Plant cells' ability to withstand abiotic stress is strongly linked to modifications in their mechanical characteristics. Nevertheless, the lack of a workable method for consistently tracking plant cell mechanical properties severely restricts our comprehension of the mechanical alterations in plant cells under stress. With Polyethylene Glycol (PEG6000), we created a drought-like environment, and we used the Double Resonator Piezoelectric Cytometry (DRPC) method to dynamically and non-invasively track changes in the stress (ΔS) created and viscoelasticity (storage modulus G' and loss modulus G") of protoplasts and suspension cells of rice during drought stress. The findings demonstrate that, rice suspension cells and protoplasts react mechanically differently to 5%–15% PEG6000 stress, implying distinct resistance mechanisms. However, neither of them can withstand 25% PEG6000 stress, they respond mechanically similarly to 25% PEG6000 stress. The results of DRPC are further corroborated by the morphological alterations of rice cells and protoplasts observed under an optical microscope. To sum up, the DRPC technique functions as a precise cellular mechanical sensor and offers novel research tools for the evaluation of plant cell adversity and differentiating between the mechanical reactions of cells and protoplasts under abiotic stress.

Here, we present a staged approach for an innovative repurposing of a portable infant humidicrib into a live cell growth, observation, and imaging system. Furthmore, humidicrib can support different variations of “umbilical” bioreactors, and can be used to conduct electrophysiology experiments and in situ immunohistochemistry. Modifications incorporate a closed loop carbon dioxide (CO₂) concentration control system with umbilical CO₂ and heating support for tailored bioreactors. The repurposing cost is inexpensive and allows for the continued observation and imaging of cells. This prototype unit has been used to continuously observe and image live primary neurons for up to 21 days. This demonstrates the repurposed units’ suitability for use in tissue culture based research, particularly where modifications to microscopes are required or where sensitive manipulation outside of a standard incubator is needed.

Conventional plaque assays rely on the use of overlays to restrict viral infection allowing the formation of distinct foci that grow in time as the replication cycle continues leading to counta-ble plaques that are visualized with standard techniques such as crystal violet, neutral red or immunolabeling. This classical approach takes several days until large enough plaques can be visualized and counted with some variation due to subjectivity in plaque recognition. Since plaques are clonal lesions produced by virus-induced cytopathic effect, we applied DNA fluores-cent dyes with differential cell permeability to visualize them by live cell imaging. We could observe different stages of that cytopathic effect corresponding to an early wave of cells with chromatin-condensation followed by a wave of dead cells with membrane permeabilization within plaques generated by different animal viruses. This approach enables an automated plaque identification using image analysis to increase single plaque resolution compared to crystal violet counterstaining and allows its application to plaque tracking and plaque reduction assays to test compounds for both antiviral and cytotoxic activities. This fluorescent real-time plaque assay sums to those next-generation technologies by combining this robust classical method with modern fluorescence microscopy and image analysis approaches for future applica-tions in virology.

Amyloid β-peptide (Aβ) oligomerization is believed to contribute to the neuronal dysfunction in Alzheimer disease (AD). Despite decades of research, many details of Aβ oligomerization in neurons still need to be revealed. Förster Resonance Energy Transfer (FRET) is a simple but effective way to study molecular interactions. Here we use a confocal microscope with a sensitive Airyscan detector for FRET detection. By live cell FRET imaging, we detect Aβ42 oligomerization in primary neurons. The neurons were incubated with fluorescently labelled Aβ42 in the cell culture medium for 24 hours. Aβ42 were internalized and oligomerized into the lysosomes/late endosomes in a concentration-dependent manner. Both the cellular uptake and intracellular oligomerization of Aβ42 were significantly higher than for Aβ40. These findings provide a better understanding of Aβ42 oligomerization in neurons.

Climate change is increasing air temperatures and altering the precipitation and hydrological re-gime on a global scale. Challenges arise when assessing the impacts of climate change on the local scale for water resources management proposes, especially for low mountain headwater catch-ments that not only serve as important water towers for local communities, but also have distinct hydrological characteristics. Until now, no low flow or hydrological drought studies have been carried out on the Lauter River. This study is unique in that it compares the Lauter River, a transboundary Rhine tributary with a nearby station on the River Rhine just below its confluence at the French-German border. The Lauter catchment is a mostly natural, forested catchment, however, its water course has been influenced by past and present cultural activities. Climate change disturbances cascade through the hydrologic regime right down to the local scale. As we are expecting more low flow events, the decrease in water availability could cause conflicts be-tween different water user groups in the Lauter catchment. However, the choice of different methods for identifying low flow periods may cause confusion for local water resources manag-ers. Using flow rate time series of the Lauter River between 1956 and 2022 we compare for the first time three low flow identification methods: the variable threshold method (VT), the fixed threshold method (FT), and the Standardized Streamflow Index (SSI). Similar analyses are applied and compared to the adjacent Maxau station on the Rhine River for the same time period. This study aims at 1) interpreting the differences amongst the various low flow identification methods and 2) revealing the differences in low flow characteristics of the Lauter catchment compared to the Rhine River. It appears that the FT reacts faster to direct climate or anthropogenic impacts whereas VT is more sensitive to indirect factors such as decreasing subsurface flow which is typical for small headwater catchments, such as the Lauter where flow dynamics react faster to flow disturbances. Abnormally low flow during the early Spring in tributaries such as the Lauter can help predict low flow conditions of the Rhine River during the following half year and espe-cially for the summer. The results could facilitate Early Warning of hydrological droughts and drought management for water users in the Lauter catchment and further downstream along some of the Rhine.

The profile of secondary metabolites occurring in apple cuticular layer is not only characteristic for particular apple cultivar, but also dynamically reflects various external factors in growing environment. In this study, the possibility to authenticate apple samples based on analysis of their cuticular layer extracts was investigated. Ultra-high-performance liquid chromatography coupled to high resolution tandem mass spectrometry (UHPLC-HRMS/MS) was employed for obtaining metabolomic fingerprints. In total, 274 authentic apple samples, four cultivars harvested within the seasons 2020 to 2022 in the Czech Republic and Poland were analysed. The generated complex data processed by univariate and multivariate statistical methods enabled to build classification models for discrimination of apple cultivars as well as their geographical origin. The models showed very good performance in differentiation of Czech and Polish samples for three of four cultivars: ‘Gala’, ‘Golden Delicious’ and ‘Idared’, moreover, the validity of the models was verified over harvest seasons. The diagnostic markers, besides metabolites of triterpene biosynthetic pathway, mainly represented wax esters. ‘Jonagold’, which is known to be susceptible to mutations, was the only cultivar for which the unambiguous classification of geographical origin was not possible.

Quantitative phase imaging by digital holographic microscopy (DHM) is a nondestructive and label-free technique, that has been playing an indispensable role in the field of science, technology, and biomedical imaging. The technique is competent for the imaging and analysis of label-free living cells and the investigation of reflective surfaces. Herein, we introduce a new configuration of a wide field-of-view single-shot common-path off-axis reflective DHM for the quantitative phase imaging of biological cells that leverages with several advantages including less-vibration sensitive to external perturbations due to common-path configuration, compact in size, simple in optical design, highly stable, and cost-effective. A detailed description of the proposed DHM system, including its optical design, working principle, and capability of phase imaging, is presented. The applications of the proposed system are demonstrated by quantitative phase imaging results obtained from the reflective surface (USAF resolution test target) as well as transparent samples (microlens array and living plant cells). The proposed system could find its applications in the investigations of several biological specimens and optical metrology of micro-surfaces.

Dynamic optical structural color is always desired in various display applications and usually involves active materials. Full-color generation, especially bi-directional full-color generation in both reflective and transmissive modes, without any active materials included has been rarely investigated. Herein, we demonstrate a scheme of bi-directional full-color generation based on a plasmonic metasurface modulated by rotation of the polarization angle of the incident light without varying the geometry and the optical properties of the materials and the environment where the metasurface resides. The metasurface unit cell consists of plasmonic modules aligning in three directions and is patterned in a square array. The metasurface structural color device generates full colors in both reflection and transmission. Based on the proposed polarization-dependent structural color, information encoding of three multiplexed animal images and quick-responsive (QR) codes is demonstrated to verify the efficient information encoding and decoding of the proposed scheme. The animals can be seen under different polarization incidence and the QR codes can be successfully decoded by the polarization rotation in transmission. The proposed bi-directional full-color generation metasurface has great potential in applications such as kaleidoscope generation, anti-counterfeiting, dynamic color display, and optical information encoding.

Farmers are continually looking for new reliable, objective and non-invasive methods for evaluation of ewe condition. Live weight (LW) and body condition score (BCS) are used by farmers as a basis to determine the condition of the animal. Body composition is an important aspect of monitoring animal condition. The body composition is the amount of fat, lean and bone; knowing the amount of each is important because the information can be used for better strategic management interventions. Experiments were conducted to establish the relationship between body composition and body parameters, at key life’s stages (weaning and pre-mating), using measurements automatically determined by an image processing ap-plication at Lincoln University sheep farm for 88 Coopworth ewes. Computerized Tomography technology was used to develop relationship with body parameters and a subset was used to validate the predicted model. Multivariate linear regression (MLR), artificial neural network (ANNs) and regression tree (RT) statistical analysis methods were evaluated to determine their efficacy to predict body fat, lean and bone. The results showed a correlation between fat, lean and bone determined by CT and the fat, lean, bone weight estimated by live weight and body parameters calculated using the image processing application with R2 values of 0.90 for fat, 0.72 for lean and 0.50 for bone using ANNs statistical model. From these results, farmers can utilize accurate measures of fat which will enhance nutritional and management practices.

The blood count is crucial for assessing bone marrow's cell production and differentiation during infections, gauging disease severity, and monitoring therapeutic responses. The profile of blood count in chronic form of paracoccidioidomycosis (PCM) has been insufficiently explored. To better understand the changes in hematological cells in different stages of the PCM chronic form, we evaluated the blood count, including immature blood cells in automated equipment, before and during the treatment follow-up of 62 chronic PCM patients. Predominantly male (96.8%) with an average age of 54.3 (SD 6.9) years, participants exhibited pre-treatment conditions such as anemia (45.2%), monocytosis (38.7%), and leukocytosis (17.7%) which became less frequent after clinical cure. Anemia was more prevalent in severe cases. Notably, hemoglobin and reticulocyte hemoglobin content increased, while leukocytes, monocytes, neutrophils, immature granulocytes, and platelets decreased. Chronic PCM induced manageable hematological abnormalities, mainly in the red blood series. Monocytosis, indicating monocytes' role in PCM's immune response, was frequent. Post-treatment, especially after achieving clinical cure, significant improvements were observed in various hematological indices, including immature granulocytes and reticulocyte hemoglobin content, underscoring the impact of infection on these parameters.

Acute and chronic graft versus host disease (a/cGVHD) are major complications of allogeneic stem cell transplantation and a frequent cause of non relapse-mortality. The treatment of a/cGVHD consists of steroid administration at full dose. In the case of aGVHD, around 60 % of patients with grade III/IV fails to respond (steroid-resistant, SR), requiring a second line therapy. Extracorporeal photopheresis (ECP) represents a good option for second line treatment, leading to multiorgan response in several patients. Here, we have evaluated the performances of a cytofluorimetric quality control of the first ECP procedure in 29 patients with severe SR aGVHD who underwent ECP. The quality control represents a check control of in vitro cell apoptosis/death induction in collected and irradiated CD3+ cells by ECP. Our analysis shows that in more than 70 % quality controls a relevant apoptotosis/death of CD3+ cells was observed after cell irradiation, as compared to non-irradiated controls. Albeit observed in a small patients’ series, the occurrence of relevant in vitro effect after ECP relates to less procedures and/or to a better survival on ECP. Age, disease, gender, aGVHD grade and time from transplant to the first ECP had not impact on ECP efficacy in this patients’ series.

Shigella and Salmonella have been implicated in neonatal sepsis, food poisoning, and bacteremia. Their increasing resistance to antibiotics has undermined treatment options, constituting a public health crisis. Also, the scarcity of new antibiotics in the development pipeline compounds multidrug-resistance (MDR). Hence, there is a need to potentiate antibiotics to mitigate resistance. This study leveraged riboflavin to re-sensitize fluoroquinolones and carbapenems against MDR Shigella and Salmonella spp. Disk diffusion, microbroth-dilution, chequerboard, and time-kill assays were used to determine resistance profiles and riboflavin-antibiotic activity. Biofilm-formation and bacterial viability (after riboflavin-antibiotic challenge) was determined with crystal-violet and Macrophage infection assays, respectively. Strains exhibited >80% resistance to fluoroquinolones and carbapenems, with 160–320 µg MIC ranges and 0.5–2 FIC, indicating no synergy. At 40–80 µg riboflavin, there was two-fold reduction in bacterial survival, with meropenem-riboflavin exhibiting significant reduction and FIC of 0.075 indicating high synergistic activity. Biofilm-formation was altered (from strong to weak phenotype) with 40 µg riboflavin-antibiotic treatment. There was a 40–70% reduction in bacterial survival in THP-1 cells treated with meropenem-riboflavin after 3 hours, and complete clearance between 24-48 hpi. Riboflavin potentiated fluoroquinolones and meropenem, leading to modulation of the host immune response for the clearance of the strains.

