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.

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.

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.

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.

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.

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.

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.

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

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

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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 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.

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.

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.

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.

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 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.

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.

Alcoholic liver disease (ALD) is a major cause of chronic liver disease. This term covers a broad spectrum of liver lesions, from simple steatosis to alcoholic hepatitis and cirrhosis. The patho-genesis of ALD is multifactorial and not fully elucidated due to complex mechanisms related to direct ethanol toxicity with subsequent hepatic and systemic inflammation. The accumulation of pro-inflammatory cytokines and the reduction of anti-inflammatory cytokines promote the development and progression of ALD. To date, there are no targeted therapies to counteract disease progression and prevent acute liver failure. Corticosteroids reduce mortality by acting on the hepatic-systemic inflammation. On the other hand, several studies analyzed the effect of the inhibition of pro-inflammatory cytokines and the stimulation of anti-inflammatory cytokines as potential therapeutic targets in ALD. This narrative review aims to clarify the role of the cy-tokines in the pathogenesis and treatment of ALD.

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.

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:

There is a large body of research on determining the impact of field variability of soil on crop yields. In contrast, site-specific information about crop responses to agronomic treatments is less frequent. On-Farm Precision Experimentation (OFPE) brings important information to understand the spatial variation of crop response to agronomic practices and thus to improve agronomic decisions. The objective of this work was to investigate the spatial variability of corn yield responses to nitrogen and seed rates using OFPE in four fields in the US Midwest. Geographically weighted regression was applied to generate local regression coefficients, which were used to delineate response zones in each field. The results showed the existence of great potential to adjust the rates of these inputs according to the response of each zone identified by the proposed method. The results of this study can be applied to reevaluate expectations on variable rate prescriptions guided largely by soil and variability.

Coordinated sets of extremely numerous digital data, on a given social or economic event, are treated by Artificial Intelligence tools to obtain reasonably accurate, valuable predictions. The same approach, applied to biomedical issues, as how to choose the right drug to completely cure a given cancer patient, does not reach satisfactory results. It is the “organized biological complexity”, which requires a different systems approach, to integrate, in an Augmented Intelligence strategy, statistical computations of digital data, network construction of “omics” findings, well-designed mathematical models and new experiments in an iterative pathway to reconstruct the “logic” beneath the “organized complexity”, as shown here for Systems Metabolomics of cancer. On this basis new diagnostic approaches, able to identify precision drug treatments, as well as new discovery strategy for more effective anti-cancer drugs are described.

The current study compared the goodness of fit and estimation precision of Bayes and maximum likelihood (ML) structural equation modeling across sample sizes. Data consisted of Markov Chain Monte Carlo generated samples of 50, 75, 100, 125, 150, 200, 250, 500, 750, 1000, and 1500 observations. The Bayes and ML methods were used to estimate a structural equation model with 40 parameters, including 12 continuous observed indicators, four latent variables, and three structural paths. Analyses were conducted with the Mplus 8.0 statistical software and were replicated 500 times with each of the 22 conditions (11 sample sizes x 2 estimation methods). Indices of model fit were the Bayes information criterion, the Tucker-Lewis index, the comparative fit index, the root mean square error of approximation, the posterior predictive p-value (for Bayes models), and the ꭓ2 test (for ML models). Results showed that as the sample size increased, both estimation methods exhibited improved model fit and enhanced estimation precision, and both methods performed well with samples of 200 or larger; however, ML demonstrated a marginally superior fit with smaller samples. These findings were not consistent with previous research and suggest that ML remains a better choice with continuous data and small samples.

Introduction: Determination of radiological bone age (BA) is a diagnostic method that consists of estimating chronological age (CA) from the bone maturation of children. Although environmental and hormonal factors can interfere with bone ossification, the process is essentially dependent on ancestral inheritance. These ethnic backgrounds are not usually considered, which can lead to biased interpretations and inadequate decision-making by pediatricians or forensic experts. The objective of this study was to determine the precision and accuracy of these radiological procedures among different ethnic groups. Methods: A qualitative systematic review carried out following MOOSE statement and previously registered in the International Prospective Registry of Systematic Reviews PROSPERO (CRD42023449512). Search was performed in MEDLINE (PubMed) (n=561), Cochrane Library (n=261), CINAHL (n=103), Web of Science (WOS) (n=181) and public institutional repositories (n=37) from inception to 31st December 2022 using Mesh as “Diagnostic Techniques and Procedures”, “Diagnostic imaging”, “Radiography”, “Age Determination by Skeleton” and free terms combining with booleans “AND” and “OR”. PEDro scale, and Risk of Bias in non-randomized Studies of Exposure (ROBINS-E) were used to assess methodological quality and risk of bias of included studies respectively. Results: 51 articles (n=19,531) were included according to the inclusion criteria previously established. There was a good to moderate methodological quality and a high to very high risk of bias. Skeletal methods for determining BA were precised in terms of intra-observer and inter-observer reliability in all ethnic groups. Regarding to accuracy of Skeletal methods in Caucasians and Hispanic children, GPA was accurate at all ages, but in youths, TW3 RUS could be a consistent alternative. In Asian and Arab, GPA and TW3 overestimated BA in adolescents near adulthood. In African youths, GPA overestimated BA while TW3 was more accurate in estimating CA. Dental and Cervical radiographic methods are equally precise but lesser accurate than Skeletal BA determination. Conclusion: Skeletal radiographic methods GPA and TW3 are both precise for BA determination among all ethnical groups, but their accuracy in estimating CA can be altered by racial bias.

Measurement uncertainty is typically expressed in terms of a symmetric interval , where denotes the measurement result and the expanded uncertainty. However, in the case of heteroscedasticity, symmetric uncertainty intervals can be misleading. In this paper, a different approach for the calculation of uncertainty intervals is introduced. This approach is applicable when a validation study has been conducted with samples with known concentrations. It will be shown how, under certain circumstances, asymmetric uncertainty intervals arise quite naturally and lead to more reliable uncertainty intervals.

For several years, drugs bearing reactive electrophilic appendages have been developed. These units typically confer prolonged residence time of the drugs on their protein targets, and may assist targeting shallow binding sites and/or improving drug-protein target spectrum. Studies on natural electrophilic molecules have indicated that in many instances natural electrophiles use similar mechanisms to alter signaling pathways. However, natural reactive species are also endowed with other important mechanisms to hone signaling properties that are uncommon in drug design. These include ability to be active at low occupancy and elevated inhibitor kinetics. Here we discuss how we have begun to harness these properties in inhibitor design.

