Respiratory Syncytial Virus is a common respiratory pathogen known for causing respiratory illnesses, ranging from mild symptoms to severe lower respiratory tract infections in infants and the elderly. This virus is responsible for one-third of pneumonia deaths in the pediatric population, but there are currently only a few effective vaccines and pharmacological treatments. The frac-talkine (CX3CL1) receptor (CX3CR1) is a co-receptor for RSV expressed by airway epithelial cells and a diversity of immune cells. Recent research has highlighted the role of CX3CR1 in the host immune response to RSV infection, and its potential role in the development of an efficient vaccine and novel treatments.

This study extends the CGAN-based MODIS cloud vertical-profile (64×64-scene, about 70km width×15km height) retrieval technique developed by Leinonen et al. (2019) [1] to construct seamless 3D cloud fields for the MODIS granules [2]. Firstly, the accuracy and spatial continuity of the Leinonen et al. [1] retrievals are statistically evaluated. Then, according to the characteristics of the retrieval error, a spatially overlapping-scene ensemble generation method and a bi-directional Ensemble Binning Probability Fusion (CGAN-BEBPF) technique are developed, which improves the CGAN retrieval accuracy and support to construct seamless 3D clouds. The CGAN-BEBPF technique involves three steps: cloud masking, intensity scaling, and optimal value selection. It ensures adequate coverage of the low reflectivity areas while preserving the high reflectivity cloud cores. The technique is applied to retrieve the 3D cloud fields of Typhoon Chaba and a multi-cell convective system. The cloud structures of the CGAN-BEBPF results are highly consistent with the ground-based radar observations. It can retrieve weak clouds at the top levels of convective storms and hurricanes that were missed by ground-based radars and fill the gaps of the ground-based radar lower-level PPI scan. Overall, CGAN-BEBPF can be automated to retrieve the rich informative 3D structures of typhoons and severe convective systems over the oceans along the MODIS swath, which certainly presents great value for improving maritime and near-shore typhoons and convection prediction and other cloud-sensitive applications.

The central promise of personalized medicine is individualized treatments that target molecular mechanisms underlying the physiological changes and symptoms arising from disease. We demonstrate a bioinformatics analysis pipeline as a proof-of-principle to test the feasibility and practicality of comparative transcriptomics to classify two of the most popular in vivo diet-induced models of coronary atherosclerosis, apolipoprotein E null mice and New Zealand White rabbits. Transcriptomics analyses indicate the two models extensively share dysregulated genes albeit with some unique pathways. For instance, while both models have alterations in the mitochondrion, the biochemical pathway analysis revealed, Complex IV in the electron transfer chain is higher in mice, whereas the rest of the electron transfer chain components are higher in the rabbits. Several fatty acids anabolic pathways are expressed higher in mice, whereas fatty acids and lipids degradation pathways are higher in rabbits. This reflects the differences between two translational models of atherosclerosis. This study validates transcriptome analysis as a potential method to precisely identify altered cellular and molecular pathways in atherosclerotic disease, which can be used to individualize treatment even in the absence of genetic data.

Nowadays, Multi-needle electrospinning has used to be an efficient method for producing nanofiber membranes. But in the process, the fluctuation errors in electrospinning fluid flow rate have directly influence the membrane quality, which bringing instability and still have not been solved yet. For finding out the solution to the inaccurate flow control in multi-needle, it is necessary to develop a multi-stage flow runner spinneret for large-scale production of nanofiber membrane. At first, the paper uses the finite element software COMSOL to numerically simulate the polymer flow in the runner of spinneret. Based on it, the velocity field distribution curve was draw and the velocity instability coefficient of the polymer flow was attained, which are providing theoretical guidance for the optimal design of spinneret. Next, the response surface analysis method (RSM) was used to establish a regression model between the average diameter of the nanofiber membrane and the process parameters. Then, above regression model used to perform variance data processing between the average diameter and the process parameter configuration. After that, the 2D and 3D response surface profiles and the significance analysis were completed. Finally, the stability of the nanofibers production was improved, which attained the optimal adjustment of process configuration in multi-needle electrospinning.

Lichen Amyloidosis (LA) is a type of cutaneous amyloidosis characterized by brownish hyperkeratotic and itchy papules on the lower leg, back, forearm or thigh. It is associated with itching and Atopic Dermatitis (AD) according to an etiopathogenetic mechanism that has not yet been fully elucidated. Currently, available therapies for this condition include oral antihistamines, laser, cyclosporine, topical corticosteroids and phototherapy, but in light of the overlap with AD, Dupilumab may also be indicated. We report the case of a female, 52 years old, suffering from AD and LA for about 27 years. She had lesions attributable to both diseases on the trunk and lower limbs associated with severe itching and had proved resistant to cyclosporine therapy. It was decided to opt for Dupilumab with induction of 2 fl of 300 mg and maintenance with 1 fl every other week. The therapy proved effective, returning a total resolution of both diseases one year after the beginning of the treatment. Dupilumab demonstrated efficacy and safety in LA related to AD and led to clinical and quality of life improvement in this patient. Therefore, Dupilumab should be considered when treating LA. Further studies should be conducted focusing on the efficacy of the drug on LA (whether or not related to AD), changes in skin lesions after discontinuation, and the safety of long-term application.

Background: Studies have tried to identify predictors of functional markers of keratoconus progression, whereas structural outcomes of collagen cross-linking have not been properly investigated. Objective: find and set up a combination of diagnostic modalities and measurements to reliably reflect CXL efficiency. Material and methods: We retrospectively reviewed the medical histories of 107 KC patients (131 eyes) who underwent CXL in a medical center from January 2018 to December 2022. The dataset included preoperative examinations and follow-up results, which provided a total of 796 observations. The cases of other corneal diseases, repeated CXL, pregnancy, and observations without follow-up examinations were excluded. Results: The study findings revealed a significant drop in the central and corneal thickness during 14.01±9.98 months of observations. Linear and polynomial equations suggested different trends in pachymetry change after CXL. The linear model showed corneal thinning, and according to the polynomials, pachymetry findings return to baseline values two years after CXL. Conclusion: The most reliable prognosis of postoperative corneal thickness is achieved with the models trained on keratometry readings and topography indices. BAD indices are also reliable predictors of the corneal thickness after CXL. A combination of the aforementioned structural parameters and their derivatives can reliably predict CXL efficiency.

D-dimer testing in combination with clinical assessment is commonly used to diagnose suspected DVT and pulmonary embolism. However, as D-dimer levels are physiologically higher in elderly patients, this test loses clinical specificity in the setting of geriatric patients. The aim of our study was to investigate the changes in D-dimer levels with age and the specificity of the D-dimer test in the diagnosis of pulmonary embolism. 1617 elderly patients consecutively admitted to the Geriatric Emergency Care Department of the INRCA-IRCCS hospital between January 2013 and February 2014 and for whom D-dimer results were available were included. The D-dimer immunoturbidimetric assay was performed on a cs-2100i Sysmex Siemens using a conventional cut-off level of 500 µg/L. The median D-dimer level was 1200 [550-2990] µg/L FEU, and the percentage of patients with D-dimer levels below the conventional cut-off was only 22.1%. As expected, there was a significant age-related increase in D-dimer, even when sex was considered, and a significant decrease in the specificity of D-dimer assay with age. The most established use of D-dimer testing is in the diagnosis of deep vein thrombosis or pulmonary embolism. Our study confirms the need for an age-adjusted cut-off for D-dimer testing in geriatric patients.

Introduction. Autopsy is a medical procedure that consists of the corpse’s examination to determine the cause of death and obtain information on pathological conditions or injuries present in the body. In recent years there has been a reduction in hospital autopsies and an increase in forensic autopsies. Material and methods. A retrospective observational study was conducted on the reports of all the 645 hospital autopsies performed at Polyclinic of Bari from 2006 to 2021.Results. Group A, 2006-2009, 174 cases were studied: 58% male, 58% adults, 55% neonatology, pulmonary disease was the cause of death in 23%, and 55% of discrepancy between clinical and autopsy diagnosis. Group B, 2010-2013, comprised 119 cases: 52% male, 46% infants, 48% neonatology, pulmonary disease was the cause of death in 25%, and 56% of discrepancy between clinical and autopsy diagnosis. Group C, 2014-2017, comprised 168 cases: gender equality, 37% infants, 25% gynecology, pulmonary disease was the cause of death in 24%, and 58% of discrepancy between clinical and autopsy diagnosis. Group D, 2018-2021 comprised 184 cases: 56% male, 38% adult, 32% gynecology, pulmonary disease was the cause of death in 27%, and 58% discrepancy between clinical and autopsy diagnosis.Conclusion. The study of hospital autopsies at the Polyclinic of Bari shows that the discrepancy between clinical and autopsy diagnosis is around 56.75%. Thus, the hospital autopsy is helpful in the modern age, especially for diagnosing fetal and neonatal diseases.

Merkel cell carcinoma (MCC) is a rare, highly aggressive skin cancer characterized by two etiologies: the first one, observed in most cases, is attributed to Merkel cell polyomavirus (MCPyV), a DNA tumor virus, while the second one is due to ultraviolet (UV) radiation exposure. Currently, there are limited diagnostic, prognostic, and therapeutic biomarkers available for MCC. MicroRNAs (miRNAs) are small RNA molecules that do not code for proteins but regulate various cellular processes, including cell cycle progression, proliferation, differentiation, and apoptosis. The dysregulation of miRNAs has been associated with a wide range of cancers since they can act as either tumor suppressors or oncogenes. This paper aims to collect, summarize and re-organize the recent discoveries concerning miRNAs, whose dysregulation in the pathogenesis and progression of MCC is slowly emerging. Furthermore, the potential clinical applications of miRNAs as diagnostic biomarkers, possible differentiation between MCPyV-related and unrelated MCC, predictors of response to therapy and prognostics are also discussed. From this review, it emerges that miRNAs may have, in the near future, the potential to become clinically significant in improving the diagnostic accuracy, prognostic evaluation, and therapeutic options of MCCs. Finally, the new perspectives on the poorly explored field of artificial intelligence in MCC are provided.

Tumid Lupus Erythematosus has been the subject of heated debate as to its correct nosographic classification. In particular, its definition has changed over time, depending on the different studies performed. In this review we address the initial definition of TLE, the changes that have taken place in the understanding and placement within the classification of Lupus Erythematosus (LE), and focus on clinical, histopathological, immunophenotypical and differential diagnosis aspects.

The African continent has the highest prevalence of hypertension globally, with South Africa reporting the highest prevalence in Southern Africa. While the influence of genetic variability in the pathogenesis of hypertension is well described internationally, limited reports are available for African populations. This study aimed to assess the association of genetic variants and essential hypertension in a cohort of two ethnic South African population groups. Two hundred and seventy-seven hypertensive and one hundred and seventy-six normotensive individuals were genotyped for 79 variants. Genotyping was performed using the Illumina GoldenGate Assay and allele-specific polymerase chain reaction. The association of variants was assessed using the Fisher Exact test under the additive and allelic genetic models, while multivariate logistic regression was used to predict the development of hypertension. Five variants (CYP11B2 rs179998, AGT rs5051 and rs699, AGTR1 rs5186 and ACE rs4646994) were significantly associated with essential hypertension in the cohort under study. Furthermore, AGTR1 rs5186 and AGT rs699 were identified as risk factors for the development of hypertension in both ethnic groups. In two ethnic South African populations an association was observed between Renin-Angiotensin-Aldosterone System (RAAS) related genes and the development of hypertension.

Aim: The aim of this study is to evaluate the possibility of discriminating between the two sexes through the simultaneous use of cheiloscopy and palatine rugoscopy. Materials and methods: 80 palate and lip impressions (40 male and 40 female) were analyzed. The palate impressions were acquired using both analogue and digital methods. The lip impres-sions were first acquired analogically and then scanned. The morphology of the palatine rugae in the palate impressions was analyzed, with the exclusion of segmental rugae. The lip prints were analyzed in sextants and the morphology of the lip wrinkles was evaluated. Results: Palatine rugoscopy shows that women have more curvy rugae than men and that men have more rugae on the left side than on the right. Cheiloscopy gave no statistically significant re-sults. Discussion: Palatine rugoscopy showed some differences between the sexes, however, these re-sults were not solid enough for reliable sex discrimination. Differences between the results of dif-ferent studies can be attributed to ethnic and methodological variations. Cheiloscopy, on the other hand, did not provide statistically significant results for sex discrimination. Conclusions: Given the scant results obtained from palatine rugoscopy and the lack of statisti-cally relevant results in cheiloscopy, together with a lack of concordance among the data found in literature, it is believed that it is not possible to discriminate between the two sexes by using the two methods contemporarily. Further research is needed.

Microscopic vision plays an important role in automated micro-assembly. However, some uncertain factors in the assembly process, such as occlusion and stains can lead to the mistakes of feature extraction. Herein, to solve the problem, the deep learning techniques are introduced into the feature recognition tasks, focusing on the attention mechanism and visualizing CNNs for DL-based microscopic vision. The main contributions are summarized as follows: The CBAM attention mechanism is combined with the YOLOv5 algorithm to improve the accuracy and robustness of feature extraction. The micropart feature occlusion experiment results show that at 70% occlusion degree, YOLOV5-CBAM can reach 97.9% [email protected], which is 4.6% higher than the original one. Visualization analysis of DL-based model is conducted using Grad-CAM to make the decision result more transparent and avoid potential visual detection risks during assembly. The heatmap matching degree between GT area and high-light area is increased by 27.81% on average, which further verify the effectiveness of attention mechanism in micropart feature localization. Additionally, micropart surface stain and droplet quality classification models based on ResNet50 are trained to replace the manual sorting. The visual results are consistent with human eye discernment and judgement, confirming the reliability of parts and droplets sorting.

This review will explore the four major pillars required for design and development of an saRNA vaccine: antigen design, vector design, non-viral delivery systems, and manufacturing (both saRNA and lipid nanoparticles (LNP)). In will report on the major innovations, preclinical and clinical data reported in the last five years and will discuss future prospects.