River regulations ultimately degrade fluvial forms and morphodynamics and simplify riparian and aquatic habitats. For several decades, river restoration actions have been performed to recover geomorphic processes and diversify these habitats to enhance both river biodiversity and ecosystem services. The objective of this study is to provide quantitative feedback on the experimental restoration of a large regulated and by-passed river (the Upper Rhine downstream of the Kembs Dam, France/Germany). This restoration consisted of the construction of two transverse groynes and the removal of bank protection. A monitoring framework composed of topo-bathymetric surveys as well as flow velocity and grain size measurements was established to assess the channel morphodynamic responses and evaluate their effects on habitat suitability for five native fish species using habitat models. A riverscape approach was used to evaluate the landscape changes in terms of both the configuration and the composition, which cannot be considered with classic approaches (e.g., WUA). Our results show that the two transverse groynes and, to a lesser extent, bank erosion, which was locally enhanced by the two groynes, increased habitat diversity due to the creation of new macroforms (e.g., pools and mid-bars) and fining of the bed grain size. Using a riverscape approach, our findings highlight that the restoration improved lentic fish habitats (eel and juvenile nase species) due to slowing of the local current and the deposition of fine sediments downstream of both groynes. As a consequence, the restoration improved the habitat suitability of the studied reach for more fish species compared with the pre-restoration conditions. This study also demonstrates that the salmon habitats downstream of the restored reach were improved due to fining of the bed grain size. This finding highlights that for restorations aimed at fish habitats, the grain size conditions must be taken into consideration along with the flow conditions. Furthermore, the implementation of groynes, while not a panacea, can be a strategy for improving fish habitats on highly regulated rivers, but only when more functional and natural options are impossible due to major constraints.

Rigid-body visual tracking is an active research field with many practical applications including visual surveillance and intelligent transport system. In this paper, we define a new problem domain, called visual growth tracking, to track different parts of an object that grow non-uniformly over space and time for application in image-based plant phenotyping. The paper introduces a novel method to detect and track each leaf of a plant for automated leaf stage monitoring. The method has four phases: optimal view selection, plant architecture determination, leaf tracking and generation of a leaf status report. The proposed method uses a graph theoretic approach to reliably detect and track individual leaves by overcoming the challenge of leaf-losses based on temporal image sequence analysis for automatically generating the leaf status report containing the following phenotypes, i.e., the emergence timing of each leaf, total number of leaves present at any time, the day on which a particular leaf stopped growing, and the length and relative growth rate of individual leaves. The proposed method demonstrates high accuracy in detecting leaves and tracking them through the early vegetative stages of maize plants based on experimental evaluation on a publicly available benchmark dataset.

Separase is a key cysteine protease to separate sister chromatids through digestion of cohesion ring that inhibits chromosome segregation as trigger of metaphase–anaphase transition in eukaryotes and highly regulated its activity by binding with securin and cyclinB-CDK1 complex. Those bindings prevent proteolytic activity of separase until the onset of anaphase. Chromosome missegregation and aneuploidy are frequently observed in malignancies. However, there are some difficulties for biochemical examinations due to instability of separase in vitro and a few spatiotemporal resolution approaches monitoring live separase activity throughout mitotic processes. Here we have developed FRET-based molecular sensors including GFP variants with separase cleavable sequences as donor and covalently attached fluorescent dye as acceptor molecules applicable to conventional live cell imaging and flow cytometric analysis because of efficient live cell uptake. We investigated performance of equivalent molecular sensors localized or not localized inside nucleus under cell cycle control using flow cytometry. Synchronized cell cycle progression rendered significant separase activity detections in both molecular sensors. We obtained consistent outcomes with localized molecular sensor introduction and cell cycle control by fluorescent microscopic observations. We thus established live cell separase activity monitoring systems specifically or statistically that could lead to elucidate separase properties in detail.

Cell-culture meat is a novel concept to replace traditional meat produced from animals, and to satisfy rising food demands due to the world population growth. It is still a young field and requires to solve technical issues including cell sources and types, efficient culture for large scale production, taste and texture for customer satisfaction. The current review summarized traditional beef meat production and evaluation methods for customer satisfaction. In particular, serum biomarkers for beef meat evaluation are discussed, which can be applicated for cell-culture meat production.

Reconstructing the Tree of Life remains a central goal in biology. Early methods, relying on small numbers of morphological or genetic characters, often yielded conflicting evolutionary histories, undermining confidence in the results. Investigations based on phylogenomics, which use hundreds to thousands of loci for phylogenetic inquiry, have provided a clearer picture of life's history, but certain branches remain problematic. To resolve difficult nodes on the Tree of Life, two recent studies tested the utility of synteny, the conserved collinearity of orthologous genetic loci in two or more organisms, for phylogenetics. Synteny exhibits compelling phylogenomic potential while also raising new challenges. We identify and discuss specific opportunities and challenges that bear on the value of synteny data and other rare genomic changes for phylogenomic studies. Synteny-based analyses of highly contiguous genome assemblies mark a new chapter in the phylogenomic era and the quest to reconstruct the Tree of Life.

Morphological dissimilarity and its evolution over time are one of the most unexpected variations found when comparing cell cultures in 2D and 3D. Monolayer cells appear to flatten in the lower part of the plate, adhering to and spreading in the horizontal plane while not extending vertically. Consequently, cells developed in two dimensions have a forced apex-basal polarity. Co-cultivation and crosstalking between multiple cell types, which control development and formation in the in vivo counterpart, are possible in 3D cultures. With or without a scaffold matrix, 3D model culture may exhibit more in vivo-like morphology and physiology. 3D cultures mimic relevant physiological cellular processes, transforming them into one-of-a-kind drug screening platforms. The structures and dynamics of regulatory networks, which are increasingly studied with live-imaging microscopy, must be considered to help and guarantee the functional maintenance of a 3D structure. However, commercially available technologies that can be used for current laboratory needs are minimal, despite the need to make it easier to acquire cellular kinetics with high spatial and temporal resolution, in order to improve visual efficiency and, as a result, experimentation performance. The CELLviewer is a newly developed multi-technology instrument that integrates and synchronizes the work of various scientific disciplines. The aim of this study is to test the device using two different models: a single Jurkat cell and an MCF-7 spheroid. The two models are loaded into the microfluidic cartridge for each experiment after they have been grown and captured in time-lapse for a total of 4 hours. The samples used are tracked under the operation of the optics after adaptive autofocus, while slipping inside the cartridge chamber, and the 3D rotation was successfully obtained experimentally. The MitoGreen dye, a fluorescence marker selectively permeable to live cells, was then used to determine cell viability. To measure the model diameter, construct fluorescence intensity graphs along a straight line passing through the cell, and visualize the spatial fluorescence intensity distribution in 3D, ImageJ software was used.

An evaluation of image contrast in a controlled foggy environment has been made by comparison between intensity imaging and different modes of polarimetric imaging. A small-scale fog chamber has been designed and constructed to create the necessary controlled foggy environment. A linear polarimetric camera of division of focal plane and a linearly polarized light source has been used for performing the experiments with polarized light. In order to evaluate the image contrast of the different imaging modes, the Michelson’s contrast of samples of different materials relative to their background has been calculated. The higher the image contrast, the easier is to detect and segment the targets of interest that are surrounded by fog. It has been proved quantitatively that in the studied situations polarimetric images present an improvement in contrast when compared to conventional intensity images.

Pulmonary hypertension (PH) is a progressive and potentially fatal complication of sickle cell disease (SCD) affecting 6-10% of adult SCD patients. Various mechanisms and theories have been evaluated to explain the pathophysiology of the disease. However, questions remain, particularly regarding the clinical heterogeneity of the disease in terms of symptoms, complications, and survival. Beyond the classical mechanisms that have been thoroughly investigated and include hemolysis, nitric oxide availability, endothelial disorders, thrombosis, and left heart failure, attention is focused nowadays on the potential role of the genes involved in such processes. Potential candidate genes are investigated through next-generation sequencing, with the TGF-β pathway being the initial target. This field of research may also provide novel targets for pharmacologic agents in the future as is already the case with idiopathic PH. The collection and processing of data and samples from multiple centers can yield safe results that will allow a better understanding of SCD-related PH as a part of the disease’s clinical spectrum. The following review attempts to capture the most recent findings of studies on gene polymorphisms that have been associated with PH in SCD patients.

Music has the innate potential to reach all parts of the brain, stimulates certain brain areas which are not achievable through other modalities. Music Therapy (MT) is being used for more than a century to treat individuals who needs personalized care. MT optimizes motor, speech and language responsibilities of the brain and improves cognitive performance. Pervasive developmentdisorder (PDD) is a multifaceted, neuro developmental disorder and autism spectrum disorder (ASD) comes under PDD, which is defined by deficiencies in three principal spheres: social connection with others, communicative and normal movement skills. The conventional imaging studies illustrate reduced brain area connectivity in people with ASD, involving selected parts of the brain cortex. People with ASD express much interest in musical activities which engages the brain network areas and improves communication and social skills.The main objective of this review is to analyze the potential role of MT in treating the neurological conditions, particularly ASD. Evidence based studies have reported the extensive therapeutic application of music on various part of the brain in a nonverbal child with autism through hearing or making music.Hence we hypothesized that MT intervention can improve the communication capacity in people with ASD, than customary neurorestoration therapy alone.

Epilepsy is a common neurological condition, typically diagnosed using Electroencephalogram (EEG). Large scale EEG datasets have recently been made publicly available, allowing the use of advanced Machine Learning (ML) algorithms to analyze EEG patterns associated with epilepsy. While most existing studies focus on identifying seizures in the EEG, few have tried to identify preictal EEG segments. Identifying preictal EEG segments are not only useful in developing early warning systems but also helps inform the course of treatment for the patient. In this study, we propose to represent EEG segments as images, instead of time-series data, and identify preictal EEG segments using a preexisting ML algorithm (YOLOv8) designed for image processing. Multiplexed images (containing the original EEG signal represented on a 2D grid, kurtosis, and spectral entropy) achieve the best accuracy of 95.75% on the dataset while images just containing the EEG signal result in an accuracy of 91.25%. Using only spectrograms, generated from the original EEG signal, results in an accuracy of 90.15%.

In this study, we investigate the growth of EV ownership among Swedish households. Through detailed socio-economic data and the Swedish vehicle register, we are able compare characteristics of households that bought an EV in 2016 with those that became EV owners in 2020. We are thus able to describe and analyse the transition from the early EV owners to the early majority. Unlike many prior studies in the field, which are based on surveys or interviews, this study relies on registry data, thereby encompassing all car owners in Sweden. In this study, we have also scrutinized the significance of ownership mode by distinguishing between households that have chosen to purchase a car and those that have chosen to lease one. The results show that the demand for EVs has reached new consumer groups, e.g. both younger and older car buyers, and that the option to lease a car privately has broadened the market for new cars in general, and for EV’s in particular.

Imaging living cells by atomic force microscopy (AFM) promises not only high-resolution topographical data, but additionally, mechanical contrast, which are not obtainable with other microscopy techniques. Such imaging is however challenging, as cells need to be measured with low interaction forces to prevent either deformation or detachment from the surface. Off-resonance modes which periodically probe the surface have been shown to be advantageous, as they provide excellent force control combined with large amplitudes, which help reduce lateral force interactions. However, the low actuation frequency in traditional off-resonance techniques limits the imaging speed significantly. Using photothermal actuation, we probe the surface by directly actuating the cantilever. Due to the much smaller mass that needs to be actuated, the achievable measurement frequency is increased by two orders of magnitude. Additionally, photothermal off-resonance tapping retains the precise force control of conventional off-resonance modes and is therefore well suited to gentle imaging. Here we show how photothermal off-resonance tapping can be used to study live cells by AFM. As an example of imaging mammalian cells, the initial attachment, as well as long term detachment of a human thrombocytes are presented. The membrane disrupting effect of the antimicrobial peptide CM-15 is shown on the cell wall of E. coli. Finally, the dissolution of the cell wall of B. subtilis by lysozyme is shown. Taken together, these evolutionarily disparate forms of life exemplify the usefulness of PORT for live cell imaging in a multitude of biological disciplines.

Previous high throughput screening studies indicated trans-eQTLs tend to be tissue specific. In this study, I probed if this feature can be used to identify tissue-specific gene regulatory networks. eQTL data (P<5e-8) for 16,259 genes were identified and their summary association statistics were obtained from the eQTLGene database. Next, eQTLs that display both cis and trans regulatory effects were selected and the association between their corresponding genes were examined using Mendelian randomization. A total of 169 genes that exerted trans-regulatory impacts on 692 genes were identified. 90% of the genes (N=749) aggregated into a gene regulatory network significantly enriched in hemo-immune processes. The robustness of finding was confirmed through simulation. The identified network displayed the scale-free topology. This provided the reason to examine the association of the network’s hub genes with the phenome. The outcome of analyses revealed GSDMB and ORMDL3 impact several disorders of immune origin and ALDH2 overexpression contributes to obesity. This study reports trans-eQTLs can be used to identify tissue-specific gene regulatory networks and describes a workflow to achieve this purpose. The network identified in this study showed scale-free topology indicating the hub genes of a GRN could be targeted to prevent disease outcomes.