Centimeter-level localization and rotation angle estimation of flatbed trucks are major problems in unmanned forklift automated loading scenes. To solve it, this study proposes a method for high-precision positioning and rotation angle estimation of flatbed trucks based on BeiDou and vision. First, an unmanned forklift equipped with a ToF camera and double antenna mobile receiver for BeiDou positioning collects depth images and localization data near the front and rear endpoints of the flatbed. Then DDRNet-23-slim was used to segment the flatbed from the depth image and extract the straight lines at the edges of the flatbed using the Hough transform and compute the set of intersection points of the lines. A neighborhood feature vector was designed to locate the endpoint of a flatbed from a set of intersection points by feature screening. Finally, the relative coordinates of the endpoints were converted to a customized forklift navigation coordinate system by BeiDou positioning, and rotation angle estimation was performed by the front and rear endpoints. Experiments showed that the endpoint positioning error was less than 3 cm and the rotation angle estimation error was less than 0.1°. The precision of the method meets the demand for automated flatbed truck loading by unmanned forklifts.

Each individual has a unique gut microbiota; therefore the genes in our microbiome outnumber the genes in our genome by about 150 to 1. Perturbation in host nutritional status influences gut microbiome composition and vice versa. The gut microbiome can help in producing vitamins, hormones, and other active metabolites that support the immune system; harvest energy from food; aid in digestion; protect against pathogens; improve gut transit and function; send signals to the brain and other organs, oscillating the circadian rhythm and coordinate with host metabolism through multiple cellular pathways. Gut microbiota can be influenced by host genetics, medications, diet, and lifestyle factors from preterm to aging. So before prescribing a customized treatment, it is crucial to monitor and count the gut flora as a focused biomarker. Many nutritional approaches that have been developed help in maintaining and restoring an optimal microbiome such as specific diet therapy, nutrition interventions and customized eating patterns. One of these approaches is time-restricted feeding/eating (TRF/E), a type of intermittent fasting (IF) in which a subject abstains from food intake for a specific time window. Such a dietary modification might alter and restore the gut microbiome for proper alignment of cellular and molecular pathways throughout the lifespan. In this review, we have highlighted that gut microbiota would be a targeted biomarker and TRF/E would be a targeted approach for restoring the gut microbiome associated molecular pathways like hormonal signaling, the circadian system, metabolic regulators, neural responses, and immune-inflammatory pathways. Consequently, modulation of gut microbiota through TRF/E could contribute in proper utilization and availability of the nutrients and in this way confer protection against diseases for harnessing personalized nutrition approaches to improve human health.

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.

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.

In areas like commercial and policy decisions—it's important to know how different treatments affect things and how well they work. However, treatments affect things on average—but now—with fancy computer methods and big sets of data, interest lies in figuring out how treatments might work differently for different people. For instance, let's say instead of just looking at what works for most people, researchers want to know which treatment is best for each person. This would help doctors to decide the right treatment for each patient. This idea isn't just for healthcare—it can help in other areas too, like deciding how to govern employees or making government policies. There are some ways people have been trying to do this already, but they struggle when there are lots of different factors to consider. New computer methods, though, are good at handling lots of factors, but they need a ton of data to work well. Therefore, this study looks at three computer methods that are—Virtual Twin Random Forest (VTRF), Causal Forest (CF), and Causal Net (CN) using real-world data about encouraging people to do manual labour. One problem this study is trying to solve is something called the "winner's curse", where a treatment might seem better than it really is because of how it's predicted by the computer. We are also testing some ideas to see if we can fix this problem and make the predictions more accurate.

To realize precise control of the quantum dots (Qdots) device, multi-channel precision bias source plays the key role. In this paper, the 16-channel high precision bias source with 18-bit resolution for Qdots device was designed. The prototype was made and its performance was tested. The short time fluctuations can reach 50μV. The step response time is less than 3μs. The resolution, stability, linearity and dynamic range of the bias source exhibits good performance. What's more, the bias source can be controlled locally and online. The results show that it is one effective and feasible topology for experiments in Qdots device application.

Rheumatoid arthritis (RA) is a complex disease triggered by the interaction between genetics and environment, especially through the shared epitope (SE) and cell surface calreticulin (CSC) theory. However, the available evidence shows that genetic diversity and environmental exposure cannot explain all the clinical characteristics and heterogeneity of RA. In contrast, recent studies demonstrate that epigenetics play important roles in the pathogenesis of RA, especially DNA methylation and histone modification. DNA methylation and histone methylation are involved in innate and adaptive immune cell differentiation, and migration, proliferation, apoptosis, and mesenchymal characteristics of fibroblast-like synoviocytes (FLS). Epigenetic-mediated regulation of immune-related genes and inflammation pathways explains the dynamic expression network of RA. In this review, we summarized the comprehensive evidence to show that methylation of DNA and histones is significantly involved in the pathogenesis of RA and could be applied as a promising biomarker in the disease progression and drug response prediction. We also explained the advantages and challenges of the current epigenetics research in RA. In summary, epigenetic modules provide a possible interface, through which genetic and environmental risk factors connect to contribute to the susceptibility and pathogenesis of RA. Additionally, epigenetic regulators provide promising drug targets to develop novel therapeutic drugs for RA. Finally, DNA methylation and histone modifications could be important features for providing a better RA subtype identification, to accelerate personalized treatment and precision medicine.

In this study, the temporal variation in soil salinity dynamics was monitored and analyzed using Electromagnetic Induction (EMI) in an agricultural area in Port Said, Egypt, which is at risk of soil salinization. To assess soil salinity, repeated CMD2 soil apparent electrical conductivity (ECa) measurements were taken and inverted to generate electromagnetic conductivity imaging (EMCI), representing soil electrical conductivity (σ) distribution through time-lapse inversion. This process involved converting EMCI data into salinity cross sections using a site-specific calibration equation that correlates σ with the electrical conductivity of saturated soil paste extract (ECe) for the collected soil samples. The study was performed from August 2021 to April 2023, involving six surveys during two agriculture seasons. The results demonstrated the accurate prediction ability of soil salinity with R2 value of 0.81. The soil salinity cross sections generated on different dates observed changes in the soil salinity distribution. These changes can be attributed to shifts in irrigation water salinity resulting from canal lining, winter rainfall events, and variations in groundwater salinity. This approach is effective for evaluating agricultural management strategies in irrigated areas where it is necessary to continuously track soil salinity to avoid soil fertility degradation and a decrease in agricultural production and farmer’s income.

Precision Agriculture is the ability to handle variations in productivity within a field and maximize financial return, optimize resource utilization and minimize impact of the environment. It is also the process of automated data collection, cloud storage and utilization to build robust decision support system. In case of Ethiopia, due to poor communication infrastructure coverage and absence of the state-of-the-art technology in the agriculture sector, implementing precision farming system is a challenging tasks in the domain area. In this work, we proposed a fusion of multiple sensors using IOT and IIOT infrastructure to collect critical data from farming fields to develop precision farming facility for decision makers. The main purpose was to monitor weather variability, automate irrigation process, extract critical soil properties. In addition, we have used time series data collected from sensor devices to build forecasting model. Fusion of multiple IoT device provide a mechanism in the agriculture area to deal with real-time monitoring of crops. It is cost-effective technology and required low-energy with edge computing sensor device. We employed the Message Queuing Telemetry Transport (MQTT) protocol to connect the Industrial/Internet of Things (I/IoT) to the cloud server. The communication between system user and sensor device has been done via cloud using Node-RED platform, web android APIs. The cloud-based Eco-system allows us to aggregate, visualize, and analyze live streams output from each sensor in real-time manner. Finally, we have built time series forecasting model using records collected by each sensor device. Using the multi-variate time series data-set, we have obtained about 99 forecasting accuracy on some important variables. Finally, we have developed mobile and web-based application for the end-user to monitor the proposed system remotely.