Aortic stenosis continues to be still the most frequently valvular pathology in the elderly population of the Western countries. According to the latest guidelines, the therapeutic choice of aortic stenosis depends on the age of the patient (<75 years or >75 years) and the risk class (STS-Prom/ Euroscore II < o > 4%). Therefore, if the surgical indication is clear in young and low risk patients and percutaneous treatment is the gold standard in older and high-risk patients, the therapeutic choice is still debated in intermediate-risk patients. In this group of patients the aortic valve stenosis treatment depends on the patient's global evaluation, on the experience of the center and no less important on the patient's will. Two main therapeutic options are debated: surgical aortic valve replacement with sutureless prosthesis versus transcatheter aortic valve implantation. In addition, the progressive development of mininvasive techniques for aortic valve surgery (right -anterior minitoracotomy) has also reduced the peri and post -operative risk in this group of patients. The goal of our review is to analyze the literature evidence of the best therapeutic choice in intermediate risk patients with severe aortic stenosis.

Obesity is a highly prevalent chronic disease characterized by excessive weight gain and fat accumulation. There is growing evidence that Lactiplantibacillus plantarum strains with bile salt hydrolase (BSH) activity are effective in preventing and alleviating obesity. However, few of them has confirmed the anti-obesity effect of BSH by gene deletion. In this study, 23 BSH-positive Lp. plantarum strains were screened by bile salt precipitation halo assay. Among these strains, Lp. plantarum Lp20 showed the highest BSH activity with a hydrolysis rate to glycodeoxycholic acid (GDCA) by 91.62%. Genomic sequencing and bioinformatics analysis indicated that gene bsh1 was associated with BSH activity of Lp. plantarum Lp20 and gene deletion of bsh1 confirmed the role of Bsh1 in GDCA hydrolysis. To investigate if the Bsh1 from Lp. plantarum Lp20 could alleviate obesity, the wild type and bsh1 deletion mutant strains were administered daily for 8 weeks to diet-induced obese mice, respectively. Compared with mutant strain, the wild type strain Lp20 significantly reduced body weight gain, inguinal white adipose tissue and mesenteric fat mass in obese mice. In addition, Lp20 also reduced serum total cholesterol levels and LDL-C by 20.8% and 33.3%, respectively. Furthermore, the recovered liver steatosis was only observed in Lp. plantarum Lp20 treated mice. Taken together, the bile salt hydrolase Bsh1 from Lp. plantarum Lp20 was a key functional factor on ameliorating HFD-induced obesity by reducing cholesterol levels and suppressing adipogenesis in mice.

To achieve a narrow bandwidth optical filter with a wide swept range for new generation optical spectrum analysis (OSA), an optoelectronic equivalent narrowband filter (OENF) is investigated and a swept optical filter with bandwidth of several MHz and swept range of several ten nanometers is built using electric filters and a swept laser as local oscillator (LO). The principle of OENF is introduced and analysis of OENF system is presented. Two electric filters are optimized to be RBW filters for high and medium spectral resolution application. Both simulations and experiments are conducted to verify OENF principle and results show that power uncertainty is less than 1.2% and spectral resolution can reach 6 MHz. Then, a real-time wavelength calibration system consisting of HCN gas cell and FP etalon is proposed to guarantee a wavelength accuracy of ±0.4 pm at C-band and to reduce the influence of phase noise and nonlinear velocity of swept LO. Finally, experiments on OSA of actual spectra of various optical sensors using OENF system are conducted. Experimental results indicate that OENF system has an excellent capacity in analysis of fine spectrum structures.

Background: The aim of this experimental work was to evaluate cheiloscopy potential: to confirm the individual uniqueness of lip prints, to verify the presence of recurrent patterns according to the Suzuki and Tsuchihashi classification and to search for characteristics that can be used in establishing the sex of a subject, with a focus on assessing the method's limitations. Methods: Lip prints were taken from a sample of 172 volunteers using liquid lipstick and transparent adhesive tape and were digitally analyzed. Results: 59 prints (35.3%) were not considered for Suzuki and Tsuchihashi classification due to quality errors and the impossibility of visualizing lip wrinkles in one or more sextants. All the digitized prints revealed themselves to be unique after having been analyzed using superimposition. Qualitatively valid prints indicated a prevalence of Type II in both male and female. A major problem was that the analysis of the same print by different operators often resulted in a different attribution of a Type. Conclusions: The potential of cheiloscopy lies mainly in the matching of prints, given their uniqueness, but the absence of a database greatly limits its use. Additionally, its use for sex identification does not seem to be reliable. It is necessary to establish an acquisition protocol for taking and analyzing prints that could reduce the errors committed by the operator.

Background: The aim of this experimental work was to confirm the individual uniqueness of lip prints, to verify the presence of recurrent patterns according to the Suzuki and Tsuchihashi classification and to search for characteristics that can be used in establishing the sex of a subject. Methods: Lip prints were taken from a sample of 172 volunteers using liquid lipstick and transparent adhesive tape and were digitally analyzed. Results: 59 prints (35.3%) were not considered for Suzuki and Tsuchihashi classification due to quality errors and the impossibility of visualizing lip wrinkles in one or more sextants, all the digitized prints revealed themselves to be unique after having been analyzed using superimposition. Qualitatively valid prints indicated a prevalence of Type II in both male and female and the presence of sex-linked features. A major problem was that the analysis of the same print by different operators often resulted in a different attribution of a Type. Conclusions: The potential of cheiloscopy lies mainly in the matching of prints, given their uniqueness, and can be a valid tool in gender determination but the absence of a database greatly limits its use. It is necessary to establish an acquisition protocol for taking and analyzing prints that could reduce the errors committed by the operator.

In recent years, bioengineered and biotechnological products have gained tremendous attention to identify unique biologic features associated with oncology. To overwhelm the complexity of tumor personalized medicines (PM) or vaccines could be a promising way-out to be considered. Cancer is a heterogeneous disease. This characteristic variability of cancer provides itself to the emergent era of PM i.e., precision oncology. The prime objective of PM is merely to deliver the right treatment to the right patient at the right time. For instance, imatinib was effectively targeted chronic myeloid leukemia. To date, attainment in using PM approaches to cancer treatment have been mixed. The next-generation sequencing (NGS), and the use of several biomarkers (e.g., HER2, BRAF, ALK, etc.), to drive precise cancer treatment through a timely and cost-effective manner. In current review, author summarizes the advancement in the field of PM such as CRISPR-Cas9, NGS, pharmacogenomics, and the benefits of machine learning techniques. The precise communication between healthcare professionals, regulatory bodies, and patients is essential to translate advances in PM into immense benefits for patients with cancer globally.

A PLS-based quality prediction method is proposed for batch processes using two-dimensional extended windows. To realize the adoption of information in the direction of sampling time and batch, a new definition region of support (ROS), k-i-back-extended region of support (KIBROS), is proposed to establish the extended window, which including the data of the previous sampling times in the same batch, the data of the same sampling time in the historical batches, the data of the previous sampling times in the historical batches and the data of the later sampling times in the historical batches. According to these regions of support, extended windows are established, and different models are proposed based on the extended windows for batch processes quality prediction. Further, using a typical batch process as an example, the injection molding process, the proposed quality prediction method is experimentally verified, proving that the proposed methods have higher prediction accuracy than the traditional method and the prediction stability is also improved.

The treatment of acute myeloid leukemia (AML) with adverse genetics remains unsatisfactory, with very low response rates to standard chemotherapy and shorter durations of remission commonly observed in these patients. The complex biology of AML with adverse genetics is continuously evolving. Herein, we discuss recent advances which have investigated the contribution of cell intrinsic mechanisms as well as of the immune system to myeloid leukemogenesis in this specific subset of AML. We focus on the biological rationales for combining targeted therapy and immunotherapy, which are currently being investigated in ongoing trials, and could hopefully ameliorate the poor outcomes for these patients

YOLOv8, as an efficient object detection method, can swiftly and precisely identify objects within images. However, traditional algorithms encounter difficulties when detecting small targets in remote sensing images, such as missing information, background noise, and interactions among multiple objects in complex scenes, which may affect performance. To tackle these challenges, we propose an enhanced algorithm optimized for detecting small objects in remote sensing images, named HP-YOLOv8. Firstly, we design the C2f-D-Mixer (C2f-DM) module as a replacement for the original C2f module. This module integrates both local and global information, significantly improving the ability to detect features of small objects. Secondly, we introduce a feature fusion technique based on attention mechanisms, named Bi-Level Routing Attention in Gated Feature Pyramid Network (BGFPN). This technique utilizes an efficient feature aggregation network and reparameterization technology to optimize information interaction between different scale feature maps, and through the Bi-level Routing Attention (BRA) mechanism, it effectively captures critical feature information of small target objects. Finally, we propose the Smooth Mean Perpendicular Distance Intersection over Union (SMPDIoU) loss function. The method comprehensively considers the shape and size of detection boxes, enhances the model's focus on the attributes of detection boxes and provides a more accurate bounding box regression loss calculation method. To demonstrate our approach's efficacy, we conduct comprehensive experiments across the RSOD, NWPU VHR-10, and VisDrone2019 datasets. The experimental results show that the HP-YOLOv8 achieves 95.11%, 93.05%, and 53.49% in the [email protected] metric, and 72.03%, 65.37%, and 38.91% in the more stringent [email protected]:0.95 metric, respectively.

In order to shorten the detection time and improve the average precision on embedded devices, A lightweight and high accuracy model is proposed for passion fruit in complex environments (backlight, occlusion, overlap, sunny, cloudy, rainy). Firstly, replacing the backbone network of YOLOv5 with a lightweight GhostNet model reduces the number of parameters and computation while improving detection speed. Secondly, a new feature branch is added to the GhostNet network, and the feature fusion layer in the neck network is reconstructed to effectively combine the lower-level and higher-level features, which not only improves the accuracy of the model but also maintains its lightweight. Finally, the knowledge distillation methods are used to transfer the knowledge from the more capable teacher model to the less capable student model, which significantly improving the detection accuracy. The improved model is denoted as G-YOLO-NK. The average accuracy of the G-YOLO-NK network is 96.00%, which is 1.00% higher than the original YOLOv5s model. Furthermore, the improved model size is 7.14MB, reduced to half of the original model, and the real-time detection frame rate is 11.25 FPS on the Jetson Nano. Compared to the state-of-the-art model, the proposed model outperforms them in terms of average precision and detection performance. The present work provides an effective model for real-time detection of passion fruits in complex orchard scenes, which can provide valuable technical support for the development of orchard picking robots and greatly improve the intelligence level of orchards.

Lynch syndrome (LS) is an inherited cancer predisposition disorder associated with an elevated risk of various epithelial cancers. Despite sharing the same pathogenic variant (PV), Lynch syn-drome variant heterozygotes (LSVH) exhibit considerable phenotypic variability in cancer risk. The role of Human Leukocyte Antigen (HLA) in modifying cancer risk prompted our investiga-tion into whether HLA variations act as genetic modifiers influencing age at cancer diagnosis in a unique cohort of LSVH carrying a PV in the hMLH1 gene in South Africa. Within our extensive LS cohort, 426 individuals carried the same hMLH1 PV (MLH1:c.1528C>T). We selected 100 indi-viduals with the greatest diversity in age at cancer diagnosis and the oldest unaffected individu-als for high-throughput HLA genotyping of 12 HLA class I and II loci using next-generation se-quencing. Statistical analyses employed Kaplan-Meier survival analyses with Logrank tests and Cox proportional hazards. Following the robust application of statistical correction methods, six HLA alleles (3.2%) were significantly associated with a young age at cancer diagnosis. Notably, HLA-B*15:17 and HLA-DPB1*55:01 correlated significantly with very young colorectal cancer (CRC) diagnosis (Mean age: 21y [17-25]; HR = 71.59; q

Precision medicine (PM), also termed stratified, individualised, targeted or personalised medicine, embraces a rapidly expanding area of research, knowledge, and practice. It brings together two emerging health technologies to deliver better individualised care: the many “-omics” arising from increased capacity to understand the human genome; and “big data” and data analytics including Artificial Intelligence (AI). PM has the potential to transform an individual’s health, moving from population-based disease prevention to more personalised management. There is however a tension between the two, with a real risk that this will exacerbate health inequalities and divert funds and attention from basic healthcare requirements leading to worse health outcomes for many. All areas of medicine should consider how this will affect their practice, with PM now strongly encouraged and supported by government initiatives and research funding. In this review we discuss examples of PM in current practice and its emerging applications in primary care, such as clinical prediction tools that incorporate genomic markers and pharmacogenomic testing. We look towards potential future applications and consider some key questions for PM including evidence of real-world impact, its affordability, the risk of exacerbating health inequalities, and the computational and storage challenges of applying PM technologies at scale.

The Range–Doppler (R-D) model is extensively employed for the geometric processing of Synthetic Aperture Radar (SAR) images. Refining the sensor motion state and imaging parameters is the most common method for achieving high-precision geometric processing using the R-D model, comprising a process that involves numerous parameters and complex computations. In this study, we introduce a novel approach to improve the positioning accuracy of the R-D model, implementing a low-order polynomial to compensate for the original imaging errors without altering the initial positioning parameters. We also design low-order polynomial compensation models with different parameters. The models are evaluated on various SAR images from different platforms and bands, including spaceborne TerraSAR-X and GF3-C images, manned airborne SAR-X images, and unmanned aerial vehicle-mounted miniSAR-Ku images. Furthermore, image positioning experiments involving the use of different polynomial compensation models and various numbers and distributions of Ground Control Points (GCPs) are conducted. The experimental results demonstrate that geometric processing accuracy comparable to that of the classical rigorous positioning method can be achieved even when applying only an affine transformation model to the images. Compared to classical refinement models, however, the proposed image-compensated R-D model is much simpler and easy to implement. Thus, this study provides a convenient, robust, and widely applicable method for the geometric positioning processing of SAR images, offering a potential approach for the joint-positioning processing of multi-source SAR images.