Regulation of the diet in terms of the content of soluble and insoluble fibre is of great importance from the point of view of dietetics, microbiology of the gastrointestinal tract and immunology of the organism. Dietary fibre plays an important role in maintaining the balance between the con-tinuity of gut mucus production (intestinal mucosa), the stability of the intestinal microbiome composition, the regeneration of the intestinal epithelium and the immune status within the gut-associated lymphoid tissue (GALT) are played by pectin and their metabolic products in the form of pectin oligosaccharides. The main purpose of this review was to focus on pectin, their functions, their impact on the human's health and on some of the most promising prebiotics due to their solubility in the gastrointestinal tract in the form of pectin oligosaccharides. They have the properties of pectin, but have also additional features and postbiotics obtained in fermentation process can affect the intestinal microbiome and GALT-related immunity. They can be obtained from plant agro-wastes.

Automated machine learning (AutoML) has been heralded as the next wave in artificial intelligence with its promise to deliver high performance end-to-end machine learning pipelines with minimal effort from the user. However, despite AutoML showing great promise for computer vision tasks, to the best of our knowledge, no study has used AutoML for image-based plant phenotyping. To address this gap in knowledge, we examined the application of AutoML for image-based plant phenotyping using wheat lodging assessment with UAV imagery as an example. We compared the performance of an open-source AutoML framework, AutoKeras in image classification and regression tasks to transfer learning using modern convolutional neural network (CNN) architectures. For image classification which classified plot images as lodged or non-lodged, transfer learning with Xception and DenseNet-201 achieved best classification accuracy of 93.2%, whereas Autokeras had 92.4% accuracy. For image regression which predicted lodging scores from plot images, transfer learning with DenseNet-201 had the best performance (R2=0.8303, RMSE=9.55, MAE=7.03, MAPE=12.54%), followed closely by AutoKeras (R2=0.8273, RMSE=10.65, MAE=8.24, MAPE=13.87%). Interestingly, in both tasks, AutoKeras models had up to 40-fold faster inference times compared to the pretrained CNNs. The merits and drawbacks of AutoML compared to transfer learning for image-based plant phenotyping are discussed.

A phenotype is the composite of an observable expression of a genome for traits in a given environment. The trajectories of phenotypes computed from an image sequence and timing of important events in a plant’s life cycle can be viewed as temporal phenotypes and indicative of the plant’s growth pattern and vigor. In this paper, we introduce a novel method called FlowerPhenoNet which uses deep neural networks for detecting flowers from multiview image sequences for high throughput temporal plant phenotyping analysis. Following flower detection, a set of novel flower-based phenotypes are computed, e.g., the day of emergence of the first flower in a plant’s life cycle, the total number of flowers present in the plant at a given time, the highest number of flowers bloomed in the plant, growth trajectory of a flower and the blooming trajectory of a plant. To develop a new algorithm and facilitate performance evaluation based on experimental analysis, a benchmark dataset is indispensable. Thus, we introduce a benchmark dataset called FlowerPheno which comprises image sequences of three flowering plant species, e.g., sunflower, coleus, and canna, captured by a visible light camera in a high throughput plant phenotyping platform from multiple view angles. The experimental analyses on the FlowerPheno dataset demonstrate the efficacy of the FlowerPhenoNet.

Brain is the most complex organ in the human body and it is divided into two hemispheres - left and right hemispheres. Left hemisphere is responsible for control of right side of our body whereas right hemisphere is responsible for control of left side of our body. Brain image segmentation from different neuroimaging modalities is one of the important parts in clinical diagnostic tools. Neuroimaging based digital imagery generally contain noise, inhomogeneity, aliasing artifacts, and orientational deviations. Therefore, accurate segmentation of brain images is a very difficult task. However, the development of accurate segmentation of brain images is very important and crucial for a correct diagnosis of any brain related diseases. One of the fundamental segmentation tasks is to identify and segment inter-hemispheric fissure/mid-sagittal plane, which separate the two hemispheres of the brain. Moreover, the symmetric/asymmetric analyses of left and right hemispheres of brain structures are important for radiologists to analyze diseases such as Alzheimer's, Autism, Schizophrenia, Lesions and Epilepsy. Therefore, in this paper we have analyzed the existing computational techniques used to find brain symmetric/asymmetric analysis in various neuroimaging techniques (MRI/CT/PET/SPECT), which are utilized for detecting various brain related disorders.

This study aimed to demonstrate the potential role of Dual Energy CT in assessing Bone Mineral Density (BMD) in postmenopausal women. A retrospective study was conducted on 51 postmenopausal female patients who underwent DXA and DECT examinations for other clinical reasons. DECT images were acquired with spectral imaging using a 256-slice system. These images were processed and visualized using an HAP(fat) map. Statistical analysis was performed using the Bland-Altman method to assess agreement between DXA and DECT HAP(fat) measurements. Mean BMD, vertebral and femoral T-scores were obtained. For vertebral analysis, the Bland-Altman plot showed an inverse correlation (R2: -0.042; RMSE: 0.690) between T-scores and DECT HAP(fat) values for measurements from L1 to L4, while a good linear correlation (R2: 0.341; RMSE: 0.589) it was found for measurements at the femoral neck. In conclusion, we demonstrated the enhanced accuracy of BMD calculation through DECT, finding a statistically significant correlation only at the femoral neck where BMD results do not seem to be influenced by the overlap of the measurements on cortical and trabecular bone. This outcome could be beneficial in the future by reducing radiation exposure for patients already undergoing follow-up for chronic conditions.

An automated discharge imaging system (ADIS), a non-intrusive and safe approach, was developed for measuring river flows during flash flood events. ADIS consists of dual cameras to capture complete surface images in the near and far fields. Surface velocities are accurately measured using the Large Scale Particle Image Velocimetry (LSPIV) technique. The stream discharges are then obtained from the depth-averaged velocity (based upon an empirical velocity-index relationship) and cross-section area. The ADIS was deployed at the Yu-Feng gauging station in Shimen Reservoir upper catchment, northern Taiwan. For a rigorous validation, surface velocity measurements were conducted using ADIS/LSPIV and other instruments. In terms of the averaged surface velocity, all measured results were in good agreement with small differences, i.e., 0.004 to 0.39 m/s and 0.023 to 0.345 m/s when compared to those from acoustic Doppler current profiler (ADCP) and surface velocity radar (SVR), respectively. The ADIS/LSPIV was further applied to measure surface velocities and discharges during typhoon events (i.e., Chan-Hom, Soudelor, Goni, and Dujuan) in 2015. The measured water level and surface velocity both showed rapid increases due to flash floods. The estimated discharges from ADIS/LSPIV and ADCP were compared, presenting good consistency with correlation coefficient R = 0.996 and normalized root mean square error NRMSE = 7.96%. The results of sensitivity analysis indicate that components till (τ) and roll (θ) of the camera are most sensitive parameter to affect the surface velocity using ADIS/LSPIV. Overall, the ADIS based upon LSPIV technique effectively measures surface velocities for reliable estimations of river discharges during typhoon events.

This paper presents novel feature descriptors and classification algorithms for automated scoring of HER2 in Whole Slide Images (WSI) of breast cancer histology slides. Since a large amount of processing is involved in analyzing WSI images, the primary design goal has been to keep the computational complexity to the minimum possible level and to use simple, yet robust feature descriptors that can provide accurate classification of the slides. We propose two types of feature descriptors that encode important information about staining patterns and the percentage of staining present in ImmunoHistoChemistry (IHC) stained slides. The first descriptor is called a characteristic curve which is a smooth non-increasing curve that represents the variation of percentage of staining with saturation levels. The second new descriptor introduced in this paper is an LBP feature curve which is also a non-increasing smooth curve that represents the local texture of the staining patterns. Both descriptors show excellent interclass variance and intraclass correlation, and are suitable for the design of automatic HER2 classification algorithms. This paper gives the detailed theoretical aspects of the feature descriptors and also provides experimental results and comparative analysis.

Background and Objectives: High job strain is worsening the outcome in coronary artery disease, CAD. Severe dissatisfaction on workplace, imbalance between high demands , and low rewards, are affecting the quality of life, and diminishing the duration of life, especially in CAD patients. Our study included two samples : CAD patients (experimental group), and without CAD patients, but with traditional risk factors (control group). The main objective of the study was the role played by high sensitivity C reactive protein, hs CRP, and microalbuminuria, as mediators of job strain and CAD relationship. Secondary objectives were the following: identification of the most vulnerable patients( with significant job strain) and intervention towards this category, for cardiovascular events prevention (primary or secondary). Materials and Methods: The utilized methods were the following : clinical exam; electrocardiogram; echocardiogram; non contrast enhanced computed chest tomography; laboratory findings ( lipid profile, glycemia, hs CRP, microalbuminuria); type D scale, DS 14; depression questionnaire, PHQ-9; and satisfaction with work scale, SWWS. Results: The highest values for hs CRP and microalbuminuria were noticed in patients with the lowest Likert score, for both samples CAD, and without CAD patients. The highest job strain was noticed in females. More than half of CAD patients ,with low Likert scores, revealed myocardial infarction. Conclusions: Microalbuminuria and hs CRP were statistical significant in the study as intercessors, in job strain and CAD relationship. The most vulnerable patients for vascular events were females, in both groups : CAD and “CAD waiting desk”. In CAD sample, the vascular vulnerability was also increased by high job strain.

In this study, the microbial strain was isolated from the humus soil to ferment Sophora flavescens and Eleutherococcus sessiliflorus extracts. The isolated microbial was identified as Bacillus genus by 16S rRNA sequence analysis. The fermented plant extracts exhibited the antifungal effects against four types of the plant pathogens such as P. carotorum, B. cinerea, C. fructicola Sau-3, and C. gloeosporioides according to incubation time. Especially, the fermented plant extracts showed the most active activities for Colletotrichum genus to inhibit mycelium growth. Metabolite changes in fermented S. flavescens and E. sessiliflorus extracts were confirmed through LC-Q-TOF/MS. Flavonoid and peptide derivatives were improved in fermented S. flavescens and E. sessiliflorus extracts compared to their unfermented one, respectively. This study suggested that the isolated Bacillus microbial fermentation could be a valuable tool in improving the bioactivity of S. flavescens and E. sessiliflorus extracts to have potential with more environmentally-friendly antifungal agents.

The paper delves into the critical issue of damage detection within the guy cable of a truss steel mast, a pivotal component in structural integrity. It introduces a model wherein damage manifests as a localized cross-section reduction in a single element of the cable. Employing Discrete Wavelet Transform (DWT), a pioneering methodology, the study scrutinizes the behavior of the affected elements through static structural analyses. Signal decomposition via the Mallat pyramid algorithm facilitates comprehensive examination. Static displacements at the connection point between the cable and the truss mast serve as the measured variables. Through systematic investigation, the paper evaluates the impact of damage size and location, external loading force, and cable tension force on the efficacy of the proposed approach. Utilizing data derived from Finite Element Method (FEM) computations for wavelet analysis the authors substantiate the findings with numerical examples, thus offering valuable insights into damage detection strategies for structural health monitoring and engineering applications.

Porous ultra-high molecular weight polyethylene (UHMWPE) is a high performance bioinert polymer used in cranio-facial reconstructive surgery in procedures where relatively low mechanical stresses arise. As an alternative to much stiffer and costly polyether-ether-ketone (PEEK) polymer, UHMWPE finds further wide application in hierarchically structured hybrids for advanced implants mimicking cartilage, cortical and trabecular bone tissues within a single component. The mechanical behaviour of open-cell UHMWPE sponges obtained through sacrificial desalination of hot compression-moulded UHMWPE-NaCl powder mixtures shows a complex dependence on the fabrication parameters and microstructural features. In particular, similarly to other porous media it displays significant inhomogeneity of strain that readily localises within deformation bands that govern the overall response. In this article, we report advances in the development of accurate experimental techniques for operando studies of the structure-performance relationship applied to the porous UHMWPE medium with pore sizes of about 250 µm that are most well-suited for live cell proliferation and fast vascularization of implants. Samples of UHMWPE sponges were subjected to in situ compression using a micromechanical testing device within Scanning Electron Microscope (SEM) chamber, allowing the acquisition of high-resolution image sequences for Digital Image Correlation (DIC) analysis. Special masking and image processing algorithms were developed and applied to reveal the evolution of pore size and aspect ratio. Key structural evolution and deformation localisation phenomena were identified at both macro- and micro-structural levels in the elastic and plastic regimes. The motion of pore walls was quantitatively described, and the presence and influence of strain localisation zones were revealed and analysed using DIC technique.

