Sampling Variability Research Articles

Analysis of Burning Rate Variation of Tungsten Delay Composition Close to Zero Oxygen Balance

ABSTRACTTungsten delay compositions are widely utilized in ammunition, small detonators, and other devices. In this study, we first explored the changes in the burning rate of micron‐sized tungsten delay compositions (W/BaCrO4/KClO4) when achieving zero oxygen balance under different material tube walls. Subsequently, SiO2, CuO, BN, and shellac were added to the tungsten delay composition to investigate the burning rate variations of different samples in aluminum tubes. The experimental results reveal that the combustion rate of the tungsten delay composition decreases with an increase in the thermal conductivity of the tube wall. The content of BN exhibits a linear relationship with the combustion rate of the tungsten delay composition, the combustion rate of the tungsten delay composition decreases with the increase of the mass percentage of BN. A small amount of SiO2, CuO, and shellac accelerates the combustion rate of the tungsten delay composition, but the combustion rate decreases as the content of these additives increases. The conclusion of this study can provide a wider range of delay times for delay devices with significant spatial limitations.

Decoding Health: Exploring Essential Biomarkers Linked to Metabolic Dysfunction-Associated Steatohepatitis and Type 2 Diabetes Mellitus

The investigation of biomarkers for metabolic diseases such as type 2 diabetes mellitus (T2DM) and metabolic dysfunction-associated steatohepatitis (MASH) reveals their potential for advancing disease treatment and addressing their notable overlap. The connection between MASH, obesity, and T2DM highlights the need for an integrative management approach addressing mechanisms like insulin resistance and chronic inflammation. Obesity contributes significantly to the development of MASH through lipid dysregulation, insulin resistance, and chronic inflammation. Selective biomarker targeting offers a valuable strategy for detecting these comorbidities. Biomarkers such as CRP, IL-6, and TNF-α serve as indicators of inflammation, while HOMA-IR, fasting insulin, and HbA1c are essential for evaluating insulin resistance. Additionally, triglycerides, LDL, and HDL are crucial for comprehending lipid dysregulation. Despite the growing importance of digital biomarkers, challenges in research methodologies and sample variability persist, necessitating further studies to validate diagnostic tools and improve health interventions. Future opportunities include developing non-invasive biomarker panels, using multiomics, and using machine learning to enhance prognoses for diagnostic accuracy and therapeutic outcomes.

The Clinical Impact of Somatic Copy Number Variations in Patients with Stage IV Wilms Tumor Enrolled in the SIOP 2001 Trial and Study.

Recent research elucidated the prognostic significance of molecular biology in Wilms tumor (WT) by linking somatic genomic variants (such as gain of chromosome 1q) to unfavorable patient outcomes. This analysis describes the clinical impact of copy number variations (CNV) in tumor samples of WT patients with stage IV disease. Tumor samples of 55 WT patients with stage IV disease from the United Kingdom, France, and Germany enrolled in the SIOP 2001 study and treated with preoperative chemotherapy (pCHT) were examined for their CNVs of chromosome 1q and other regions of interest using multiplex ligation-dependent probe amplification (MLPA). The identified CNV were analyzed regarding their prognostic impact. Chromosome 1q gain (1q+) and TP53 loss occurred in 38.2% and 16.4% of tumors and were associated with older patient age at diagnosis (median [months]: 65 and 64 vs. 49 each, p = 0.03 and 0.02, respectively) and poorer 5-year event-free survival (40.0% and 11.1% vs. 67.7% and 82.6%, p = 0.04 and <0.01, respectively) compared to their specific control group of tumors without the respective CNV. In patients with pulmonary-only metastasis, 1q+ was an adverse prognostic marker irrespective of remission status after pCHT with or without metastasectomy. A simultaneous MYCN gain occurred more frequently in tumors with 1q+ than in tumors without 1q+ (p = 0.03). TP53 loss was linked to high-risk histology and inferior 5-year overall survival (p<0.001). We confirm the prognostic relevance of 1q+ and TP53 loss in stage IV WTs and emphasize their potential utility for future treatment stratification.

