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A common problem in confocal microscopy is the decrease in intensity of excitation light and emission signal from fluorophores as they travel through 3D specimens, resulting in decreased signal detected as a function of depth. Here, we report a visualization program compatible with widely used fluorophores in cell biology to facilitate image interpretation of differential protein disposition in 3D specimens. Glioblastoma cell clusters were fluorescently labeled for mitochondrial complex I (COXI), P2X7 receptor (P2X7R), β-Actin, Ki-67, and DAPI. Each cell cluster was imaged using a laser scanning confocal microscope. We observed up to ~70% loss in fluorescence signal across the depth in Z-stacks. This progressive underrepresentation of fluorescence intensity as the focal plane deepens hinders an accurate representation of signal location within a 3D structure. To address these challenges, we developed ProDiVis: a program that adjusts apparent fluorescent signals by normalizing one fluorescent signal to a reference signal at each focal plane. ProDiVis serves as a free and accessible, unbiased visualization tool to use in conjunction with fluorescence microscopy images and imaging software. [ABSTRACT FROM AUTHOR]

Background: E-cigarette advertising, which often includes various features, may prompt e-cigarette use and product switching. This study examined the associations between noticing e-cigarette ad features and perceived product appeal and interest in completely switching from cigarettes to advertised e-cigarettes among young adult dual users of both products. Methods: We analyzed data from an online heatmap experiment among young adult dual users defined as established cigarette smokers who currently used e-cigarettes (ages 18-34 years; n = 1,821). Participants viewed 12 e-cigarette ads, clicked on ad features (e.g., fruit flavors, nicotine warnings, price promotions, smoker-targeted claims) that attracted their attention (defined as "noticing"), and answered questions about e-cigarette product appeal and interest in completely switching from cigarettes to the e-cigarettes shown. We examined within-person associations between noticing specific ad features and outcomes, controlling for demographic and tobacco use-related characteristics. Results: Noticing fruit flavors (AOR = 1.67 and 1.28) and fruit images (AOR = 1.53 and 1.21) was positively associated with having any e-cigarette product appeal and switching interest. Noticing price promotions (AOR = 1.23) was positively associated with product appeal. In contrast, noticing nicotine warnings (AOR = 0.74 and 0.86), smoker-targeted claims (AOR = 0.78 and 0.89), and tobacco flavors (AOR = 0.92 and 0.90) was negatively associated with product appeal and switching interest. Conclusions: Noticing certain e-cigarette ad features (e.g., fruit flavors and nicotine warnings) may be associated with product appeal and/or switching interest among young adult dual users. More research is needed to assess the influence of e-cigarette ad features that promote product switching interests among cigarette smokers while discourage interests among tobacco-naïve individuals. [ABSTRACT FROM AUTHOR]

Cultivated and wild species of the genus rye (Secale) are important but underexploited gene sources for increasing the genetic diversity of bread wheat. Gene transfer is possible via bridge genetic materials derived from intergeneric hybrids. During this process, it is essential to precisely identify the rye chromatin in the wheat genetic background. In the present study, backcross generation BC2F8 from a cross between Triticum aestivum (Mv9kr1) and S. cereanum ('Kriszta,' a cultivar from the artificial hybrid of S. cereale and S. strictum) was screened using in-situ hybridization (GISH and FISH) and analyzed by DArTseq genotyping in order to select potentially agronomically useful genotypes for prebreeding purposes. Of the 329,267 high-quality short sequence reads generated, 27,822 SilicoDArT and 8,842 SNP markers specific to S. cereanum 1R-7R chromosomes were identified. Heatmaps of the marker densities along the 'Lo7' rye reference pseudomolecules revealed subtle differences between the FISH- and DArTseq-based results. This study demonstrates that the "exotic" rye chromatin of S. cereanum introgressed into wheat can be reliably identified by high-throughput DArTseq genotyping. The Mv9kr1-'Kriszta' addition and translocation lines presented here may serve as valuable prebreeding genetic materials for the development of stress-tolerant or disease-resistant wheat varieties. [ABSTRACT FROM AUTHOR]

Road surface quality is essential for driver comfort and safety, making it crucial to monitor pavement conditions and detect defects in real time. However, the diversity of defects and the complexity of ambient conditions make it challenging to develop an effective and robust classification and detection algorithm. In this study, we adopted a semi-supervised learning approach to train ResNet-18 for image feature retrieval and then classification and detection of pavement defects. The resulting feature embedding vectors from image patches were retrieved, concatenated, and randomly sampled to model a multivariate normal distribution based on the only one-class training pavement image dataset. The calibration pavement image dataset was used to determine the defect score threshold based on the receiver operating characteristic curve, with the Mahalanobis distance employed as a metric to evaluate differences between normal and defect pavement images. Finally, a heatmap derived from the defect score map for the testing dataset was overlaid on the original pavement images to provide insight into the network's decisions and guide measures to improve its performance. The results demonstrate that the model's classification accuracy improved from 0.868 to 0.887 using the expanded and augmented pavement image data based on the analysis of heatmaps. [ABSTRACT FROM AUTHOR]

This study aimed to define the aroma components, antioxidant activity, total phenol, and sugar contents of three geographically indicated dry beans: İspir Dry Bean, Narman Sugar Bean, and Hınıs Bean. Significant differences were found among three different dry beans in terms of total phenolic content, total antioxidant capacity (2,2-diphenyl-1-picrylhydrazil test), sugar content, and volatile compounds. The total phenolic content ranged from 14.09 mg gallic acid equivalents per 100 g dry bean sample in Narman to 36.73 mg in Hınıs. The highest total antioxidant activity was observed in İspir Bean with 8.38%, while the lowest was in Narman Bean with 6.02%. Additionally, the total sugar contents of İspir, Narman, and Hınıs beans were determined to be 6.46%, 5.60%, and 4.22%, respectively. Forty different aroma volatiles, including 10 alcohols, five terpenes, 11 aldehydes, four esters, six acids and four ketones, were identified in the bean seed samples. Narman Bean had higher levels of total alcohols, total acids, and total esters compared to İspir and Hınıs beans, while İspir Bean had higher levels of total terpenes and total ketones and Hınıs Bean had higher levels of total aldehydes. Furthermore, PCA and heatmap analysis revealed that the dry beans were divided into two main groups and that Narman beans were separated from İspir and Hınıs beans. In conclusion, this study showed that the geographically indicated İspir, Narman, and Hınıs dry bean varieties differed from each other in terms of flavor and nutritive characteristics, including bioactive and volatile compounds. [ABSTRACT FROM AUTHOR]