The Solvency II regulatory framework specifies procedures and parameters for determining solvency capital requirements (SCRs) for insurance companies. The proposed standard SCR calculations involve two steps. The Value–at–Risk (VaR) of each risk driver is measured and, in a second step, all components are aggregated to the company’s overall SCR, using the Standard Formula. This formula has two inputs: the VaRs of the individual risk drivers and their correlations. The appropriate calibration of these input parameters has been the purpose of various Quantitative Impact Studies that have been conducted during recent years. This paper demonstrates that the parameter calibration for the equity–risk module—overall, with about 25%, the most significant risk component—is seriously flawed, giving rise to spurious and highly erratic parameter values. As a consequence, an implementation of the Standard Formula with the currently proposed calibration settings for equity–risk is likely to produce inaccurate, biased and, over time, highly erratic capital requirements.

X-ray tomography is an effective non-destructive testing method for industrial quality control. Limited angle tomography can be used to reduce the amount of data that needs to be acquired and thus speed up the process. In some industrial applications, however, objects are flat and layered, and laminography is preferred. It can deliver 2D images of the structure of a layered object at a particular depth from very limited data. An image at a particular depth is obtained by summing those parts of the data that contribute to that slice. This produces a sharp image of that slice while superimposing a blurred version of structures present at other depths. In this paper, we investigate an optimal experimental design (OED) problem for laminography that aims to determine the optimal source positions. Not only can this be used to mitigate imaging artifacts, it can also speed up the acquisition process in case where moving the source and detector is time consuming (e.g., in robotic arm imaging systems). We investigate the imaging artifacts in detail through a modified Fourier Slice Theorem. We address the experimental design problem within the Bayesian risk framework using empirical Bayes risk minimization. Finally, we present numerical experiments on simulated data.

: Image guided superficial radiation therapy (IGSRT) combines superficial radiation therapy (SRT) with full dermal visualization (FDV) via high resolution dermal ultrasound (HRDUS) for the treatment of non-melanoma skin cancer (NMSC). The gold standard for IGSRT delivery includes a comprehensive cancer care model with support for the dermatologists by a multidisciplinary team. When delivered in this model, IGSRT can achieve cure rates of 99% for treatment of NMSC. This paper focuses on the benefits of IGSRT during therapy. Medical records from 7 dermatology clinics of 883 patients with 1,507 cases of NMSC treated with IGSRT be-tween 2017 and 2018 were retrospectively reviewed. 92% of the NMSC lesions showed daily depth fluctuations, 60.32% of lesions did not require changes during therapy, and nearly 40% of lesions required at least one compensatory change during therapy. 83% of NMSC lesions were labeled high-risk based on the 2024 NCCN guidelines. Increasing and decreasing tumor depth measurements during IGSRT inform dermatologists when adaptive changes in energy (kV), TDF and dose will result in more efficacy and less toxicity, respectively.

Given the accelerating scientific, clinical and consumer interest in highly-prevalent functional gastrointestinal disorders, appropriate therapeutic strategies are needed to address the many aspects of digestive dysfunction. Accumulating evidence for the crucifer-derived bioactive molecule, sulforaphane in upstream cellular defence mechanisms highlights its potential as a therapeutic candidate in targeting functional gastrointestinal conditions together with systemic disorders. This article catalogues the evolution of and rationale for a hypothesis that utilises multifunctional sulforaphane as the initial step in restoring the ecology of the gut ecosystem; it does this primarily by targeting the functions of intestinal epithelial cells. A growing body of work has identified the colonocyte as the driver of dysbiosis, so that targeting gut epithelial function could provide an alternative to targeting the microbes themselves for remediation of microbial dysbiosis. The hypothesis discussed herein has evolved over several years and is supported by case studies showing the application of sulforaphane in gastrointestinal disorders, related food intolerance and several systemic conditions. To our knowledge, this is the first time the effects of sulforaphane have been reported in a clinical environment where several of its key properties within the gut ecosystem appear to be related to its nutrigenomic effects on gene expression.

This study addresses the challenge of treating skin cancer effectively, especially in visible areas like the face, where traditional methods may be undesirable. Current options, including surgery and radiation therapy, have limitations such as complexity, expense, and imprecise dose delivery. The proposed solution involves additively manufacturing personalized patches using diagnostic imaging data, offering a targeted and controlled dose of beta-emitting isotopes like Y-90. The patches, tailored to the tumor's shape and depth, aim to minimize damage to healthy tissues and structures. Through 3D printing technology we demonstrated the feasibility of creating individualized applicators filled with radioisotopes, that have the potential to deliver therapeutic doses while ensuring patient comfort and affordability. This innovative approach, based on radiation delivery simulations, offers a promising future alternative to conventional radiation therapy methods. Clinical trials are required to validate its efficacy and safety, but the potential for reduced radiation exposure and enhanced treatment precision positions this personalized and targeted therapy as a valuable advancement in skin cancer treatment.

Cell junction that is usually associated with dynamic cytoskeletons is essential for a wide range of cellular activities such as cell migration, cell communication, barrier function and signal transduction. Real-time observation of cell junction facilitates understanding of the mechanisms by which cell junction regulate relevant cellular activities. In this study, we examined the binding capacity of a modified tridecapeptide from Cx43 to cell junction protein ZO-1 and sought to create a fluorescent peptide that could be used to label cell junction. To introduce the fluorescent peptide into the cells, a cell-penetrating peptide was linked to the modified tridecapeptide and synthesized. The binding of the modified tridecapeptide was tested using pull down and immunoprecipitation assay. The ability of the peptide to label cell junction was assessed by adding it to fixed or cultured Caco-2 cells. Testing assay revealed that the Cx43-derived peptide can bind to ZO-1. Besides, the peptide was able to label cell junction of fixed cell, although no obvious cell junction labeling was observed clearly in living cells, probably due to the inadequate affinity. These results indicate that a peptide-based strategy for labeling cell junction may be feasible, and efforts to improve its affinity are warranted in the future.

One of the tools for optimal crop production is regular monitoring and assessment of crops. During the growing season of fruit trees, the bloom period has increased photosynthetic rates that correlate with the fruiting process. This paper presents the development of an image processing algorithm to detect peach blossoms on trees. Images of an experimental peach orchard were acquired from the Parma Research and Extension Center of the University of Idaho using an off-the-shelf unmanned aerial system (UAS), equipped with a multispectral camera (Near-infrared, Green, Blue). The orchard has different stone fruit varieties and different plant training system. Individual tree images (high-resolution) and arrays of trees images (low-resolution) were acquired to evaluate the detection capability. The image processing algorithm was based on different vegetation indices. Initial results showed that the image processing algorithm could detect peach blossoms and demonstrate good potential as a monitoring tool for orchard management.

Quantum correlations, especially time correlations, are crucial in ghost imaging in order to significantly reduce the background noise on the one hand while increasing the imaging resolution. Moreover, the time correlations serve as a critical reference, distinguishing between signal and noise, which in turn enable clear visualization of biological samples. Quantum imaging also addresses the challenge involved in imaging delicate biological structures with minimal photon exposure and sample damage. Here we explore the recent progress in quantum correlation-based imaging, notably its impact on secure imaging and remote sensing protocols as well as on biological imaging. We also exploit the quantum characteristics of heralded single-photon sources (HSPS) combined with decoy-state methods for secure imaging. This method uses Quantum Key Distribution (QKD) principles to reduce measurement uncertainties and protect data integrity. It is highly effective in low-photon-number regimes for producing high-quality, noise-reduced images. The versatility of decoy-state methods with WCSs (WCS) is also discussed, highlighting their suitability for scenarios requiring higher photon numbers. We emphasize the dual advantages of these techniques: improving image quality through noise reduction and enhancing data security with quantum encryption, suggesting significant potential for quantum imaging in various applications, from delicate biological imaging to secure quantum communication.

The key links of face recognition are digital image preprocessing, facial feature extraction and pattern recognition, this article aimed at the current problem of slow speed and low recognition accuracy of face recognition , from the above three key links, on the basic of analyzing the therories of Fractional Differential Masks Operator (FDMO), Principal Component Analysis (PCA) and Support Vector Machine (SVM), design a kind of FDMO+PVA+SVM coupling algorithm that applies to face recognition to improve the speed and accuracy of it. To realize FDMO+PCA+SVM coupling algorithm, first, we should apply FDMO to face image processing binary marginalization, the purpose is getting face contour; Then, we apply PCA to get the feature of face image which is disposed by binary marainalization. At last, we can apply One-Against All of the SVM classifier and LibSVM software package to realize face recognition. Beside, the article with nine different coupling algorithm design four groups of experimental reaults on the ORL face database verified by comparative analysic FDMO+PCA+SVM coupling algorithm in the superiority of face recognition accuracy and speed.

Although ovarian cystic teratoma is the most common ovarian tumor, complications are quite rare. However, it is important to be recognized by the radiologist in order to avoid inaccurately diagnosing them as malignant lesions. This case report describes a 61-year-old postmenopausal woman, who presented to the emergency room with abdominal pain following a minor blunt abdominal trauma. In this context, a CT scan was performed, which showed the presence of round, hypodense masses randomly distributed in the peritoneum, with coexisting ascites in moderate amount; ovarian carcinoma with peritoneal carcinomatosis was suspected. The patient was hospitalized and an MRI of the abdomen and pelvis was recommended for a more detailed lesion characterization. Following this examination, the patient was diagnosed with mature cystic ovarian teratoma complicated by rupture. Surgery was performed, and the outcome was favorable. The cases of ruptured cystic teratomas are rare, and to our knowledge, this is the first occurrence described in literature. Special attention must be paid when confronting with such a case in medical practice, since it can easily misdiagnosed as peritoneal carcinomatosis

Light microscopy is indispensable for analysis of bacterial spatial organization. However, imaging in bacteria is difficult due to their small sizes and the fact that most species are non-spherical, meaning they typically lie horizontally on a microscope coverslip. This is especially problematic when considering that many essential bacterial processes—such as cell division—occur along the short axes of these cells, and so are viewed side-on by standard microscopy. We recently developed a pair of methods to overcome this problem by forcing cells to stand vertically during imaging, named VerCINI (Vertical Cell Imaging by Nanostructured Immobilisation) and µVerCINI (Microfluidic VerCINI). The concept behind both methods is that cells are imaged while confined vertically inside cell traps made from a nanofabricated mould. By doing so, the short axes of the cells are rotated parallel to the microscope imaging plane and are imaged with high resolution. μVerCINI combines the vertical cell confinement with microfluidics so that vertical imaging can be done during fluid exchange, such as during antibiotic perturbations. Here, we provide a practical guide to implementing both VerCINI and µVerCINI, with detailed protocols and experience-based tips so that interested researchers can easily use one or both imaging methods to complement their current approaches.

Open-vocabulary learning has recently gained prominence as a means to enable image segmentation for arbitrary categories based on textual descriptions. This advancement has extended the applicability of segmentation systems to a broader range of generally purpose scenarios. However, current methods often revolve around specialized architectures and parameters tailored to specific segmentation tasks, resulting in a fragmented landscape of segmentation models. In response to these challenges, we introduce OVAMTSeg, a versatile framework designed for Open-Vocabulary and Multitask Image Segmentation. OVAMTSeg harnesses adaptive prompt learning to empower the model to capture category-sensitive concepts, enhancing its robustness across diverse multi-task and scenario contexts. Text prompts are employed to effectively capture semantic and contextual features of the text, while cross-attention and cross-modal interactions enable the fusion of image and text features. Furthermore, a transformer-based decoder is incorporated for dense prediction. Extensive experimental results underscore the effectiveness of OVAMTSeg, showcasing its state-of-the-art performance and superior generalization capabilities across three segmentation tasks. Notable achievements include a 47.5 mIoU in referring expression segmentation, 51.6 mIoU on Pascal-VOC with four unseen classes, 46.6 mIoU on Pascal-Context in zero-shot segmentation, 65.9 mIoU on Pascal-5i, and 35.7 mIoU on COCO-20i datasets for one-shot segmentation.

Magnetic Particle Imaging (MPI) is a new imaging modality that sensitively and specifically detects superparamagnetic iron oxide nanoparticles (SPIONs). Many labs have been developing cellular magnetic resonance imaging (MRI) tools using both SPIONs and fluorine-19 (19F)-based contrast agents for numerous important applications, including tracking of immune and stem cells used for cellular therapies. SPION-based MRI cell tracking has very high sensitivity, but low specificity. SPIONs produce negative contrast in MRI, or signal voids. SPIO is not directly detected by MRI, but indirectly through its relaxation effects on protons, therefore, it is not possible to reliably quantify the local tissue concentration of SPION particles and cell number cannot be determined. 19F based cell tracking uses perfluorocarbons (PFC) to label cells. The number of 19F atoms can be directly measured from 19F MR images and related to cell number. 19F MRI has high specificity, but low sensitivity. MPI cell tracking displays great potential for overcoming the challenges of MRI-based cell tracking allowing for both high cellular sensitivity and high specificity and quantification of SPIO labeled cell number. In this paper we describe nanoparticle and MPI system factors that influence MPI sensitivity and resolution, quantification methods and give our perspective on testing and applying MPI for cell tracking.