Wi-Fi based localization has become one of the most practical methods for mobile users in location-based services. However, due to the interference of multipath and high-dimensional sparseness of fingerprint data, the localization system based on received signal strength (RSS) is hard to obtain high accuracy. In this paper, we propose a novel indoor positioning method, named JLGBMLoc (Joint denoising auto-encoder with LightGBM Localization). Firstly, because the noise and outliers may influence the dimensionality reduction on high-dimensional sparseness fingerprint data, we propose a novel feature extraction algorithm, named joint denoising auto-encoder (JDAE), which reconstructs the sparseness fingerprint data for a better feature representation and restores the fingerprint data. Then, the LightGBM is introduced to the Wi-Fi localization by scattering the processed fingerprint data to histogram, and dividing the decision tree under leaf-wise algorithm with depth limitation. At last, we evaluated the proposed JLGBMLoc on UJIIndoorLoc dataset and Tampere dataset, experimental results show that the proposed model increases the positioning accuracy dramatically comparing with other existing methods.

Background: As part of the Heart Healthy Lenoir Project, we developed a practice level intervention to improve blood pressure control. The goal of this study was: i) determine if single nucleotide polymorphisms (SNPs) that associate with blood pressure variation, identified in large case-control studies, are applicable to blood pressure control in subjects from a rural population; ii) measure the association of these SNPs with subjects’ responsiveness to the hypertension intervention; and iii) identify other SNPs that may help understand patient-specific responses to an intervention. Methods and Results: We used a combination of candidate SNPs and genome-wide analyses to test associations with either baseline systolic blood pressure (SBP) or change in systolic blood pressure one year after the intervention in two genetically defined ancestral groups: African Americans (AA) or Caucasian Americans (CAU). Of the 48 candidate SNPs, 13 SNPs associated with baseline SBP in our study; however, one candidate SNP, rs592582, also associated with a change in SBP after one year. Using our study data, we identified 4 and 15 additional loci that associated with a change in SBP in the AA and CAU groups, respectively. Our analysis of gene-age interactions identified genotypes associated with SBP improvement within different age groups of our populations. Moreover, our integrative analysis identified AQP4-AS1 and PADI2 as genes whose expression levels may contribute to the pleiotropy of complex traits involved in cardiovascular health and blood pressure regulation in response to an intervention targeting hypertension. Conclusions: Identification of SNPs associated with the success of a hypertension treatment intervention suggests that genetic factors in combination with age may contribute to an individual’s success in lowering SBP. If these findings prove to be applicable to other populations, the use of this genetic variation in making patient-specific interventions may help providers with making decisions to improve patient outcomes. Further investigation is required to determine the role of this genetic variance with respect to the management of hypertension such that more precise treatment recommendations may be made in the future as part of personalized medicine.

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.

(1) Background: The purpose of this study is to evaluate the full arch scan accuracy (precision and trueness) of nine digital intra-oral scanners and four lab scanners. Previous studies have compared the accuracy of some intra-oral scanners, but as this is a field of quickly developing technologies, a more up-to-date study was needed to assess the capabilities of currently available models.; (2) Methods: The present in vitro study compared nine different intraoral scanners (Omnicam 4.6; Omnicam 5.1; Primescan; CS 3600; Trios 3; Trios 4; Runyes; i500 and DL206) as well as four lab light scanners (Einscan SE; 300e; E2 and Ineos X5) to investigate the accuracy of each scanner by examining the overall trueness and precision. Ten aligned and cut scans from each of the intra-oral and lab scanners in the in vitro study were brought into CloudCompare. A comparison was made with the master STL using the CloudCompare 3D analysis best-fit algorithm. The results were recorded along with individual standard deviation and a colorimetric map of the deviation across the surface of the STL mesh; a comparison was made to the master STL, quantified at specific points. ; (3) Results: In the present study, the Primescan had the best overall trueness (17.3 ± 4.9). Followed by (in order of increasing deviation) the Trios 4 (20.8 ± 6.2), i500 (25.2 ± 7.3), CS3600 (26.9 ± 15.9), Trios 3 (27.7 ± 6.8), Runyes (47.2 ± 5.4), Omnicam 5.1 (55.1 ± 9.5), Omnicam 4.6 (57.5 ± 3.2) and Launca DL206 (58.5 ± 22.0). Regarding the lab light scanners, the Ineos X5 had the best overall trueness with (0.0 ± 1.9). Followed by (in order of increasing deviation) the 3Shape E2 (3.6 ± 2.2), Up3D 300E (12.8 ± 2.7), and Einscan SE (14.9 ± 9.5); (4) Conclusions: This study confirms that all current generations of intra-oral digital scanners can capture a reliable, reproducible full arch scan in dentate patients. Out of the intra-oral scanners tested, no scanner produced results significantly similar in trueness to the Ineos X5. However, the Primescan was the only one to be statistically of a similar level of trueness to the 3Shape E2 lab scanner. All scanners in the study had mean trueness of under 60-micron deviation. While this study can compare the scanning accuracy of this sample in a dentate arch, the scanning of a fully edentulous arch is more challenging. The accuracy of these scanners in edentulous cases should be examined in further studies.

The importance of frailty in older people is getting constant recognition as an important aspect both in terms of public health, as well in personal level for the appropriate management of an older person health condition. This is reflected by the continuously increasing number of research studies carried out in several settings across different countries. Sometimes this is very solid, but in other cases there is a considerable gap in terms of accurate and well grounded documentation of frailty status. This is the case in Greece, where we are missing clinically validated tools to approach frailty. We are missing frailty screening tools, such as for instance PRISMA 7, the gold standard tool of Fried criteria is somehow problematic since the question referring to physical activity originates from a questionnaire that has not been translated and validated, while Clinical Frailty Scale has been validated for the translation but not for the capacity to detect frailty. Aim of this study is to validate these tools for their accuracy to detect frailty by using a measurable index of frailty, previously proposed for use in clinical studies: the Short Physical Performance Battery (SPPB). 74 male and female participants have been evaluated for their frailty status using different tools. We observed that PRISMA 7 translation detects frailty only when one question is removed at a cut-off of ≥ 2 and indicated a sensitivity of 88.1%, and specificity of 99.9% with a good correlation with SPPB measurements (r = 0.858; p < 0.001). When CFS was validated using SPPB it demonstrated a very good correlation (r = -0.838; p < 0.001 respectively) as it was the case for the modified Fried Criteria (r = -0.725; p < 0.001). All items demonstrated a good correlation between them. We can here propose that we can accurately assess frailty status in the community setting by using a modified version of Fried criteria, Clinical Frailty Scale translation in Greek and we can screen for frailty by using the Greek translation of PRISMA 7 only after removing the item 6 of the questionnaire.

