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Liver metastasis (LIM) is an important factor in the diagnosis, treatment, follow-up, and prognosis of patients with gastric gastrointestinal stromal tumor (GIST). There is no simple tool to assess the risk of LIM in patients with gastric GIST. Our aim was to develop and validate a nomogram to identify patients with gastric GIST at high risk of LIM.

Grasping and twisting motions are vital when manipulating objects due to their fundamental role in enabling precision, adaptability, and effective interaction. However, few studies in soft robotics exploiting artificial muscles have achieved object manipulation in situ through the coordination of twisting and grasping motions akin to our forearm and hand’s capabilities. Especially, when using the same artificial muscle module to achieve these two motions will greatly simplify the manufacturing and control complexity. Here, we introduce identical origami artificial muscle modules (OAMMs) subjected to distinct end constraints into the design of the robotic manipulator, allowing it to achieve independent grasping and twisting motions to achieve effective, precise object manipulation. Applying different end constraints to the identical OAMMs yields distinct motions at their ends, where utilizing a fixed end and a sliding end realizes pure translation, while opting for a fixed end and a rotating end enables pure rotation. The differentially constrained OAMMs then serve as soft actuators for the manipulator’s torsional mechanism and grasping mechanism to accomplish independent, controllable twisting and grasping motions. The coordination of twisting and grasping motions finally enables the manipulator to complete various tasks, including installing a light bubble, pouring the water from a lidded bottle into a cup, and sorting and stacking puzzle blocks. Our study pioneers the utilization of OAMMs for precise and versatile object manipulation through the coordination of independent twisting and grasping motions.

Breast cancer is becoming a significant global health challenge, with millions of fatalities annually. Magnetic Resonance Imaging (MRI) can provide various sequences for characterizing tumor morphology and internal patterns, and becomes an effective tool for detection and diagnosis of breast tumors. However, previous deep-learning based tumor segmentation methods from multi-parametric MRI still have limitations in exploring inter-modality information and focusing task-informative modality/modalities. To address these shortcomings, we propose a Modality-Specific Information Disentanglement (MoSID) framework to extract both inter- and intra-modality attention maps as prior knowledge for guiding tumor segmentation. Specifically, by disentangling modality-specific information, the MoSID framework provides complementary clues for the segmentation task, by generating modality-specific attention maps to guide modality selection and inter-modality evaluation. Our experiments on two 3D breast datasets and one 2D prostate dataset demonstrate that the MoSID framework outperforms other state-of-the-art multi-modality segmentation methods, even in the cases of missing modalities. Based on the segmented lesions, we further train a classifier to predict the patients’ response to radiotherapy. The prediction accuracy is comparable to the case of using manually-segmented tumors for treatment outcome prediction, indicating the robustness and effectiveness of the proposed segmentation method. The code is available at https://github.com/Qianqian-Chen/MoSID.

Background Parenchymal Enhancement (BPE) quantification in Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) plays a pivotal role in clinical breast cancer diagnosis and prognosis. However, the emerging deep learning-based breast fibroglandular tissue segmentation, a crucial step in automated BPE quantification, often suffers from limited training samples with accurate annotations. To address this challenge, we propose a novel iterative cycle-consistent semi-supervised framework to leverage segmentation performance by using a large amount of paired pre-/post-contrast images without annotations. Specifically, we design the reconstruction network, cascaded with the segmentation network, to learn a mapping from the pre-contrast images and segmentation predictions to the post-contrast images. Thus, we can implicitly use the reconstruction task to explore the inter-relationship between these two-phase images, which in return guides the segmentation task. Moreover, the reconstructed post-contrast images across multiple auto-context modeling-based iterations can be viewed as new augmentations, facilitating cycle-consistent constraints across each segmentation output. Extensive experiments on two datasets with various data distributions show great segmentation and BPE quantification accuracy compared with other state-of-the-art semi-supervised methods. Importantly, our method achieves 11.80 times of quantification accuracy improvement along with 10 times faster, compared with clinical physicians, demonstrating its potential for automated BPE quantification. The code is available at https://github.com/ZhangJD-ong/Iterative-Cycle-consistent-Semi-supervised-Learning-for-fibroglandular-tissue-segmentation.