Nano-biochar is a source of blackish carbonaceous material, a prerequisite for sustainable crop productivity. By using a variety of feedstock materials, nanobiochar synthesis can be employed via pyrolysis. Therefore, a project was initiated to explore the morpho-physio-biochemical alteration at the vegetative stage of wheat crops after the foliar application of nanobiochar suspension (NBS). This investigation was conducted at the Botanical Research Area of the University of Lahore in a randomized complete block design (RCBD) arrangement, with four treatments (0, 1, 3, and 5% NBS) by maintaining three replications for each treatment using the wheat variety "Zincol". Nano biochar suspension in above mentioned concentrations were foliarly applied at the end of tillering/beginning of leaf sheath elongation of wheat seedlings to assess the morphological changes (root length, shoot length, number of leaves, fresh biomass/plant, dry biomass/plant), physio-biochemical alterations (total free amino acids, total sugars, chlorophyll content, protein, phenols, flavonoids), and nutrient uptake (Na, K, Ca, Mg, N, P contents. Our findings indicate that the foliar application of 3% NBS yielded the most favorable results across all measured attributes. Furthermore, Treatment-4 (5% NBS) specifically improved certain traits, including leaf area, total soluble proteins, and leaf calcium content. Finally, all NBS resulted in a decrease in carotenoid and sodium content in wheat seedlings. [ABSTRACT FROM AUTHOR]

Neuromarketing is a modern tool for researching consumer reactions to advertising stimuli and identifying relevant consumer behaviour patterns. Conducting neuromarketing research using eye tracking technology allows us to obtain objective data on consumer perceptions of advertising, websites, product packaging, etc. This article is devoted to studying the structural and content environment of the marketing category and neuromarketing research on advertised materials via the eye-tracking method. The analysis of publishing activity on the topic of neuromarketing carried out with the help of Scopus tools and the VOSviewer toolkit showed a trend of increasing interest from the scientific community in the use of neurotechniques and technologies in the study of consumer behaviour since 2004. The results of the analysis of the structural and content environment have shown the growing interest of scientists in the detailed study of consumer reactions to a product, brand, site, and advertisement, with further conclusions regarding their preferences and priorities. The work revealed that in the field of neuromarketing, there are methods that can be conditionally divided into those that register activity in the brain (neurological) and those that register activity outside the brain (biometric). The characteristics of these methods make it possible to choose the most appropriate method of eye tracking for evaluating consumers’ reactions to advertising posters. Pupil Labs Invisible mobile eyetracker was used as the main tool for neuromarketing research. According to the results of the two stages of the experiment, heatmaps were obtained, which are described by the key metrics of the study: fixations and points of view, heatmaps, areas of interest, and time spent. With the help of research, the most profitable designs of advertising posters for consumers were determined. The influence of different colors and their combinations on the brain activity of potential consumers was analysed. As a result, a conclusion was made regarding the optimal placement of such key elements on the poster as the logo, and the price, the colour range of the presented materials and the fonts that were used were determined. The application of the obtained results of marketing research made it possible to obtain information about how consumers perceive visual stimuli, which, in the future, will be the basis for perfecting marketing communication strategies with the target audience of consumers.

Abstract Maternal mortality ratio (MMR) estimates have been studied over time for understanding its variation across the country. However, it is never sufficient without accounting for presence of variability across in terms of space, time, maternal and system level factors. The study endeavours to estimate and quantify the effect of exposures encompassing all maternal health indicators and system level indicators along with space–time effects influencing MMR in India. Using the most recent level of possible -factors of MMR, maternal health indicators from the National Family Health Survey (NFHS: 2019–21) and system level indicators from government reports a heatmap compared the relative performance of all 19 SRS states. Facet plots with a regression line was utilised for studying patterns of MMR for different states in one frame. Using Bayesian Spatio-temporal random effects, evidence for different MMR patterns and quantification of spatial risks among individual states was produced using estimates of MMR from SRS reports (2014–2020). India has witnessed a decline in MMR, and for the majority of the states, this drop is linear. Few states exhibit cyclical trend such as increasing trends for Haryana and West Bengal which was evident from the two analytical models i.e., facet plots and Bayesian spatio- temporal model. Period of major transition in MMR levels which was common to all states is identified as 2009–2013. Bihar and Assam have estimated posterior probabilities for spatial risk that are relatively greater than other SRS states and are classified as hot spots. More than the individual level factors, health system factors account for a greater reduction in MMR. For more robust findings district level reliable estimates are required. As evident from our study the two most strong health system influencers for reducing MMR in India are Institutional delivery and Skilled birth attendance.

With the rapid urbanization in Malaysia, human activities have caused damage to many waterfront areas. Efforts are being made to restore the connection between the community and river waterfronts while creating attractive urban spaces. However, the implementation of modern design development strategies without sufficient consideration of cultural aspects and societal visual preferences has raised a number of questions with regard to the public's acceptance and appreciation of the newly implemented urban facades. This study aims to comprehensively examine the relationship between cultural elements and visual preferences in the context of urban waterfront walkways in Malaysia. This study implemented a photo survey to assess the visual preferences of the study's respondents. The results indicated a significant correlation between subjects' visual preferences and their cultural backgrounds. Additionally, the analysis of the collected data highlights a strong correlation between the presence of green elements and what the respondents perceived as part of the Malaysian culture. Additionally, the historical character of the study area, as represented by historical buildings, significantly influences the preferences of Malaysian respondents. Furthermore, certain elements, such as food carts, high-rise buildings, and water, are among the least preferred compared to other elements. Ultimately, incorporating these elements in the early design stages can contribute to the creation of culturally connected and visually appealing urban waterfront spaces in Malaysia. [ABSTRACT FROM AUTHOR]

Pathologic myopia (PM) is the critical factor of irreversible visual artifacts and puts patients at risk of other severe retinal diseases such as glaucoma. Early intervention can help control the disease's progression and prevent vision loss. Due to its prevalence worldwide, automated detection of PM and its severity is essential. Deep learning-aided diagnosis has proven itself in the field of ophthalmology. The proposed study automatically classifies pathologic and non-pathologic myopia from the fundus images using a guided mini U-Net (GM-U-Net) for feature extraction integrated with a customized convolutional neural network (PMNet) explicitly designed for fundus images. The proposed GM-U-Net allows a deeper network with significantly reduced parameters than conventional U-Net for feature extraction. The proposed PMNet utilizes the features extracted by GM-U-Net to discriminate between PM and a normal retina image. The PMNet classification performance is compared with the other transfer learning models based on the features provided by the GM-U-Net. The combination of GM-U-Net and PMNet outperforms the different models for PM classification. In-depth ablation tests are conducted to realize the current form of PMNet and test its effectiveness. PMNet achieves an accuracy of 90%, average sensitivity of 93%, and specificity of 97% for binary class on the test set, demonstrating it as a valuable tool for early PM detection. Further, to localize the prominent regions in the images, colored heatmap techniques are applied to visualize the affected areas with a hotter color. [ABSTRACT FROM AUTHOR]