Immune-cell reprogramming driven by mitochondria-derived reactive electrophilic immunometabolites (mt-REMs—e.g., fumarate, itaconate) is an emerging phenomenon of major biomedical importance. Despite their localized production, mt-REMs elicit significantly-large local and global footprints within and across cells, through mechanisms involving electrophile signaling. Burgeoning efforts are being put into profiling mt-REMs’ potential protein-targets and phenotypic mapping of their multifaceted inflammatory behaviors. Yet, precision indexing of mt-REMs’ first-responders with spatiotemporal intelligence and locale-specific function assignments remains elusive. Highlighting the latest advances and overarching challenges, this perspective aims to stimulate thoughts and spur interdisciplinary innovations to address these unmet chemical-biotechnological needs at therapeutic immuno-signaling frontiers.

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.

Myelodysplastic neoplasm (MDS) is a heterogenous clonal disorder of hemopoietic stem cells charac-terized by cytomorphologic dysplasia, ineffective hematopoiesis, peripheral cytopenias and risk of pro-gression to Acute Myeloid Leukemia (AML). Our understanding of this disease has continued to evolve over the last century. More recently prognostication and treatment are determined by cytogenetic and molecular data. Specific genetic abnormalities such as 5q deletion (del 5q), TP53 inactivation and SF3B1 mutation, are increasingly associated with disease phenotype and outcomes, as reflected in the recently updated 5th edition of the World Health Organization Classification of Hematolymphoid Tumors (WHO5) and International Consensus Classification 2022 (ICC 2022) classifications. Treatments are in-creasingly being tailored for patient specific phenotypes, and trials exploring the efficacy of targeted treatments are ongoing in this era of precision medicine.

Prenatal diagnosis of clubfoot traditionally relied on two-dimensional ultrasonography. To enhance diagnosis and predict postnatal outcomes, we examined the parameters that differentiate pathological clubfoot using three-dimensional ultrasonography. In our retrospective study, we examined the findings of prenatal ultrasound and the postnatal outcomes of pregnancies with suspected congenital clubfoot between 2018 and 2021. Based on the three-dimensional perspective, we measured the angles of varus, equinus, calcaneopedal block and forefoot adduction and compared the sonographic variables between the postnatal treated and non-treated groups. We evaluated 31 pregnancies (47 feet) with suspected clubfoot using three-dimensional ultrasonography. After delivery, a total of 37 feet (78.7%) underwent treatment involving serial casting only or additional Achilles tenotomy. The treated group showed significantly greater hindfoot varus deviation (60.5° vs. 46.6°, p=0.026) and calcaneopedal block deviation (65.6° vs. 26.6°, p <0.05) compared to the non-treated group. The calcaneopedal block had an area under the curve of 0.98 with a diagnostic threshold of 46.2 degrees (sensitivity of 97%, specificity of 90%, positive predictive value of 97%, and negative predictive value of 90%). During prenatal evaluation of clubfoot using three-dimensional ultrasonography, the calcaneopedal block deviation has the potential to predict postnatal treatment.

Heat shock factor 1 (HSF1) is a transcription factor crucial for regulating heat shock response (HSR), one of the significant cellular protective mechanisms. When cells are exposed to proteotoxic stress, HSF1 induces the expression of heat shock proteins (HSPs) to act as chaperones, correcting the protein-folding process and maintaining proteostasis. In addition to its role in HSR, HSF1 is overexpressed in multiple cancer cells, where its activation promotes malignancy and leads to poor prognosis. The mechanisms of HSF1-induced tumorigenesis are complex and involve diverse signaling pathways, dependent on cancer type. With its important roles in tumorigenesis and tumor progression, targeting HSF1 offers a novel cancer treatment strategy. In this article, we examine the basic function of HSF1 and its regulatory mechanisms, focus on the mechanisms involved in HSF1’s roles in different cancer types, and examine current HSF1 inhibitors as novel therapeutics to treat cancers.

The integration of Wireless Sensor Networks (WSNs) into agricultural areas has had a significant impact and has provided new, more complex, efficient, and structured solutions for enhancing crop production. This research reviews the role of Wireless Sensor Networks (WSNs) in monitoring the macro-nutrient content of plants. The review study focuses on identifying the types of sensors used to measure macro-nutrients, determining sensor placement within agricultural areas, implementing wireless technology for sensor communication, and selecting device transmission intervals and ratings. The study of NPK (Nitrogen, Phosphorus, Potassium) monitoring using sensor technology in precision agriculture is of high significance in efforts to improve agricultural productivity and efficiency. In addition to fostering technological innovations and precision farming solutions, in future this research aims to increase agricultural yields, particularly by enabling the cultivation of certain crops in locations different from their original ones.

Profitability is an underestimated concept in precision agriculture. In this research, a new module is developed within a pre-existing farm management system to assess the profitability of precision agriculture applications in extended crops. The module is regulated on a 5-meter spatial resolution, thus allowing scaling up of original and processed data on a zone-, field-, cultivar-, and farm-scale. A bottom-up approach, taking advantage of the full functionality of the farm management system, together with a flexible architecture and an easy-to-use interface, renders the new module an innovative commercial application.

Simulator training for image-guided surgical interventions may benefit from artificial intelligence systems that control the evolution of task skills in terms of time and precision of a trainee's performance on the basis of fully automatic feed-back systems. At the earliest stages of training, novice trainees frequently focus on getting faster at the task, and may thereby compromise the optimal evolution of the precision of their performance. For automatically guiding them towards attaining an optimal speed-accuracy trade-off, an effective control system for the reinforcement/correction of strategies must be able to exploit the right individual performance criteria in the right way, reliably detect individual performance trends at any given moment in time, and alert the trainee, as early as necessary, when to slow down and focus on precision, or when to focus on getting faster. This article addresses several aspects of this challenge for speed-accuracy controlled simulator training before any training on specific surgical tasks or clinical models should be envisaged. Analyses of individual learning curves from the simulator training sessions of novices and benchmark performance data of one expert surgeon, who had no specific training in the simulator task, validate the suggested approach.

A novel method based on a hybrid incremental modeling approach has been designed and applied to imbalance detection in ultra-high precision rotating machines. The model is obtained by a two-step iterative process that combines an overall model (least-squares fitting) with a local model (fuzzy k-nearest-neighbour) to take advantage of their complementary capacities. Three normalization strategies of evaluating the effect on accuracy are analyzed. A comparative study demonstrates that the hybrid incremental model provides better error-based performance indices for detecting imbalance than a nonlinear regression model and an adaptive neural-fuzzy inference system model. The suitability of Mahanolobis normalization for hybrid incremental modeling is also demonstrated in this case study. The proposed strategy for imbalance detection is simple, fast, and non-intrusive, reducing the deterioration in the performance of ultra-high precision rotating machines due to vibrations.