This review provides information on acne transcriptomics allowing deeper insights into acne pathogenesis and isotretinoin´s mode of action. Puberty-induced insulin-like growth factor 1 (IGF-1), insulin and androgen signaling activate the kinase AKT and mechanistic target of rapamycin complex 1 (mTORC1). Western diet (hyperglycemic carbohydrates, milk/dairy products) as well co-stimulate AKT/mTORC1 signaling. AKT-mediated phosphorylation of nuclear FoxO1 and FoxO3 results in their extrusion into the cytoplasm, a critical switch, which enhances the transactivation of lipogenic and proinflammatory transcription factors including androgen receptor (AR), sterol regulatory element-binding transcription factor 1 (SREBF1), peroxisome proliferator-activated receptor γ (PPARγ), and signal transducer and activator of transcription 3 (STAT3) but reduces FoxO1-dependent expression of GATA binding protein 6 (GATA6), the key transcription factor of infundibular keratinocyte homeostasis. AKT-mediated phosphorylation of the p53-binding protein MDM2 promotes the degradation of p53. In contrast, isotretinoin enhances the expression of p53, FoxO1 and FoxO3 in sebaceous glands of acne patients. Overexpression of these proapoptotic transcription factors explains isotretinoin´s desired sebum-suppressive effect via induction of sebocyte apoptosis but also its adverse effects including teratogenicity (neural crest cell apoptosis), reduced ovarian reserve (granulosa cell apoptosis), risk of depression (apoptosis of hypothalamic neurons), VLDL hyperlipidemia, intracranial hypertension and dry skin.

Abstract Background: Periodontitis is associated with systemic chronic inflammatory diseases. There is limited evidence on the influence of anti-cytokine therapies on the periodontal condition and microbiome in the tooth pocket of such patients, so the aim of this study was to elucidate this issue. Methods: In this observational trial, the periodontal status and the gingival crevicular fluid of 13 patients with different chronic inflammatory diseases were obtained before initiation of anti-cytokine treatment and 14 weeks after. Gingival crevicular fluid was collected for 16S rRNA gene sequencing from a clinically healthy tooth and the deepest measured pocket. Results: In participants with advanced attachment loss at baseline, the clinical attachment level significantly decreased from 2.9 (2.3-3.4) to 2.4 (1.3-3.0) mm (p = 0.04), and the bleeding- on-probing index dropped from 19.4 (10.0-21.0) to 7.3 (6.9-9.9) mm (p = 0.02) without any further periodontal treatment. Shannon’s diversity index significantly increased in the deepest pockets of patients (p = 0.039). Conclusion: The data showed alterations in diversity of the subgingival microbiome and improvement of periodontal parameters over the course of the study, implying a shift towards a healthier condition after starting anti-cytokine therapy. Additional investigations are needed to analyze whether the administration of selective biologicals can improve periodontal conditions in patients with or without chronic inflammatory diseases.

Hyperspectral images (HSI) contain abundant spectral information. Efficient extraction and utilization of this information for image classification remain prominent research topics. Previously, hyperspectral classification techniques primarily relied on statistical attributes and mathematical models of spectral data. Deep learning classification techniques have recently been extensively utilized for hyperspectral data classification, yielding promising outcomes. This study proposes a deep learning approach that uses polarization feature maps for classification. Initially, the polar coordinate transformation method is employed to convert the spectral information of all pixels in the image into spectral feature maps. Subsequently, the proposed Deep Context Feature Fusion Network (DCFF-NET) is utilized to classify these feature maps. The model is validated using three open-source hyperspectral datasets: Indian Pines, Pavia University, and Salinas. The experimental results indicated that DCFF-NET achieves excellent classification performance. Experimental results on three public HSI datasets demonstrate that the proposed method accurately recognizes different objects with an overall accuracy (OA) of 86.68%, 94.73%, and 95.14% based on the pixel method, and 98.15%, 99.86%, and 99.98% based on the pixel-patch method.

The global food production system faces several challenges, including significant environmental impacts due to traditional agricultural practices. The rising demands of consumers for food products that are safe, healthy, and have animal welfare standards have led to an increased interest in alternative proteins and the development of the cellular agriculture field. Within this innovative field, precision fermentation emerges as a promising technological solution to produce proteins with reduced ecological footprints. This review provides a summary of the environmental impacts related to the current global food production, and explore how precision fermentation can contribute to address these issues. Additionally, we will report on the main animal-derived proteins produced by precision fermentation, with a particular focus on those used in the food and nutraceutical industries. The general principles of precision fermentation will be explained, including strain and bioprocess optimization. Examples of efficient recombinant protein production by bacteria and yeasts, such as milk proteins, egg-white proteins, structural and flavoring proteins, will also be addressed, along with case examples of companies producing these recombinant proteins in a commercial scale. Through these examples, we will explore how precision fermentation supports sustainable food production and holds the potential for significant innovations in the sector.

Oral squamous cell carcinoma (OSCC) is a highly prevalent and aggressive malignancy, with mortality rates that reach 60%, mainly due to an excessive diagnostic delay. MiRNAs, a class of crucial epigenetic gene-expression regulators, have emerged as potential diagnostic biomarkers, with >200 molecules reported to be dysregulated in OSCC. We had previously established an in silico methodology for the identification of the most disease-specific molecules by bridging genetics and epigenetics. Here, we identified the stage-specific miRNAs that govern the asymptomatic early stages of oral tumorigenesis by exploiting the reverse interplay between miRNA-levels and their target-genes’ expression. Incorporating gene expression data from our group’s experimental hamster model of sequential oral oncogenesis, we bioinformatically detected the miRNAs that simultaneously target/regulate >75% of the genes that are characteristically upregulated or downregulated in the consecutive stages of hyperplasia, dysplasia and early-invasion OSCC, while exhibiting the opposite expressional dysregulation in OSCC-derived tissue and/or saliva specimens. We found that all stages share the downregulation of miR-34a-5p, miR124-3p, and miR-125b-5p, while miR-1-3p is underexpressed in dysplasia and early-invasion. Malignant early-invasion stage is distinguished by the downregulation of miR-147a and the overexpression of miR-155-5p, miR-423-3p and miR-34a-5p. The identification of stage-specific miRNA molecules may facilitate their utilization as biomarkers for presymptomatic OSCC diagnosis.

This paper presents the PEnsemble 4 model, a sophisticated machine learning framework that integrates IoT-based environmental data to accurately forecast maize yield. With the projected significant growth in global maize demand over the next decade, the inherent risks posed by the crop’s dependence on weather conditions necessitate improved prediction capabilities. The PEnsemble 4 model, developed with high accuracy, incorporates comprehensive datasets encompassing soil attributes, nutrient composition, weather conditions, and UAV-captured vegetation imagery. By employing a combination of Huber and M estimates, the PEnsemble 4 model effectively analyzes temporal patterns in vegetation indices, specifically CIre and NDRE, which serve as reliable indicators of canopy density and plant height. In addition, this research significantly contributes to precision agriculture by offering an efficient and sustainable alternative to conventional farming practices through precise yield predictions. Notably, the PEnsemble 4 model enables earlier estimation, advancing the timeline for yield prediction from the conventional day 100 in the R6 stage to day 79 in the R2 stage. This improvement enhances decision-making processes in farming operations. The remarkable accuracy rate of 91% underscores the importance of adopting a multifaceted data approach that harnesses IoT-derived environmental insights. Additionally, the PEnsemble 4 model extends its benefits beyond yield prediction, facilitating the detection of water and crop stress, as well as disease monitoring in broader agricultural contexts. Ultimately, the PEnsemble 4 model establishes a new standard in maize yield prediction, revolutionizing crop management and protection through the synergistic utilization of IoT and machine learning technologies.

New challenges arise in the face of global climate change which impact every ecosystem on earth, including aquatic systems. This is evident in observations made in regard to the world’s oceans, which show trends of incremental changes in ocean surface temperatures, sea levels, and ocean acidity. These environmental shifts impact human resources such as fisheries and aquaculture. In addition, according to the World Bank, the increase in human population will also require more food and nutrient production, which include industries such as aquaculture. With this increasing demand in aquaculture and fisheries, we must develop efficient and productive methods to operate these industries. We can use genetic methods, specifically transcriptomic information to better understand the biology of our source of nutrition. With the advent of RNASeq techniques, we can provide a better understanding about growth and development, immune function and stress, and adaptations. The use of population genetics or (genomics) to detect Single Nucleotide Polymorphisms (SNPs) between populations or closely related species can provide greater insight from stock structure to fishery-induced evolution. In addition, candidate loci can be investigated further to better understanding evolutionary processes, which provide clues on physiological adaptations and gene expression patterns that can help elucidate how these organisms respond to their current environment. In addition, the use of transcriptomic analyses such as differential gene expression can be used to determine resilience in various environmental conditions such as pollution, hypoxic/anoxic conditions, fluctuations in salinity, and temperature extremes. There has been an increase in transcriptomic studies for many aquaculture species, which has aimed at improving our understanding of growth, development, and metabolism, providing vital information for fisheries and aquaculture industries to make adjustments to environmental conditions such as oxygen availability, nutrition, and salinity. All of these aspects provide insightful information for advancing our knowledge of aquaculture, fisheries and conservation management.

Despite the known benefits of precision agriculture, the adoption is challenging due to the cost of investment and the farm sizes. Therefore, profitability is an important aspect to consider. This study aimed to evaluate the net returns, profitability, and investment efficiencies of PA by different economic farm sizes. The study was based on data retrieved from FADN and Eurostat. The study examined four countries (Poland, Germany, France, and Romania) under field crop farming using an investment cost of €35 941 - €71 883, and a 20% and 15% reduction in the cost of crop protection and fertilizer usage respectively without compromising productivity. There is a positive relationship between the adoption of PA and farm returns for larger-scale farms. The result of the profitability and analysis of investment efficiency using NPV showed a positive value for economic farm sizes of €100 000 and above. Hence, it is not economically advisable that all farmers use PA technologies with the hope that they will be profitable but with public support (subsidies) more farms will be able to use PA and be profitable. Also, an opportunity to meet the goal of the European Union Green Deal of minimizing emissions that cause climate change.

Abiotic stress is a significant factor restricting the normal growth and development of plants. Under abiotic stress, plants maintain their life and continuous growth by reconfiguring transcriptional regulation and metabolic networks. In recent years, with the development of sequencing technology and mass spectrometry technology, transcriptomics and metabolomics have emerged as new disciplines following genomics and proteomics, which are conducive to identifying metabolites and regulatory genes in plants under abiotic stress. In this review, we mainly analyzes the technical characteristics, advantages, and disadvantages of transcriptomics and metabolomics, focusing on reviewing the research progress in the field of plant responses to abiotic stress (temperature stress, salt stress, water stress, and heavy metal stress) using transcriptomics and metabolomics both domestically and internationally in recent years. It also provides a prospective view on current issues, which helps accelerate the understanding of the mechanisms of plant responses to abiotic stress and provides new ideas for breeding stress-resistant varieties in the future.

Laser-targeted weeding methods further enhance the sustainable development of green agriculture, with one key technology being the improvement of weed localization accuracy. Here, we propose an improved YOLOv8 instance segmentation based on bidirectional feature fusion and deformable convolution (BFFDC-YOLOv8-seg) to address the challenges of insufficient weed localization accuracy in complex environments with resource-limited laser weeding devices. Initially, by training on extensive datasets of plant images, the most appropriate model scale and training weights are determined, facilitating the development of a lightweight network. Subsequently, the introduction of the Bidirectional Feature Pyramid Network (BiFPN) during feature fusion effectively prevents the omission of weeds. Lastly, the use of Dynamic Snake Convolution (DSConv) to replace some convolutional kernels enhances flexibility, benefiting the segmentation of weeds with elongated stems and irregular edges. Experimental results indicate that the BFFDC-YOLOv8-seg model achieves a 4.9% increase in precision, an 8.1% increase in recall rate, and a 2.8% increase in mAP50 value to 98.8% on a vegetable weed dataset compared to the original model. It also shows improved mAP50 over other typical segmentation models such as Mask R-CNN, YOLOv5-seg, and YOLOv7-seg by 10.8%, 13.4%, and 1.8%, respectively. Furthermore, the model achieves a detection speed of 24.8 FPS on the Jetson Orin nano standalone device, with a model size of 6.8MB that balances between size and accuracy. The model meets the requirements for real-time precise weed segmentation, suitable for complex vegetable field environments and resource-limited laser weeding devices.

Land use land cover change(LULCC) plays a significant role in understanding the global change. Particularly in regions with high population growth and climate change, anthropogenic activities have drastically altered land use and land cover (LULC). The geographic information system (GIS) and remote sensing are prominent tools for monitoring LULC changes. The land use land cover(LULC) analysis and carbon loss assessment were carried out in Ayubia national park. The area was classified into five major LULC classes, namely grassland, built-up areas, mixed forests, conifer forests and bare land. Landsat temporal satellite data of Ayubia national park acquired on 1992,2002,2012 and 2022 were analysed to access land use change and Markov’s Chain Model was used to predict the land use land cover changes for 2040. The accuracy of the images were assessed using Kappa matrix. Our results showed that a total of 371.94 ha forest land has been converted into other land uses from 1992 to 2022 and Indicating the increasing scale of anthropogenic pressure on forest resources of the Park. The carbon pool was assessed in two forest types including temperate coniferous forest and Mixed forest. The estimated carbon value recorded in TCF is 135.19 ±9.74 MgC ha-1, whereas it is in 86.43 ±8.25 MgC ha-1 in MF. Using satellite data, a thorough examination of the forest inventory was conducted to determine the LULCC, the rate of biomass carbon loss, and the factors that influenced these trends.

This study introduces a novel algorithm tailored for the precise detection of lower outliers in univariate datasets, particularly suited for scenarios with a single cluster. The approach leverages a combination of transformative techniques and advanced filtration methods to efficiently segregate anomalies from normal values. Notably, the algorithm emphasizes high precision, ensuring minimal false positives, and requires only a few parameters for configuration. Its unsupervised nature enables robust anomaly filtering without the need for extensive manual intervention. To validate its efficacy, the algorithm is rigorously tested using real-world data obtained from photovoltaic (PV) module strings with similar DC capacities, containing various anomalies. Results demonstrate the algorithm's capability to accurately identify lower outliers while maintaining computational efficiency and reliability in practical applications.