Drought has a significant impact on plants, affecting their growth, development and survival. This study focuses on evaluating the impact of drought stress, a significant abiotic factor, on the agronomic and fiber parameters of potential cotton (Gossypium hirsutum L.) lines with the aim of developing drought-tolerant varieties. The experiment involved two irrigation regimes—well-watered (100% field capacity) and deficit irrigation (50% field capacity)—conducted on F9–F10 generations. Key fiber parameters, including fiber length, boll weight, fiber strength, and lint percentage, were identified as crucial selection criteria under both well-watered and deficit irrigation conditions. Notably, boll number emerged as the decisive parameter in both F9 and F10 generations. The study employed univariate and multivariate analyses, such as PCA, heat map cluster, correlation analysis, and decision tree, which consistently highlighted fiber length, boll weight, fiber strength and lint (ginning) percentage the key factor. In the F10 generation, the integration of decision tree and heat map cluster results led to the identification of 8 promising lines. These selected genotypes have potential for inclusion in future cotton breeding programmes, offering the opportunity to increase drought tolerance and improve cotton yield and productivity. Their resilience to environmental stresses makes them promising candidates for improving overall cotton performance under challenging conditions. [ABSTRACT FROM AUTHOR]

BACKGROUND: Barley flour, known to be rich in various phytochemicals, has been demonstrated to improve the technological and nutritional properties of pasta; however, its volatile profile, on which its aromatic properties depend, also plays an important role in the acceptance of barley‐enriched pasta. In the present work, volatile organic compounds (VOCs) of semolina doughs enriched with different percentages of barley and of the related pasta were characterized by solid phase micro‐extraction (HS‐SPME) coupled to gas‐chromatography/mass spectrometry (GC–MS), and evaluated using a multivariate statistical approach, including principal component analysis (PCA), cluster heatmaps, Pearson's and Spearman's correlations, and partial least squares correlation (PLSC). RESULTS: The effects of single raw materials, and their interactions, were studied to establish their importance in the volatile profile of the samples, and the correlation between the dough VOCs and the processed product VOCs was assessed. The presence of barley flour markedly affected the volatile profile in comparison with the dough obtained with only durum wheat. For alcohols, esters, terpenes, and some aldehydes there was a clear correlation with the percentage of barley. For some of the VOCs, on the other hand, a strong dependence on the ingredients interaction effect due to the mixing stage has been demonstrated. CONCLUSION: The heatmaps allowed a good graphical visualization of the relationship between molecules and barley percentage, offering the possibility to select the best one according to the desired volatolomic footprint. Pasta with 40% of barley was demonstrated to give pasta with the most complex volatile profile. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

The simplest approach to convey the results of scientific analysis, which can include complex comparisons, is typically through the use of visual items, including figures and plots. These statistical plots play a critical role in scientific studies, making data more accessible, engaging, and informative. A growing number of visual representations have been utilized recently to graphically display the results of oncologic imaging, including radiomic and radiogenomic studies. Here, we review the applications, distinct properties, benefits, and drawbacks of various statistical plots. Furthermore, we provide neuroradiologists with a comprehensive understanding of how to use these plots to effectively communicate analytical results based on imaging data. [ABSTRACT FROM AUTHOR]

Matrix factorization exploits the idea that, in complex high-dimensional data, the actual signal typically lies in lower-dimensional structures. These lower dimensional objects provide useful insights, with interpretation favored by sparse structures. Sparsity, in addition, is beneficial in terms of regularization and, thus, to avoid over-fitting. By exploiting Bayesian shrinkage priors, we devise a computationally convenient approach for high-dimensional matrix factorization. The dependence between row and column entities is modeled by inducing flexible sparse patterns within factors. The availability of external information is accounted for in such a way that structures are allowed while not imposed. Inspired by boosting algorithms, we pair the proposed approach with a numerical strategy relying on a sequential inclusion and estimation of low-rank contributions, with a data-driven stopping rule. Practical advantages of the proposed approach are demonstrated by means of a simulation study and the analysis of soccer heatmaps obtained from new generation tracking data. for this article are available online. [ABSTRACT FROM AUTHOR]

Maternal mortality ratio (MMR) estimates have been studied over time for understanding its variation across the country. However, it is never sufficient without accounting for presence of variability across in terms of space, time, maternal and system level factors. The study endeavours to estimate and quantify the effect of exposures encompassing all maternal health indicators and system level indicators along with space–time effects influencing MMR in India. Using the most recent level of possible -factors of MMR, maternal health indicators from the National Family Health Survey (NFHS: 2019–21) and system level indicators from government reports a heatmap compared the relative performance of all 19 SRS states. Facet plots with a regression line was utilised for studying patterns of MMR for different states in one frame. Using Bayesian Spatio-temporal random effects, evidence for different MMR patterns and quantification of spatial risks among individual states was produced using estimates of MMR from SRS reports (2014–2020). India has witnessed a decline in MMR, and for the majority of the states, this drop is linear. Few states exhibit cyclical trend such as increasing trends for Haryana and West Bengal which was evident from the two analytical models i.e., facet plots and Bayesian spatio- temporal model. Period of major transition in MMR levels which was common to all states is identified as 2009–2013. Bihar and Assam have estimated posterior probabilities for spatial risk that are relatively greater than other SRS states and are classified as hot spots. More than the individual level factors, health system factors account for a greater reduction in MMR. For more robust findings district level reliable estimates are required. As evident from our study the two most strong health system influencers for reducing MMR in India are Institutional delivery and Skilled birth attendance. [ABSTRACT FROM AUTHOR]

Cowpeas (Vigna unguiculata L. Walp) have been credible constituents of nutritious food and forage in human and animal diets since the Neolithic era. The modern technique of Diversity Array Technology (DArTseq) is both cost-effective and rapid in producing thousands of high-throughputs, genotyped, single nucleotide polymorphisms (SNPs) in wide-genomic analyses of genetic diversity. The aim of this study was to assess the heterogeneity in cowpea genotypes using DArTseq-derived SNPs. A total of 92 cowpea genotypes were selected, and their fourteen-day-old leaves were freeze-dried for five days. DNA was extracted using the CTAB protocol, genotyped using DArTseq, and analysed using DArTsoft14. A total of 33,920 DArTseq-derived SNPs were recalled for filtering analysis, with a final total of 16,960 SNPs. The analyses were computed using vcfR, poppr, and ape in R Studio v1.2.5001-3 software. The heatmap revealed that the TVU 9596 (SB26), Orelu (SB72), 90K-284-2 (SB55), RV 403 (SB17), and RV 498 (SB16) genotypes were heterogenous. The mean values for polymorphic information content, observed heterozygosity, expected heterozygosity, major allele frequency, and the inbreeding coefficient were 0.345, 0.386, 0.345, 0.729, and 0.113, respectively. Moreover, they validated the diversity of the evaluated cowpea genotypes, which could be used for potential breeding programmes and management of cowpea germplasm. [ABSTRACT FROM AUTHOR]