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.

Cow’s Milk (CM) is a healthy food consumed worldwide by individuals of all ages. Unfortunately, 'lactase deficient' individuals cannot digest milk's carbohydrate lactose depriving themselves of highly beneficial milk proteins like casein, lactoalbumin and lactoglobulin, due to lactose intolerance (LI), while other individuals develop cow milk allergy specifically against these proteins (CMPA). The management of these conditions is distinctly different and an inappropriate diagnosis or treatment may have significant implications for the patients, especially if they are infants or very young children, resulting in unnecessary dietary restriction or avoidable adverse reactions. Omics technologies play a pivotal role in elucidating the intricate interactions between nutrients and the human body, spanning from genetic factors to the microbiota profile and metabolites. This comprehensive approach enables the precise delineation and identification of distinct cohorts of individuals with specific dietary requirements, so that tailored nutrition strategies can be developed. This is what is called personalized nutrition or precision nutrition (PN), the area of nutrition that focuses on the effects of the nutrients over the genome, proteome and metabolome, promoting well-being and health, preventing diseases, reducing chronic disease incidence, and so increasing life expectancy. Here, we report the scientific community opinion proposing to replace the approach “one size fits all” with tailor-made nutrition programs, designed by integrating nutrigenomic data together with clinical parameters and microbiota profiles, taking into account the individual lactose tolerance threshold and needs in terms of specific nutrients intake. This customized approach could help LI patients to improve their quality of life, overcoming depression or anxiety, often resulting from the individual perception of this condition as different from a normal state.

(1) Background: This manuscript introduces the "Lines and Dots" (LAD) technique, a novel method for administering Botulinum Toxin Type A (BoNT-A) in treating horizontal forehead lines. This technique emphasizes precision in targeting nerve endplates, considering individual anatomical variations, and optimizing treatment efficacy. (2) Methods: The LAD technique focus on using the frontalis muscle contraction dynamics to identify the specific distribution of the supraorbital and supratrochlear nerves It involves creating a grid of predetermined lines and points on the forehead that align with nerve paths and motor endplate zones. (3) Results: By targeting injections along these precise lines and points, the LAD technique ensures more effective utilization of BoNT-A. This approach not only enhances the toxin's effect but also allows for a reduction in the overall dosage. The strategic placement of injections results in a more natural, symmetrical, and sustained therapeutic effect, ensuring an improved aesthetic outcome. (4) Conclusions: The LAD technique represents a significant advancement in the realm of cosmetic injectables. It offers a tailored, patient-specific approach, maximizing the effectiveness of BoNT-A injections for treating forehead lines while minimizing risks and enhancing patient satisfaction. This method underscores the importance of a deep anatomical understanding and precision in cosmetic procedures.

Esophageal cancer is a formidable challenge in the realm of cancer treatment. Conventional methods such as surgery, chemotherapy, and immunotherapy have demonstrated limited success rates in managing this disease. In response, targeted drug therapies have emerged as a promising strategy to improve outcomes for patients. These therapies aim to disrupt specific pathways involved in the growth and development of esophageal cancer cells. This review explores various drugs used to target specific pathways, including cetuximab and monoclonal antibodies (gefitinib) that target the epidermal growth factor receptor (EGFR), trastuzumab that targets human epidermal growth factor receptor 2 (HER-2), drugs targeting the vascular endothelial growth factor receptor (VEGFR), mTOR inhibitors, and cMET inhibitors. Additionally, the article discusses the impact of drug resistance on the effectiveness of these therapies, highlighting factors such as cancer stem cells, cancer-associated fibroblasts, immune-inflammatory cells, cytokines, hypoxia, and growth factors. While drug targeting approaches do not provide a complete cure for esophageal cancer due to drug resistance and associated side effects, they offer potential for improving patient survival rates.

The globe and more particularly the economically developed regions of the world are currently in the era of the fourth Industrial revolution (4IR). Conversely; the economically developing regions in the world and more particularly the African continent have not yet even fully passed through the Third Industrial Revolution (3IR) wave and its economy is still heavily dependent on the agricultural field. On the other hand, the state of global food insecurity is worsening on an annual basis thanks to the exponential growth of the global human population which continuously heightens the food demand in both quantity and quality. This justifies the significance of the focus on digitizing agricultural practices to improve the farm yield to meet up with the steep food demand and stabilize the economy of the African continent and countries like India whose economy is mainly dependent on Agriculture. The tools we have at our disposal to utilize in the digitization of farming practices include space technology and Global Navigation and Satellite System (GNSS) in particular, Machine learning (ML), precision agriculture and communication systems such as the Internet of Things (IoT) and Information And Communication Technologies (ICT). The most pressing challenges in the farming field include the monitoring of diseases, pests, weeds and nutrient deficiencies in the crops as early detection translates to swift and timely correction actions and hence more yield at the end of a farming cycle. Vast opportunities in the field of precision agriculture still exist that can amount to further research studies such as the lack of real-time monitoring and real-time corrective action focus.

The aim was to assess the precision and accuracy of cephalometric analyses performed by artificial intelligence (AI) with and without human augmentation. Four dental professionals with varying experience levels and AI identified 31 landmarks on 30 cephalometric radiographs twice. These landmarks were re-identified by all examiners with the aid of AI. Precision and accuracy were assessed by using intraclass correlation coefficients (ICCs) and mean absolute errors (MAEs). AI revealed the highest precision with a mean ICC of 0.97, while the dental student had the lowest (mean ICC: 0.77). AI/human augmentation method significantly improved precision of the orthodontist, resident, dentist, and dental student by 3.26%, 2.17%, 19.75%, and 23.38%, respectively. The orthodontist demonstrated the highest accuracy with a MAE of 1.57 mm/˚. AI/human augmentation method improved the accuracy of the orthodontist, resident, dentist, and dental student by 12.74%, 19.10%, 35.69%, and 33.96%, respectively. AI demonstrated excellent precision and good accuracy in automated cephalometric analysis. The precision and accuracy of the examiners with the aid of AI improved by 10.47% and 27.27%, respectively. The AI/human augmentation method significantly improved the precision and accuracy of less experienced dental professionals to that of an experienced orthodontist.