Introduction. Anesthesia is widely used in various fields of dentistry to reduce patient discomfort when the treatment would be painful. The dentist may need to infiltrate the anesthetic into multiple locations and this is usually done with the same needle which passes through the soft tissues several times to distribute the anesthetic solution over a larger area. The present study aims to evaluate, through a morphological analysis under a scanning electron microscope (SEM), how the beveled tip of a 30G needle, used to inject anesthetic several times into different areas of the vestibular mucosa, changes. Materials and methods. 30G needles with two different brands were tested, in vivo used for a variable number of sequential injections from 1 to 6 on 192 patients. The specimens were analyzed under SEM observation to evaluate any deformations induced by contact with the vestibular soft tissues. Results. The amount of deformation, measured along the axis parallel to the needle, was proportional to the number of injections. The protrusion of deformations, measured on an axis perpendicular to the needle, were maximum on the needles used 3 times. For the needles used 4,5 and 6 injections, no further increase in deformations was noted. Conclusion. From the data it seems that the tested needles are not produced to withstand multiple injections consequently after the first injection on soft tissues, they undergo a deformation which could increase the risk of injury to the mucosa itself or to nerves and vessels with which they come into contact. It would be advisable to use the single needle for a reduced number of injections.

The environment is seldom optimal for plant growth and changes in abiotic and biotic signals, including temperature, water availability, radiation and pests, induce plant responses to optimise survival. The New Zealand native plant species and close relative to Arabidopsis thaliana, Pachycladon cheesemanii grows under environmental conditions that are unsustainable for many plant species. Here we compare the responses of both plant species to different stressors (low temperature, salt and UV-B radiation) to help understand how P. cheesemanii can grow in such harsh environments. The stress transcriptomes were then determined and comparative transcriptome and network analyses discovered similar and unique responses within species between different stresses, and between the two plant species. A number of widely studied plant stress processes were highly conserved in A. thaliana and P. cheesemanii. However, in response to cold stress, Gene Ontology terms related to glycosinolate metabolism were only enriched in P. cheesemanii. Salt stress was associated with alteration of the cuticle and proline biosynthesis in A. thaliana and P. cheesemanii, respectively. Anthocyanin production may be a strategy to cope with UV-B radiation stress in P. cheesemanii only. These results allowed us to construct broad stress response pathways in A. thaliana and P. cheesemanii and identify possible novel plant strategies that help mitigate environmental stress.

Employing remote sensing techniques to evaluate crop residue cover (CRC) enables rapid mapping of tillage practices over large cropland areas, which has significant practical implications for monitoring the implementation of conservation tillage practices. This research utilized Sentinel-2 MSI and Landsat-8 OLI remotely sensed data, various spectral indices were calculated and the datasets were correlated with the field-measured CRC data. Selected spectral indices, highly significantly correlated, were chosen to build a correlation model between the indices and the CRC. Considering Sentinel-2 MSI and Landsat 8 OLI remotely sensed data variations in spectral and spatial scales, the accuracy of CRC models was evaluated using R2 and RMSE values. From the results, it's evident that all six spectral indices, including STI, NDTI, SRNDR, NDI5, NDI7 and NDSVI, registered correlation coefficients with the CRC exceeded 0.3 in the study area. STI and NDTI showed a stronger correlation to CRC, with coefficients of 0.810 and 0.803, respectively. For the Landsat 8 data, the lowest correlation obtained was for NDSVI with the Landsat 8 data with a correlation coefficient of 0.358. Applying the linear regression technique, STI and NDTI obtained higher accuracy compared to other indices in the Sentinel-2 MSI data. For NDTI and STI, The R2 values were 0.650 and 0.665, respectively, while their corresponding RMSE values were 3.96% and 4.03%. The highest model accuracy was obtained for the STI and NDTI constructed from the Landsat 8 OLI data (R2 values 0.41 and 0.40; RMSE values 5.25% and 5.27%, respectively). The CRC model built using partial least squares regression (PLSR) exhibited greater precision when applied to the Sentinel-2 MSI data (R2 value 0.894; RMSE value 2.18%) in comparison to the Landsat 8 OLI data (R2 value 0.695; RMSE value 3.70%). PLSR has the potential to enhance the precision of the model. In addition, the study area shows that the estimation of the CRC is better in the one-dimensional linear regression model and the PLSR model created with the Sentinel-2 MSI optical data compared to the Landsat 8 OLI optical remotely sensed data. The validation suggests that the CRC estimation in the study area is more precise when using the model relying on Sentinel-2 satellite imagery rather than the Landsat 8 OLI imagery.

Stress during a pandemic increased the risk of alcohol consumption, which may require pharmacological management. The characteristics of emergency calls for alcohol-related issues for patients pharmacologically treated, admitted to, or not admitted to the emergency department (ED) were analyzed. An observational single-center retrospective study was conducted from January 1, 2018 to December 31, 2021, and divided into 2-year periods (2018–2019 and 2020–2021). This study focused on calls to one of the EDs of seven hospitals in the Bari (Italy) metropolitan area for patients requiring emergency services (ES) who were either admitted or not admitted, due to their refusal. A 30% reduction in emergency calls for alcohol-related issues and a 41.17% reduction in calls for patients who refused to be admitted to the ED were observed during the pandemic. During the pandemic, a reduced rate of pharmacological treatment for calls coded green (non-critical), and an increase in calls coded yellow (fairly critical) and red (very critical) were recorded. Metadoxine was administered in almost all alcohol-related emergencies, primarily in conjunction with drugs acting on the gastrointestinal tract, irrespective of age, the period considered, and whether patients were admitted or not admitted to the ED. ES is the first and only out-of-hospital service encountered by numerous patients with alcohol-use disorders who refuse to be admitted to the ED. These patients should be directed by ES personnel to a multidisciplinary program to receive treatment for drinking, improve their quality of life, and reduce sanitation costs.

With the progression of AI, embedding advanced AI technologies into small robotics and mobile devices has become essential, driving research towards lightweighting AI models. Our study enhances the EPINET depth estimation model for light field images, aiming for compactness and faster inference while preserving accuracy. We conducted two-step experiments aimed at enhancing inference efficiency: Initially, by adjusting input streams and convolution layers, we simplified the CNN model, achieving faster inference times at the cost of reduced accuracy. To address this reduction in accuracy, we then applied knowledge distillation, allowing the simplified model to learn from the original model’s more complex patterns. In our quantitative experiments using two error metrics, MSE (Mean Squared Error) and BadPix, we identified optimal knowledge positions and evaluated the required complexity for the student model. As a result, our method improved MSE by 21% and BadPix by 14% compared to training without it. Furthermore, the student model achieved an inference speed 13% faster than the teacher model and surpassed its accuracy by 10% in MSE. Additionally, we demonstrated that repeatedly applying our approach could further enhance both model compactness and accuracy.

During the infection by SARS-CoV-2, the virus is changing infected host cell into its own factory producing new viral particles. As infection progresses, infected cell undergoes many changes in various pathways. One of the events caused by changes is cytokine storm, which leads to the severe symptoms. In this study, we investigated transcriptomic changes caused by COVID-19 disease using RNA-seq data obtained from COVID-19-positive patients and COVID-19-negative donors. RNA-seq data were collected for the purpose of identification of potential biomarkers associated with a different course of the disease. Here, the first datasets of 96 samples were analyzed to validate the methods. The aim of this publication is to report pilot results. In search of potential biomarkers associated with different disease severity, we performed differential expression analysis of human transcriptome, focusing on COVID-19 positivity and symptom severity. Since we detected plenty of potential biomarkers, we performed KEGG enrichment analysis to get better view of altered pathways. Results show, that affected were pathways related to immune processes and response to infection, also multiple signaling pathways, while most of them were also reported to be influenced by SARS-CoV-2 infection in previous studies.

Spatial biology is a rapidly growing research field which focuses on the transcriptomic or proteomic profiling of single cells within tissues with preserved spatial information. Imaging-based spatial transcriptomics uses epifluorescence microscopy, which has shown remarkable results for identification of multiple targets in situ. Nonetheless, the number of genes that can be reliably visualized is limited by the diffraction of light. Here, we investigate the effect of structured illumination (SIM), a super-resolution microscopy approach, over the performance of single gene transcript detection in spatial transcriptomics experiments. We performed direct mRNA-targeted hybridization in situ sequencing for multiple genes in mouse coronal brain tissue sections. We evaluated spot detection performance in widefield and confocal images versus those with SIM in combination with 20X, 25X and 60X objectives. In general, SIM increases the detection efficiency of gene transcripts spots compared to widefield and confocal modes. For each case, the specific fold increase in localizations is dependent on gene transcripts density and the numerical aperture of the objective used, which showed to play an important role especially for densely clustered spots. Taken together, our results suggest that SIM has the capacity to improve spot detection and overall data quality in spatial transcriptomics.

Over the past few decades, phototherapy, encompassing the utilization of both natural and artificial light, has emerged as a dependable and non-invasive strategy for addressing a diverse range of illnesses, diseases, and infections. This therapeutic approach, primarily known for its efficacy in treating skin infections such as herpes and acne lesions, involves the synergistic use of specific light wavelengths and photosensitizers like methylene blue. Photodynamic therapy, as it is termed, relies on the generation of antimicrobial reactive oxygen species (ROS) through the interaction between light and externally applied photosensitizers. Recent research, however, has highlighted the intrinsic antimicrobial properties of light itself, marking a paradigm shift in focus from exogenous agents to the inherent photosensitivity of molecules found naturally within pathogens. Chemical analyses have identified specific organic molecular structures and systems, including porphyrins and conjugated C=C bonds, as pivotal components in molecular photosensitivity. Given the prevalence of these systems in organic life forms, there is an urgent need to investigate the potential impact of phototherapy on individual molecules expressed within pathogens and discern their contributions to the antimicrobial effects of light. This review delves into the recently unveiled key molecular targets of phototherapy, offering insights into their potential downstream implications and therapeutic applications. By shedding light on these fundamental molecular mechanisms, we aim to advance our understanding of phototherapy's broader therapeutic potential and contribute to the development of innovative treatments for a wide array of microbial infections and diseases.

Introduction: Parkinson’s disease (PD) patients harboring recessive gene variants exhibit a distinct clinical phenotype with an early disease onset and relatively mild symptoms. Data concerning individualized therapy for autosomal recessive PD forms are still scarce. Methods: Demographic and treatment data of a cohort of PD carriers of recessive genes (9 homozygous or compound heterozygous PRKN carriers, 4 Heterozygous PRKN carriers and 3 biallelic PINK1 carriers) were evaluated. Results: The average Levodopa Equivalent daily dose (LEDD) was 806.8±453.5 (range 152-1810) in PRKN carriers and 765±96.6 (range 660-850) in PINK1 carriers. The majority responded to low/moderate doses of Levodopa. The response to Dopamine Agonists (DA) was often favorable both as initial and longitudinal therapy. 8/13 PRKN and 1/3 PINK1 carriers were treated with amantadine successfully, and this also applied to patients who could not tolerate Levodopa or DA. Conclusions: In the era of personalized treatment, the therapeutic approach in recessive PD gene carriers might differ as compared to idiopathic PD. Lower LEDD doses were efficient even in patients with a very long disease duration, while a few patients were doing well without any Levodopa treatment decades after disease initiation. DA or amantadine could be used as a first and main line treatment regimen if well tolerated. Literature data on therapeutic strategies in carriers of pathogenic mutations in recessive PD genes, including device-aided treatments will be further discussed.

Injuries to the anterior cruciate ligament (ACL) often result in post-traumatic osteoarthritis (PTOA). To better understand the molecular mechanisms behind PTOA development following ACL injury, we profiled ACL injury-induced gene expression changes in knee joints of three mouse strains with varying susceptibility to OA: STR/ort (highly susceptible), C57BL/6 (moderately susceptible) and super-healer MRL/MpJ (not susceptible). Right knee joints of the mice were injured using a non-invasive tibial compression injury model that closely mimics ACL rupture in humans and global gene expression was quantified before and at 1-day, 1-week, and 2-weeks post-injury using RNA-seq. Following injury, STR/ort displayed severe cartilage degeneration while MRL/MpJ had little cartilage damage. Gene expression analysis suggested that prolonged inflammation and elevated catabolic activity in STR/ort injured joints, compared to the other two strains may be responsible for the severe PTOA phenotype observed in this strain. MRL/MpJ had the lowest expression values for several inflammatory cytokines and catabolic enzymes activated in response to ACL injury. Furthermore, we identified several genes highly expressed in MRL/MpJ compared to the other two strains including B4galnt2 and Tpsab1 which may contribute to enhanced healing in the MRL/MpJ. Overall, this study has increased our knowledge of early molecular changes associated with PTOA development.

Applying the iterative methodology for dimensionality reduction/feature selection using categorical gradient boosted trees, as it has been defined in and has been successfully applied on similar datasets in and , on a dataset consisted of 12708 gene expressions coming from 5052 individuals from 105 studies, we classify whether a person has acute myeloid leukaemia (AML) or is healthy. A CatBoost model on a dataset with reduced dimensions of 72 genes reached a ROC-AUC score of 0.9973 using ten fold cross validation (10CV) and ROC-AUC: 0.9988 on an inference dataset. We further investigate the options of using less genes that potentially could be used in clinical practise and genes than have not been associated to AML yet, or to blood cancer in general. On the same folds of the 10CV and on the same inference dataset the performance of the tuned CatBoost models show that it could be the case that not all genes with an association to AML have been found yet and 19 genes could be enough to predict AML: CatBoost63 (ROC-AUC: 0.9941, Test: 0.9942), CatBoost19: (ROC-AUC: 0.9946, Test: 0.9941) and CatBoost15: (ROC-AUC: 0.9922, Test: 0.9900). In addition, our results verify that a gene diagnostic test for AML could be possible in the future as well as further research is needed on these 15 genes that it could lead to new and better drugs.