The present investigation was conducted to explore the role of potassium nutrition in improving biomass and physio-chemical alterations to reduce the adverse effects of salinity in mungbean. A sand-culture experiment was carried out under different salinity levels (0, 50, and 100 mM NaCl) with two levels of potassium (0 and 50 mM K2SO4) and two mungbean cultivars (NM-92 and Ramzan), and the alterations in mungbean biomass and metabolic activities were investigated. The results suggested that salinity significantly reduced the biomass, nitrate reductase activity (NRA), nitrite reductase activity (NiRA), total soluble proteins, chlorophyll a, chlorophyll b, total chlorophyll, nitrogen, potassium, calcium, magnesium, and phosphorous contents in both mungbean cultivars in comparison to the control. However, K2SO4 at 50 mM significantly improved all the parameters in both mungbean cultivars except for the sodium content. A significant increase in the total free amino acids, carotenoids, and sodium content in both mungbean cultivars was observed due to salt stress. Moreover, principal component analysis and heatmaps were developed individually for both mungbean cultivars to assess the variability and correlation among the studied attributes under all applied treatments. Under saline conditions, the mungbean 'Ramzan' showed more marked reductions in almost all the growth parameters as compared to 'NM-92'. The results suggest that the application of K2SO4 ameliorates the adverse effects of salinity by regulating osmolyte production, NRA, and NiRA, thus promoting plant growth and productivity. [ABSTRACT FROM AUTHOR]

Transitioning from full to deficit irrigation (DI) has become a key strategy in arid regions to combat water scarcity and enhance irrigation water use efficiency (IWUE). However, implementing DI requires additional approaches to counter its negative effects on wheat production. One effective approach is the foliar application of salicylic acid (SA), micronutrients (Mic; zinc and manganese), and macronutrients (Mac; nitrogen, phosphorus, and potassium). However, there is a lack of knowledge on the optimal combinations and timing of foliar application for these components to maximize their benefits under arid conditions, which is the primary focus of this study. A two-year field study was conducted to assess the impact of the foliar application of SA alone and in combination with Mic (SA + Mic) or Mic and Mac (SA + Mic + Mac) at various critical growth stages on wheat growth, physiology, productivity, and IWUE under DI conditions. Our result demonstrated that the foliar application of different components, the timing of application, and their interaction had significant effects on all investigated wheat parameters with few exceptions. Applying different components through foliar application at multiple growth stages, such as tillering and heading or tillering, heading, and grain filling, led to significant enhancements in various wheat parameters. The improvements ranged from 7.7% to 23.2% for growth parameters, 8.7% to 24.0% for physiological traits, 1.4% to 21.0% for yield and yield components, and 14.8% to 19.0% for IWUE compared to applying the components only at the tillering stage. Plants treated with different components (SA, Mic, Mac) exhibited enhanced growth, production, and IWUE in wheat compared to untreated plants. The most effective treatment was SA + Mic, followed by SA alone and SA + Mic + Mac. The foliar application of SA, SA + Mic, and SA + Mic + Mac improved growth parameters by 1.2–50.8%, 2.7–54.6%, and 2.5–43.9%, respectively. Yield parameters were also enhanced by 1.3–33.0%, 2.4–37.2%, and 3.0–26.6% while IWUE increased by 28.6%, 33.0%, and 18.5% compared to untreated plants. A heatmap analysis revealed that the foliar application of SA + Mic at multiple growth stages resulted in the highest values for all parameters, followed by SA alone and SA + Mic + Mac applications at multiple growth stages. The lowest values were observed in untreated plants and with the foliar application of different components only at the tillering stage. Thus, this study suggested that the foliar application of SA + Mic at various growth stages can help sustain wheat production in arid regions with limited water resources. [ABSTRACT FROM AUTHOR]

Abstract This study delves into the antioxidant potential of Justicia gendarussa, commonly known as gendarussa, and its response to varying doses of nitrogen fertilizer. Gendarussa exhibits the potential for antioxidant activity. The diverse ecological conditions in which it thrives may influence its biological activity and lead to inconsistent production of secondary metabolites. Nitrogen, a pivotal factor in secondary metabolite production in plants, has become a focal point of this research. This research aims to determine the optimal nitrogen fertilizer dose on gendarussa antioxidant capacity and metabolites using a metabolomics approach. Employing a randomized block design for cultivation, the investigation revealed that a maximum harvest weight of 10.9 g/aerial parts of the plant was achieved with 270 kg/ha of nitrogen fertilizer. This study explored the DPPH, ABTS, FRAP, and CUPRAC assays of antioxidant effect, and found insignificant differences between the various nitrogen treatments. UHPLCQ-Orbitrap HRMS was employed to identify 30 metabolites in positive and 18 in negative ionization modes. Gendarusin A, a major metabolite in gendarussa, is identified in both positive and negative ionization. PCA and heatmap analysis successfully categorized these metabolites in the aerial parts of gendarussa at different nitrogen fertilizer dosages. Based on the metabolomics approach, variations in nitrogen fertilizer made metabolites at doses of 90 kg/ha had higher relative concentrations of metabolites compared to doses of 180 kg/ha and 270 kg/ha. So, 90 kg/ha are the optimal nitrogen fertilizer dose for cultivation and utilization strategies.

Most phenolic compounds in beans exist in complex, insoluble binding forms that bind to cell wall components via ether, ester, or glucoside bonds. In the process of solid-state fermentation, Eurotium Cristatum can produce many hydrolase enzymes, such as α-amylase, pectinase, cellulase and β-glucosidase, which can effectively hydrolyze ether, ester or glucoside bond, release bound polyphenols, and increase polyphenol content in soybeans. When the fermentation conditions of soybean were fermentation time 12 days, inoculation amount 15% and initial pH 2, the content of free polyphenols in fermented soybean was 2.79 mg GAE/g d.w, which was 4.98 times that of unfermented soybean. The contents of bound polyphenols and total phenols in fermented soybean were 0.62 mg GAE/g d.w and 3.41 mg GAE/g d.w, respectively, which were 2.38 times and 4.16 times of those in unfermented soybean. At the same time, the inhibitory effect of free polyphenols in fermented soybean on acetylcholinesterase reached 91.51%. Thus, our results demonstrated that solid state fermentation and Eurotium Cristatum can be used as an effective way to increase soybean polyphenol content and combat Alzheimer's disease.