This study examines the capacity of a Site-Specific Fodder Management Decision Support System (SSFM-DSS) model to assist in successful cultivation of sericea lespedeza [SL; Lespedeza cuneata (Dum.-Cours.) G. Don.], with a specific focus on small-scale agricultural systems in the southeastern United States (U.S.). The study emphasizes incorporating advanced geospatial technologies, such as Geographic Information Systems (GIS), remote sensing, and Global Navigation Satellite Systems (GNSS), to improve fodder production. The research showcases the versatility of SL under varying environmental conditions, underscoring its significance in promoting sustainable livestock production. The methodology integrates empirical field data, geospatial analysis, and predictive modeling to formulate an SSFM strategy customized to address the distinct difficulties climate change presents, such as sudden and severe drought conditions. The study introduces an automated geospatial model for assessing the appropriateness of SL production throughout Alabama, Georgia, and South Carolina. The model uses many environmental criteria, including soil properties, climate fluctuations, and terrain. Furthermore, the research was instrumental in development of a WebGIS Dashboard, which offers farmers a decision assistance system to enhance the sustainable production of SL. The results highlight the significant impact that a SSFM-DSS can have on promoting sustainable agricultural practices, providing a solution to address the issues of food security and environmental concerns in agriculture.

Spinal metastatic tumors are common and often cause debilitating symptoms. Image-guided percutaneous thermal ablation (IPTA) has gained significant recognition in managing spinal column tumors due to its exceptional precision and effectiveness. Conventional guidance modalities, including computed tomography, fluoroscopy, and ultrasound, have been important in targeting spinal column tumors while minimizing harm to adjacent critical structures. This study presents a novel approach utilizing a fusion of Cone Beam Computed Tomography with Magnetic Resonance Imaging to guide percutaneous thermal ablation for four patients with secondary spinal column tumors. The visual analog scale (VAS) evaluated procedure effectiveness during an 18-month follow-up. Percutaneous vertebroplasty was performed in two cases, and a thermostat was used during all procedures. Imaging was performed using the Stealth Station navigation system Spine 8 (SSS8) and a 1.5T MRI machine. The fusion of CBCT with MRI allowed precise tumor localization and guidance for thermal ablation. Initial results indicate successful tumor ablation and symptom reduction, emphasizing the potential of CBCT-MRI fusion in spinal column tumor management. This innovative approach is promising in optimizing therapy for secondary spinal column tumors. Further studies are necessary to validate its efficacy and applicability.

Agricultural production needs technologies that assist the management of natural resources, for example, the collection of real-time data on soil, water, weather, crops, and biodiversity conditions. Sensor technology solutions and open-source software are appropriate for promoting more sustainable agricultural production. Among the advantages of using open-source technologies and software is its potential for extension, collaboration, customization, flexibility, maintenance cost, transparency, speed, and better security. Given the above, the objective of this research was to find, in different electronic databases, exclusively open-source software for precision agriculture, offering a systematic review, and addressing considerations and challenges. This survey considers up-to-date open-source software available in repositories such as GitHub and GitLab, to understand its characteristics and application formats.

Parkinson’s disease (PD) is characterized by motor deficits and a wide variety of non-motor symptoms. The age of onset, rate of disease progression and the precise profile of motor and non-motor symptoms display considerable individual variation. Neuropathologically, the loss of substantia nigra dopaminergic neurons is a key feature of PD. The vast majority of PD patients exhibit alpha-synuclein aggregates in several brain regions, but there is also great variability in the neuropathology between individuals. While the dopamine replacement therapies can reduce motor symptoms, current therapies do not modify the disease progression. Numerous clinical trials using a wide variety of approaches have failed to achieve disease modification. It has been suggested that the heterogeneity of PD is a major contributing factor to the failure of disease modification trials, and that it is unlikely that a single treatment will be effective in all patients. Precision medicine, using drugs designed to target the pathophysiology in a manner that is specific to each individual with PD, has been suggested as a way forward. PD patients can be stratified according to whether they carry one of the risk variants associated with elevated PD risk. In this review we assess current clinical trials targeting two enzymes, leucine-rich repeat kinase 2 (LRRK2) and glucocerebrosidase (GBA), which are encoded by two most common PD risk genes. Because the details of the pathogenic processes coupled to the different LRRK2and GBA risk variants are not fully understood, we ask if these precision medicine-based intervention strategies will prove “precise“ or “personalized“ enough to modify the disease process in PD patients. We also consider at what phases of the disease that such strategies might be effective, in light of the genes being primarily associated with the risk of developing disease in the first place, and less clearly linked to the rate of disease progression. Finally, we critically evaluate the notion that therapies targeting LRRK2 and GBA might be relevant to a wider segment of PD patients, beyond those that actually carry risk variants of these genes.

COVID-19 severity is heterogeneously distributed over age strata, but current mitigation strategies are homogeneously applied to all population. Social-distancing and stay-home are effective conservative approaches but lack economic sustainability on long term. Conversely, herd-immunity is a nonrestrictive strategy which can cost remarkable number of human lives and can melt the healthcare system down. Here I propose an Age Adaptive Social Distancing (AASD) engineering strategy to mitigate COVID-19 outbreak. AASD is based on the scientific evidence that the fatality rate grows nonlinearly with age, hence also the containing strategy should adapt nonlinearly. Essentially, AASD suggests that ‘silent spreaders’ (age 0-39) should avoid/minimize direct and indirect contacts with individuals in ‘dangerous zone’ (age 40+). The rationale is: 0-19 should follow parents strategy, healthy 20-39 (low fatality rate) might conduct screened life under active surveillance, to sustain economy and acquire rational immunity; 40-59 should respect social distancing (waiting a therapy); 60+ should stay at home (waiting a vaccine). This might save human lives, reduce healthcare demand and improve economic sustainability. The final take-home message is that future studies should design precision and personalized strategies for specific contagious diseases that integrate different social constrains, active surveillance and contact tracing.

Autonomous unmanned aerial vehicles (UAVs) require highly reliable navigation information. Generally, navigation systems with the inertial navigation system (INS) and global navigation satellite system (GNSS) have been widely used. However, the GNSS is vulnerable to jamming and spoofing. The terrain referenced navigation (TRN) technique can be used to solve this problem. In this study, to obtain reliable navigation information even if a GNSS is not available or the degree of terrain roughness is not determined, we propose a federated filter based INS/GNSS/TRN integrated navigation system. we also introduce a TRN system that combines batch processing and an auxiliary particle filter to ensure stable flight of UAVs even in a long-term GNSS-denied environment. As an altimeter sensor for the TRN system, we use an interferometric radar altimeter (IRA) to obtain reliable navigation accuracy in high altitude flight. In addition, a parallel computing technique with general-purpose computing on graphics processing units (GPGPU) is applied to process a high resolution terrain database and a nonlinear filter in real time on board. Finally, we verify the performance of the proposed system through software-in-the-loop (SIL) tests and captive flight tests in a GNSS unavailable environment.