In the recent decades, per- and polyfluoroalkyl substances (PFAS) have garnered widespread public attention due to their persistence in the environment and detrimental effects on the health of living organisms, spurring the generation of several transcriptome-centered investigations to understand the biological basis of their mechanism. In this study, we collected 2144 publicly available samples from 7 distinct animal species to examine the molecular responses to PFAS exposure and to determine if there are conserved responses. Our comparative transcriptional analysis revealed that exposure to PFAS is conserved across different tissues, molecules and species. We identified and reported several genes exhibiting consistent and evolutionarily conserved transcriptional response to PFAS, such as ESR1, HADHA and ID1, as well as several pathways including lipid metabolism, immune response and hormone pathways. This study provides the first evidence that distinct PFAS molecules induce comparable transcriptional changes and affect the same metabolic processes across inter-species borders. Our findings have significant implications for understanding the impact of PFAS exposure on living organisms and the environment. We believe that this study offers a novel perspective on the molecular responses to PFAS exposure and provides a foundation for future research into developing strategies for mitigating the detrimental effects of these substances in the ecosystem.

Using coherent optical detection and digital signal processing, laser phase noise and equalization enhanced phase noise can be effectively mitigated using the feed-forward and feed-back carrier phase recovery approaches. In this paper, theoretical analyses of feed-back and feed-forward carrier phase recovery methods have been carried out in the long-haul high-speed n-level phase shift keying (n-PSK) optical fiber communication systems, involving a one-tap normalized least-mean-square (LMS) algorithm, a block-wise average algorithm, and a Viterbi-Viterbi algorithm. The analytical expressions for evaluating the estimated carrier phase and for predicting the bit-error-rate (BER) performance (such as the BER floors) have been presented and discussed in the n-PSK coherent optical transmission systems by considering both the laser phase noise and the equalization enhanced phase noise. The results indicate that the Viterbi-Viterbi carrier phase recovery algorithm outperforms the one-tap normalized LMS and the block-wise average algorithms for small phase noise variance (or effective phase noise variance), while the one-tap normalized LMS algorithm shows a better performance than the other two algorithms for large phase noise variance (or effective phase noise variance). In addition, the one-tap normalized LMS algorithm is more sensitive to the level of modulation formats.

3D printing is a non-traditional additive manufacturing process. It is different from the traditional subtractive manufacturing process. It offers exceptional rapid prototyping capabilities and results that traditional subtractive manufacturing methods cannot attain, especially in applications involving curved or intricately shaped components. Despite its advantages, metal 3D printing will face porosity, warpage, and surface roughness issues. These issues will affect the future practical application of the parts indirectly, for example, the structural strength and the parts assembly ca-pability. Therefore, this study compares the qualities of the warpage, weight, and surface rough-ness after milling and grinding processes under the same materials (316L stainless steel) between general rolled steel and 3D additive steel. Experimental results show that 3D printing parts are approximately 13% to 14% lighter than the general rolled steel. The surface roughness performance of 3D printing steel is better than general rolled steel under the same material after milling or grinding processing. The hardness of the 3D printing steel is better than that of the gen-eral rolled steel. The research verifies that 3D additive manufacturing can use surface processing to optimize surface performance and achieve the functions of lightness and hardness.

The objective of this study is to evaluate the effect of constitutive equations on the prediction accuracy for springback in cold stamping with various deformation modes. In this study, two types of yield functions—Hill’48 and Yld2000-2d—were considered to describe yield behavior. Isotropic and kinematic hardening models based on the Yoshida–Uemori model were also adopted to describe hardening behavior. Various material tests (such as uniaxial tension, tension- compression, loading-unloading, and hydraulic bulging tests) were carried out to determine the material parameters of the models. The obtained parameters were implemented in the finite element (FE) simulation to predict springback, and the results were compared with experimental data. U-bending and T-shape drawing were employed to evaluate the springback prediction accuracy. Obviously, the springback prediction accuracy was greatly influenced by constitutive equations. Therefore, it is important to choose appropriate constitutive equations for accurate description of material behaviors in FE simulation.

Ovarian cancer (OC) is an umbrella term for cancerous malignancies affecting the ovaries, yet treatment options for all subtypes are predominantly derived from high-grade serous ovarian cancer, the largest subgroup. The concept of "functional precision medicine" involves gaining personalized insights on therapy choice, based on direct exposure of patient tissues to drugs. This especially holds promise for rare subtypes like low-grade serous ovarian cancer (LGSOC). This study aims to establish an in vivo model for LGSOC using zebrafish embryos, comparing treatment responses previously observed in mouse PDX models, cell lines, and 3D tumor models. To address this goal, a well characterized patient-derived LGSOC cell line with the KRAS mutation c.35 G > T (p.(Gly12Val)) was used. Fluorescently labelled tumor cells were injected into the perivitelline space of 2 days post fertilization zebrafish embryos. At 1 day post-injection xenografts were as-sessed for tumor size, followed by random allocation into treatment groups with trametinib, luminespib, and trametinib + luminespib. Subsequently xenografts were euthanized and analysed for apoptosis and proliferation by confocal microscopy. Tumor cells formed compact tumor masses (n=84) in vivo and with clear Ki67 staining, indicating proliferation. Zebrafish xenografts exhibited sensitivity to trametinib and luminespib, individually or combined, within a two-week period, establishing them as a rapid and complementary tool to existing in vitro and in vivo models for evaluating targeted therapies in LGSOC.

This research investigates precision enhancement in regional quasi-geoid refinement through ADS80 tri-linear array scanning stereoscopic imagery for aerial triangulation, coupled with GNSS gravity-potential modeling. Acquiring stereoscopic imagery and analyzing triangulation accuracy with the ADS80 camera, we performed this study over the Qinghai-Tibet Plateau's elevated, desolate terrain, collecting 593 GNSS points following high-precision stereoscopic imagery model. Utilizing 12 gravity satellite models, we computed geoid heights and China 1985 Yellow Sea elevations for 28 benchmarks and GNSS points, thereby refining the Qinghai Province Quasi-Geoid Model (QPQM) using geometric techniques. The findings reveal that POS-assisted ADS80 stereoscopic imagery yields high-precision triangulation with maximal horizontal and elevation accuracies of 0.083/0.116 cm and 0.053/0.09 cm, respectively, across five control point arrangements. The RMSE of normal heights for 1985, processed via these GNSS points, achieved decimeter precision. By applying error corrections from benchmarks to the 1985 elevation data from gravity satellites and performing weighted averaging, the precision of EGM2008, SGG-UGM-2, and SGG-UGM-1 models improved to 8.61 cm, 9.09 cm, and 9.38 cm, respectively, surpassing the QPQM by 9.22 cm to 9.99 cm. This research demonstrates that the proposed methods can significantly enhance the precision of regional quasi-geoid surfaces. Additionally, these methods offer a novel approach for rapidly establishing regional quasi-geoid models in the uninhabited areas of the Qinghai-Tibet Plateau.

As the most serious of the many worse new pathological changes caused by diabetes, there are many risk factors for the occurrence and development of DR. It mainly includes hyperglycemia, hypertension, hyperlipidemia and so on. Among them, hyperglycemia is the most critical cause, which plays a key role in the pathological changes of DR.High-sucrose diets(HSDs) lead to elevated blood glucose levels in vivo, which, through oxidative stress, inflammation, the production of AGEs and VEGF, causes plenty of pathological damages to the retina and ultimately bring about loss of vision. The existing therapies for DR primarily target the terminal stage of the disease, when irreversible visual impairment has appeared. Therefore, early prevention is particularly critical. Early prevention of DR-related vision loss requires adjustments to dietary habits, mainly by reducing sugar intake. This article primarily discusses the risk factors, pathophysiological processes, and molecular mechanisms associated with the development of DR caused by HSDs. It aims to raise awareness of the crucial role of diet in the occurrence and progression of DR, promote timely changes in dietary habits, prevent vision loss, and improve the quality of life. The aim is to make people aware of the importance of diet in the occurrence and progression of DR. According to the dietary modification strategies we give, patients can change their poor eating habits timely, avoid the retinopathy that can be avoided theoretically, and obtain a excellent outcome.

The Geopyörä breakage test uses two counter-rotating wheels to nip and crush rock specimens with a tightly controlled gap between rollers. The force applied and energy consumed during breakage are measured with instrumentation. In contrast to drop weigh test (DWT) methods, the breakage energy is a response to the rock compressive strength and degree of compression, not a controlled test input, such as the kinetic input energy of a falling weight. This paper presents the detailed measures conducted to evaluate the accuracy and precision of energy measurements across various ore types using the Geopyörä device. While force measurement was assessed just for its precision. The outputs are compared directly to the DWT measures of fragmentation at the same energy and fitted A and b parameters. The test reproducibility was evaluated using a Round-Robin methodology, testing several samples in multiple laboratories. The results confirmed that the new test has sufficient accuracy to match DWT results and excellent precision to assure reproducibility.

The art of observing and describing behaviors has driven diagnosis and informed basic science in Psychiatry. In recent times, studies of mental illness are focused on understanding the brain’s neurobiology but there is a paucity of information on the potential contributions from peripheral activity to mental health. In Precision Medicine, this common practice leaves a gap between bodily behaviors and genomics that we here propose to address with a new layer of inquiry that includes genes’ expression on tissues inclusive of brain, heart, muscle-skeletal and organs for vital bodily functions. We interrogate genes’ expression on human tissue as a function of disease-associated genes. By removing genes linked to disease from the typical human set, and recomputing the genes’ expressions on the tissues, we can compare the outcomes across mental illnesses, well-known neurological conditions, and non-neurological ones. We find that major neuropsychiatric conditions that are behaviorally defined today (e.g. Autism, Schizophrenia, Depression) through DSM-observation criteria, have strong convergence with well-known neurological ones (e.g. Ataxias, Parkinson), but less overlap with non-neurological ones. Surprisingly, tissues majorly involved in the central control, coordination, adaptation and learning of movements, emotion and memory are maximally affected in psychiatric diagnoses along with peripheral heart and muscle-skeletal tissues. Our results underscore the importance of considering both the brain-body connection and the contributions of the peripheral nervous systems to mental health.

Background: Peripheral Ossifying Fibroma (POF) is a benign gingival tumor generally presenting as a slow-growing mass, usually not ulcerated, and without bleeding. Histopathological exam shows fibrous connective tissue with spots or extensive areas of intralesional calcifications, which can also be detectable on radiograms. Methods and case presentation: We present the case of a 4-year-old male with a retro-incisive swelling of the hard palate, associated with speaking and feeding difficulties. On CT scans, a small intralesional calcification was detected. After conscious sedation, the patient was subjected to diode laser excision (910 nm, 2W, CW, 300 m fiber) with-out post-operative complications. A review was conducted by searching the words “peripheral ossifying fibroma” AND “children” OR “infant” and including manuscripts of the last 15 years. Results: POF is an uncommon lesion of the oral mucosa and may sporadically affect the palatal mucosa in children. Considering its epidemiology and conventional clinical presentation as epu-lis, the pre-operative diagnosis may be relatively difficult. Risk factors could be represented by plaque, calculus, or trauma, all supporting a possible reactive pathogenesis, but a certain etiology is still missing. The main problem of POF is the recurrence rate, which is particularly high (8-20%), thus suggesting that a deep excision is recommended with full extension to the hard tis-sue of bone and to the adjacent periodontal ligament. Diode laser surgery may be recommended for such treatments, especially in pediatric patients, associated with intraoperative conscious se-dation. Data emerging from the relevant literature on POF management in children have been collected and discussed. Conclusions: The authors suggest the use of diode laser for the complete excision of POF, especially in less-compliant patients as children, with a low grade of comorbidi-ties and good outcomes, giving the surgeon a viable alternative to conventional surgery.

Intermittent religious fasting increases a risk of hypo- and hyperglycemia in individuals with diabetes, but its impact in those without diabetes is poorly investigated. The study aim was to examine the effects of religious Bahá'í fasting (BF) on glycemic control and variability and compare these effects with time-restricted eating (TRE). In a three-arm randomized controlled trial, 16 subjects without diabetes were assigned to a BF, 16:8 TRE or control group. Continuous glucose monitoring and food intake documentation was conducted before and during the 19 days of intervention, and 24-hour mean glucose, glycemic variability indices, and glucotypes were assessed. BF and TRE groups, but not the control group, markedly reduced the daily eating window while maintaining macronutrient composition. Only the BF group decreased caloric intake (-677.8 ± 357.6 kcal, p=0.013), body weight (-1.92 ± 0.95 kg, p=0.011) and BMI (-0.65 ± 0.28 kg, p=0.006). Higher maximum glucose values were observed during BF in the within-group (+1.41 ± 1.04, p=0.039) and between-group comparison (BF vs. Control: p= 0.010 and TRE vs. BF: p= 0.022). However, there were no alterations of 24-hour mean glucose, intra- and inter-day glycemic variability indices, or glucotypes in any group. Proportions of time above and below range (70-180mg/dl) and HbA1c values remained unchanged. BF and TRE do not exhibit negative effects on glycemic control and variability in subjects without diabetes.

Somatic embryogenesis-mediated plant regeneration is essential for genetic manipulation of agronomically important traits in upland cotton. Genotype specific recalcitrance to regeneration is a primary challenge in deploying genome editing and incorporating useful transgenes into elite cotton germplasm. In this study, transcriptomes of a semi-recalcitrant cotton (Gossypium hirsutum L.) genotype ‘Coker312’ were analyzed at two critical stages of Somatic Embryogenesis that includes non-embryogenic callus (NEC) and embryogenic callus (EC) cells, and the results compared to a non-recalcitrant genotype ‘Jin668’. We discovered of 305 differentially expressed genes in Coker312, whereas, in Jin668, about 6-fold more genes (2,155) were differentially expressed. A total of 154 differentially expressed genes were common between the two genotypes. Gene enrichment analysis of upregulated genes identified functional categories such as lipid transport, embryo development, regulation of transcription, sugar transport, vitamin biosynthesis, among others. In Coker312 EC cells, five major transcription factors were highly upregulated: LEAFY COTYLEDON 1 (LEC1), WUS-related homeobox 5 (WOX5), ABSCISIC ACID INSENSITIVE3 (ABI3), FUSCA3 (FUS3), and WRKY2. In Jin668, LEC1, BABY BOOM (BBM), FUS3, and AGAMOUS-LIKE15 (AGL15) were highly expressed in EC cells. We also found that gene expression of these embryogenesis genes was typically higher in Jin668 when compared to Coker312. We conclude that significant differences in expression of the above genes between Coker312 and Jin668 may be a critical factor affecting the regenerative ability of these genotypes.