This study analyses regional development in one of the poorest provinces in China, Guizhou Province, between 2000 and 2012 using a multiscale and multi-mechanism framework. In general, regional inequality has been declining since 2000. In addition, economic development in Guizhou Province presented spatial agglomeration and club convergence, which shows how the development pattern of one core area, two-wing areas and a contiguous area at the edge of the province have been developed between 2006 and 2012. Multilevel regression analysis revealed that industrialization and investment level were the primary driving forces of regional economic disparity in Guizhou Province. The influences of marketization and decentralization on regional economic disparity were relatively weak. Investment level reinforced regional economic disparity and the development of core-periphery structure in the province. However, investment level actually weakened the regional economic disparity in Guizhou Province when the variable of time was considered. In addition, both the topography and urban–rural differentiation were the two main reasons for forming a core-periphery structure in Guizhou Province.

This study considers an application of the first-order Grey Markov Model to foresee the values of Italian power generation in relation to the available energy sources. The model is used to fit data from the Italian energy system from 2000 to 2022. The integration of Markovian error introduces a random element to the model, which is able now to capture inherent uncertainties and misalignments between the Grey model predictions and the real data. This application provides valuable insights for strategic planning in the energy sector and future developments. The results show good accuracy of the predictions, which provides powerful information for the effective implementation of energy policies concerning the evolution of energy demand in the country. Despite advancements, Italy’s 2032 energy mix will still significantly rely on fossil fuels, emphasizing the need for sustained efforts beyond 2032 to enhance sustainability.

The COVID-19 pandemic has caused ~ 2 million fatalities. Significant progress has been made in advancing our understanding of the disease process, one of the unanswered questions, however, is the anomaly in the case/mortality ratio with Mexico as a clear example. Herein, this anomaly is explored by spatial analysis and whether mortality varies locally according to local factors. To address this, hexagonal cartogram maps (hexbin) used to spatially map COVID-19 mortality and visualise association with patient-level data on demographics and pre-existing health conditions. This was further interrogated at local Mexico City level by choropleth mapping. Our data show that the use of hexagonal cartograms is a better approach for spatial mapping of COVID-19 data in Mexico as it addresses bias in area size and population. We report sex/age-related spatial relationship with mortality amongst the Mexican states and a trend between health conditions and mortality at the state level. Within Mexico City, there is a clear south, north divide with higher mortality in the northern municipalities. Deceased patients in these northern municipalities have the highest pre-existing health conditions. Taken together, this study provides an improved presentation of COVID-19 mapping in Mexico and demonstrates spatial divergence of the mortality in Mexico.

Predictive biomarkers for immune checkpoint inhibitors (ICI), such as PD-L1 expression (TPS), remain limited in clinical applications. Improved novel ICI predictive biomarkers are urgently needed. We evaluated a live single-cell functional liquid biopsy cytokine profiling platform to track immunotherapy treatment response and outcomes. Peripheral blood mononuclear cell samples of healthy donors and NSCLC patients undergoing ICI-based therapies were collected longitudinally pre-/post-treatment under IRB-approved protocols. Samples were enriched for CD4+ and CD8+ T-lymphocytes and analyzed on the IsoLight platform. The single T-cells were captured in microchambers on IsoCode chips for cytokines profiling. Functional polyfunctionality data from 55,775 single cells were analyzed using the IsoSpeak software, kappa coefficient, and Kaplan-Meier survival plots. We found a significant difference between responders and non-responders in CD8+ T-cells’ changes in overall polyfunctionality and polyfunctional strength index (ΔPSI). ΔPSI score in CD8+ T-cells performed better than PD-L1 TPS and, when combined with PD-L1 TPS, strongly correlated with early ICI treatment response with a kappa coefficient of 1.0 (p=0.003). High CD4+ T-cells ΔPSI predicted a strong trend of improved PFS (3.9-fold) and OS (3-fold). In conclusion, single peripheral T-cell polyfunctionality dynamics analysis is a promising liquid biopsy profiling platform as NSCLC ICI predictive biomarker regardless of oncogene-addiction status.

This paper presents a two-stage hierarchical neural network using image classification and object detection algorithms as key building blocks for a system that automatically detects a potential design right infringement. This neural network is trained to return the Top-N original design right records that highly resemble the input image of a counterfeit. Design rights specify the unique aesthetic characteristics of a product. Due to the rapid change of trends, new design rights are continuously generated. This work proposes an Ensemble Neural Network (ENN), an artificial neural network model that aims to deal with a large amount of counterfeit data and design right records that are frequently added and deleted. At first, we performed image classification and objection detection learning per design right using the existing models with a proven track record of high accuracy. The distributed models form the backbone of the ENN and yield intermediate results aggregated at a master neural network. This master neural network is a deep residual network paired with a fully connected network. This ensemble layer is trained to determine the sub-models that return the best result for a given input image of a product. In the final stage, the ENN model multiples the inferred similarity coefficients to the weighted input vectors produced by the individual sub-models to assess the similarity between the test input image and the existing product design rights to see any sign of violation. Given 84 design rights and the sample product images taken meticulously under various conditions, our ENN model achieved average Top-1 and Top-3 accuracies of 98.409% and 99.460%, respectively. Upon introducing new design rights data, a partial update of the inference model was done an order of magnitude faster than the single model. ENN maintained a high level of accuracy as it scaled out to handle more design rights. Therefore, the ENN model is expected to offer practical help to the inspectors in the field, such as the customs at the border that deal with a swarm of products.

Densely urbanized areas, with a low percentage of green vegetation, are highly exposed to Heat Waves (HW) which nowadays are increasing in terms of frequency and intensity also in the middle-latitude regions, due to ongoing Climate Change (CC). Their negative effects may combine with those of the UHI (Urban Heat Island), a local phenomenon where air temperatures in the compact built up cores of towns increase more than those in the surrounding rural areas, with significant impact on the quality of urban environment, on citizens health and energy consumption and transport, as it has occurred in the summer of 2003 on France and Italian central-northern areas. In this context this work aims at designing and developing a methodology based on aero-spatial remote sensing (EO) at medium-high resolution and most recent GIS techniques, for the extensive characterization of the urban fabric response to these climatic impacts related to the temperature within the general framework of supporting local and national strategies and policies of adaptation to CC. Due to its extension and variety of built-up typologies, the municipality of Rome was selected as test area for the methodology development and validation. First of all, we started by operating through photointerpretation of cartography at detailed scale (CTR 1: 5000) on a reference area consisting of a transect of about 5x20 km, extending from the downtown to the suburbs and including all the built-up classes of interest. The reference built-up vulnerability classes found inside the transect were then exploited as training areas to classify the entire territory of Rome municipality. To this end, the satellite EO HR (High Resolution) multispectral data, provided by the Landsat sensors were used within a on purpose developed "supervised" classification procedure, based on data mining and “object-classification” techniques. The classification results were then exploited for implementing a calibration method, based on a typical UHI temperature distribution, derived from MODIS satellite sensor LST (Land Surface Temperature) data of the summer 2003, to obtain an analytical expression of the vulnerability model, previously introduced on a semi-empirical basis.

Tumor immunotherapy represented by immune target blocking and chimeric antigen receptor cell therapy has developed rapidly. Among them, Natural Killer Cell (NK) is gradually becoming another preferred immunotherapy method after T cell immunotherapy because of its unique killing effect in innate immunity and adaptive immunity. Although NK cell therapy has achieved promising results in clinical trials, there are still some problems, such as low efficacy in solid tumors, insufficient penetration of NK cells and high cost of treatment. Nanomaterials are expected to be an excellent tool to improve the anti-tumor immune response of NK cells due to their advantages such as chemical specificity, biocompatibility and simple synthesis. Nanomaterials can enhance the cytotoxicity of NK cells, participate in the modification and noninvasive cell-tracking patterns of NK cells，and mediate their homing and infiltration, and thus significantly improve the efficiency of NK cell immunotherapy. In this review, we will briefly introduce the methods and mechanisms of tumor immunotherapy, emphasize the research progress of NK cell-based immunotherapy and imaging, and finally summarize the direction and research progress of nanomaterials in NK cell-based immunotherapy.

To ensure consumers can purchase high-quality rice, accurate identification of rice varieties is particularly important. Methods: This article conducts research based on machine learning algorithms from the perspectives of image, spectrum, and spectrogram fusion. Six types of hyperspectral image data of rice were preprocessed using convolutional smoothing (SG) and multiple scatter correction (MSC). Texture information of the images was extracted using gray-level co-occurrence matrix. Spectra, texture, and spectrogram data were fused into a new matrix. Spectral, texture, and spectrogram fusion data were used as inputs for the model, and support vector machines (SVM), logistic regression (LR), and K-nearest neighbors (KNN) classification models were constructed and compared. Results: From the classification results, the spectrogram fusion classification performance was better than classification models using only spectra or texture. Conclusion: The research results showed that the accuracy of SVM and LR classification models exceeded 90%, and the LR model performed the best, effectively classifying rice varieties.

Knowledge is not merely the characteristic of certain pieces of information being deemed true and justifiable, but rather a process. Within an axiomatic system, the set of information that reaches the status of knowledge forms a universe that provides an interpretation of the reality one aims to describe. Language exchanges information through communication and the process of justification (exchange of information), if it consistently justifies a particular piece of information, it elevates it to knowledge. In this paper, we provide a framework for knowledge within an axiomatic model where we define the fundamental elements of communication and grammar. As a result, the structure dictates that knowledge, to be considered as such, must be investigateable by anyone who utilizes it in its entirety, both in form and content. Ultimately, the development of knowledge processing, referred to as \emph{knowmatics}, represents a clear evolution of the computer science that processes information (subset of knowledge).

Currently, drug screening is primarily based on human cell culture for initial high-throughput screening, and subsequently, rodent model to confirm the biological effects. However, the mammalian system is known for time-consuming and highly-cost to be difficult to perform high-throughput drug screening, which exists a critical gap between in vitro cell-based models and the in vivo mammalian models. Therefore, the zebrafish could bridge this gap in preclinical toxicity screening along the drug development pipeline because of its efficiency. We aimed to develop an in vivo zebrafish platform for rapid drug screening. Zebrafish, due to its high genomic conservation with mammals and rapid development and differentiation, it has many advantages, such as short life span, large number of offspring and low cost, easy manipulation for generating transgenic species, to serve as animal model for disease-based research. In 96-well microplates, zebrafish embryos were incubated with small molecular compounds that affected bone mineralization. The level of osteogenic mineralization was evaluated by fluorescent dye staining and quantified by image analysis software. Quantitative real time-PCR (qRT-PCR) was performed to evaluate the biological pathways involved in bone metabolism at the molecular level. The system was validated by demonstrating that response to alendronate and Dorsomorphin in zebrafish. In our study, we screened for 24 compounds within the CYCU-1120~1152 chemical library and identified 3 compounds, pentamidine (CYCU-1140), BML-267 (CYCU-1147), and alendronate (CYCU-1152), increased embryonic mineralization; while 6 compounds, RWJ-60475 (CYCU-1126), levamisole HCL (CYCU-1128), tetramisole HCL (CYCU-1129), fenvalerate (CYCU-1132), NSC-663284 (CYCU-1138), and BML-267ester (CYCU-1148), were inhibitory to bone mineralization. We also found that alendronate enhanced the level of bone mineralization by inhibiting osteoclast-related genes. To sum up, our research showed that zebrafish may have potential to be a drug-screening and mechanism-analysis platform for bone mineralization.

Hepatocellular carcinoma (HCC) is a heterogeneous malignancy with complex carcinogenesis. Although there has been significant progress in the treatment of HCC over the past decades, drug resistance to chemotherapy remains a major obstacle in its successful management. In this study we were able to reduce chemoresistance in cisplatin-resistant HepG2 cells by either silencing the expression of transglutaminase type 2 (TG2) using siRNA or by pre-treatment of cells with the TG2 enzyme inhibitor cystamine. Further analysis revealed that, whereas the full-length TG2 isoform (TG2-L) was almost completely cytoplasmic in its distribution, the majority of the short TG2 isoform (TG2-S) was membrane-associated in both parental and chemoresistant HepG2 cells. Following induction of cisplatin toxicity in non-chemoresistant parental cells, TG2-S together with cisplatin quickly relocated to the cytosolic fraction. Conversely, no cytosolic relocalisation of TG2-S or nuclear accumulation cisplatin was observed following identical treatment of chemoresistant cells, where TG2-S remained predominantly membrane-associated. This suggests that deficient subcellular relocalisation of TG2-S from membranous structures into the cytoplasm may limit the apoptic response to cisplatin toxicity in chemoresistant cells. Structural analysis of TG2 revealed the presence of binding motifs for interaction of TG-S with the membrane scaffold protein LC3/LC3 homologue that could contribute to a novel mechanism of chemotherapeutic resistance in HepG2 cells.