Previous studies have reported certain sex differences in motor performance precision. The aim of the present study was to analyse sex differences in fine motor precision performance for both hands in different tests conditions. 220 Spanish participants (ages: 12-95) performed fine motor tasks - tracing over the provided models – lines of 40 mm for both hands, two sensory conditions (PV – proprioceptive-visual; P – proprioceptive only) and three movement types (F – frontal, T – transversal and S - Sagittal). Differences in line length (the task focused on precision) were observed through MANOVA analysis for all test conditions, both sexes and different age groups. Sex differences in precision were observed in F and T movement types (statistically significance level and higher Cohens’ d was observed in condition with vision). No any statistically significant differences were observed in both hands and sensory conditions in sagittal type. Sex differences in fine motor precision were more frequently observed in the PV sensory condition in the frontal movement type and less in the sagittal one.

Amidst the burgeoning global concerns surrounding skin cancer, the quest for precise and efficient diagnostic methodologies has intensified. Conventional diagnostic approaches relying on subjective visual inspection by dermatologists often grapple with subjectivity and resource constraints. This predicament underscores the critical role of Artificial Intelligence (AI) in revolutionizing diagnostic paradigms by significantly enhancing accuracy and accessibility. This study embarks on an extensive exploration of skin cancer classification, specifically targeting the intricate task of 8-class skin cancer classification. Employing cutting-edge deep learning models on the ISIC2019 dataset, a comprehensive analysis was conducted utilizing a diverse array of pre-trained ImageNet architectures and Vision Transformer models. To counteract the inherent class imbalance in skin cancer datasets, a pioneering "Naturalize" augmentation technique was introduced, leading to the creation of indispensable tools—the Naturalized 2.4K ISIC2019 and groundbreaking Naturalized 7.2K ISIC2019 datasets—catalyzing advancements in classification accuracy. The pivotal role of AI in mitigating the risks of misdiagnosis and under-diagnosis in skin cancer becomes evident through this research. AI’s proficiency in analyzing vast datasets and discerning subtle patterns augments the diagnostic prowess of dermatologists significantly. Integration of AI-powered systems in skin cancer diagnosis holds immense promise in facilitating early detection and substantially improving patient outcomes. The meticulous evaluation employed quantitative measures such as confusion matrices, classification reports, and visual analyses using Score-CAM across diverse dataset variations. Astonishingly, the culmination of these endeavors culminated in an unprecedented achievement—100% average accuracy, precision, recall, and F1-Score—within the groundbreaking Naturalized 7.2K ISIC2019 dataset. This remarkable feat underscores the transformative potential of AI-driven methodologies, exemplifying their prowess in redefining the landscape of skin cancer diagnosis and management. In summary, this research represents a pivotal stride towards redefining dermatological diagnosis, showcasing the remarkable impact of AI-powered solutions in surmounting the challenges inherent in skin cancer diagnosis. The attainment of 100% across crucial metrics within the Naturalized 7.2K ISIC2019 dataset serves as a testament to the transformative capabilities of AI-driven approaches in reshaping the trajectory of skin cancer diagnosis and patient care.

The term neurodegeneration with brain iron accumulation (NBIA) brings together a broad set of progressive and disabling neurological genetic disorders in which iron is deposited preferentially in certain areas of the brain. Among NBIA disorders, the most frequent subtype is pantothenate kinase-associated neurodegeneration (PKAN) caused by pathologic variants in the PANK2 gene codifying the enzyme pantothenate kinase 2 (PANK2). Nowadays, there are no effective treatments to stop the progression of these diseases. This review discusses the utility of patient-derived cell models as a valuable tool for the identification of commercial pharmacological or natural compounds for implementing polytarget precision medicine in PKAN. In the last years, several studies have described that PKAN patient-derived fibroblasts manifest the main pathological changes associated with the disease including intracellular iron overload. Interestingly, treatment of mutant cell cultures with various supplements such as pantothenate, pantethine, thiamine, L-carnitine, vitamin E, omega 3, and -lipoic acid improved all pathophysiological alterations in PKAN fibroblasts with residual expression of the PANK2 enzyme. The information provided by pharmacological screenings in patient-derived cellular models can help to optimize therapeutic strategies in individual PKAN patients.

Melanoma is widely recognized as one of the most lethal forms of skin cancer, with its incidence showing an upward trend in recent years. Nonetheless, the timely detection of this malignancy substantially enhances the likelihood of patients’ long-term survival. Several computer-based methods have recently been proposed in the pursuit of diagnosing skin lesions at their early stages. Despite achieving some level of success, there still remains a margin of error that the machine learning community considers to be an unresolved research challenge. This study presents a novel framework for the classification of skin lesions. The framework incorporates deep features to generate a highly discriminant feature vector, while also maintaining the integrity of the original feature space. Recent deep models including Darknet53, DenseNet201, InceptionV3, and InceptionResNetV2 are employed in our study for the purpose of feature extraction. Additionally, transfer learning is leveraged to enhance the performance of our approach. In the subsequent phase, the extracted feature information from the chosen pre-existing models is combined, with the aim of preserving maximum information, prior to undergoing the process of feature selection using a novel entropy-controlled grey wolf optimization (ECGWO) algorithm. The integration of fusion and selection techniques is employed to initially incorporate the feature vector with a high level of information and subsequently eliminate redundant and irrelevant feature information. The efficacy of our design is substantiated through the evaluation on three benchmark dermoscopic datasets, namely PH2, ISIC-MSK, and ISIC-UDA. In order to validate the proposed methodology, a comprehensive evaluation is conducted, including a rigorous comparison with established techniques in the field.

Sowing accuracy is a critical factor in evaluating the precision of sowing equipment, and the sowing rate of the sowing equipment developed at home and abroad cannot reach 100 %, so this paper designs a seed reseeding device, aiming to improve the sowing rate secondly. Firstly, the structural composition of the seed reseeding device is introduced; secondly, according to the production object selected in this paper, kale seed (Jingfeng No.1), the best combination of factors and levels of the suction nozzle is 8 m/s inlet speed, 1.8 mm aperture, 18 mm lead and V-shaped nozzle by using orthogonal experiment; then, the EDEM software was used to conduct the vibration test, and it was found that the seed group “boils” steadily at about 15.13 mm (the depth of the reseeding tray is 23 mm), which can effectively improve the suction rate of the suction nozzle; finally, by combining the simulation results of the flow field using Fluent/ANSYS software and the spatial distribution of seeds, the number of seeds in the effective adsorption range of a single suction nozzle is about 4. The determined number of seeds, approximately 4, accounts for only 0.04% of the total number of seeds generated by the simulation. This percentage ensures high suction precision and a single seed rate, particularly under working conditions where a 72-hole tray is being sowed. In general, domestic sowing accuracy ranges from 90% to 95%. With this configuration, the purpose of improving the sowing rate twofold can be achieved.

Background: We previously developed a refined, tumor selective adenovirus, Ad5NULL-A20, har-boring tropism ablating mutations in each major capsid protein, to ablate all native means of infection. We incorporated a 20mer peptide (A20) in the fiber knob for selective infection via αvβ6 integrin, a marker of aggressive epithelial cancers. Methods: To ascertain the selectivity of Ad5NULL-A20 for αvβ6 positive tumor cell lines of pancreatic and breast cancer origin, we performed reporter gene and cell viability assays. Biodistribution of viral vectors in mice harboring xenografts with low, medium, and high αvβ6 levels was quantified by qPCR for viral genomes 48 hours post intravenous administration. Results: Ad5NULL-A20 vector transduced cells in an αvβ6 selective manner, whilst cell killing me-diated by oncolytic Ad5NULL-A20 was αvβ6 selective. Biodistribution analysis following intrave-nous administration into mice bearing breast cancer xenografts demonstrated that Ad5NULL-A20 resulted in significantly reduced liver accumulation coupled with increased tumor accumulation compared to Ad5 in all three models, with tumor: liver ratios improved as a function of αvβ6 expression. Conclusions: Ad5NULL-A20 based virotherapies efficiently target αvβ6 integrin positive tumors following intravenous administration, validating the potential of Ad5NULL-A20 for systemic ap-plications, enabling tumor selective overexpression of virally encoded therapeutic transgenes.

Diffuse intrinsic pontine gliomas (DIPGs) account for ~15% of pediatric brain tumors, which invariably present with poor survival regardless of treatment mode. Several seminal studies have revealed that 80% of DIPGs harbor H3K27M mutation coded by HIST1H3B, HIST1H3C and H3F3A genes. The H3K27M mutation has broad effects on gene expression and is considered a tumor driver. Determination of the effects of H3K27M on posttranslational histone modifications and gene regulations in DIPG is critical for identifying effective therapeutic targets. Advanced animal models play critical roles in translating these cutting-edge findings into clinical trial development. Here, we review current molecular research progress associated with DIPG. We also summarize DIPG animal models, highlighting novel genomic engineered mouse models (GEMMs) and innovative humanized DIPG mouse models. These models will pave the way towards personalized precision medicine for the treatment of DIPGs.

Monitoring complex electro-mechanical processes is not straightforward despite the arsenal of techniques nowadays availanle. This paper presents a method based on Adaptive-Network-based Fuzzy Inference System (ANFIS) to estimate eccentricity of its spinning axis. The method is experimentally tested on an ultra-precision rotating device commonly used for micro-scale turning. The developed model has three inputs, two obtained from a frequency domain analysis of a vibration signal and the third, which is the device rotation frequency. A comparative study demonstrates that an adaptive neural-fuzzy inference system model provides better error-based performance indices for detecting imbalance than a non-linear regression model. This simple, fast, and non-intrusive imbalance detection strategy is proposed to counteract eventual deterioration in the performance of ultra-high precision rotating machines due to vibrations.

The texture properties of cooked rice are a dominant property which can indicate the eating quality. Evaluation of back extrusion (BE) test precision and sensitivity for cooked germinated brown rice (GBR) texture in production process was investigated in this study. BE test on texture properties of cooked GBR rice show high precision of measurement of hardness, toughness and stickiness tests which indicated by the repeatability and reproductivity test but the sensitivity indicated by coefficient of variation of the texture properties. The findings of the study of the effect of different soaking and aging duration in the production of Khao Dawk Mali 105 (KDML 105) GBR on cooked GBR texture measured by BE test confirmed the developed protocol for evaluation of high precision and sensitivity of the texture measurement method. Repeatability and reproducibility of reliable measurements have a low standard deviation of the farthest different between replicates, which is considered high precision. High coefficient of variation where the relative wide variation of the absolute value of the property can be detected indicate high sensitivity which even small resolution can be detected or vice versa. But the correlation coefficient among the texture properties by BE were not related to the precision or sensitivity of the test. By these results, the original protocol for determination of precision and sensitivity of food texture measurement was successfully verified as the usable evaluation protocol for GBR texture measurement.

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.

Lung cancer remains the main cause of global cancer-related deaths. Currently low-dose computed tomography (LD-CT) shows a 20% reduction of mortality. However, its clinical suitability is hindered by cost, radiation, and some false positives. Thus, the discovery non-invasive and less-expensive biomarker is urgent needed. In this scenario, proteins released in biological fluids could be considered interesting biomarkers for their role in cancer development. We conducted a pioneering case-control pilot study aimed at assessing the usefulness of Raf Kinase Inhibitory Protein (RKIP) and its phosphorylated form (pRKIP) as potential early-stage lung cancer biomarkers. Urine and Blood samples of two consecutive and independent cohorts of lung cancer patients and healthy controls were screened by means of a novel experimental ELISA assay designed to evaluate both forms of the biomarker. Although urinary levels of RKIP were not statistically different between the lung cancer group and high-risk healthy group, serum ones instead increased significantly in early stage LC patients and permitted to discriminate LC patients from both low-risk (HS) and high-risk healthy subjects (HR-HS) with an overall 93% accuracy (AUC 0.93) when LC was compared to HS and 74% (AUC 0.73) when it was compared to high-risk group. Of note, the analysis of RKIP/pRKIP ratio also showed a high accuracy (90% - AUC 0.95) in discriminating LC patients from HS and more importantly, it allowed to identify LC patients compared to HR-HS, with a higher accuracy (79% - AUC 0.79) than RKIP alone. We hypothesize that serum RKIP levels may be correlated to the activation of the immune systems against the tumor during the first phases of its development. Our study identified a preliminary, non-invasive, RKIP and pRKIP profile able to discriminate with high specificity and sensitivity the early-stage lung cancer patients from high-risk Healthy Subjects. These results, although preliminary, suggest the utility of measuring RKIP/pRKIP as potential new screening test for lung cancer and provide the basis for future validation studies.

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.

The HER2-targeted antibody-drug conjugates (ADCs) trastuzumab emtansine (T-DM1) and Trastuzumab deruxtecan (T-DXd) improved outcomes in breast cancer. Novel combinations and sequential approaches are under investigation to overcome resistance to T-DM1 and T-DXd. Furthermore, the landscape of HER2-targeted therapy is rapidly advancing with the development of ADCs designed to attack cancer cells with greater precision and reduced toxicity. Disitamab Vedotin (RC48) incorporates a HER2 antibody with unique binding epitope and a potent microtubule inhibitor as its cytotoxic moiety. ARX-788 comprises a non-cleavable linker and a novel maytansinoid derivative as the cytotoxic drug, engineered for increased stability and a higher drug-to-antibody ratio. SYD985, a duocarmycin-based ADC, is designed to be activated only in the acidic environment of the tumor, thus reducing off-target effects. BL-M0701 targets a different epitope on the HER2 protein, which might be beneficial in circumventing resistance that affects the binding of other ADCs and incorporate a camptothecin derivative as its payload. Zanidatamab zovodotin (ZW25) is an ADC that binds to two different HER2 epitopes simultaneously, enhancing the internalization and delivery of its cytotoxic payload. These and other ADCs in phase I trials hold the potential to improve outcomes for HER2-positive and HER2-Low breast cancer and possibly other solid tumors.