Regardless of the SARS-CoV-2 pandemic containment, it remains paramount to comprehensively understand its underlying mechanisms to mitigate potential future health and economic impacts, comparable to those experienced throughout the course of the pandemic. The angiotensin-converting enzyme 2 (ACE2) provide anchorage for SARS-CoV-2 binding, thus implicating that ACE and ACE2 might contribute to the variability in infection severity. This study aimed to elucidate predisposing factors influencing the disease course among people infected by SARS-CoV-2, focusing on angiotensin-converting enzyme (ACE) and ACE2 polymorphisms. Notably, despite similar demographics and comorbidities, COVID-19 patients exhibit substantial differences in prognosis. Genetic polymorphisms in ACE and ACE2 have been implicated in disease progression, prompting our investigation into their role in COVID-19 evolution. Using next-generation sequencing (NGS), we analyzed ACE and ACE2 genes in a sample group comprising six subjects infected by SARS-CoV-2. Our findings revealed a correlation between specific polymorphisms and COVID-19 outcomes. Specifically, ACE and ACE2 intronic deletions were observed in all deceased patients, suggesting a potential association with mortality. These results highlight the significance of genetic factors in shaping the clinical course of COVID-19, emphasizing the importance of further research into the impact of genetic variations on COVID-19 severity.

Heat stress affects the granulosa cells (GCs) and ovarian follicular microenvironment, causing poor oocyte developmental competence and fertility. This study aimed to investigate the physical responses and global transcriptomic changes in bovine GCs to acute heat stress (43 ℃ for 2 h) in-vitro and gave essential insights into the general interaction at cell–stress nexus. Heat-stressed GCs exhibited transient proliferation senescence, resumed proliferation at 48 h post-stress. While post-stress immediate culture-media change had a relatively positive effect on proliferation resumption. Increased accumulation of reactive oxygen species and apoptosis was observed in heat stress group. In spite of the upregulation of pro-apoptotic and caspase executioner genes, antioxidants and anti-apoptotic genes were also upregulated in heat-stressed GCs. Progesterone and Estrogen hormones along with steroidogenic genes expression, declined significantly, in spite of the upregulation of genes involved in cholesterol synthesis. Out of 12385 differentially expressed genes (DEGs), 330 significant DEGs (75 upregulated, 225 downregulated) were subjected to KEGG functional pathway annotation, gene ontology enrichment, and STRING network analyses. Based on the manual query of DEGs, pathway and enrichment analyses, a vast interplay observed among all major signaling pathways strongly evidence the repression of cellular transcriptional and proliferation activity, averting the effects of heat stress through remodeling of cellular structural proteins and energetic-homeostasis. This study presents detailed responses of acute heat-stressed GCs at physical, transcriptional, and pathway levels and presents interesting insights into future studies regarding GCs adaptation and their interaction with oocyte and reproductive system at ovarian level.

In the current era of emerging technologies and cutting-edge inventions, it is equally important to consider the ethical aspects of research and innovations to have an ethical space for the sustainable progress and safety of humanity. The field of nanotechnology research and innovation often faces criticism due to its potential adverse impacts on health, the environment, limited accessibility, high costs, and concerns about data privacy. Therefore, it is crucial to address and resolve these ethical issues systematically. In this regard, we present our findings and methods for exploring the application of the ethics technology assessment method by considering its relevance at different stages of innovation development. Specifically, the present work provides a conceptual framework for ethics technology assessment focused on evaluating a significant innovation in nanotechnology within the domain of food safety with specific reference to mycotoxin remediation. By leveraging the value-sensitive design approach, we described various strategies for analyzing the ethical implications of nanotechnology-based innovations in mycotoxin remediation. Through comprehensive ethics technology assessment, nanotechnology-based innovations can be guided toward becoming more value-oriented, thereby making substantial contributions to social progress.

With the rapid growth of the global mask market, consumers have put forward higher requirements for the functionality and ingredients of mask products. Conventional facial masks generally use ordinary non-woven fabrics as the mask base fabric, are frequently used with essence and contain preservatives. At the same time, they are susceptible to the influence of the external environment and are easily oxidized, which will cause the mask to deteriorate and skin allergic reactions. In addition, conventional facial masks exhibit poor skin fit, limited breathability, inadequate absorption of nutrient solutions, and easy to drip. The nanofiber mask prepared by electrospinning technology has a high specific surface area and high porosity, which enabling improved nutrient absorption by the skin, superior skin fit, and exceptional breathability. Nanofiber facial mask products can effectively solve consumer pain points and promote the high-end development of facial mask products. Therefore, this research combines needleless electrospinning technology to develop a novel solid-state, preservative-free, and quick-dissolving nanofiber facial mask that can be prepared on a large scale. Based on needleless electrospinning technology, this research deeply explores the process parameters and influencing mechanisms of quick-dissolving nanofiber facial masks, ultimately achieving the consistent production of nanofiber facial masks with optimal morphology. Comprehensive and evaluation of the structure and performance of nanofiber facial masks from micro and macro perspectives. The experimental results show that the mask morphology is optimal under the process conditions of using a spinning liquid of 20% collagen peptide solution, a spinning voltage of 30kV, a collection distance of 19cm, and a liquid supply speed of 130ml/h.

Over the past 10 years, developments in precision oncology have led to a rise in the demand for predictive tools that assist the screening and stratification of patients for treatment. The possibility digitizing tissue slide images has led to the creation of artificial intelligence (AI) and machine learning algorithms in digital pathology, which could ultimately improve patient care. In this context, we assessed a variety of AI-based computational models for digital pathology. In this review, we described the latest AI applications in oncology, current challenges and future prospects.

Uterine pathologies pose a challenge to women’s health on a global scale. Despite extensive research, the causes and origin of some of these common disorders are not well defined yet. This study presents a comprehensive analysis of transcriptome data from diverse datasets encompassing relevant uterine pathologies such as endometriosis, endometrial cancer and uterine leiomyomas. Leveraging the Comparative Analysis of Shapley values (CASh) technique, we demonstrate its efficacy in improving the outcomes of classical differential expression analysis on transcriptomic data derived from microarray experiments. CASh integrates the Microarray game algorithm with Bootstrap resampling, offering a robust statistical framework to mitigate the impact of potential outliers in the expression data. Our findings unveil novel insights into the molecular signatures underlying these gynecological disorders, highlighting CASh as a valuable tool for enhancing the precision of transcriptomics analyses in complex biological contexts. This research contributes to a deeper understanding of gene expression patterns and potential biomarkers associated with these pathologies, offering implications for future diagnostic and therapeutic strategies.

The advancement of precision medicine in medical care has led behind the conventional symptom-driven treatment process by allowing early risk prediction of disease through improved diagnostics and customization of more effective treatments. It is necessary to scrutinize overall patient data alongside broad factors to observe and differentiate between ill and relatively healthy people to take the most appropriate path toward precision medicine, resulting in an improved vision of biological indicators that can signal health changes. Precision and genomic medicine combined with artificial intelligence have the potential to improve patient healthcare. Patients with less common therapeutic responses or unique healthcare demands are using genomic medicine technologies. AI provides insights through advanced computation and inference, enabling the system to reason and learn while enhancing physician decision-making. Many cell characteristics, including gene up-regulation, proteins binding to nucleic acids, and splicing, can be measured at high throughput and used as training objectives for predictive models. Researchers can create a new era of effective genomic medicine with the improved availability of a broad range of data sets and modern computer techniques such as machine learning. This review article has elucidated the contributions of ML algorithms in precision and genome medicine.

The agricultural industry places a high priority on crop protection, especially when it comes to protecting cotton crops against pink bollworm infestations. Rapid disease identification is essential for effective crop management at the same time. In order to overcome these difficulties, this study offers a thorough process for utilizing the Robot Operating System (ROS) to deploy an autonomous robot. The robot is intended to identify infestations of pink bollworm in addition to counting and tracking red Disease that impact cotton harvests. Drones are used to check the condition of cotton fields, and the autonomous robot's path planning component is closely related to this process. Large fields are photographed by these drones in multispectral mode, and the robot's course planning technique is done by calculating the Normalized Difference Vegetation Index (NDVI) based on these images. This approach ensures targeted surveillance and intervention, optimizing the use of resources. A customized dataset was created especially for this application to improve the robot's detecting abilities. The dataset was utilized to train a YOLOV8 model, a state-of-the-art object detection architecture. Performance characteristics of the trained model are impressive; it has a mean Average Precision (mAP) of 67.1%, Precision of 67.9%, and Recall of 61.8%. These metrics highlight how well the model works to precisely locate and measure interesting occurrences in the cotton fields. In order to address the particular challenges presented by pink bollworm infestations and crop diseases in the context of cotton cultivation, this research contributes a comprehensive solution for autonomous crop monitoring and protection by seamlessly integrating ROS, drones, NDVI calculations, and a robust detection model.

The strictness of the result of an excavation, whether mechanical or by means of explosives, is naturally conditioned by the objective, and therefore by the type of technique applied to achieve it. To attain the best results in terms of rock breakage and respect of the final profile, it’s important to evaluate the excavation specific energy and its optimization. This research focuses on evaluating the effects of different techniques on the quality of final walls in open-pit and underground operations. Different geometries and configurations can be applied to both quarrying and tunnelling blasts. The research is aimed to push contour blasts to their limits, and the main aspects are discussed in order to improve the blast parameters in the daily practice.

Organic electrophoretic coatings have gained significant attention due to their ability to provide a uniform and controlled coating thickness, high coating adhesion, and excellent corrosion protection properties. In this work, to investigate the aging behaviors of an organic electrophoretic coating for magnesium alloys, water absorption kinetics curves of the coatings after immersion in 3.5 wt.% NaCl at 20℃ and 40℃ were measured, and parameters such as the saturated water absorption and saturation time of the free film coatings were fitted to determine the densification evolution of the organic coatings. A tensile test was used to test the tensile strength of the organic coating during service to determine the evolution of the strength and toughness of the organic coating. The evolution of wet adhesion of organic coatings with service time was tested by ASTM D4541-02. The impedance spectra of the organic coatings under the above conditions were detected by EIS, and finally, the failure behavior of the organic coatings under immersion was analyzed.

Allopolyploidy in plants involves the merging of two or more distinct parental genomes into a single nucleus, a significant evolutionary process in the plant kingdom. Transcriptomic analysis provides valuable insights into the fate of duplicated genes, evolutionary novelties, and environmental adaptations exhibited by allopolyploid plants. However, transcriptome profiling is challenging due to genomic redundancy, which is further complicated by the presence of multiple chromosomes sets and the variations among homoeologs and allelic genes. Prior to transcriptome analysis, sub-genome phasing and homoeology inference are essential for obtaining a comprehensive view of gene expression. This review aims to clarify the terminology in this field, identify the most challenging aspects of transcriptome analysis, explain their inherent difficulties, and suggest reliable analytic strategies. Furthermore, bulk RNA-seq is highlighted as a primary method for studying allopolyploid expression, with an emphasis on the critical steps of read mapping and normalization in differential gene expression analysis.

In this pilot study, 84 subjects with a history of poor oral hygiene were enrolled in an interventional, randomized, prospective, open-label controlled trial. The aim was to provide preliminary clinical data on the products, the recruitment monthly rate, and patient satisfaction to estimate resources for the main study, as well as descriptive data on oral microbiota rebalancing. The population was divided into five groups: 20 participants received delicate mint toothpaste, 12 mint toothpaste, 20 mint mouthwash, and 20 delicate mint toothpaste and mint mouthwash using a specifically custom-designed toothbrush. The 12 subjects in the control group continued to use their oral care products without changing their normal routine. The study duration was 28 days.All subjects tolerated the products well, and there were no adverse events. Recruitment capability and procedures allowed a realistic estimation for the future main trial. The products did not cause any changes in tooth color. The participants who completed the treatment consisting of delicate mint toothpaste, mint mouthwash, and the specifically custom-designed toothbrush showed the gingival sensitivity best results (p≤0.000). The products induced a statistically significant decrease in plaque in all treated groups (p≤0.000) and an improvement of gingival sensitivity (delicate mint toothpaste, p≤0.005; mint toothpaste, p≤0.015; and mint mouthwash, p≤0.015). All the products were effective in stabilizing the oral microbiota.The tested products showed an optimal safety profile. They also stabilized the biodiversity of the oral microbiota, making it less susceptible to microbial fluctuations than the control group.

This paper presents high-resolution digital measurements at low cost, combining oversample and averaging. Experiments of processing data between samples and between multiple temperature sensors in the same environment are conducted. They provide results with effective increase in the analog to digital converter bits, in correspondence with the minimization on the effects of noise. It is shown that averaging a set of small, low-cost sensors at a small microcontroller can give flexible access to improved measurement accuracy.

The addition of 50% vitreous humor to the media bathing lens explants results in the induction of both fiber cell differentiation and a significant immune/inflammatory response. Although Fgfr loss blocks differentiation in lens epithelial explants, this loss is partially rescued by deleting Pten. To investigate the functions of the Fgfrs and Pten during lens fiber cell differentiation, we utilized a lens epithelial explant system and conducted RNA sequencing on vitreous humor-exposed explants lacking Fgfrs, or Pten or both Fgfrs and Pten. We found that Fgfr loss impairs both vitreous-induced differentiation and inflammation while the additional loss of Pten restores these responses. Furthermore, transcriptomic analysis suggested that PDGFR-signaling in FGFR-deficient explants is required to mediate the rescue of vitreous-induced fiber differentiation in explants lacking both Fgfrs and Pten. The blockage of β-crystallin induction in explants lacking both Fgfrs and Pten in the presence of a PDGFR-inhibitor supports this hypothesis. Our findings demonstrate that a wide array of genes associated with fiber cell differentiation are downstream of FGFR-signaling and that the vitreous-induced immune responses also depend on FGFR-signaling. Our data also demonstrates that many of the vitreous-induced gene expression changes Fgfr-deficient explants are rescued in explants lacking both Fgfrs and Pten.