A common problem in confocal microscopy is the decrease in intensity of excitation light and emission signal from fluorophores as they travel through 3D specimens, resulting in decreased signal detected as a function of depth. Here, we report a visualization program compatible with widely used fluorophores in cell biology to facilitate image interpretation of differential protein disposition in 3D specimens. Glioblastoma cell clusters were fluorescently labeled for mitochondrial complex I (COXI), P2X7 receptor (P2X7R), β-Actin, Ki-67, and DAPI. Each cell cluster was imaged using a laser scanning confocal microscope. We observed up to ∼70% loss in fluorescence signal across the depth in Z-stacks. This progressive underrepresentation of fluorescence intensity as the focal plane deepens hinders an accurate representation of signal location within a 3D structure. To address these challenges, we developed ProDiVis: a program that adjusts apparent fluorescent signals by normalizing one fluorescent signal to a reference signal at each focal plane. ProDiVis serves as a free and accessible, unbiased visualization tool to use in conjunction with fluorescence microscopy images and imaging software.

Cultivated and wild species of the genus rye (Secale) are important but underexploited gene sources for increasing the genetic diversity of bread wheat. Gene transfer is possible via bridge genetic materials derived from intergeneric hybrids. During this process, it is essential to precisely identify the rye chromatin in the wheat genetic background. In the present study, backcross generation BC2F8 from a cross between Triticum aestivum (Mv9kr1) and S. cereanum (‘Kriszta,’ a cultivar from the artificial hybrid of S. cereale and S. strictum) was screened using in-situ hybridization (GISH and FISH) and analyzed by DArTseq genotyping in order to select potentially agronomically useful genotypes for prebreeding purposes. Of the 329,267 high-quality short sequence reads generated, 27,822 SilicoDArT and 8,842 SNP markers specific to S. cereanum 1R–7R chromosomes were identified. Heatmaps of the marker densities along the ‘Lo7’ rye reference pseudomolecules revealed subtle differences between the FISH- and DArTseq-based results. This study demonstrates that the “exotic” rye chromatin of S. cereanum introgressed into wheat can be reliably identified by high-throughput DArTseq genotyping. The Mv9kr1-’Kriszta’ addition and translocation lines presented here may serve as valuable prebreeding genetic materials for the development of stress-tolerant or disease-resistant wheat varieties.

It is necessary to integrate data and information effectively in order to build an integrated digital and refined urban system. A multi-granularity and multi-view urban heat map formed by integrating multi-source urban information flow can assist in immediate decision-making. Managing urban big data in the form of streams requires a stable operating environment that can efficiently handle millions of sensors and devices connected to the Internet of Things (IoT) in a highly concurrent environment. Urban management data contain spatiotemporal multi-dimensional information that presents the complexity of spatiotemporal dynamic associations, further increasing the difficulty of data streaming. To overcome these challenges, we have proposed a spatiotemporal-pyramid (ST-pyramid) model that organizes multidimensional and dynamic data streams logically using a data partition strategy based on geographical grid subdivision. Accordingly, we have proposed a load-balanced heatmap pyramid computation framework that can be used to build streaming processing procedures in a distributed environment. In addition, we implemented an urban traffic heatmap prototype system based on an open-source Flink framework. The experimental results show that the real-time heatmap pyramid construction algorithm proposed in this paper has high throughput, low latency, and flexible scalability and can provide large-scale public services in time in digital earth construction.

Simple Summary: Fish fry counting finds applications in various scenarios, including fish fry trading and the management of breeding densities. However, current computer-based methods struggle to accurately and effectively count large numbers of fish fry and locate them. To address these challenges, this study proposes a fish fry counting method based on a single keypoint as a feature. Additionally, a large-scale fish fry dataset, FishFry-2023, has been constructed, which includes universal point annotations for use. The number of fry in a single image ranges from 204 to 1935, ensuring an effective evaluation of the proposed method. Our approach was trained and tested on FishFry-2023, with experimental results demonstrating an average counting accuracy of 98.59%, exhibiting higher counting precision compared to the density map algorithms now available. Fish fry counting has been vital in fish farming, but current computer-based methods are not feasible enough to accurately and efficiently calculate large number of fry in a single count due to severe occlusion, dense distribution and the small size of fish fry. To address this problem, we propose the deconvolution enhancement keypoint network (DEKNet), a method for fish fry counting that features a single-keypoint approach. This novel approach models the fish fry as a point located in the central part of the fish head, laying the foundation for our innovative counting strategy. To be specific, first, a fish fry feature extractor (FFE) characterized by parallel dual branches is designed for high-resolution representation. Next, two identical deconvolution modules (TDMs) are added to the generation head for a high-quality and high-resolution keypoint heatmap with the same resolution size as the input image, thus facilitating the precise counting of fish fry. Then, the local peak value of the heatmap is obtained as the keypoint of the fish fry, so the number of these keypoints with coordinate information equals the number of fry, and the coordinates of the keypoint can be used to locate the fry. Finally, FishFry-2023, a large-scale fish fry dataset, is constructed to evaluate the effectiveness of the method proposed by us. Experimental results show that an accuracy rate of 98.59% was accomplished in fish fry counting. Furthermore, DEKNet achieved a high degree of accuracy on the Penaeus dataset (98.51%) and an MAE of 13.32 on a public dataset known as Adipocyte Cells. The research outcomes reveal that DEKNet has superior comprehensive performance in counting accuracy, the number of parameters and computational effort. [ABSTRACT FROM AUTHOR]

Although, in recent years, it has been common to monitor players in team sports using EPTSs (Electronic Performance and Tracking Systems) devices, most of the studies have focused on the optimization of individual performance rather than collective work or tactical analysis. Moreover, almost all these studies focus on men's teams with little focus on women's teams. In this work, data from women's soccer teams at different levels (competition and grassroots) have been collected using both a low-cost personally developed EPTS and a commercial EPTS. With these systems, we have built a dataset consisting of more than 16 million records, paying special attention to spatio-temporal variables collected in the form of geographical coordinates. Different methods have been applied to the collected dataset to solve the problem of determining the position (individual role) of each player on the field based solely on spatio-temporal variables. The methods include algorithms based on clustering, centroid calculation, and computer vision. We have verified the effectiveness of these methods and propose an alternative method based on image recognition algorithms applied to heat maps generated from the position of the players monitored during the matches. As shown in this paper, the validity of the proposed method has been verified, exceeding the performance of existing methods and extending the range of application of these techniques. [ABSTRACT FROM AUTHOR]