Proteins need to be located in appropriate spatiotemporal contexts to carry out their diverse biological functions. Mislocalized proteins may lead to a broad range of diseases, such as cancer and Alzheimer’s disease. Knowing where a target protein resides within a cell will give insights into tailored drug design for a disease. As the gold validation standard, the conventional wet lab uses fluorescent microscopy imaging, immunoelectron microscopy, and fluorescent biomarker tags for protein subcellular location identification. However, the booming era of proteomics and high-throughput sequencing generates tons of newly discovered proteins, making protein subcel-lular localization by wet-lab experiments a mission impossible. To tackle this concern, in the past decades, artificial intelligence (AI) and machine learning (ML), especially deep learning methods, have made significant progress in this research area. In this article, we review the latest advances in AI-based method development in three typical types of approaches, including sequence-based, knowledge-based, and image-based methods. We also elaborately discuss existing challenges and future directions in AI-based method development in this research field.

Experiments to measure a single molecule/particle, i.e. an individual molecule/particle, at room temperature or under physiological conditions without immobilization, for example on a surface, or without significant hydrodynamic flow have so far failed. This failure has given impetus to the underlying theory of Brownian molecular motion towards its stochastic due to diffusion. Quantifying the thermodynamic jitter of molecules/particles inspires many and forms the theoretical basis of single-molecule/single-particle biophysics and biochemistry. For the first time, our simulation results for a live cell (cytoplasm) show that the tracks of individual single molecules are localized in Brownian motion, while there is fanning out in fractal diffusion (anomalous diffusion).

Abstract: Although experimental attempts to measure a single molecule/particle at room temperature or under physiological conditions without immobilization, for example on a surface, or without hydrodynamic flow have so far failed, this failure has given impetus to the underlying theory of Brownian motion towards its stochastic due to diffusion. Quantifying the thermodynamic jitter of molecules/particles inspires many and forms the theoretical basis of single-molecule/single-particle biophysics and biochemistry. For the first time, our simulation results for a live cell (cytoplasm) show that the tracks of individual single molecules are localized in Brownian motion, while there is fanning out in fractal diffusion (anomalous diffusion).

African swine fever (ASF) is a lethal and highly contagious transboundary animal disease with the potential for rapid international spread. Currently, there is no ASF vaccine commercially available. All infected animals must be isolated and culled immediately upon confirmation of the presence of the virus. Works leading to the rational development of protective ASF vaccines are urgently needed. Here, we generated a safe and efficacious live-attenuated vaccine (LAV) VNUA-ASFV-LAVL2 by serial passaging of a field isolate (VNUA-ASFV-05L1, genotype II) in porcine alveolar macrophage (PAMs, 65 passages) and an immortalized porcine alveolar macrophage cell line (3D4/21, 55 passages). VNUA-ASFV-LAVL2 can efficiently replicate in both PAMs and 3D4/21 cells. It provides 100% protection, even with the low dose of 10^2 HAD50, for the vaccinated pigs against the challenge of contemporary pandemic ASFV field isolate. Pigs vaccinated with this LAV at a dose range of 10^2 to 10^5 HAD50 remained clinically healthy during both the 28-day observation period of immunization and the 28-day observation period of challenge. VNUA-ASFV-LAVL2 was completely eliminated from blood by 28 days post inoculation (DPI), and from feces or oral fluids by 17 DPI. It remained a safe, stable, and attenuated phenotype after five passages in pigs. Consistently high levels of ASFV-specific IgG antibodies and significant cellular immunity were detected in vaccinated pigs before the ASFV challenge. These results indicate that the VNUA-ASFV-LAVL2 strain is a safe and efficacious LAV against the genotype II ASFV strain responsible for current ASF outbreaks in Asia.

The pollution of the bays in Abidjan is a major concern for the Ivorian policy makers. In fact, the pollution of the bays induce high costs to the society while impacting population health dramatically. As a result, pollution reduction management of production activities has been undertaken in the Cocody Bay area. To our knowledge, no study has yet proposed a model to evaluate the cost-effectiveness of these pollution management strategies. A cost-effectiveness model, based on Monte Carlo simulation, was developed to assess the economic and environmental impacts of various scenarios characterized by a set of production practices, both in the short term and in the long term. The authors discuss the steps and input parameters of the model presented. The proposed model may serve as the basis for identifying an optimal production scenario defined as the scenario with the best incremental cost-effectiveness ratio considering a willingness to pay (WTP) threshold. The WTP, to be estimated based on the gross domestic product of Côte d’Ivoire, represents the opportunity costs associated with selecting the optimal scenario. The current framework can also be applied to other settings facing similar challenge.

Pituitary adenylate cyclase-activating polypeptide receptor 1 (PAC1-R) has a higher affinity for pituitary adenylate cyclase- activating polypeptide (PACAP) than for vasoactive intestinal peptide (VIP). PACAP exerts its neuroprotective effects through PAC1-R. PAC1-R has become a target for drug development, and we targeted PAC1-R to screen small positive allosteric modulator 1 (SPAM1). SPAM1 has a neuroprotective effects, and in the D-galactose (D-gal) mouse model, SPAM1 improved the structure of the hippocampal dentate gyrus and restored the number of neurons. Compared with D-gal model mice, SPAM1 treatment upregulated the expression of Sirtuin 6 (SIRT6) and Lamin B1, and downregulated the expression of YinYang 1 (YY1) and p16. Consistent with this, the same results were obtained in senescent RGC-5 cells. Meanwhile, SPAM1 was also found to up-regulate SIRT6 and down-regulate YY1 expression in normal RGC-5 cells. Then, we further explored the mechanism of SPAM1 neuroprotection by whole transcriptome sequencing and proteomic analysis, and found that SPAM is involved in the longevity regulating pathway, and confirmed the up-regulation of neurofilament light polypeptide and neurofilament medium polypeptide by western blot, thus laying a pharmacological foundation for SPAM1 as a therapeutic agent for neurodegenerative diseases. polypeptide and neurofilament medium polypeptide. Thus, it lays a pharmacological foundation for SPAM1 as a therapeutic drug for neurodegenerative diseases.

Owing to the advantages of cost-effectiveness, compactness, and the avoidance of complicated camera synchronization, single-camera three-dimensional (3D) digital image correlation (DIC) techniques have gained increasing attention for deformation measurement of materials and structures. In the traditional single-camera 3D-DIC system, the left and right view images can be recorded by a single camera using a diffraction grating, a bi-prism or a set of planar mirrors. To further improve the measurement accuracy of single-camera 3D-DIC, this paper introduces a single-camera four-view imaging technique by installing a pyramidal prism in front of the camera. The 3D reconstruction of the measured points before and after deformation is realized with eight governing equations induced by four views, and the strong geometric constraints of four views can help to improve the measurement accuracy. A static experiment, a rigid body translation experiment and a four-point bending experiment show that the proposed single-camera 3D-DIC method can achieve higher measurement accuracy than the dual-view single-camera 3D-DIC techniques and has advantages in reducing both random error and systematic error.

Stripe rust ​​(caused by Puccinia striiformis f. sp. tritici) is one of the most devastating diseases of wheat and causes large-scale epidemics and severe yield loss. Applying fungicides during early epidemic development is crucial to controlling the disease but is often challenged by resource-limited human visual scouting. Deep learning has the potential to process images and videos captured from affordable devices to empower high-throughput phenotyping for early detection of stripe rust for timely application of fungicides and improve control efficiency. Here, we developed RustNet, a neural network-based image classifier, for efficiently monitoring fields for stripe rust. RustNet was built on a ResNet-18 architecture pre-trained with ImageNet Large-Scale Visual Recognition Challenge (ILSVRC) dataset using transfer learning. RGB images and videos of multiple wheat fields with different wheat types (winter and spring wheat), conditions (irrigated and non-irrigated), and locations were acquired using smartphones or unmanned aerial vehicles near the canopy. A semi-automated image labeling approach was conducted to improve labeling efficiency by combining automated machine labeling and human correction. Cross-validations across multiple categories (sensor platforms, wheat types, and locations) achieved Area Under Curve from 0.72 to 0.87. Independent validation on a published dataset from Germany achieved accuracies ranging from 0.79 to 0.86. The visualization of the last convolutional layer of RustNet demonstrated the identification of pixels with stripe rust. RustNet is freely available at https://zzlab.net/RustNet.

A 'little brother' of pain, itch is an unpleasant sensation that creates a specific urge to scratch. To date, various machine-learning based image classifiers (MBICs) have been proposed for quantitative analysis of itch-induced scratch behaviour of laboratory animals in an automated, non-invasive, inexpensive and real-time manner. In spite of MBICs' advantages, the overall performances (accuracy, sensitivity and specificity) of current MBIC approaches remains inconsistent, with their values varying from ~50% to ~99%, for which the reasons underlying have yet to be investigated further, both computationally and experimentally. To look into the variation of the performance of MBICs in automated detection of itch-induced scratch, this article focuses on the experimental data recording step, and reports here for the first time that MBICs' overall performance is inextricably linked to the sharpness of experimentally recorded video of laboratory animal scratch behaviour. This article furthermore demonstrates for the first time that a linearly correlated relationship exists between video sharpness and overall performance (accuracy and specificity, but not sensitivity) of MBICs, and highlight the primary role of experimental data recording in rapid, accurate and consistent quantitative assessment of laboratory animal itch.

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.

Precision agriculture is a farm management technology that involves sensing and then responding to the observed variability in the field. Remote sensing is one of the tools of precision agriculture. The emergence of small unmanned aerial vehicles (sUAV) have paved the way to accessible remote sensing tools for farmers. This paper describes the comparison of two popular off-the-shelf sUAVs: 3DR Iris and DJI Phantom 2. Both units are equipped with a camera gimbal attached with a GoPro camera. The comparison of the two sUAV involves a hovering test and a rectilinear motion test. In the hovering test, the sUAV was allowed to hover over a known object and images were taken every second for two minutes. The position of the object in the images was measured and this was used to assess the stability of the sUAV while hovering. In the rectilinear test, the sUAV was allowed to follow a straight path and images of a lined track were acquired. The lines on the images were then measured on how accurate the sUAV followed the path. Results showed that both sUAV performed well in both the hovering test and the rectilinear motion test. This demonstrates that both sUAVs can be used for agricultural monitoring.

Intrahepatic cholangiocarcinoma (ICCA) is a malignant epithelial neoplasm characterized by biliary differentiation within the liver. ICCA is molecularly heterogeneous and exhibits a broad spectrum of histopathological features. It is a highly aggressive carcinoma with high mortality and poor survival rates. ICCAs are classified into two main subtypes: small duct type and large duct types. These two tumor types have different cell origins and clinicopathological features. ICCAs are characterized by numerous molecular alterations, including mutations in KRAS, TP53, IDH1/2, ARID1A, BAP1, BRAF, SAMD4, and EGFR, and FGFR2 fusion. Two main molecular subtypes–inflammation and proliferation–have been proposed. Recent advances in high-throughput assays using next-generation sequencing have improved our understanding of ICCA pathogenesis and molecular genetics. The diagnosis of ICCA poses a significant challenge for pathologists because of its varied morphologies and phenotypes. Accurate diagnosis of ICCA is essential for effective patient management and prognostic determination. This article provides an updated overview of ICCA pathology, focusing particularly on molecular features, histological subtypes, and diagnostic approaches.

Many fields of human activity benefit from the ability to create images of obscured objects placed behind walls and to map their displacement in a noninvasive way. Usually, imaging devices like Synthetic Aperture Radars (SAR) and Ground Penetrating Radars (GPR) use expensive dedicated electronics which results in prohibitive prices. This paper presents the experimental implementation and the results obtained from an imaging system capable of performing SAR imaging and interferometric displacement mapping of targets located behind walls, as well as 3D GPR imaging using a low-cost general-purpose radar sensor. The proposed solution uses for the RF section of the system a K-band microwave radar sensor module implementing Frequency Modulated Continuous Wave operation. The low-cost sensor was originally intended for simple presence detection and ranging for domestic applications. The proposed system was tested in several scenarios and proved to operate as intended for a fraction of the cost of a commercial imaging device.

Medical image analysis is an important branch in the field of medicine, which mainly uses image processing and analysis techniques to interpret and diagnose medical image data. Medical image data helps doctors to effectively observe and diagnose patients' body structures, tissues and lesions. Medical image analysis has been an important research area in the medical field, and it is important for disease diagnosis, treatment planning, and condition monitoring. In recent years, the rapid development of deep learning and computer vision technologies has contributed greatly to the automation, multimodal data fusion, real-time application, and accuracy improvement of medical image analysis. In addition, the development of deep learning has given rise to some new research areas in medical image analysis, such as Generative Adversarial Networks (GANs) for synthetic medical images, self-supervised learning for unsupervised feature learning, and neural network interpretability. In this paper, we will introduce some optimisation methods for medical images which are effective in improving the accuracy, efficiency and reliability of medical image analysis.

Recent advances in microscopy imaging and image analysis motivate more and more institutes world-wide to establish dedicated core-facilities for bio-image analysis. To maximize the benefits research groups at these institutes gain from their core-facilities, they should be established to fit well into their respective environment. In this article, we introduce common collaborator requests and corresponding potential services core-facilities can offer. We also discuss potential conflicts of interests between the targeted missions and implementations of services to guide decision makers and core-facility founders to circumvent common pitfalls.

In rye, there is a considerable variety of grain color which is determined by the diversity of compounds localized in different parts of the grain (caryopsis) - pericarp, testa, and aleurone. The localization of anthocyanins and proanthocyanidins was analyzed in 26 rye samples with identified anthocyanin genes, along with the analysis of CIE color coordinates. The Grain Scan program [1] was used to analyze images of individual grains. The localization of anthocyanins and proanthocyanidins was studied on longitudinal and cross sections of grains using light microscopy and MALDI-imaging. The violet-grained samples contain anthocyanins in the pericarp, and the green-grained samples contain anthocyanins in the aleurone layer. The green, violet and yellow-grained rye, with the exception of two anthocyaninless mutants vi3 and vi6, shows the presence of proanthocyanidins in the brown-colored testa. Four main color groups of the rye grains (yellow, green, brown, violet) could be differentiated using the color coordinate h° (hue angle). Interspecies and intraspecies variability for the localization of colored flavonoids in cereal grains is discussed.

Dendritic Mesoporous Organosilica Nanoparticles (DMON) are a new class of biodegradable nanoparticles suitable for biomolecules delivery. In order to escape from the endosomes-lysosomes and to deliver biomolecules in the cytoplasm of cells, we studied photochemical internalization (PCI) and photodynamic therapy (PDT) of DMON. We added photosensitizers in the framework of DMON. DMON were also loaded with siRNA or FVIII factor protein. We made four formulations with four different photosensitizers, The photosensitizers allowed to perform imaging of DMON in cancer cells, but the presence of the tetrasulfide bond in the framework of DMON quenched the formation of singlet oxygen. Fortunately one formulation allowed to efficiently deliver proapoptotic siRNA in MCF-7 cancer cells leading to 31% of cancer cell death, without irradiation. For FVIII protein, it was loaded in two formulations with drug loading capacities (DLC) up to 25%. In conclusion DMON are versatile nanoparticles which allowed to load siRNA and to deliver it in cancer cells, and to load FVIII protein with good DLC. Due to the presence of tetrasulfide, it was not possible to perform PDT and PCI.

The supply chain has incorporated products by putting them into hair scarfs. This study introduces the use of mini chips in health and beauty products and can reduce fatigue through enhanced sleep patterns. The mini chip could be placed in the scarf and used as a prototype. RFID technology provides the supply chain with specific information that is used to identify products and make communication easier. (Muhammad, et. al. 2013) This paper presents a new tool herein referred to as a scarf prototype which is developed to analyse EMG (electromyogram), ECG (electrocardiography), EEG (electroencephalogram), and EOG (electro-oculogram) signals that focuses in the area of sleep disorders. The mini chips used can be used to determine a solution for sleep disruption by using automated analytics. This could lead to improvement in our understanding of sleep disruption and overall sleep physiology. Automated technology allows repeated measurements, evaluation of sleep patterns, and provide suggestions to improve a person’s quality of sleep. This analysis compares the use of polysomnography and the scarf prototype. The analytics provide models and shows correlation between variables, such as EMG, ECG, EEG, and EOG. This study shows that the results from the scarf prototype is just as reliable as the original method, polysomnography.

This paper proposes a new approach for multi-focus images fusion based on Region Mosaicing on Contrast Pyramids (REMCP). A density-based region growing method is developed to construct a focused region mask for multi-focus images. The segmented focused region mask is decomposed into a mask pyramid, which is then used for supervised region mosaicking on a contrast pyramid. In this way, the focus measurement and the continuity of focused regions are incorporated and the pixel level pyramid fusion is improved at the region level. Objective and subjective experiments show that the proposed REMCP is more robust to noise than compared algorithms and can fully preserves the focus information of the multi-focus images meanwhile reducing distortions of the fused images.

Next-generation sequencing (NGS) has been a widely-used technology in biomedical research for understanding the role of molecular genetics of cells in health and disease. A variety of computational tools have been developed to analyse the vastly growing NGS data, which often require bioinformatics skills, tedious work and significant amount of time. To facilitate data processing steps minding the gap between biologists and bioinformaticians, we developed CSI NGS Portal, an online platform which gathers established bioinformatics pipelines to provide fully automated NGS data analysis and sharing in a user-friendly website. The portal currently provides 16 standard pipelines for analysing data from DNA, RNA, smallRNA, ChIP, RIP, 4C, SHAPE, circRNA, eCLIP, Bisulfite and scRNA sequencing, and is flexible to expand with new pipelines. The users can upload raw data in fastq format and submit jobs in a few clicks, and the results will be self-accessible via the portal to view/download/share in real-time. The output can be readily used as the final report or as input for other tools depending on the pipeline. Overall, CSI NGS Portal helps researchers rapidly analyse their NGS data and share results with colleagues without the aid of a bioinformatician. The portal is freely available at: https://csibioinfo.nus.edu.sg/csingsportal

Diagnosing melanocytic lesions is among the most challenging problems in the practice of pathology. The difficulty of physically masking melanin pigment and the similarity of its color to commonly used chromogens often complicate examination of the cytomorphology and immunohistochemical staining results for tumor cells. Melanin bleach can be very helpful for histopathological diagnosis of heavily pigmented melanocytic lesions. Although various depigmentation methods have been reported, no standardized methods have been developed. This study developed a fully automated platform that incorporates hydrogen peroxide-based melanin depigmentation in an automated immunohistochemical analysis. The utility of the method was tested in one cell block of malignant melanoma cells in pleural effusion, ten ocular melanoma tissue samples, and ten cutaneous melanoma tissue samples. Our results demonstrated that the proposed method, which can be performed in only 3 hours, effectively preserves cell cytomorphology and immunoreactivity. The method is particularly effective for removing melanin pigment to facilitate histopathological examination of cytomorphology and for obtaining an unmasked tissue section for immunohistochemical analysis.

Rapid and accurate detection of road cracks is of great significance for road health monitoring, but currently this work is mainly completed through manual site surveys. Low-altitude UAV remote sensing can obtain images with centimeter or even subcentimeter ground resolution, which provides a new efficient and economical approach for rapid crack detection. Nevertheless, crack detection networks face challenges such as edge blurring and misidentification due to the heterogeneity of road cracks and the complexity of the background. To address these issues, we propose a real-time edge reconstruction crack detection network (ERNet) which adopts multi-level information aggregation to reconstruct crack edges and improve the accuracy of segmentation between the target and background. To capture global dependencies across spatial and channel levels, we propose an efficient bilateral decomposed convolutional attention module (BDAM) that combines depth separable convolution and dilated convolution to capture global dependencies across spatial and channel levels. To enhance the accuracy of crack detection, we use a coordinate-based fusion module that integrates spatial, semantic, and edge reconstruction information. In addition, we propose an automatic measurement of crack information for extracting the crack trunk and its corresponding length and width. Experimental results demonstrate that our network achieves the best balance between accuracy and inference speed.

This paper describes the first results of an Image Recognition Based Location (IRBL) for mobile application focusing on the procedure to generate a Database of range images (RGB-D). In an indoor environment, to estimate the camera position and orientation, a prior spatial knowledge of the surrounding is needed. In order to achieve this objective a complete 3D survey of two different environment (Bangbae metro station of Seoul and E.T.R.I. building in Daejeon – Republic of Korea) was performed using LiDAR (Light Detection And Ranging) instrument and the obtained scans were processed in order to obtain a spatial model of the environments. From this, two databases of reference images were generated using a specific software realized by the Geomatics group of Politecnico di Torino (ScanToRGBDImage). This tool allow to generate synthetically different RGB-D images) centered in the each scan position in the environment. Later, the external parameters (X, Y, Z, ω, φ, κ) and the range information extracted from the DB images retrieved, are used as reference information for pose estimation of a set of acquired mobile pictures in the IRBL procedure. In this paper the survey operations, the approach for generating the RGB-D images and the IRB strategy are reported. Finally the analysis of the results and the validation test are described.

Effectively mapping and monitoring rubber plantation is still changing. Previous studies have explored the potential of phenology features for rubber plantation mapping through a pixel-based approach (pixel-based phenology approach). However, in fragmented mountainous Xishuangbanna, it could lead to noises and low accuracy of resultant maps. In this study, we investigated the capability of an integrated approach by combining phenology information with an object-based approach (object-based phenology approach) to map rubber plantations in Xishuangbanna. Moderate Resolution Imaging Spectroradiometer (MODIS) data were firstly used to acquire the temporal profile and phenological features of rubber plantations and natural forests, which delineates the time windows of defoliation and foliation phases. Landsat images were then used to extract a phenology algorithm comparing three different approaches: pixel-based phenology, object-based phenology, and extended object-based phenology to separate rubber plantations and natural forests. The results showed that the two object-based approaches achieved higher accuracy than the pixel-based approach, having overall accuracies of 96.4%, 97.4%, and 95.5%, respectively. This study proved the reliability of a phenology-based rubber mapping in fragmented landscapes with a distinct dry/cool season using Landsat images. This study indicated that the object-based phenology approaches can effectively improve the accuracy of the resultant maps in fragmented landscapes.

Ultrasound (US) has become a widely used imaging modality in clinical practice, characterized by its rapidly evolving technology, advantages, and unique challenges such as low imaging quality and high variability. There is a critical need to develop advanced automatic US image analysis methods to enhance diagnostic accuracy and objectivity. Vision transformer, a recent innovation in machine learning, has demonstrated significant potential in various research fields, including general image analysis and computer vision, due to its capacity to process large datasets and learn complex patterns. Its suitability for automatic US image analysis tasks, such as classification, detection, and segmentation, has been recognized. This review provides an introduction to vision transformer and discusses its applications in specific US image analysis tasks, while also addressing the open challenges and potential future trends in its application in medical US image analysis. Vision transformer has shown promise in enhancing the accuracy and efficiency of ultrasound image analysis and is expected to play an increasingly important role in the diagnosis and treatment of medical conditions using ultrasound imaging as technology progresses.

Plant-based protein sources derived from microalgae offer promise as low-carbon emission and nutrient-dense sources for human consumption. The current management of microalgae cultivation relies heavily on manual microscopic examination in order to identify desired and competing species, as well as predators. In this study, we trained and tested a transfer learning model modified from EfficientNetV2 B3 on 434 and 161 prospectively acquired images of the preferred Nannochloropsis sp microalgae and competitor Spirulina, respectively, and achieved &gt;98% classification for both species on tenfold cross-validation. The model was further enhanced with gradient-weighted class activation mapping, which allowed visualization of regions of the input images that were relevant to the classification, thereby improving its explainability. In this paper, we demonstrate that a simple deep transfer learning model can be used to identify microalgae species. The application addresses the practical need for robust automated monitoring of microalgae populations on industrial microalgae farms.

The purpose of this study was to explore and develop high-resolution infrared thermal (HRIT) imaging for screening toddler’s fracture. A Toddler’s fracture is a common tibial fracture of children younger than 6 years old. Initial x-ray radiographs may not show the fracture and another x-ray during a follow-on visit is usually required. The study included 39 participants admitted to an emergency department with a suspected toddler’s fracture. X-ray confirmed 8 cases with a toddler’s fracture (20.5%). Infrared images of participants were recorded on their index visit, focusing on region-of-interests on the injured and the contralateral (uninjured) legs. The uninjured leg acted as a thermal reference. Nine statistical measures were extracted from the images: maximum, mini-mum, mean, standard deviation, median, mode, interquartile range, skewness, and kurtosis. Wilcoxon rank sum test indicated that maximum, mean, standard deviation, median, and inter quartile range temperature measures were significantly different (p<0.05) when comparing fractured and non-fractured legs. Principal component analysis of these measures highlighted distinct separation of toddler’s fracture and non-fracture cases. Similarly, plots of the statistical measures further confirmed clustering of toddler’s fracture cases. The study demonstrated that HRIT imaging can be a valuable tool for screening for toddler’s fracture.

Over recent years, the importance of patent literature has become more recognized in the academic setting. In the context of artificial intelligence, deep learning, and data sciences, patents are relevant to not only industry but also academe and other communities. In this article, we focus on deep tomographic imaging and perform a preliminary landscape analysis of the related patent literature. Our search tool is PatSeer . Our patent bibliometric data is summarized in various figures and tables. In particular, we qualitatively analyze key deep tomographic patent literature.

Uniform water soluble MoS2 quantum dots (WS-MSQDs) are synthesized via a sequential combination of sintering/etching/exfoliation method and solvothermal route. The obtained WS-MSQDs with average size of approximately 3.4 nm exhibit sufficient water solubility and remarkable fluorescence properties. The WS-MSQDs have been utilized as a probe for detection of Fe3+ ions with high selectivity and specificity. Furthermore, the WS-MSQDs exhibit high fluorescence stability under different conditions. Finally, the WS-MSQDs are successfully applied for the fluorescence imaging of Fe3+ in living cells, which exhibited practical potential for biomedical applications.

In order to improve contrast and restore color for underwater image captured by camera sensors without suffering from insufficient details and color cast, a fusion algorithm for image enhancement in different color spaces based on contrast limited adaptive histogram equalization (CLAHE) is proposed in this article. The original color image is first converted from RGB color space to two different special color spaces: YIQ and HSI. The color space conversion from RGB to YIQ is a linear transformation, while the RGB to HSI conversion is nonlinear. Then, the algorithm separately operates CLAHE in YIQ and HSI color spaces to obtain two different enhancement images. The luminance component (Y) in the YIQ color space and the intensity component (I) in the HSI color space are enhanced with CLAHE algorithm. The CLAHE has two key parameters: Block Size and Clip Limit, which mainly control the quality of CLAHE enhancement image. After that, the YIQ and HSI enhancement images are respectively converted backward to RGB color. When the three components of red, green, and blue are not coherent in the YIQ-RGB or HSI-RGB images, the three components will have to be harmonized with the CLAHE algorithm in RGB space. Finally, with 4 direction Sobel edge detector in the bounded general logarithm ratio operation, a self-adaptive weight selection nonlinear image enhancement is carried out to fuse YIQ-RGB and HSI-RGB images together to achieve the final fused image. The enhancement fusion algorithm has two key factors: average of Sobel edge detector and fusion coefficient, and these two factors determine the effects of enhancement fusion algorithm. A series of evaluate metrics such as mean, contrast, entropy, colorfulness metric (CM), mean square error (MSE) and peak signal to noise ratio (PSNR) are used to assess the proposed enhancement algorithm. The experiments results showed that the proposed algorithm provides more detail enhancement and higher values of colorfulness restoration as compared to other existing image enhancement algorithms. The proposed algorithm can suppress effectively noise interference, improve the image quality for underwater image availably.

Live video streaming has become one of the main multimedia trends in networks in recent years. Providing Quality of Service (QoS) during live transmissions becomes a challenge due to the stringent requirements for low latency and minimal interruptions. This scenario has led to a high dependence on cloud services, implying a widespread usage of Internet connections, which constrains contexts where an Internet connection is not available. Thus, alternatives such as Mobile Ad Hoc Networks (MANETs) emerge as potential communication techniques. These networks operate autonomously with mobile devices serving as nodes, without the need for coordinating centralized components. However, these characteristics lead to challenges to live-video streaming, such as dynamic node topologies or periods of disconnection. Considering these constraints, this paper investigates the application of Artificial Intelligence (AI)-based classification techniques to provide adaptive streaming in MANETs. For this, a software-driven architecture is proposed to route stream in offline MANETs, predicting the stability of individual links and compressing video frames accordingly. The proposal has been implemented and assessed in a laboratory context, analyzing the model performance and QoS metrics. As a result, the model is implemented in a decision forest algorithm, which provides 95.9% of accuracy. Also, obtained latency values become assumable for video streaming, manifesting a reliable response for routing and node movements.

In this paper we describe a method which combines sentiment analysis with machine learning techniques and/or multivariate statistical analysis. By applying this methodology it is possible to classify a collection of texts into two or more groups or clusters. On the basis of a number of previously defined clusters, the novelty of the outlined approach is the use of the sentiment analysis results as input to the machine learning model or multivariate statistical analysis. Once the classifier has been obtained, we can assign a given text into one of the pre-established clusters. The groups or clusters can represent different time periods, classes of texts transcribed from different conversations, etc. The method is illustrated through an example taken from one of the two studies in which we have applied this methodology. In one of the studies, the method was used to classify press news of a volcanic eruption, while in the other study it was used to classify the conversations recorded between a chatbot with different kinds of speakers (humans or chatbots). This last study was the seminal work in which we introduced this methodology.

Objective: to evaluate the feasibility of the intra-operative application of a specimen PET/CT imager in a clinical setting. Materials and methods: this is a pilot analysis performed in three patients who received an intra-operative administration of 68Ga-PSMA-11 (n=2) and 68Ga-DOTA-TOC (n=1), respectively. Patients were administrated with PET radiopharmaceuticals to perform radio-guided surgery with a beta-probe detector during radical prostatectomy for prostate cancer (PCa) and salvage lymphadenectomy for recurrent neuroendocrine tumor (NET) of the ileum, respectively. All procedures have been performed within two ongoing clinical trials in our Institute (NCT05596851 and NCT05448157). Pathologic assessment with immunohistochemistry (PSMA-staining and SSA immunoreactivity) was considered as standard of truth. Specimen images were compared with baseline PET/CT images and histopathological analysis. Results: Patients received 1 MBq/Kg of 68Ga-PSMA-11 (PCa) or 1.2 MBq/Kg of 68Ga-DOTA-TOC (NET) prior to surgery. Specimens were collected, positioned in the dedicated specimen container, and scanned to obtain high resolution PET/CT images. In all cases a perfect match was observed between the findings detected by the specimen imager and histopathology. Overall, the PET spatial resolution was sensibly higher for the specimen images compared to the baseline whole-body PET/CT images. Furthermore, the use of the PET/CT specimen imager did not significantly interfere with any procedures, and the overall length of the surgery was not affected using the PET/CT specimen imager. Finally, the radiation exposure of the operating theater staff was lower than 40 µSv per procedure (range 26 – 40 Sv). Conclusion: the image acquisition of specimens obtained by patients who received intra-surgery injection of 68Ga-PSMA-11 and 68Ga-DOTA-TOC was feasible and reliable also in a live-experience session and has been easily adapted to surgery daily-practice. The high sensitivity, together with the evaluation of intra-lesion tumor heterogeneity, were the most relevant results since the data derived from specimen PET/CT imaging matched perfectly with the histopathological analysis.

Keywords: olfactory rehabilitation; chronic pain; fMRI;

We present a tutorial to realize a multidimensional fitting procedure capable of extracting all the relevant information contained in a sequence of charged particle images acquired as a function of time in femtosecond pump-probe experiments. The images are reproduced using a 3D fitting method, which provides the velocity (or center-of-mass kinetic energy) and angular distributions contained in the images and their time evolution. A detailed example of the method is shown through the analysis of the time-resolved predissociation dynamics of CH₃I on the B-band origin [Gitzinger et al., J. Chem. Phys. 133, 234313 (2010)]. We show that the multidimensional approach is essential for the analysis of complex images that contain several overlapping contributions where reduced dimensionality analyses cannot provide a reliable description of the features present in the image sequence. This methodology can be generalized to many types of multidimensional data analysis.

Spectral image analysis has the potential to replace traditional approaches for assessing plant responses to different types of stress including herbicide, through non-destructive and high-throughput screening (HTS). Therefore, this study was conducted to develop a rapid bioassay method using a multi-well plate and spectral image analysis for diagnosis of herbicide activity and mode of action. Crabgrass (Digitaria ciliaris) as a model weed was cultivated in multi-well plates and subsequently treated with 6 herbicides with different modes of action when they reached the 1-leaf stage, using only a quarter of the recommended dose. To detect plant response to herbicides, plant spectral images were acquired after herbicide treatment using RGB, infrared (IR) thermal, and chlorophyll fluorescence (CF) sensors and analyzed for diagnosing herbicide efficacy and mode of action. Principal Component Analysis (PCA) using all spectral data successfully distinguished herbicides and clustered depending on their modes of action. The performed experiments have shown that the multi-well plate assay combined with spectral image analysis can be successfully applied for herbicide bioassay. In addition, the use of spectral image sensors, especially in CF image, would facilitate HTS by enabling rapid observation of herbicide response, as early as 3 hours after herbicide treatment.

As sustainability gains significance within Higher Education Institutions (HEIs) worldwide, the University of British Columbia (UBC) stands as one of the global champions of sustainability. In 2019, Times Higher Education ranked UBC as number one in the world for taking urgent action to combat climate change and its impacts and ranked one in Canada for making cities inclusive, safe, resilient, and sustainable. Over the years, UBC students have been instrumental to sustainability on the UBC campus by advocating for divestment, climate justice, and other sustainability commitments and projects in the university. Hence, this qualitative study examines students’ engagement with or their perception of the university’s sustainability programs and image. The study found that students acknowledged and commended the university’s sustainability efforts in teaching, research, providing sustainability-related opportunities for students, and in sustainability operations. However, students also addressed hesitation on the part of university administration in championing climate justice and bolder climate action. The conclusion is that continued support and engagement with students are critical for UBC to achieve its climate action plans and sustainability goals in general. The study contributes to the ongoing discourse on the influential role of young people and the youth climate movement in catalyzing ambitious global climate action at all levels.

Background: Microscopic examination of acid-fast stained sputum smears for detecting mycobacterial acid-fast bacilli (AFB) remains the most economical and readily available method for laboratory diagnosis of tuberculosis (TB). However, this conventional approach has limitations, including low sensitivity and labor-intensive procedures. Methods: An automated microscopy system incorporating artificial intelligence (AI) and machine learning for AFB identification was evaluated. The study was conducted at an Infectious Disease Hospital in Jiangsu Province, China, utilizing an intelligent microscope system (TB-Scan, Wellgen Medical, Kaohsiung). A total of 1,000 sputum smears were included in the analysis, with the system capturing digital microscopic images and employing an image recognition model to automatically identify and classify AFB. Referee technicians provided the gold standard for discrepant results. Results: The automated system demonstrated an overall accuracy of 95.00% (950/1,000), sensitivity of 91.24% (177/194), and specificity of 95.91% (773/806). Notably, the system identified 21 smears as positive that were previously reported as negative, with referee technicians confirming 17 of these as true positive and retracting the test results accordingly. Recalculating the performance, the accuracy increased to 96.70% (967/1,000), sensitivity to 91.94% (194/211), and specificity to 97.97% (773/789), with a false negative rate of 8.06% (17/211) and a false positive rate of 2.03% (16/789). Conclusions: The incorporation of AI and machine learning into an automated microscopy system demonstrated the potential to enhance the sensitivity and efficiency of AFB detection in sputum smears compared to conventional manual microscopy. This approach holds promise for widespread application in TB diagnostics and potentially other fields requiring labor-intensive microscopic examination.

Chronic inflammatory diseases are considered the most significant cause of death worldwide. Current treatments for inflammatory diseases are limited due to the lack of understanding of the biological factors involved in early-stage disease progression. Nerve growth factor (NGF) is a neurotrophic factor directly associated with inflammatory and autoimmune diseases like osteoarthritis, multiple sclerosis, and rheumatoid arthritis. It has been shown that NGF levels are significantly upregulated at the site of inflammation and play a crucial role in developing a robust inflammatory response. However, little is known about NGF's temporal expression profile during the initial progressive phase of inflammation. This study aimed to determine the temporal expression pattern of NGF in rat skin during Adjuvant-Induced Arthritis (AIA). Sprague Dawley rats were randomly divided into control and Complete Freund's Adjuvant (CFA) treated groups. Levels of NGF were evaluated following unilateral AIA at different time points and it was found that peripheral inflammation due to AIA significantly upregulated the expression of NGF mRNA and Protein in a biphasic pattern. These results suggest that NGF signaling is crucial for initiating and maintaining peripheral neurogenic inflammation in rats during AIA.

Aims: To study the effects of pyridoxal phosphate (PLP) on oxidative stress in isolated pancreatic acinar cells. We have previously shown that PLP has cytoprotective and insulinotropic effects on mice islet cells in vivo and in vitro studies. Main methods: Acinar cells were isolated from three months old WNIN male rats and were cultured in vitro for a period of 24 h in CO₂ incubator. Later the cells were divided into four groups as untreated (group 1), H₂O₂ treatment (group 2)_, PLP treatment (group 3) and PLP followed by H₂O₂ treatment (group 4). Cell viability was confirmed using MTT assays, oxidative stress levels were measured with ROS assay, change in different protein levels were recorded by flow cytometry. The acinar cells insulin secretion assay was performed with ELISA. The amylase protein expression was assessed using confocal microscopy. Key findings: The cell viability of acinar cell in group 1 was considered as 100%, while in group 2 it was reduced to 82% due to H₂O₂ effect, and in group 3 (99.8%) and group 4 (99.5%) were near to group 1 due to the cytoprotective effect of PLP. The ROS levels were increased by 1.47 folds in group 2, while PLP decreased to 1.02 fold in group 4, which was comparable with the changes in group 1. Beneficial effects of PLP were also observed from the increased expression levels of acinar cells are amylase -2.01, neurogenin-3-9.51, PDX-1- 23.6 and insulin-13.5 in group 3 compared to group 1. The specificity of PLP’s response was confirmed by amino oxy acetic acid (AOAA), a specific PLP inhibitor. The increased amylase protein localization with PLP was confirmed by confocal microscopy. Insulin secretion efficiency of acinar cells was observed to be 6.13 folds higher at basal and 24.63 fold higher at stimulated levels in group3 compared to group1. Significance: Our results advocate the antioxidant and cytoprotective effects of PLP on the pancreatic acinar cell along with increased pancreatic marker expressions of amylase,PDX-1, neurogenin-3 and insulin proteins.