A time-sequential working, spatially multiplexed autostereoscopic 3D display design consisting of a fast switchable RGB-color filter array is presented. The wavelength-selective filter barrier emits the light from a display over a larger active area than common autostereoscopic barrier displays. An optical modelling of wavelength-selective barriers has been used for instance to calculate the light ray distribution properties of that arrangement. To find well working display designs, automated searches by simulation and computational evaluation has been proceeded. Wavelength-selective filter barrier arrangements exhibit characteristics different from common barrier displays with similar barrier pitch and ascent. In particular, these constructions show strong angular luminance dependency under barrier inclination specified by correspondent slant angle. In time-sequential implementation it is important to avoid that quick eye or eyelid movement lead to visible color artifacts. In the millisecond regime tunable liquid crystal Fabry-Pérot color filters for the colors red, green and blue are presented. They consist of a sub-micrometer thick nematic layer sandwiched between dielectric mirrors and ITO-electrodes. These cells shall switch narrow-banding light of red, green or blue. An array for a glasses-free 3D display has to be equipped with several thousand switchable filter elements having different color apertures. The newly introduced design is usable as a multi user display as well as a single user system with user adaptive control.

The straightness error of guideway is one of the key indicators of ultra-precision machine, which plays an important role in the machining accuracy of work-piece. In order to measure straightness error of long distance ultra-precision guideway accurately, a splicing measurement for straightness error of guideway using high-precision flat mirror and displacement sensor was proposed in this paper, and the data splicing processing algorithm based on coordinate transformation was studied. Then comparative experiments on splicing measurement and direct measurement of straightness error were carried out on a hydrostatic guideway grinder. The maximum difference between the two measurements was about 0.3μm, which was far less than the straightness error of 5.8μm. The experiment demonstrated the correctness of the proposed splicing measurement method and data processing algorithm. To suppress the influence of straightness error on machining accuracy, a straightness error compensation algorithm based on error rotation transformation and vertical axis position correction was proposed, and the grinding experiment of a plane optics with size of 1400mm×500mm was carried out, whose flatness error after grinding was about 2.98μm. The straightness error of the grinding machine had been well suppressed.

The makeup of human microbiota has been linked to a number of autoimmune disorders. Recent developments in whole metagenome sequencing and 16S rRNA sequencing technology have considerably aided research into the microbiome and its relationship to disease. Due to the inherent high dimensionality and complexity of data generated by high-throughput platforms, conventional bioinformatics techniques could only provide an inadequate explanation for the most relevant changes and seldom provide correct predictions. Machine learning, on the other hand, is a subset of artificial intelligence applications that enable the untangling of high-dimensional systems and intricate knots in correlation by learning complex patterns and improving automatically from training data without being explicitly programmed. Machine learning is increasingly being utilized to research the influence of microbes on the onset of illness and other clinical features since computer power has increased dramatically in the last few decades. In this review paper, we focused on emerging methodological approaches of supervised machine learning algorithms for identification of autoimmune disorders utilizing metagenomics data, as well as the potential benefits and limitations of machine learning models in clinical applications.

Purpose: Investigation for the co-occurrence of two unrelated genetic disorders of muscular dystrophy and Prader-Willi syndrome (PWS) (OMIM # 176270) using joint whole genome sequencing (WGS). Methods: Trio WGS joint analysis was performed to investigate the genetic etiology in a proband with PWS, prolonged muscular hypotonia associated hyperCKemia, and early-onset obesity. The parents were unaffected. Results: Results showed maternal isodisomy uniparental disomy (UPD) in chromosome 15 expanding from 15q11.2 to 15q22.2, including PWS regions at 15q11.2-15q13. Maternal heterodisomy was detected from 15q22.2 to 15q26.3. A pathogenic variant, NM_000070.3(CAPN3):c.550del (p.Thr184fs), was identified at 15q15.1 in a heterozygous state in the mother that was homozygous in the proband due to maternal isodisomy. Conclusion: This is the first study of the concurrent molecular etiology of PWS and calpainopathy (OMIM # 253600) in the same patient. This report highlights the utility of joint analysis and need for the assessment of autosomal recessive disease in regions of isodisomy in patients with complex and unexplained phenotype.

This paper introduces a method to achieve optimal Gaussian estimation even when operating with low precision numerical representations of the full covariance matrix, such as 16-bit integer or 32-bit single precision formats, instead of more expensive double precision floating point representations (64 bits). The approximation's numerical error is accurately estimated in a conservative manner, resulting in minimal covariance inflation. When combined with a compressed estimator like the GCKF (Global Compressed Kalman Filter), which performs global updates at low frequency, this approach produces estimates that are nearly identical to the optimal ones. The deviation between the results obtained using this new approach and those obtained with the standard GCKF (which operates in double precision) is negligible. This new method is particularly relevant in high-dimensional estimation problems that require high-frequency operation. By leveraging the compressed operation of the GCKF, the estimation process applies the necessary approximation only during low-frequency global updates, using the single precision format instead of the more expensive double precision representations. The introduced numerical approximation is accurately estimated while maintaining a conservative approach, resulting in minimal covariance inflation. When combined with the GCKF, which performs global updates at low frequency, the estimates produced are almost identical to the optimal ones. This approach is particularly useful in high-dimensional estimation problems that require high-frequency operation. By representing the full covariance matrix in a low precision numerical format and applying appropriate scaling, memory usage is significantly reduced, offering substantial benefits for high-dimensional estimation problems and enabling real-time smoothing processes in such cases. By reducing the memory requirements for storing the full covariance matrix, the CPU avoids encountering a high number of page faults, resulting in improved processing performance. Lastly, the experimental section verifies the performance of the proposed approach in a single precision (32-bit floating point) GCKF SLAM process, assessing expected values and standard deviations of marginal PDFs.

We communicate the theoretical results in the study of utilizing the quantum phenomena in optical current for quantum computation in the context of high precision voltage measurements. These results can be used around computation by quantum sampling and quantum communication as the basis for further research and physical implementations. We propose a Main Optical Setup (MOS) for such computation which allows to make a superstructure to implement specific computations and algorithms. To create MOS we used the nonlinear units (e.g., beta- barium borate crystal), arranged in series, powered with pulsed laser pump, and ended with the beam splitter to generate the output state of a number of entangled photon pairs. The computation is made by propagation of entanglement with beam splitters applied crossword and adjustable phase shifters that are tools for parameter steering. We show how to implement the series of cosine-based components on the example of two-component case. The results opens a broad area for future research in the area of building quantum optimizer using the quantum sampling methods and in the area of high precision temporal measurement of voltage, which is the important process for building high-fidelity devices.

The emergence of three-dimensional human organoids has opened the door for development of patient-derived cancer organoid (PDO) models, which closely recapitulate parental tumor tissue. Mainstays of preclinical cancer modeling include in vitro cell lines and patient-derived xenografts, but these models lack the cellular heterogeneity seen in human tumors. Moreover, xenograft establishment is resource- and time-intensive, rendering these models difficult to use to inform clinical trials and decisions. PDOs, however, can be created efficiently and retain tumor-specific properties such as cellular heterogeneity, cell-cell and cell-stromal interactions, tumor microenvironment, and therapeutic responsiveness. PDO models and drug screening protocols have been described for several solid tumors and, more recently, for gliomas. Since PDOs can be developed in clinically relevant timeframes and share many characteristics of parent tumors, they may enhance the ability to provide precision oncologic care for patients. This review explores the current literature on cancer organoids, highlighting the history of PDO development, organoid models of glioma, and potential clinical applications of PDOs.

In this paper, a novel integrated high precision vacuum microelectronic accelerometer is put forward based on the theory of field emission, the accelerometer consists of sensitive structure and interface ASIC. The sensitive structure has a mass of a cathode cone tips array, a folded beam, an emitter electrode and a feedback electrode. The sensor is fabricated on a double side polished (1 0 0) N-type silicon wafer, the tips array of cathode are shaped by wet etching with HNA (HNO3, HF and CH3COOH) and metalized by TiW/Au thin film. The structure of sensor is released by ICP process finally. The interface ASIC was designed and fabricated based on the P-JFET high voltage bipolar process. The accelerometer is tested through static field rollover test, and the test results show the integrated vacuum microelectronic accelerometer has good performances, which sensitivity is 3.081V/g and non-linearity is 0.84% in the measuring range of −1g~1g.

Ionizing radiation exposure can cause damage to diverse tissues and organs, with the hematopoietic system being the most sensitive. However, limited information is available regarding the radiosensitivity of various hematopoietic cell populations in the bone marrow due to the high heterogeneity of the hematopoietic system. In this study, we observed that granulocyte-macrophage progenitors, hematopoietic stem/progenitor cells and B cells within the bone marrow showed the highest sensitivity, exhibiting a rapid decrease in cell numbers following irradiation. Nonetheless, neutrophils, natural killer (NK) cells, T cells, and dendritic cells demonstrated a certain degree of radioresistance, with neutrophils exhibiting the most pronounced resistance. By employing single-cell transcriptome sequencing, we investigated the early responsive genes in various cell types following irradiation, revealing that distinct gene expression profiles emerged between radiosensitive and radioresistant cells. In B cells, radiation exposure led to a specific upregulation of genes associated with mitochondrial respiratory chain complexes, suggesting a connection between these complexes and cell radiosensitivity. In neutrophils, radiation exposure resulted in fewer gene alterations, indicating their potential for distinct mechanisms in radiation resistance. Collectively, this study provides insights into the molecular mechanism for the heterogeneity of radiosensitivity among the various bone marrow hematopoietic cell populations.

Background: Metastatic tumors to the oro-facial tissues are rare, with an incidence ranging between 1% and 8% of all oral malignant tumors. Generally reported with a peak of incidence in the 5th–7th decades, but possibly occurring at any age, metastases may represent the first sign of an occult cancer or manifest in patient with an already known history of a primary carcinoma, mostly from the lungs, kidney, prostate, and colon-rectum in males, or uterus, breast, lung, and ovary in females. In the oro-facial tissues the sites mostly involved are the oral mucosa, gingiva/jawbones, tongue, and the salivary glands. Methods: A systemic Review of the literature on the oro-facial metastases from Renal Cell Carcinoma has been conducted by searching the most used databases, also focusing the attention on the Clear Cell histological variant, representing the most frequent one. Results: Among the 160 analyzed studies, 211 cases of oral metastases of renal cancer were found (148 in males, 70.1%; 63 in females, 29.8%), with an average age of 59.8 years. In almost 40% of cases, metastasis represented the first clinical manifestation of the primary tumor and 122 were histologically diagnosed as ccRCCs (57.8%). The tongue was involved in most of the cases (26% - 55 cases), followed by mandible (18.9% - 40 cases), gingiva (18.4% - 39 cases), maxilla (10.9% - 23 cases), parotid gland (10.4% - 22 cases), buccal mucosa (5.2% - 11 cases), lips (3.3% - 7 cases), hard palate (2.8% - 6 cases), soft palate, masticatory space and submandibular gland (0.9% - 2 cases), lymph nodes, tonsil and oral floor (0.4% - 1 case). Among the 122 ccRCCs (84 were males, 68.8 %; 38 females, 31.1%), with an average age of 60.8 years and representing in 33.6% the first clinical manifestation, the tongue remained the most involved site (25.4% - 31 cases), followed by the gingiva (17.2% - 21 cases), parotid gland (13,1% - 16 cases), mandibular bone (12.2% - 15 cases), maxillary bone (11.4% - 14 cases), buccal mucosa and lips (4.9% - 6 cases), hard palate (4% - 5 cases), submandibular gland and soft palate (1.6% - 2 cases), lymph nodes, tonsil, oral floor and masticatory space (0.8%. 1 case). The clinical presentation in soft tissues was represented by a fast-growing exophytic mass, sometimes accompanied by pain. Instead inn bone appeared as radiolucent lesions, with ill-defined borders and cortical erosion. Conclusions: Our data focus the attention on the incidence of Renal cancer Metastases in the oro-facial tissues. Their early identification surely represents a diagnostic challenge, especially when the clinical work-up is limited to the cervico-facial region. Nevertheless, early diagnosis and recently introduced adjuvant therapies may represent the key for better outcomes. Therefore, general guidelines about clinical and radiological identification of oro-facial potentially malignant lesions should be part of the cultural background of any dentists.

In current severe global emergency situation of 2019-nCov outbreak, it is imperative to identify vulnerable and susceptible groups for effective protection and care. Recently, studies found that 2019-nCov and SARS-nCov share the same receptor, ACE2. In this study, we analyzed five large-scale bulk transcriptomic datasets of normal lung tissue and two single-cell transcriptomic datasets to investigate the disparities related to race, age, gender and smoking status in ACE2 gene expression and its distribution among cell types. We didn’t find significant disparities in ACE2 gene expression between racial groups (Asian vs Caucasian), age groups (>60 vs <60) or gender groups (male vs female). However, we observed significantly higher ACE2 gene expression in former smoker’s lung compared to non-smoker’s lung. Also, we found higher ACE2 gene expression in Asian current smokers compared to non-smokers but not in Caucasian current smokers, which may indicate an existence of gene-smoking interaction. In addition, we found that ACE2 gene is expressed in specific cell types related to smoking history and location. In bronchial epithelium, ACE2 is actively expressed in goblet cells of current smokers and club cells of non-smokers. In alveoli, ACE2 is actively expressed in remodelled AT2 cells of former smokers. Together, this study indicates that smokers especially former smokers may be more susceptible to 2019-nCov and have infection paths different with non-smokers. Thus, smoking history may provide valuable information in identifying susceptible population and standardizing treatment regimen.