The goal of this study was to understand the effect of light intensity on the chlorogenic acid content and biosynthesis-related gene expression in Marsdenia tenacissima. In this study, M. tenacissima plants were treated with different light intensities; the chlorogenic acid content was determined by high-performance liquid chromatography; and transcriptome sequencing was performed. The amount of chlorogenic acid in the control was the highest and differed significantly from that under three different shading treatments. With a decrease in light intensity, the content of chlorogenic acid also showed a decreasing trend. A total of 1149 differentially expressed genes were identified by transcriptome sequencing, and most of the genes were down-regulated under the 90% shading treatment. A weighted gene co-expression network analysis identified the differentially expressed genes associated with light-induced chlorogenic acid biosynthesis. The different shading treatments down-regulated the expression of the chlorogenic acid biosynthesis pathway structural genes (HCTs). The MIKC family genes were the main transcription factors regulating light-induced chlorogenic acid biosynthesis, but the MYB and SBP family genes were also involved. In summary, combined physiological and transcriptome analysis, candidate structural genes, and transcription factors in the biosynthesis pathway of chlorogenic acid were identified in M. tenacissima. [ABSTRACT FROM AUTHOR]

Chocolate spot and heat stress devastatingly impact the production of faba bean, particularly under prevailing climatic changes and rising drastic environmental conditions. Hence, the adaptability of faba bean performance is a decisive objective of plant breeders to ensure its sustainable production. The present study aimed to evaluate the agronomic performance and stability of diverse eleven faba bean genotypes for yield characters, chocolate spot, and heat stress in eight different growing environments. The faba bean genotypes were evaluated at two sowing dates in two different locations during two growing seasons. The evaluated eleven faba bean genotypes were sown timely in autumn (25 October) and late sowing in early winter (25 November) in Bilbeis and Elkhatara during 2020 and 2021 growing seasons. The results exhibited substantial differences among the evaluated sowing dates, locations, and faba bean genotypes for all studied characters. The genotypes Sakha-3, Nubaria-3, Nubaria-5, Misr-3, and Wadi-1 were able to produce acceptable yield and quality characters under timely sowing in autumn and late sowing in early winter in all tested environments. Moreover, the genotypes Nubaria-3, Nubaria-4, Nubaria-5, Sakha-4, Giza-3, and Triple White exhibited better resistance to chocolate spot. The assessed faba bean genotypes were evaluated under late sowing to expose the plants to high temperature stress at flowering and throughout the anthesis and seed-filling stages. The genotypes Nubaria-5, Nubaria-3, Nubaria-4, Sakha-3, Sakha-4, Wadi-1, and Misr-3 possessed tolerance to heat stress more than the other genotypes. Different statistical methods were applied to study the stability of assessed genotypes such as joint regression, Additive Main Effect and Multiplicative Interaction (AMMI) analysis, AMMI stability value, Wricke's and Ecovalence values. The estimated stability parameters were consistent in depicting the stability of the assessed faba bean genotypes. The findings revealed that Sakha-1, Misr-3, Nubaria-4, and Nubaria-5 demonstrated stable and desirable performance across all tested environments. The heatmap was employed to classify the assessed faba bean genotypes into different groups based on agronomic performance, chocolate spot resistance and heat stress tolerance. Nubaria-3, Nubaria-4, Nubaria-5, and Misr-3 had the best performance for agronomic performance, chocolate spot resistance, and heat stress tolerance. The obtained results provide evidence of employing promising faba bean genotypes for improving the stability of agronomic performance, chocolate spot resistance, and heat stress tolerance in breeding programs principally under unprecedented climate fluctuations. [ABSTRACT FROM AUTHOR]

Silicon (Si) as silicic acid, Si(OH) 4 offers several benefits to the growth of plants, especially under adverse environmental conditions. Therefore, the present study aimed to assess the role of exogenous Si treatments (0.5 and 1.0 mM) in the tolerance of Fagopyrum esculentum Moench to Al stress applied at two different concentrations (0.2 and 0.4 mM). A set of agrophysiological, biochemical and antioxidant parameters were evaluated during the investigation. The exogenous Si application to Fagopyrum esculentum plants exposed to Al treatments significantly modulated the physiological and antioxidant responses to overcome the Al phytotoxicity and provide beneficial effects. The results indicated that the application of different doses of Al significantly affected the physiological parameters viz., plant growth, tolerance index, biomass accumulation (BA), relative water content (RWC), lipid peroxidation (LP), membrane stability index (MSI) and reduced glutathione (GSH) content. Likewise, aluminum-treated leaves also displayed increased hydrogen peroxide accumulation signifying the extent of the damage in F. esculentum. However, the individual and combined doses of silicon (Si) yielded beneficial effects on the physiological and antioxidant attributes. Multivariate analysis also suggested that individual and combined doses of Si improved physiological (root length (RL), shoot length (SL), root and shoot tolerance index (TI), BA, RWC, MSI and osmolytes) and modulates antioxidant defense enzymes (SOD, APX, GPX, CAT, GR and GST). The study reveals that exogenous Si application acts as a potent stress-modulating agent either via the formation of aluminum-silicate complexes or by improving the efficiency of antioxidant enzymatic activities in Al-contaminated soils. Graphical abstract of the proposed mechanism of Si-mediated Al detoxification in Fagopyrum esculentum plants [Display omitted] • Hydroxyl aluminum-silicate (HAS) complex formation in the rhizosphere is the key factor in reducing plant heavy metals (HM) uptake. • Al-treated leaves exhibit increased hydrogen peroxide (H 2 O 2) accumulation vs. silicon (Si) treated leaves. • Exogenous Si treatment modulates osmolytes and antioxidant defense mechanisms. [ABSTRACT FROM AUTHOR]

Neuromarketing is a modern tool for researching consumer reactions to advertising stimuli and identifying relevant consumer behaviour patterns. Conducting neuromarketing research using eye tracking technology allows us to obtain objective data on consumer perceptions of advertising, websites, product packaging, etc. This article is devoted to studying the structural and content environment of the marketing category and neuromarketing research on advertised materials via the eye-tracking method. The analysis of publishing activity on the topic of neuromarketing carried out with the help of Scopus tools and the VOSviewer toolkit showed a trend of increasing interest from the scientific community in the use of neurotechniques and technologies in the study of consumer behaviour since 2004. The results of the analysis of the structural and content environment have shown the growing interest of scientists in the detailed study of consumer reactions to a product, brand, site, and advertisement, with further conclusions regarding their preferences and priorities. The work revealed that in the field of neuromarketing, there are methods that can be conditionally divided into those that register activity in the brain (neurological) and those that register activity outside the brain (biometric). The characteristics of these methods make it possible to choose the most appropriate method of eye tracking for evaluating consumers' reactions to advertising posters. Pupil Labs Invisible mobile eyetracker was used as the main tool for neuromarketing research. According to the results of the two stages of the experiment, heatmaps were obtained, which are described by the key metrics of the study: fixations and points of view, heatmaps, areas of interest, and time spent. With the help of research, the most profitable designs of advertising posters for consumers were determined. The influence of different colors and their combinations on the brain activity of potential consumers was analysed. As a result, a conclusion was made regarding the optimal placement of such key elements on the poster as the logo, and the price, the colour range of the presented materials and the fonts that were used were determined. The application of the obtained results of marketing research made it possible to obtain information about how consumers perceive visual stimuli, which, in the future, will be the basis for perfecting marketing communication strategies with the target audience of consumers. [ABSTRACT FROM AUTHOR]

Improving drought tolerance of potato (Solanum tuberosum L.) is integral, particularly under current climate fluctuations. The objective of this study was to assess the impact of different concentrations of nanoparticles of ascorbic acid (AS), benzoic acid (BA), and salicylic acid (SA) individually and in combined treatments on potato under drought-induced stress. The assessed biostimulants with different concentrations (0.50 and 0.75 mM) were applied to two potato cultivars (Spunta and Lady-Rosetta). Nodal cutting of each cultivar was exposed to drought stress via 30% polyethylene glycol (PEG) in MS media. Five growth characters (plantlet length, number of leaves plant-1, number of roots, number of lateral branches plantlet-1, and root length) were measured after 7, 14, and 21 d. The results indicated that the evaluated cultivars exhibited highly significant differences (< 0.001) in all characters. Moreover, all evaluated nano biostimulants recorded highly significant differences (< 0.001) compared to the untreated control. The co-application of two nano biostimulants was stronger than the sole use of one material. The co-application of AS+BA was the most effective, and its impact was more considerable compared to the other treatments. The assessed cultivars displayed significant interaction with the application of nano biostimulants. 'Lady-Rosetta' responded more to the applied nano biostimulants in all studied characters. In conclusion, applying AS, SA, and BA improved the growth of 'Lady-Rosetta' under drought stress conditions. Furthermore, the combined treatment AS+BA is more powerful in modulating drought stress's adverse impacts on potato plants. [ABSTRACT FROM AUTHOR]

Heatmaps are preferred visualization modes for biologists to display high-dimensional information from high-throughput omics data. Many software including website services and R packages are available to generate various types of customized heatmaps. Here, we describe oppHeatmap (omics pilot platform of heatmap), a new tool constructed to render different kinds of heatmaps through MATLAB. oppHeatmap is available for plotting ordinary heatmaps, hierarchical clustering, TreeMaps, microplates graph, sample correlation (full heatmap and upper and lower triangle parts), gene correlation (between columns or tables), and polar heatmaps. oppHeatmap can support the modification of borders, fonts, and colors to customize the final plots. oppHeatmap can not only read data from Microsoft Excel to generate specific heatmaps but also make Excel heatmaps by coloring each cell in Excel. The graphs can be stored in SVG (supported vector graph) format and modified by other SVG recognition software. oppHeatmap is designed by MATLAB AppDesigner with GUI (graphical user interface) operation. The program for oppHeatmap was available at https://github.com/HangZhouSheep/oppHeatmap.are diagramed by All kinds of heatmaps oppHeatmap. The main function of oppHeatmap is to enable visualization of two types of heatmaps and a total of 11 graphs. The first type is a heatmap with a polar or rectangular coordinate system, including ordinary heatmap, microplates graph, polar heatmap, and Excel heatmap. The last one involves writing the heatmap in Excel and others in MATLAB. The second type is with rearranged rows and columns, which includes hierarchical clustering, TreeMaps, and sample/gene correlation between columns or rows. All plots support row standardization for better color contrast. Eleven base functions implement heatmap plotting in the command line environment of MATLAB. GUI designed by AppDesigner supports the interactive construction of heatmaps. [ABSTRACT FROM AUTHOR]

Objective: Susceptibility weighted imaging (SWI) of neonatal hypoxic-ischemic brain injury can provide assistance in the prognosis of neonatal hypoxic-ischemic encephalopathy (HIE). We propose a convolutional neural network model to classify SWI images with HIE. Materials and methods: Due to the lack of a large dataset, transfer learning method with fine-tuning a pre-trained ResNet 50 is introduced. We randomly select 11 datasets from patients with normal neurology outcomes (n = 31) and patients with abnormal neurology outcomes (n = 11) at 24 months of age to avoid bias in classification due to any imbalance in the data. Results: We develop a rule-based system to improve the classification performance, with an accuracy of 0.93 ± 0.09. We also compute heatmaps produced by the Grad-CAM technique to analyze which areas of SWI images contributed more to the classification patients with abnormal neurology outcome. Conclusion: Such regions that are important in the classification accuracy can interpret the relationship between the brain regions affected by hypoxic-ischemic and neurodevelopmental outcomes of infants with HIE at the age of 2 years. [ABSTRACT FROM AUTHOR]

This paper provides the first known “heatmap” representing Australian public narratives across a range of groups experiencing disadvantage developed from a comprehensive literature review of primary Australian studies between 2020 and 2021. Eleven narratives were identified across 14 population groups with the most frequent being deficit narratives, misrepresentation of the issue and ‘absent’ narratives (group being described not represented in the narrative). The most frequently described groups were Aboriginal and Torres Strait Islander people, people living with a disability, racial minorities and women. The heatmap resonated with three global review‐level studies and findings of supplementary qualitative interviews. Examination of relationships between the heatmap and empirical Australian disadvantage data enabled identification of missing and misleading public narratives. Attempts to disrupt such narratives can improve the understanding of disadvantage by more truthfully reflecting the lives, experiences and challenges of groups experiencing disadvantage. Updates to the heatmap can enable analysis of the impact of disruption strategies. [ABSTRACT FROM AUTHOR]

Recently, there have been cases of phishing attempts to steal personal information through fake sites disguised as major sites. Although phishing attacks continue and increase, countermeasures remain in the form of defense after identifying the attack. Therefore, in this paper, we designed a phishing detection model using machine learning that provides knowledge and prediction by learning patterns from data input to a computer. For this, an analysis model was built using sklearn logistic regression, and the phishing probability was visualized using a heatmap. In addition, a graph was used to visually indicate the result, and a function for attribute information of a phishing website was provided. [ABSTRACT FROM AUTHOR]

This paper proposes a novel multivariate performance diagnostic approach for the Oracle database systems to detect performance degradation and crashes during database operations and maintenance. It was based on three technologies: image encoding, image concatenation, and deep convolutional network. Instantaneous variation and magnitude information of the time series were acquired by Heatmap and Recurrence Plot (RP) from databases. Moreover, the Heatmap and RP were concatenated in order to fully extract complementary information. Finally, the concatenated images of Heatmap and RP were used to train the VGG16 model for database performance diagnosis. The quantitative analysis demonstrated a good increment of accuracy in Heatmap and RP based on the same deep learning networks compared with other image encodings of Gramian Angular Difference Field (GADF), Gramian Angular Summation Fields (GASF), and Markov Transition Fields (MTF). Meanwhile, concatenated images of Heatmap and RP can improve the accuracy by 1% compared to single Heatmap input. The result of the trained model applied to multivariate database diagnosis shows an accuracy of 95.3%. Thus, our method can more effectively and accurately diagnose the performance of the Oracle database systems.

3D human pose estimation (HPE) has become increasingly important in baseball analytic, but there are several difficulties pertaining to pose estimation in real-world baseball pitching. First, in-the-wild baseball pitching lacks related 3D pose datasets and contains lots of joints occluded by other body parts. Second, baseball pitching contains dramatic velocity changes during arm acceleration phases. Due to these properties of pitching, it is difficult to use common filters to remove random noises while preserving high-frequency critical joint movements in pitching. To solve these problems, we propose joint-wise volumetric triangulation to reconstruct 3D human poses by utilizing the information of multi-view 2D joint heatmaps generated by 2D HPE methods. We also designed a baseball-customized filter system to remove noisy signal from pose movement while preserving the high-frequency pitching motion. Our proposed pose reconstruction scheme yields a 33.1 mm average position error and 0.35m/s (1.28 km/h) average velocity error on baseball pitching motion. Our work can be directly applied to estimate human poses either in indoor environment or real-world baseball field.

This paper introduces a novel method for accurately estimating the 2D coordinates of hand keypoints from single static images, utilizing a sequential convolutional neural network optimized with Maximum Likelihood Estimation Loss. Unlike traditional heatmap-based techniques, our approach eliminates the need to generate label heatmaps and sidesteps the direct optimization of model parameters based on noisy labels. Instead, it concentrates on modeling the distribution of the discrepancies between predicted results and ground truth, rather than the potential presence of noisy labels, thus enabling the direct prediction of hand keypoint coordinates. Furthermore, we propose a sequential training and inference framework that consists of a deep convolutional backbone network and a multi-stage sequential network. Each stage of this network features similar structures, facilitating the progressive and precise prediction of hand keypoint coordinates. Our extensive experimental results demonstrate that our approach is both highly accurate and robust, outperforming mainstream methods under the experimental conditions detailed in this paper.

Abstract Background Radiation-induced damage (RID) after radiotherapy (RT) of primary brain tumors and metastases can be challenging to clinico-radiographically distinguish from tumor progression. RID includes pseudoprogression and radiation necrosis; the latter being irreversible and often associated with severe symptoms. While histopathology constitutes the diagnostic gold standard, biopsy-controlled clinical studies investigating RID remain limited. Whether certain brain areas are potentially more vulnerable to RID remains an area of active investigation. Here, we analyze histopathologically confirmed cases of RID in relation to the temporal and spatial dose distribution. Methods Histopathologically confirmed cases of RID after photon-based RT for primary or secondary central nervous system malignancies were included. Demographic, clinical, and dosimetric data were collected from patient records and treatment planning systems. We calculated the equivalent dose in 2 Gy fractions (EQD22) and the biologically effective dose (BED2) for normal brain tissue (α/β ratio of 2 Gy) and analyzed the spatial and temporal distribution using frequency maps. Results Thirty-three patients were identified. High-grade glioma patients (n = 18) mostly received one normofractionated RT series (median cumulative EQD22 60 Gy) to a large planning target volume (PTV) (median 203.9 ccm) before diagnosis of RID. Despite the low EQD22 and BED2, three patients with an accelerated hyperfractionated RT developed RID. In contrast, brain metastases patients (n = 15; 16 RID lesions) were often treated with two or more RT courses and with radiosurgery or fractionated stereotactic RT, resulting in a higher cumulative EQD22 (median 162.4 Gy), to a small PTV (median 6.7 ccm). All (n = 34) RID lesions occurred within the PTV of at least one of the preceding RT courses. RID in the high-grade glioma group showed a frontotemporal distribution pattern, whereas, in metastatic patients, RID was observed throughout the brain with highest density in the parietal lobe. The cumulative EQD22 was significantly lower in RID lesions that involved the subventricular zone (SVZ) than in lesions without SVZ involvement (median 60 Gy vs. 141 Gy, p = 0.01). Conclusions Accelerated hyperfractionated RT can lead to RID despite computationally low EQD22 and BED2 in high-grade glioma patients. The anatomical location of RID corresponded to the general tumor distribution of gliomas and metastases. The SVZ might be a particularly vulnerable area.

Road surface quality is essential for driver comfort and safety, making it crucial to monitor pavement conditions and detect defects in real time. However, the diversity of defects and the complexity of ambient conditions make it challenging to develop an effective and robust classification and detection algorithm. In this study, we adopted a semi-supervised learning approach to train ResNet-18 for image feature retrieval and then classification and detection of pavement defects. The resulting feature embedding vectors from image patches were retrieved, concatenated, and randomly sampled to model a multivariate normal distribution based on the only one-class training pavement image dataset. The calibration pavement image dataset was used to determine the defect score threshold based on the receiver operating characteristic curve, with the Mahalanobis distance employed as a metric to evaluate differences between normal and defect pavement images. Finally, a heatmap derived from the defect score map for the testing dataset was overlaid on the original pavement images to provide insight into the network’s decisions and guide measures to improve its performance. The results demonstrate that the model’s classification accuracy improved from 0.868 to 0.887 using the expanded and augmented pavement image data based on the analysis of heatmaps.

Simple Summary: Small-target classification in an image is still challenging in spite of emerging deep learning-based techniques. This study focused on the development of deep learning models for small-lesion classification. The proposed Rense block and edge conservative module enables deep learning models to extract better features of small lesions in images. Our RenseNet was validated with quantitative classification accuracy and a qualitative explanation heatmap for kidney stone and lung tumor computed tomography (CT) image datasets. Deep learning has become an essential tool in medical image analysis owing to its remarkable performance. Target classification and model interpretability are key applications of deep learning in medical image analysis, and hence many deep learning-based algorithms have emerged. Many existing deep learning-based algorithms include pooling operations, which are a type of subsampling used to enlarge the receptive field. However, pooling operations degrade the image details in terms of signal processing theory, which is significantly sensitive to small objects in an image. Therefore, in this study, we designed a Rense block and edge conservative module to effectively manipulate previous feature information in the feed-forward learning process. Specifically, a Rense block, an optimal design that incorporates skip connections of residual and dense blocks, was demonstrated through mathematical analysis. Furthermore, we avoid blurring of the features in the pooling operation through a compensation path in the edge conservative module. Two independent CT datasets of kidney stones and lung tumors, in which small lesions are often included in the images, were used to verify the proposed RenseNet. The results of the classification and explanation heatmaps show that the proposed RenseNet provides the best inference and interpretation compared to current state-of-the-art methods. The proposed RenseNet can significantly contribute to efficient diagnosis and treatment because it is effective for small lesions that might be misclassified or misinterpreted. [ABSTRACT FROM AUTHOR]