Atezolizumab neutralizing assay development: a novel way of evaluating assay cutpoint after sample pretreatment

Atezolizumab, a biotherapeutic monoclonal antibody directed against PD-L1, has been shown to be efficacious in multiple oncology indications. In clinical trials, a subset of patients developed anti-atezolizumab antibodies, necessitating the development of a neutralizing antibody (NAb) assay. A bead-based sample pretreatment method was developed to reduce high levels of therapeutic in patient serum samples. Untreated sample variability is reduced by this sample pretreatment, based on this, a novel approach was taken for determining an appropriate assay decision threshold (cutpoint). The therapeutic was added to the samples to mimic typical patient samples. The resulting assay was successfully validated and applied to sample analysis from multiple clinical trials.

Boar-to-Boar Variations in Quality Characteristics of Sperm from Different Ejaculates Following Freezing–Thawing

The main objective of this study was to investigate boar-to-boar variations in the quality characteristics of sperm from the sperm-rich fractions (SRFs) and whole ejaculates (WEs) following freezing–thawing. Several sperm attributes, such as motility patterns analyzed by the computer-assisted sperm analysis (CASA) system, mitochondrial function, membrane integrity, and DNA fragmentation were used to compare the cryo-survival of sperm from SRFs and WEs from boars with good and poor semen freezability (GSF and PSF, respectively). In this study, boars with post-thaw total motility (TMOT) more than 30% (&gt;30%) were classified as having GSF, while those with post-thaw TMOT less than 30% (&lt;30%) were classified as having PSF. Principal component analysis 1 (PCA1), which is the main component of the sample variation, explained approximately 75% of the variance between the GSF and PSF groups, reaffirming the reliability of post-thaw TMOT as a reliable criterion used to classify the animals. Most of the post-thaw sperm parameters of the SRFs and WEs were positively correlated. Furthermore, scatter plot analyses show stronger relationships between the analyzed post-thaw parameters of the frozen–thawed (FT) sperm of SRFs than those of WEs. Individual boar variations or the sperm source had marked effects on the quality characteristics of FT sperm. The higher TMOT, velocity straight line (VSL), and velocity average path (VAP) of FT sperm were more enhanced in the SRFs compared with the WEs of the PSF group. Furthermore, the mitochondrial function, membrane integrity, and DNA fragmentation of FT sperm were markedly higher in the SRFs than in the WEs, particularly for the poor freezability boars. We suggest that the freezability potential of sperm of the GSF group does not differ significantly between the SRFs and WEs, reaffirming that boar variability is an important factor that affects the cryo-survival of sperm.

Systematic evaluation of sampling rate influences and variability in POCIS using meta-analysis and quantitative structure property relationship (QSPR).

Despite the significant benefits of aquatic passive sampling (low detection limits and time-weighted average concentrations), the use of passive samplers is impeded by uncertainties, particularly concerning the accuracy of sampling rates. This study employed a systematic evaluation approach based on the combination of meta-analysis and quantitative structure-property relationships (QSPR) models to address these issues. A comprehensive meta-analysis based on extensive data from 298 studies on the Polar Organic Chemical Integrative Sampler (POCIS) identified essential configuration parameters, including the receiving phase (type, mass) and the diffusion-limiting membrane (type, thickness, pore size), as key factors influencing uptake kinetic parameters. The incomplete availability of these details across studies potentially impacts data reproducibility and comparability. The subsequent meta-regression and subgroup analysis were performed to reveal the most significant factors contributing to sampling rate variability and inter-study heterogeneity. The flow rate and octanol-water partitioning (Kow or pH-dependent Dow) were identified from all environmental factors and chemical properties. Furthermore, the impact of chemical properties on the sampling rates of POCIS was predicted by Quantitative Structure-Property Relationship (QSPR) models using 2D descriptors and random forest regression. The analysis highlighted that the electrotopological state and molecular mass are the most important chemical properties influencing the sampling rate. This study systematically unraveled the most important impact factors on reliable estimates of passive sampling rates, and these causes of uncertainty should be further considered in aquatic monitoring and assessment with passive samplers.