The G protein-coupled receptor (GPCR) Smoothened (Smo) is a central signal transducer of the Hedgehog (Hh) pathway which has been linked to diverse forms of tumours. Stimulated by advancements in structural and functional characterisation, the Smo receptor has been recognised as an important therapeutic target in Hh-driven cancers, and several Smo inhibitors have now been approved for cancer therapy. This receptor is also known to be an oncoprotein itself and its gain-of-function variants have been associated with skin, brain, and liver cancers. According to the COSMIC database, oncogenic mutations of Smo have been identified in various other tumours, although their oncogenic effect remains unknown in these tissues. Drug resistance is a common challenge in cancer therapies targeting Smo, and data analysis shows that healthy individuals also harbour resistance mutations. Based on the importance of Smo in cancer progression and the high incidence of resistance towards Smo inhibitors, this review suggests that detection of Smo variants through tumour profiling could lead to increased precision and improved outcomes of anti-cancer treatments.

We are in a new chapter of crop and livestock improvement with the emergence of genome editing. This latest generation of molecular tools can be used to make targeted changes in a genome including insertions, deletions, and mutations. With new advances comes new risks for unintended changes and impacts, thus the need for appropriate risk assessment for product development and to inform regulatory measures. Though CRISPR/Cas has arisen as the predominant technology, there are multiple types of genome editing tools each with pros and cons depending on the organism and desired outcome. Furthermore, each editing tool differs in specificity as they may edit non-intended sites, referred to as off-target edits. The consensus of the agricultural editing community is to avoid off-target editing through design and detection, instead of determining whether off-target editing in each case is detrimental. The design of a targeting component, the tool chosen, and the identification of the edit(s) made are the critical factors in avoiding off-target edits and confirming intended edits in final products that are released commercially. The limited amount of head-to-head comparisons of genome editing tools in diverse crops and livestock make it difficult to develop broad conclusions and best practices, which is further compounded by the diversity of techniques, targets, and processes. Developers and breeders should consult the literature and test as needed to determine which editing technology will be the most effective for their purposes, especially as more tools with altered efficiency and specificity become available. Yet, the lack of off-target edits in studies that employed careful design of targeting components followed by wide testing for on- and off-target edits bodes well for the use of genome editing with proper precautions of target selection and screening.

Precision and accuracy estimation is an important index used to reflect the measurement performance and quality of a measurement system. To reveal the significance and connotations of the precision and accuracy estimation index of a close-range photogrammetry system, several common precision and accuracy estimation methods used in close-range photogrammetry are explained from a theoretical perspective, and the mechanism of the internal coincidence precision estimation and the external coincidence accuracy estimation are deduced, respectively. Through detailed experimental design and testing, the validity and reliability of the proposed precision and accuracy estimation methods are verified, which provides strong evidence for the quality control, optimisation, and evaluation of the measurement results from a close-range photogrammetry system. At the same time, it has significance for the further development of precision and accuracy estimation analysis of close-range photogrammetry systems.

Endothelial cell (EC) injury is a crucial contributor to progression of diabetic kidney disease (DKD), but the specific EC populations and mechanisms involved remain elusive. Kidney ECs (n=5,464) were collected at 3 timepoints from diabetic BTBRob/ob mice and non-diabetic littermates. Their heterogeneity, transcriptional changes, and alternative splicing during DKD progression were mapped using SmartSeq2 single-cell RNA sequencing (scRNAseq) and elucidated by pathway, network, and gene ontology enrichment analyses. We identified 13 distinct transcriptional EC phenotypes corresponding to different kidney vessel subtypes, confirmed by in-situ hybridization and immunofluorescence. EC subtypes along nephrons displayed extensive zonation related to their functions. Differential gene expression analyses in peritubular and glomerular EC in DKD underlined regulation of DKD-relevant pathways including EIF2 signaling, oxidative phosphorylation, and IGF1 signaling. Importantly, it revealed differential alteration of these pathways between the two EC subtypes and changes during disease progression. Furthermore, glomerular and peritubular EC also displayed aberrant and dynamic alteration of al-ternative splicing (AS), strongly associated with DNA repair. Strikingly, genes displaying differential transcription or alternative splicing participate in divergent biological processes. Our study reveals spatiotemporal regulation of gene transcription and AS linked to DKD progression, providing insight into pathomechanisms and clues to novel therapeutic targets for DKD treatment.

To understand the molecular mechanism underlying yellowing in pineapples during ripening, coupled with alterations in fruit quality, comprehensive metabolome and transcriptome investigations were carried out. These investigations were conducted using the pulp samples collected at three distinct stages of maturity: young fruit (YF), mature fruit (MF), and fully mature fruit (FMF). This study revealed a noteworthy increase in the levels of total phenols and flavones, coupled with a concurrent decline in lignin and total acid content, as the fruit transitioned from YF to FMF. Furthermore, the analysis yielded 167 differentially accumulated metabolites (DAMs) and 2194 differentially expressed genes (DEGs). Kyoto Encyclopedia of Genes and Genomes enrichment analysis based on DAMs and DEGs revealed that the biosynthesis of plant secondary metabolites, particularly the flavonol, flavonoid, and phenypropanoid pathways, plays a pivotal role in fruit yellowing. Additionally, a comprehensive regulatory network encompassing genes that contribute to metabolisms of flavones, flavonols, lignin, and organic acids was proposed. This network sheds light on the intricate processes that underlies fruit yellowing and quality alterations. These findings enhance our understanding of the regulatory pathways governing pineapple ripening and offer valuable scientific insight into the molecular breeding of pineapples.

Herein, we examined the effects of weather parameters and individual clinicodemographic risk factors on hemorrhagic stroke (HS) incidence, severity on admission, and disability at discharge in a harsh desert climate. In a retrospective design we studied patients admitted to a stroke unit in Arab Emirates in 2016-2019. With a distributed lag nonlinear model we explored immediate and delayed effects of weather on stroke incidence. Supervised machine learning was used to build models predictive of scores in NIHSS and mRS scales. We assessed model performance by calculating ROC AUC, F1 scores, specificity, and sensitivity. HS risk increased after a significant change in any weather parameter. The risk was reduced below baseline after 1-week adjustment to environmental changes. Climate input from the previous 3 days yielded reliable classification models. Humidex was a better predictor of severity than air temperature, whereas body mass index, age, and time of day at stroke onset were the strongest clinical predictors. Weather parameters before admission were stronger predictors of disease severity than individual clinical factors. We addressed limitations of previous studies by analyzing a full set of climate parameters: ambient temperature, relative humidity, humidex, atmospheric pressure, and wind speed. Predictive models may help to optimize patient management and develop preventative strategies.

Arguably the most important parameter in treating cognitive decline associated with Alzheimer’s disease is the length of time in which improvement, if achieved at all, is sustained. However, monotherapies such as donepezil and memantine are associated with a more rapid decline than no treatment in patients over multi-year follow-ups {Kennedy, 2018 #535}. Furthermore, anti-amyloid antibody treatment, which at best simply slows decline, is associated with accelerated cerebral atrophy [2], resulting in earlier dementia-associated brain volumes for those treated at the MCI stage than untreated patients. In contrast, a precision medicine approach, in which the multiple potential drivers of cognitive decline are identified for each patient and then targeted with a personalized protocol (such as ReCODE), has led to documented improvement in patients with cognitive decline [3]; [4]; [5], but long-term follow-up (>5 years) has not been reported previously. Therefore, we report here sustained cognitive improvement, in some cases for over a decade, in patients treated with a precision medicine protocol—something that has not been reported in patients treated with anti-cholinesterase, glutamate receptor inhibitory, anti-amyloid, or other therapeutic methods. These case studies warrant long-term cohort studies to determine how frequently such sustained cognitive improvements occur in patients treated with precision medicine protocols.

Precision farming is transforming how antibiotics get used on farms. It fights the overuse that creates superbugs and harms nature. Farmers now have high-tech tools to use antibiotics ultra-precisely. Drones, AI, and advanced watering equipment help them pinpoint only sick plants and animals needing treatment. This shields crops while avoiding environmental damage. More careful antibiotic use makes livestock healthier and boosts harvests too. And it prevents resistant germs, where antibiotics stop working altogether. The approach helps farms in multiple ways, using fewer resources for even better results. There are some growing pains, like high costs for small farms. And questions around data privacy also cause concern. But overcoming these hurdles could help precision farming spread far and wide. This technology promises safer, greener, and more productive agriculture. Targeted use of antibiotics protects both crops and people from drug-proof germs. And nature benefits when less medicine spreads into soil and water. The future looks bright for farming with precision.

Significant advances in Penning trap measurements of atomic masses and mass ratios of the proton, deuteron, triton, helion, and alpha-particle have occurred in the last five years. These include a measurement of the mass of the deuteron against 12C with 8.5 x 10-12 fractional uncertainty; resolution of vibrational levels of H2+ as mass and the application of a simultaneous measurement technique to the H2+/D+ cyclotron frequency ratio, yielding a deuteron-proton mass ratio at 5 x 10-12; new measurements of HD+/3He+, HD+/T+ and T+/3He+ leading to a tritium beta-decay Q-value with an uncertainty of 22 meV, and atomic masses of the helion and triton at 13 x 10-12; and a new measurement of the mass of the alpha-particle against 12C at 12 x 10-12. Some of these results are in strong disagreement with previous literature values. Their impact in determining a precise proton/electron mass ratio and electron atomic mass from spectroscopy of the HD+ molecular ion is also discussed.

For both conventional and renewable energy conversion processes, computational fluid dynamics (CFD) has been used to address more energy-related challenges in recent decades. Using CFD to investigate vertical-axis wind turbines has become more common in recent years. The main goals of this application have been to more accurately predict the turbine's performance and to comprehend the complicated nature of the complex turbulent flow. The Vertical Axis Wind Turbine (VAWT) simulation for energy-generating applications has several intricate components. One of them is being aware of the chaotic flow that occurs during the first stages of the starting process, which influences overall effectiveness. In this article, the performance of the wind turbine was increased by using a passive flow control approach. The numerical research was carried out using Large Eddy Simulation for four alternative tip speed ratios in both cases, the classic and the optimized, equipped with a vortex trap on the exterior of the blades. The power and torque coefficient variations, as well as the velocity magnitude contours, show that the starting process may begin with a significant improvement in efficiency when flow control is used.

This study introduces an original comprehensive system centered on identifying specific terms that indicate a user's position, particularly the discrete values representing latitude and longitude. This system not only detects these terms but also retrieves the corresponding numerical data for accurate and efficient determination of locations. The importance of this study can be applied various fields, notably aiding offline operations of military personnel, who often lack internet access. In such scenarios, precise awareness of location is vital for strategic manoeuvres, rescue operations, and navigating unfamiliar landscapes. The system allows these personnel by allowing them to extract exact location coordinates from spoken terms, thereby enhancing their awareness even in challenging surroundings. Apart from its military utility, the project holds broader significance. Teams responding to emergencies, personnel involved in disaster management, and exploratory missions can all gain from this technology during disruptions in communication infrastructure. Furthermore, travelers, adventurers, and outdoor enthusiasts can utilize this system to accurately determine their positions in remote areas without relying on online maps. We used offline speech recognition techniques to precisely transcribe spoken terms, achieving an accuracy of over 91.3% and a word error rate of 4.2%. For sound recognition, the OpenAI Whisper model was used, and a conversion process from SpeechRecognition to AudioSegmentation was implemented, followed by transforming the audio into .wav format, we have also developed the interface of the app to use it efficiently using Streamlit. This was done to ensure seamless compatibility with the Whisper model and uninterrupted audio input. By training the system to identify specific linguistic linked to location, it achieves robust detection and extraction of relevant terms. This approach eliminates the necessity for constant internet connectivity, rendering it exceptionally useful in remote, offline, and resource-limited situations.

Understanding microbial composition in upper respiratory infections (URIs) is critical for effective diagnosis and treatment. Precision Metagenomic next-generation sequencing p(mNGS) can provide a comprehensive yet clinically relevant profile of respiratory infection. The hybridization capture-based targeted sequencing generated a precision metagenomics profile of 29 patients with acute URIs. Nasopharyngeal samples were collected from the subjects suspected of respiratory infection, and p(mNGS) was performed using the Illumina®/IDbyDNA Respiratory Pathogen ID/AMR Panel (RPIP). The dataset obtained from the mNGS analysis contains a wealth of information on the composition of acute URI microbiota, including the relative abundance of known pathogens and potential clinical implications. The dataset represents a valuable resource for future research endeavors in respiratory medicine, infectious disease epidemiology, and therapeutic interventions. In addition, the dataset offers significant potential for reuse and integration with other omics datasets. The comprehensive nature of the mNGS data allows the exploration of associations between the respiratory microbiota and host factors such as clinical outcomes, immune responses, or genetic predisposition. Combining this dataset with other relevant datasets, such as transcriptomics or metabolomics, could provide a deeper understanding of the complex interactions between the microbiota and the host in acute URIs.

Cancer is fundamentally a disease of perturbed genes. Although many mutations can be marked in the genome of a cancer or transformed cell, the initiation and progression were shown to be driven by only a few mutational events viz. driver mutations that progressively govern and execute the functional impacts. The driver mutations are thus believed to dictate and dysregulate the subsequent cellular proliferative function/decisions thereby producing a cancerous state. Therefore, identifying the driver events from the genomic alterations in a patient’s cancer cell gained large attention recently for designing better targeting therapies towards paving way for the precision cancer medicine. With rolling advancements in high-throughput omics technologies, analysis of genetic variations and gene expression profiles for cancer patients has become a routine clinical practice. However, it is anticipated that protein structural alterations resulting from such driver mutations can provide more direct and clinically relevant evidence of disease states than genetic signatures alone. This review comprehensively discusses various aspects and approaches that have been developed for the prediction of cancer drivers using genetic signatures and protein structures, and their potential application in developing precision cancer therapies.Keywords: