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IntroductionWith the continuous development of exploration and development, tight sandstone reservoirs have become an essential field of oil and gas exploration. The tight sandstone reservoirs are characterized by complex lithology, poor pore structure, and strong heterogeneity, which bring great difficulties to formation evaluation by well logs. Especially, the accuracy of the Archie formula for calculating the water saturation of tight sandstone reservoirs containing fractures is not high, saturation evaluation faces greater challenges. Therefore, how to calculate the saturation of tight sandstone reservoirs more accurately is an urgent problem to be solved.MethodsIn this paper, the tight sandstone reservoirs of the Jurassic Ahe Formation in the Tarim Basin were taken as the research area. A saturation model that combines the effects of shale and fractures was proposed. Specifically, if the shale content is more than 20%, the Indonesian formula is used to calculate the saturation. If the shale content is less than 20%, the dual porosity model is adopted. Based on the rock resistivity and nuclear magnetic resonance (NMR) experiment results, the porosity and T2 logarithmic mean values are selected as influencing factors to calculate the key parameters of the dual porosity model.Results and DiscussionThe saturation of tight sandstone reservoirs in the Jurassic Ahe Formation of the Tarim Basin is calculated through the method proposed in this paper. The case study shows that the accuracy of the proposed method is higher than that of the Archie model. The method proposed in this paper demonstrates excellent adaptability in the quantitative evaluation of saturation for tight sandstone reservoirs.

Abstract Auricularia heimuer, the third most frequently cultivated edible mushroom species worldwide, has high medicinal value. However, a shortage of molecular marker hinders the efficiency and accuracy of genetic breeding efforts for A. heimuer. High-throughput transcriptome sequencing data are essential for gene discovery and molecular markers development. This study aimed to clarify the distribution of SSR loci across the A. heimuer transcriptome and to develop highly informative EST-SSR markers. These tools can be used for phylogenetic analysis, functional gene mining, and molecular marker-assisted breeding of A. heimuer. This study used Illumina high-throughput sequencing technology to obtain A. heimuer transcriptome data. The results revealed 37,538 unigenes in the A. heimuer transcriptome. Of these unigenes, 24,777 (66.01%) were annotated via comparison with the COG, Pfam, and NR databases. Overall, 2510 SSRs were identified from the unigenes, including 6 types of SSRs. The most abundant type of repeats were trinucleotides (1425, 56.77%), followed by mononucleotides (391, 15.58%) and dinucleotides (456, 18.17%). Primer pairs for 102 SSR loci were randomly designed for validity confirmation and polymorphism identification; this process yielded 53 polymorphic EST-SSR markers. Finally, 13 pairs of highly polymorphic EST-SSR primers were used to analyze the genetic diversity and population structure of 52 wild A. heimuer germplasms, revealing that the 52 germplasms could be divided into three categories. These results indicated that SSR loci were abundant in types, numbers, and frequencies, providing a potential basis for germplasm resource identification, genetic diversity analysis, and molecular marker-assisted breeding of A. heimuer.

Fish stock assessment is crucial for sustainable marine fisheries management in rangeland ecosystems. To address the challenges posed by the overfishing of offshore fish species and facilitate comprehensive deep-sea resource evaluation, this paper introduces an improved fish sonar image detection algorithm based on the you only look once algorithm, version 5 (YOLOv5). Sonar image noise often results in blurred targets and indistinct features, thereby reducing the precision of object detection. Thus, a C3N module is incorporated into the neck component, where depth-separable convolution and an inverse bottleneck layer structure are integrated to lessen feature information loss during downsampling and forward propagation. Furthermore, lowercase shallow feature layer is introduced in the network prediction layer to enhance feature extraction for pixels larger than $ 4 \times 4 $. Additionally, normalized weighted distance based on a Gaussian distribution is combined with Intersection over Union (IoU) during gradient descent to improve small target detection and mitigate the IoU's scale sensitivity. Finally, traditional non-maximum suppression (NMS) is replaced with soft-NMS, reducing missed detections due to occlusion and overlapping fish targets that are common in sonar datasets. Experiments show that the improved model surpasses the original model and YOLOv3 with gains in precision, recall and mean average precision of 2.3%, 4.7% and 2.7%, respectively, and 2.5%, 6.3% and 6.7%, respectively. These findings confirm the method's effectiveness in raising sonar image detection accuracy, which is consistent with model comparisons. Given Unmanned Underwater Vehicle advancements, this method holds the potential to support fish culture decision-making and facilitate fish stock resource assessment.

With the increasing adoption of composite materials in aircraft construction, traditional anti-icing technologies face significant challenges due to the low thermal conductivity and heat resistance of composite resins. These limitations have spurred the development of lightweight, efficient, durable, and cost-effective integrated anti-icing technologies as a critical area of research. This paper begins with an overview of advancements in electrothermal anti-icing and de-icing technologies for aircraft. It then explores the configurations and applications of functional-structural integration technology for anti-icing and de-icing, emphasizing pivotal technologies and current challenges in this field. Finally, the study forecasts the development trends in the multifunctional integration of thermal conductivity/insulation, anti-icing, and electromagnetic wave transparency/wave-absorbing properties. These advancements are driven by the evolution of composite materialization in aircraft and the progression of multi-electrical/all-electrical technologies. The objective is to provide a comprehensive guide for technological development in anti-icing, aiding researchers and relevant departments to further enhance the application of anti-icing technology in composite material aircraft.

Fish object detection and counting in pelagic fisheries face many challenges in complex environments. Sonar imaging technology offers a solution because it generates high-resolution images underwater. In this paper, we propose a sonar-based fish object detection and counting method using an improved YOLOv8 combined with BoT-SORT to address issues such as missed detection, false detection, and low accuracy caused by complex factors such as equipment motion, light changes, and background noise in pelagic environments. The algorithm utilizes the techniques of lightweight upsampling operator CARAFE, generalized feature pyramid network GFPN, and partial convolution. It integrates with the BoT-SORT tracking algorithm to propose a new region detection method that detects and tracks the schools of fish, providing stable real-time fish counts in the designated area. The experimental results indicate that while focusing on maintaining a lightweight design, the improved algorithm achieved a 3.8% increase in recall and a 2.4% increase in mAP0.5 compared to the original algorithm. This significantly impacts scientific and rational fishery planning, marine resource protection, and improved productivity. At the same time, it provides important data support for marine ecological monitoring, environmental protection, and fishery management, contributing to sustainable fishery development and marine ecology preservation.

Introduction: Permeability is one of the most important parameters for reservoir evaluation. It is commonly measured in laboratories using underground core samples. However, it cannot describe the entire reservoir because of the limited number of cores. Therefore, petrophysicists use well logs to establish empirical equations to estimate permeability. This method has been widely used in conventional sandstone reservoirs, but it is not applicable to tight sandstone reservoirs with low porosity, extremely low permeability, and complex pore structures.Methods: Machine learning models can identify potential relationships between input features and sample labels, making them a good choice for establishing permeability prediction models. A stacking model is an ensemble learning method that aims to train a meta-learner to learn an optimal combination of expert models. However, the meta-learner does not evaluate or control the experts, making it difficult to interpret the contribution of each model. In this study, we design a gate network stacking (GNS) model, which is an algorithm that combines data and model-driven methods. First, an input log combination is selected for each expert model to ensure the best performance of the expert model and selfoptimization of the hyperparameters. Petrophysical constraints are then added to the inputs of the expert model and meta-learner, and weights are dynamically assigned to the output of the expert model. Finally, the overall performance of the model is evaluated iteratively to enhance its interpretability and robustness.Results and discussion: The GNS model is then used to predict the permeability of a tight sandstone reservoir in the Jurassic Ahe Formation in the Tarim Basin. The case study shows that the permeability predicted by the GNS model is more accurate than that of other ensemble models. This study provides a new approach for predicting the parameters of tight sandstone reservoirs.

Simple Summary: Lynch syndrome (LS) is an autosomal dominant disorder linked to increased risks of colorectal and endometrial cancers, caused by pathogenic variants in MMR genes (MLH1, MSH2, MSH6). In a cohort of 42,828 participants from the Taiwan Precision Medicine Initiative (TPMI), 89 individuals carried MMR gene variants: 25% MLH1, 53% MSH2, and 22% MSH6, with a prevalence of 1 in 481. Cancer incidence rates were 40.9% for MLH1, 29.8% for MSH2, and 40% for MSH6 carriers. Colonoscopy screening revealed no significant differences in polyp prevalence compared to controls. The study underscores the need for improved LS diagnosis and surveillance in the Taiwanese population. Lynch syndrome (LS) is an autosomal dominant disorder characterized by increased risks of colorectal and endometrial cancers. LS is defined by pathogenic variants in mismatch repair (MMR) genes, including MLH1, MSH2, and MSH6. Data on the prevalence and associated cancer risks of LS in the Han Chinese population remain limited. In this study, using a broad biobank approach through the Taiwan Precision Medicine Initiative (TPMI), we identified LS-associated MMR gene variants within a cohort of 42,828 participants from a Taiwanese medical center. A total of 89 individuals were found to carry pathogenic MMR variants: MLH1 (n = 22, 25%), MSH2 (n = 47, 53%), and MSH6 (n = 20, 22%). The overall prevalence of MMR variants was calculated, and cancer incidence rates among carriers were determined. The prevalence of MMR variants in the study population was 1 in 481. The distribution of MLH1, MSH2, and MSH6 variants were 24.7%, 52.8%, and 22.5%, respectively. Cumulative cancer incidence rates of carriers were 40.9% for MLH1 carriers, 29.8% for MSH2, and 40% for MSH6. Among the 19 individuals who underwent colonoscopy screening, the prevalence of polyps was similar to that of the control group (adenoma detection rate: 32% vs 26%, p = 0.585). A meticulous analysis of the detected polyps in seven participants, considering factors such as location, size, morphology, and pathological features, showed no significant differences from controls. A significant cancer risk is associated with LS-related MMR variants in the Taiwanese population. The apparent under diagnosis of LS highlights the urgent need for enhanced surveillance and genetic counseling in this demographic. Our findings suggest that adjustments in the current screening protocols may be warranted to better identify and manage at-risk individuals. [ABSTRACT FROM AUTHOR]

Demand is strong for sensitive, reliable, and cost-effective diagnostic tools for cancer detection. Accordingly, bead-based biosensors have emerged in recent years as promising diagnostic platforms based on wide-ranging cancer biomarkers owing to the versatility, high sensitivity, and flexibility to perform the multiplexing of beads. This comprehensive review highlights recent trends and innovations in the development of bead-based biosensors for cancer-biomarker detection. We introduce various types of bead-based biosensors such as optical, electrochemical, and magnetic biosensors, along with their respective advantages and limitations. Moreover, the review summarizes the latest advancements, including fabrication techniques, signal-amplification strategies, and integration with microfluidics and nanotechnology. Additionally, the challenges and future perspectives in the field of bead-based biosensors for cancer-biomarker detection are discussed. Understanding these innovations in bead-based biosensors can greatly contribute to improvements in cancer diagnostics, thereby facilitating early detection and personalized treatments. [ABSTRACT FROM AUTHOR]

Abstract The nonlinear Hall effect (NLHE) is a phenomenon which could produce a transverse Hall voltage in a time-reversal-invariant material. Here, we report the real space characterizations of NLHE evaluated through quantum transport in TaIrTe4 nanoribbon without the explicit Berry curvature dipole (BCD) information. We first characterize the NLHE in both transverse confined directions in global-level measurement. The impact of quantum confinement in NLHE is evaluated by adjusting the width of nanoribbons. Then, the probing area is trimmed to the atomic scale to evaluate the local texture, where we discover its patterns differ among the probed neighboring atomic groups. The analysis of charge distribution reveals the connections between NLHE’s local patterns and its non-centrosymmetric nature, rendering nearly an order of Hall voltage enhancement through probe positioning. Our work paves the way to expand the range of NLHE study and unveil its physics in more versatile material systems.

Small extracellular vesicles (sEVs) are vital for cellular communication and serve as critical biomarker carriers for diseases such as cancer. However, quantifying and profiling sEV surface markers presents significant challenges due to the low concentration of specific sEV-bound proteins and interference by more abundant dispersed proteins. This paper presents Immunojanus Particles (IJPs), a new method that enables the direct detection of sEVs in less than an hour without isolation. The design of IJPs incorporates fluorescent and non-fluorescent halves, utilizing rotational Brownian motion to detect captured sEVs through the change in the blinking rate, without interference from the smaller dispersed proteins. We demonstrate a detection limit of 2E5 sEVs/mL with low sample volumes and the capability to characterize sEVs directly from plasma, serum, cell culture media, and urine. In a small pilot study involving 87 subjects, including individuals with colorectal cancer, pancreatic ductal adenocarcinoma, glioblastoma, Alzheimer's disease, and healthy controls, our method accurately identified the type of disease with high 0.90-0.99 AUC in a blind setting. Compared with an orthogonal ultracentrifugation plus surface plasmon resonance (UC+SPR) method that requires about 24 hours, the sensitivity and dynamic range of IJP are better by 2 logs.

A genomic understanding of the oncogenic processes and individual variability of human cancer has steadily fueled improvement in patient outcomes over the past 20 years. Mutations within tumour tissues are routinely assessed through clinical genomic diagnostic assays by academic and commercial laboratories to facilitate diagnosis, prognosis and effective treatment stratification. The application of genomics has unveiled a wealth of mutation-based biomarkers in canine cancers, suggesting that the transformative principles that have revolutionized human cancer medicine can be brought to bear in veterinary oncology. To advance clinical genomics and genomics-guided medicine in canine oncology, we have developed and validated a canine cancer next-generation sequencing gene panel for the identification of multiple mutation types in clinical specimens. With this panel, we examined the genomic landscapes of 828 tumours from 813 dogs, spanning 53 cancer types. We identified 7856 alterations, encompassing copy number variants, single nucleotide variants, indels and internal tandem duplications. Additionally, we evaluated the clinical utility of these alterations by incorporating a biomarker framework from comprehensive curation of primary canine literature and inferences from human cancer genomic biomarker literature and clinical diagnostics. Remarkably, nearly 90% of the cases exhibited mutations with diagnostic, prognostic or therapeutic implications. Our work represents a thorough assessment of genomic landscapes in a large cohort of canine cancers, the first of its kind for its comprehensive inclusion of multiple mutation types and structured annotation of biomarkers, demonstrating the clinical potential of leveraging mutation-based biomarkers in veterinary oncology., (© 2023 Vidium Animal Health and The Authors. Veterinary and Comparative Oncology published by John Wiley & Sons Ltd.)