Bayesian identification of differentially expressed isoforms using a novel joint model of RNA-seq data.

We develop a Bayesian approach, BayesIso, to identify differentially expressed isoforms from RNA-seq data. The approach features a novel joint model of the sample variability and the deferential state of isoforms. Specifically, the within-sample variability and the between-sample variability of each isoform are modeled by a Poisson-Lognormal model and a Gamma-Gamma model, respectively. Using a Bayesian framework, the differential state of each isoform and the model parameters are jointly estimated by a Markov Chain Monte Carlo (MCMC) method. Extensive studies using simulation and real data demonstrate that BayesIso can effectively detect isoforms of less differentially expressed and differential transcripts for genes with multiple isoforms. We applied the approach to breast cancer RNA-seq data and uncovered a unique set of isoforms that form key pathways associated with breast cancer recurrence. First, PI3K/AKT/mTOR signaling and PTEN signaling pathways are identified as being involved in breast cancer development. Further integrated with protein-protein interaction data, pathways of Jak-STAT, mTOR, MAPK and Wnt signaling are revealed in association with breast cancer recurrence. Finally, several pathways are activated in the early recurrence of breast cancer. In tumors that occur early, members of pathways of cellular metabolism and cell cycle (such as CD36 and TOP2A) are upregulated, while immune response genes such as NFATC1 are downregulated.

Policies and Practice of Force in Nigerian Policing: A Case Study of Yelwa Division Bauchi Police Command

This study aims to evaluate the policies and practice of force in Nigerian policing in yelwa division Bauchi Police command. The research objectives: know the mechanisms in existing policies that police use for mitigating excessive force, analyze the impact of police accountability mechanisms on public trust and community relations, evaluate the effectiveness of existing policies that police use in mitigating incidents of excessive force, assess the influence of training in de-escalation techniques and to access the implicit bias recognition on police officers' use of force in Yelwa division Bauchi Police command. This study employed survey research technique and developed a set of structured questionnaire as instrument utilized in collecting primary data. The data was analyzed using descriptive and inferential statistical techniques to provide a comprehensive overview of the respondents' perceptions and experiences. The findings indicate that majority of respondents believe that these mechanisms have increased trust and improved community relations, though some still report no change or a decrease in trust, respondents generally perceive existing policies as moderately effective in reducing excessive force, there is room for improvement. The study concludes that current policies require enhancement to improve their effectiveness in addressing excessive force. It recommended that regular policy reviews, improved community engagement, and enhanced transparency in accountability mechanisms. Additionally, it suggests the implementation of continuous training programs to better support officers in de-escalation and bias recognition. Limitations include a small sample size and variability in respondents' perceptions, which underscores the need for further research with a larger and more diverse sample to validate and expand upon these findings.

Criminal Justice System Involvement Among U.S. Military Veterans: Prevalence, Correlates, and Mental Health Burden

Studies of justice system involvement (JSI) among U.S. military veterans have yielded mixed results regarding both prevalence and correlates due in part to sampling variability and limitations. The present study examined factors associated with lifetime JSI in a nationally representative sample of 4,049 U.S. veterans. Bivariate and multivariate analyses indicate that JSI was associated with racial/ethnic minority, lower income/education, combat, and other lifetime traumas. Relative importance analysis revealed that lifetime substance use disorders, cumulative trauma, adverse childhood experiences, and male sex accounted for the majority of variance in predicting JSI. JSI was also independently associated with current substance use, suicidal ideation, and psychiatric treatment utilization. The study provides population-based findings that extend previous studies. Results underscore the need for informed identification, prevention, and intervention for veterans in the community who are at increased for JSI.

Enhanced multiscale human brain imaging by semi-supervised digital staining and serial sectioning optical coherence tomography

A major challenge in neuroscience is visualizing the structure of the human brain at different scales. Traditional histology reveals micro- and meso-scale brain features but suffers from staining variability, tissue damage, and distortion, which impedes accurate 3D reconstructions. The emerging label-free serial sectioning optical coherence tomography (S-OCT) technique offers uniform 3D imaging capability across samples but has poor histological interpretability despite its sensitivity to cortical features. Here, we present a novel 3D imaging framework that combines S-OCT with a deep-learning digital staining (DS) model. This enhanced imaging modality integrates high-throughput 3D imaging, low sample variability and high interpretability, making it suitable for 3D histology studies. We develop a novel semi-supervised learning technique to facilitate DS model training on weakly paired images for translating S-OCT to Gallyas silver staining. We demonstrate DS on various human cerebral cortex samples, achieving consistent staining quality and enhancing contrast across cortical layer boundaries. Additionally, we show that DS preserves geometry in 3D on cubic-centimeter tissue blocks, allowing for visualization of meso-scale vessel networks in the white matter. We believe that our technique has the potential for high-throughput, multiscale imaging of brain tissues and may facilitate studies of brain structures.

Intelligent classification of lung cancer pathology images through comparative morphological feature learning

Lung cancer pathology images are categorized with enhanced accuracy in this research, addressing the significant challenge posed by the limited availability of labeled images, a limitation exacerbated by the complexity of cellular morphologies. Background The accurate classification of lung cancer pathology images is of paramount importance for both diagnostic and therapeutic purposes. However, the development of robust classification models is often hindered by the intricate cellular morphologies and the scarcity of labeled images, which is a critical bottleneck in the field. Objectives The study is designed to incorporate unlabeled data into the training process, thereby enhancing the classification of lung cancer pathology images through the use of comparative learning techniques. Methods A methodology is introduced wherein confidently classified unlabeled images are integrated with labeled ones, enriching the training dataset. This approach draws on principles of farthest and nearest neighbor contrastive learning to cultivate a more challenging learning environment and to augment the variability of contrastive samples. To effectively extract key cellular morphological features, an encoder based on the ResNet50 architecture, fortified with deformable and dynamic convolutional techniques, is utilized. Results Demonstrated by experimental results, the proposed classification strategy achieves a significant improvement in the accuracy of lung cancer image classification, even under conditions characterized by a limited availability of labeled data, thus underscoring the robustness of the method. Conclusion The integration of comparative learning with both labeled and unlabeled images, complemented by the application of advanced convolutional techniques, is shown to be a promising avenue for enhancing the classification of lung cancer pathology images. This research is presented as a practical solution to the urgent need for accurate and efficient diagnostic tools in the field of oncology.

Research on Small-Target Detection of Flax Pests and Diseases in Natural Environment by Integrating Similarity-Aware Activation Module and Bidirectional Feature Pyramid Network Module Features

In the detection of the pests and diseases of flax, early wilt disease is elusive, yellow leaf disease symptoms are easily confusing, and pest detection is hampered by issues such as diversity in species, difficulty in detection, and technological bottlenecks, posing significant challenges to detection efforts. To address these issues, this paper proposes a flax pest and disease detection method based on an improved YOLOv8n model. To enhance the detection accuracy and generalization capability of the model, this paper first employs the Albumentations library for data augmentation, which strengthens the model’s adaptability to complex environments by enriching the diversity of training samples. Secondly, in terms of model architecture, a Bidirectional Feature Pyramid Network (BiFPN) module is introduced to replace the original feature extraction network. Through bidirectional multi-scale feature fusion, the model’s ability to distinguish pests and diseases with similar features and large scale differences is effectively improved. Meanwhile, the integration of the SimAM attention mechanism enables the model to learn information from three-dimensional channels, enhancing its perception of pest and disease features. Additionally, this paper adopts the EIOU loss function to further optimize the model’s bounding box regression, reducing the distortion of bounding boxes caused by high sample variability. The experimental results demonstrate that the improved model achieves a significant detection performance on the flax pest and disease dataset, with notable improvements in the detection accuracy and mean average precision compared to the original YOLOv8n model. Finally, this paper proposes a YOLOv8n model with a four-headed detection design, which significantly enhances the detection capability for small targets such as pests and diseases with a size of 4 × 4 pixels or larger by introducing new detection heads and optimizing feature extraction. This method not only improves the detection accuracy for flax pests and diseases but also maintains a high computational efficiency, providing effective technical support for the rapid and precise detection of flax pests and diseases and possessing an important practical application value.

Calculating the water production rate in a low-saturation reservoir based on the modified curved capillary theory

Low-saturation reservoirs have complex geological backgrounds, diverse genetic mechanisms, poor physical properties, variable pore structures, and low permeabilities. It also has a wide range of oil–water coexistences and low oil saturations, which make it difficult to accurately calculate their water production rate. To address these issues, this study investigates low-saturation reservoirs in the Nanpu Sag, focusing on petrophysical characteristics and relative permeability variations in samples with different pore structures. A range of petrophysical experiments, including coupled oil-water phase percolation, petroelectrical testing, nuclear magnetic resonance (NMR), and X-ray diffraction (XRD), were conducted to analyze fluid interactions and pore structure impacts. From these experiments, a new pore structure factor was proposed to enhance the existing curved capillary theory. The results showed that this modification improved calculation accuracy for water production rate. The enhanced model was applied to actual logging data and validated against test production results, demonstrating its effectiveness. This study offers both theoretical and practical advancements. It improves the understanding of fluid dynamics in low-saturation reservoirs and provides a reliable tool for fluid identification during exploration. This model can guide more accurate reservoir assessments and support optimized production strategies, improving efficiency in managing these challenging reservoir environments.

A Post-Mortem Molecular Damage Profile in the Ancient Human Mitochondrial DNA.

Mitochondrial DNA (mtDNA) analysis is crucial for understanding human population structure and genetic diversity. However, post-mortem DNA damage poses challenges, that make analysis difficult. DNA preservation is affected by environmental conditions which, among other factors, complicates the differentiation of endogenous variants from artefacts in ancient mtDNA mix profiles. This study aims to develop a molecular damage profile for ancient mtDNA that can become a useful tool in analysing mtDNA from ancient remains. A dataset of 427 whole genomes or capture of mtDNA sequences from individuals representing different historical periods and climatic regions was compiled from the ENA database. Present-day and UDG-treated ancient samples were also included and used to establish levels of damaged reads. Results indicated that samples from cold regions exhibited the lowest percentage of damaged reads, followed by arid, cold, tropical and temperate regions, with significant differences observed between cold and temperate regions. A global damage profile was generated, identifying 2933 positions (25% of the positions considered) with damage in more than 23.8% of the samples analysed, deemed as damage hotspots. Notably, 2856 of these hotspots had never been reported as damage or mutational hotspots, or heteroplasmic positions. Damage hotspot frequency by position was slightly higher in the non-coding region compared with the coding region. In conclusion, this study provides a molecular damage profile for ancient mtDNA analysis that is expected to be a valuable tool in the interpretation of mtDNA variation in ancient samples.

Electrophysiological findings of brainstem auditory evoked potentials in infants with down syndrome: A systematic review and meta-analysis.

to summarize the evidence on the electrophysiological findings in the auditory brainstem response (ABR) in infants with DS. This is a systematic review study, whose protocol was registered in the International Prospective Register of Systematic Reviews (PROSPERO CRD42023424139) and conducted in accordance with the recommendations of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Observational studies were included: cross-sectional and cohort studies that performed ABR evaluation in infants with DS up to two years of age, which had their results compared with normal infants, of the same age group. The search for studies was performed in the following databases: PubMed, LILACS, Scopus, CINHAL, Web of Science, Scielo, Embase and LIVIVO, and in the gray literature: Google Scholar and Proquest. There were no restrictions on language or publication date. The methodological quality of the included studies was assessed using the JBI (Joanna Briggs Institute) checklist. Phases 1 (reading of titles and abstracts) and 2 (reading in full), data extraction, assessment of methodological quality and certainty of evidence were performed independently by the reviewers. Existing disagreements were resolved in a consensus meeting. A total of 494 articles were obtained, which after removal of duplicates and independent analysis by the reviewers, ten studies were selected for qualitative synthesis and four studies were selected for meta-analysis. There was heterogeneity between the effects observed in the ABR parameters (I2=78%) with an overall pooled effect size of -0.05 (95% confidence interval of -0.13-0.03; p=0.22) indicating no significant difference in ABR responses between groups. The certainty of the evidence assessed by GRADE was considered very low due to inconsistency and imprecision. The results of the meta-analysis indicate that there are no significant diferences in ABR parameters, including waves I, III, and V and the I - V interpeak interval, between infants with and without DS up to two years of age. However, the limitations found, such as methodological heterogeneity, small sample sizes and variability in the age range of the participants, generated uncertainty in the results. Therefore, the certainty of the evidence was classified as very low, according to the GRADE methodology.

A Streamlined High-Throughput LC-MS Assay for Quantifying Peptide Degradation in Cell Culture.

Peptides are widely used in biomaterials due to their ease of synthesis, ability to signal cells, and modify the properties of biomaterials. A key benefit of using peptides is that they are natural substrates for cell-secreted enzymes, which creates the possibility of utilizing cell-secreted enzymes for tuning cell-material interactions. However, these enzymes can also induce unwanted degradation of bioactive peptides in biomaterials, or in peptide therapies. Liquid chromatography-mass spectrometry (LC-MS) is a widely used, powerful methodology that can separate complex mixtures of molecules and quantify numerous analytes within a single run. There are several challenges in using LC-MS for the multiplexed quantification of cell-induced peptide degradation, including the need for nondegradable internal standards and the identification of optimal sample storage conditions. Another problem is that cell culture media and biological samples typically contain both proteins and lipids that can accumulate on chromatography columns and degrade their performance. Removing these constituents can be expensive, time-consuming, and increases sample variability. However, loading unpurified samples onto the column without removing lipids and proteins will foul the column. Here, we show that directly injecting complex, unpurified samples onto the LC-MS without any purification enables rapid and accurate quantification of peptide concentration and that hundreds of LC-MS runs can be done on a single column without significantly diminishing the ability to quantify the degradation of peptide libraries. To understand how repeated injections degrade column performance, a model library was injected into the LC-MS hundreds of times. It was then determined that column failure is evident when hydrophilic peptides are no longer retained on the column and that failure can be easily identified by using standard peptide mixtures for column benchmarking. In total, this work introduces a simple and effective method for simultaneously quantifying the degradation of dozens of peptides in cell culture. By providing a streamlined and cost-effective method for the direct quantification of peptide degradation in complex biological samples, this work enables more efficient assessment of peptide stability and functionality, facilitating the development of advanced biomaterials and peptide-based therapies.

Feasibility of Using Pulsed Electromagnetic Field Therapy to Improve the Dynamic Postural Balance of Children with Cerebral Palsy: A Randomized, Sham-Controlled Pilot Study

Cerebral palsy (CP) manifests with abnormal posture and impaired selective motor control, notably affecting trunk control and dynamic balance coordination, leading to inadequate postural control. Previous research has indicated the benefits of pulsed electromagnetic field (PEMF) therapy for various musculoskeletal and neurological conditions. Therefore, we conducted a randomized pilot study to assess the feasibility of our preliminary research design and examine the effect of the PEMF treatment among children with CP. Methods: Twelve children with spastic CP participated, with the study group undergoing PEMF treatment three times a week for four weeks. The treatment involved sine signal form, 20/200 Hz frequencies at an amplitude of 150 μT, initially administered for 8, 12, and 16 min per session. The control group received a sham treatment. Dynamic postural balance was evaluated using a force platform at baseline and post-intervention, and the data were analyzed. Data were processed using IBM SPSS 27 by repeated factorial analysis of variance. The significance level was α = 0.05. Results: No side effects of PEMF therapy were detected; this is important, because this intervention has not yet been applied among CP patients. The treatment group demonstrated a positive trend in fine balance coordination tests (p = 0.049); however, the small sample size and variability in control group performance suggest caution in interpreting these findings. Other test domains did not show significant differences. Conclusion: Our pilot study reveals the safety, feasibility, and potential efficacy of pulsed electromagnetic field (PEMF) therapy for children with cerebral palsy. With no observed side effects, the significant improvement in fine balance coordination suggests a promising avenue.

Non-Invasive Biomarkers and Breath Tests for Diagnosis and Monitoring of Chronic Liver Diseases.

Background: Chronic liver disease (CLD) presents a significant global health burden, demanding effective tools for diagnosis and monitoring. Traditionally, liver biopsy has been the gold standard for evaluating liver fibrosis and other chronic liver conditions. However, biopsy's invasiveness, associated risks, and sampling variability indicate the need for reliable, noninvasive alternatives. This review examines the utility of noninvasive tests (NITs) in assessing liver disease severity, progression, and therapeutic response in patients with CLD. Result: Key modalities discussed include serum biomarker panels (e.g., FIB-4, APRI, ELF), imaging techniques like transient elastography, and magnetic resonance elastography, each offering unique benefits in fibrosis staging. Emerging biomarkers such as extracellular vesicles and circulating microRNAs show promise in early detection and personalized monitoring. Comparative studies indicate that while no single NIT matches biopsy precision, combinations of these modalities improve diagnostic accuracy and patient outcomes by reducing unnecessary biopsies. Moreover, NITs are instrumental in monitoring dynamic changes in liver health, allowing for more responsive and patient-centered care. Conclusions: Challenges remain, including standardization across tests, cost considerations, and the need for larger, diverse population studies to validate findings. Despite these limitations, NITs are increasingly integrated into clinical practice, fostering a paradigm shift toward noninvasive, accessible liver disease management. Continued advancements in NITs are essential for improved patient outcomes and will likely shape the future standard of care for CLD.

Immunohistochemistry-Free Enhanced Histopathology of the Rat Spleen Using Deep Learning.

Enhanced histopathology of the immune system uses a precise, compartment-specific, and semi-quantitative evaluation of lymphoid organs in toxicology studies. The assessment of lymphocyte populations in tissues is subject to sampling variability and limited distinctive cytologic features of lymphocyte subpopulations as seen with hematoxylin and eosin (H&E) staining. Although immunohistochemistry is necessary for definitive characterization of T- and B-cell compartments, routine toxicologic assessments are based solely on H&E slides. Here, a deep learning (DL) model was developed using normal rats to quantify relevant compartments of the spleen, including periarteriolar lymphoid sheaths, follicles, germinal centers, and marginal zones from H&E slides. Slides were scanned, destained, dual labeled with CD3 and CD79a chromogenic immunohistochemistry, and rescanned to generate exact co-registered images that served as the ground truth for training and validation. The DL model identified individual splenic compartments with high accuracy (97.8% Dice similarity coefficient) directly from H&E-stained tissue. The DL model was utilized to study the normal range of lymphoid compartment area and cellularity. Future implementation of our DL model and expanding this approach to other lymphoid tissues have the potential to improve accuracy and precision in enhanced histopathology evaluation of the immune system with concurrent gains in time efficiency for the pathologist.

Fault detection for industrial processes based on time-serial maximal deviation analysis

Abstract The application of feature extraction algorithms in data-driven fault detection has been widely investigated for industrial processes, with a wide range of multivariate analytical methods available in the literature for this purpose. However, mainstream methods all focus on exploiting the systematic variation in historical normal samples and a fault is usually isolated as a deviation from the characterized normal signatures. For the sole purpose of fault detection in dynamic processes, it may be more effective to timely and adaptively uncover deviations inherent in time-serial variations for online sequential samples of interest, rather than focusing solely on extracting latent representations from normal samples. Therefore, a novel feature extraction algorithm called time-serial maximal deviation analysis (TSMDA) is proposed. TSMDA is designed to derive a projecting scheme with dynamically determined projecting vectors in a timely manner, so that possible time-serial deviations in newly measured sequential samples can be adaptively uncovered to the greatest extent possible. Most importantly, TSMDA cannot be executed without the involvement of online monitored samples and it is expected to guarantee consistently enhanced fault detectability. The effectiveness of the proposed fault detection method is validated through practical experiments on two industrial processes and the influence of key model parameters on fault detectability is also evaluated.