Numerous patch-based methods have recently been proposed for histological image based breast cancer classification. However, their performance could be highly affected by ignoring spatial contextual information in the whole slide image (WSI). To address this issue, we propose a novel hierarchical Graph V-Net by integrating 1) patch-level pre-training and 2) context-based fine-tuning, with a hierarchical graph network. Specifically, a semi-supervised framework based on knowledge distillation is first developed to pre-train a patch encoder for extracting disease-relevant features. Then, a hierarchical Graph V-Net is designed to construct a hierarchical graph representation from neighboring/similar individual patches for coarse-to-fine classification, where each graph node (corresponding to one patch) is attached with extracted disease-relevant features and its target label during training is the average label of all pixels in the corresponding patch. To evaluate the performance of our proposed hierarchical Graph V-Net, we collect a large WSI dataset of 560 WSIs, with 30 labeled WSIs from the BACH dataset (through our further refinement), 30 labeled WSIs and 500 unlabeled WSIs from Yunnan Cancer Hospital. Those 500 unlabeled WSIs are employed for patch-level pre-training to improve feature representation, while 60 labeled WSIs are used to train and test our proposed hierarchical Graph V-Net. Both comparative assessment and ablation studies demonstrate the superiority of our proposed hierarchical Graph V-Net over state-of-the-art methods in classifying breast cancer from WSIs. The source code and our annotations for the BACH dataset have been released at https://github.com/lyhkevin/Graph-V-Net.

Purpose: This paper aims to explore the impact of digital inclusive finance (DIF) on regional economic growth and innovation-driven development. Design/methodology/approach: Based on the panel data of 31 provinces (autonomous regions and municipalities directly under the central government) in China from 2011 to 2018, this paper explores the impact of DIF on economic growth and innovative development. Findings: (1) DIF has a direct positive effect on economic growth and innovative development; (2) there is significant regional heterogeneity in the impact of DIF on economic growth and innovative development. (3) DIF can indirectly affect economic growth and innovative development by increasing residents' personal disposable income, increasing fiscal expenditure and improving educational level. Social implications: Exploring the relationship between them and digital inclusive financial development can provide a reference for national productivity construction and development. Originality/value: Economic growth and innovation-driven development have been one of the main concerns of China's policymakers. Exploring the relationship between them, digital inclusive financial development can provide a reference for national productivity construction and development. [ABSTRACT FROM AUTHOR]

Multiphoton microscopy (MPM) is an enabling technology for visualizing deep-brain structures at high spatial resolution in vivo. Within the low tissue absorption window, shifting to longer excitation wavelengths reduces tissue scattering and boosts penetration depth. Recently, the 2200 nm excitation window has emerged as the last and longest window suitable for deep-brain MPM. However, multiphoton fluorescence imaging at this window has not been demonstrated, due to the lack of characterization of multiphoton properties of fluorescent labels. Here we demonstrate technologies for measuring both the multiphoton excitation and emission properties of fluorescent labels at the 2200 nm window, using (1) 3-photon (ησ3) and 4-photon action cross sections (ησ4) and (2) 3-photon and 4-photon emission spectra both ex vivoand in vivoof quantum dots. Our results show that quantum dots have exceptionally large ησ3and ησ4for efficient generation of multiphoton fluorescence. Besides, the 3-photon and 4-photon emission spectra of quantum dots are essentially identical to those of one-photon emission, which change negligibly subject to the local environment of circulating blood. Based on these characterization results, we further demonstrate deep-brain vasculature imaging in vivo. Due to the superb multiphoton properties of quantum dots, 3-photon and 4-photon fluorescence imaging reaches a maximum brain imaging depth of 1060 and 940 μm below the surface of a mouse brain, respectively, which enables the imaging of subcortical structures. We thus fill the last gap in multiphoton fluorescence imaging in terms of wavelength selection.

Ultrasonography is an important routine examination for breast cancer diagnosis, due to its non-invasive, radiation-free and low-cost properties. However, the diagnostic accuracy of breast cancer is still limited due to its inherent limitations. Then, a precise diagnose using breast ultrasound (BUS) image would be significant useful. Many learning-based computer-aided diagnostic methods have been proposed to achieve breast cancer diagnosis/lesion classification. However, most of them require a pre-define region of interest (ROI) and then classify the lesion inside the ROI. Conventional classification backbones, such as VGG16 and ResNet50, can achieve promising classification results with no ROI requirement. But these models lack interpretability, thus restricting their use in clinical practice. In this study, we propose a novel ROI-free model for breast cancer diagnosis in ultrasound images with interpretable feature representations. We leverage the anatomical prior knowledge that malignant and benign tumors have different spatial relationships between different tissue layers, and propose a HoVer-Transformer to formulate this prior knowledge. The proposed HoVer-Trans block extracts the inter- and intra-layer spatial information horizontally and vertically. We conduct and release an open dataset GDPH&SYSUCC for breast cancer diagnosis in BUS. The proposed model is evaluated in three datasets by comparing with four CNN-based models and three vision transformer models via five-fold cross validation. It achieves state-of-the-art classification performance (GDPH&SYSUCC AUC: 0.924, ACC: 0.893, Spec: 0.836, Sens: 0.926) with the best model interpretability. In the meanwhile, our proposed model outperforms two senior sonographers on the breast cancer diagnosis when only one BUS image is given (GDPH&SYSUCC-AUC ours: 0.924 vs. reader1: 0.825 vs. reader2: 0.820).

Epidermal growth factor receptor (EGFR) genotype is crucial for treatment decision making in lung cancer, but it can be affected by tumour heterogeneity and invasive biopsy during gene sequencing. Importantly, not all patients with an EGFRmutation have good prognosis with EGFR-tyrosine kinase inhibitors (TKIs), indicating the necessity of stratifying for EGFR-mutant genotype. In this study, we proposed a fully automated artificial intelligence system (FAIS) that mines whole-lung information from CT images to predict EGFRgenotype and prognosis with EGFR-TKI treatment.

In this study, we show a way to improve the antibacterial activity of lysozyme by incorporating guanidino functional groups onto its surface (Lyz-Gua), which could treat pathogenic bacteria without accumulated resistance and shows advantages over commercial antibiotics.

Accurate prediction of tumour response to neoadjuvant chemoradiotherapy enables personalised perioperative therapy for locally advanced rectal cancer. We aimed to develop and validate an artificial intelligence radiopathomics integrated model to predict pathological complete response in patients with locally advanced rectal cancer using pretreatment MRI and haematoxylin and eosin (H&E)-stained biopsy slides.

The wind power forecasting (WPF) technology can reduce the adverse impact of wind power grid connection. Based on the characteristics of wind power data, an algorithm based on improved variational mode decomposition (IVMD) and long short-term memory (LSTM) Network is proposed to predict the wind power, and hyper parameter optimization search of LSTM using Whale Swarm Algorithm with Iterative Counter (WSA-IC). Firstly, through correlation analysis, the characteristics of 10 different wind power data are screened, and two kinds of data with large correlation with wind power are determined as input of the mode. Secondly, IVMD is used to calculate the maximum envelope kurtosis, determine the best decomposition parameters of the variational mode decomposition (VMD), and the original wind power and wind speed sequences are decomposed to obtain the IMF with different time scales. Finally, to address the problems of difficult optimization of hyper parameter and difficulty in obtaining optimal solutions for LSTM neural network modes, the WSA-IC algorithm is proposed to optimize its key hyper parameter, and the IVMD-WSA-IC-LSTM forecasting mode is established to obtain the short-term forecasting results of wind power. The algorithm is tested with the data of China Longyuan Power Group Corporation Limited. Compared with other common forecasting approaches using same data, the mean absolute error (MAE) of the forecasting approach is reduced to 0.007859, the mean square error (MSE) is reduced to 0.00011, and the determination coefficient is improved to 0.998828, which has higher forecasting accuracy.

Background: The correlation between the preoperative splenic area measured on CT scans and the overall survival (OS) of early-stage non-small cell lung cancer (NSCLC) patients remains unclear. Methods: A retrospective discovery cohort and validation cohort consisting of consecutive NSCLC patients who underwent resection and preoperative CT scans were created. The patients were divided into two groups based on the measurement of their preoperative splenic area: normal and abnormal. The Cox proportional hazard model was used to analyse the correlation between splenic area and OS. Results: The discovery and validation cohorts included 2532 patients (1374 (54.27%) males; median (IQR) age 59 (52–66) years) and 608 patients (403 (66.28%) males; age 69 (62–76) years), respectively. Patients with a normal splenic area had a 6% higher 5-year OS (n = 727 (80%)) than patients with an abnormal splenic area (n = 1805 (74%)) (p= 0.007) in the discovery cohort. A similar result was obtained in the validation cohort. In the univariable analysis, the OS hazard ratios (HRs) for the patients with abnormal splenic areas were 1.32 (95% confidence interval (CI): 1.08, 1.61) in the discovery cohort and 1.59 (95% CI: 1.01, 2.50) in the validation cohort. Multivariable analysis demonstrated that abnormal splenic area was independent of shorter OS in the discovery (HR: 1.32, 95% CI: 1.08, 1.63) and validation cohorts (HR: 1.84, 95% CI: 1.12, 3.02). Conclusion: Preoperative CT measurements of the splenic area serve as a prognostic indicator for early-stage NSCLC patients, offering a novel metric with potential implications for personalized therapeutic strategies in top-tier oncology research.

Soil heavy metal remediation has always been the focus of research. This work synthesized a recoverable, reusable, and efficient material for Cr(VI)-contaminated soil through a new strategy. The main structure is a bead that encapsulates the layered double hydroxides(LDHs) and magnetic particles into an alginate bead (LDHs-M). Varying applications in soil suggested LDHs-M showed high immobilization performance. Including LDHs interlayer ions, proportion of components, pH, dosage, and initial Cr(VI) concentration, many influence factors were systemically explored, suggesting that LDHs contained in the bead played a prominent role during the immobilization and LDHs-M with the interlayer anions of NO3-showed excellent immobilization efficiency. Different characterizations were used to investigate the immobilization mechanism, indicating that interlayer anion exchange and electrostatic adsorption were the main ways for soil Cr(VI) immobilization. The toxicity also decreased with the reduction of Cr(VI). Meanwhile, the simulation of natural aging processes, plant growth, and changes in the microbial community in the soil after remediation were also investigated, which verified the immobilizing stability and bioavailability of LDHs-M. Furthermore, the magnetic belt recovery system showed superior recovery performance in solid phase separation. Then, the separated beads were regenerated in an electrolyte solution and reused in soil remediation. The perfect immobility, instantaneous separation, and regenerability made it a promising material in heavy metal contaminated soil.

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) allows screening, follow up, and diagnosis for breast tumor with high sensitivity. Accurate tumor segmentation from DCE-MRI can provide crucial information of tumor location and shape, which significantly influences the downstream clinical decisions. In this paper, we aim to develop an artificial intelligence (AI) assistant to automatically segment breast tumors by capturing dynamic changes in multi-phase DCE-MRI with a spatial-temporal framework. The main advantages of our AI assistant include (1) robustness, i.e., our model can handle MR data with different phase numbers and imaging intervals, as demonstrated on a large-scale dataset from seven medical centers, and (2) efficiency, i.e., our AI assistant significantly reduces the time required for manual annotation by a factor of 20, while maintaining accuracy comparable to that of physicians. More importantly, as the fundamental step to build an AI-assisted breast cancer diagnosis system, our AI assistant will promote the application of AI in more clinical diagnostic practices regarding breast cancer.

Methane (CH4) enters waters in hydrocarbon-rich basins because of natural processes and problems related to oil and gas wells. As a redox-active greenhouse gas, CH4degrades water or emits to the atmosphere and contributes to climate change. To detect if methane migrated from hydrocarbon wells (i.e., anomalous methane), we examined 20 751 methane-containing groundwaters from the Upper Appalachian Basin (AB). We looked for concentrations (mg/L) that indicated AB brine salts (chloride concentrations ([Cl]) > 30; [Ca]/[Na] < 0.52) to detect natural methane, and we looked for concentrations of redox-active species ([SO4] ≥ 6; [Fe] ≥ 0.3) to detect anomalous methane. These indicators highlight natural contamination by methane-containing brines or recent onset of microbial oxidation of methane coupled to iron- or sulfate-reduction. We hypothesized that only waters recently contaminated by methane still exhibit high iron and sulfate concentrations. Of the AB samples, 17 (0.08%) from 12 sites indicated potential contamination. All were located in areas with high densities of shale-gas or conventional oil/gas wells. In contrast, in southwestern Pennsylvania where brines are shallow and coal, oil, and gas all have been extracted extensively, no sites of recent methane migration were detectable. Such indicators may help screen for contamination in some areas even without predrill measurements.

Supercapacitor has gained significant attention due to its fast charging/discharging speed, high power density and long-term cycling stability in contrast to traditional batteries. In this review, state-of-the-art achievements on supercapacitor electrode based on carbon materials is summarized. In all-carbon composite materials part, various carbon materials including graphene, carbon nanotube, carbon foam and carbon cloth are composited to fabricate larger specific surface area and higher electrical conductivity electrodes. However, obstacles of low power density as well as low cycling life still remain to be addressed. In metal-oxide composites part, carbon nanotube, graphene, carbon fiber fabric and hollow carbon nanofibers combine with MnO2respectively, which significantly address drawbacks of all-carbon material electrodes. Additionally, TiO2is incorporated into graphene electrode to overcome the low mechanical flexibility of graphene. In organic active compounds part, conducting polymers are employed to combinate with carbon materials to fabricate high specific capacitance, long-term thermal stability and outstanding electroconductivity flexible textile supercapacitors. In each part, innovation, fabrication process and performance of the resulting composites are demonstrated. Finally, future directions that could enhance the performance of supercapacitors are discussed.

A new approach - the digital holographic surface imaging (DHSI) - is proposed for in situ observation of the electrode surface during a typical localized corrosion process. The test results show that the experimental system is sensitive in detecting ions' concentration changes in the vicinity of the electrode surface. The phase maps obtained with this method capture the dynamic concentration changes in front of the steel surface and exhibit the initiation, propagation or re-passivation processes of metastable pits. The proposed method can provide visual evidence of localized dissolution on the electrode surface and offer an in situ and simple means to monitor early-stage corrosion in real time, thereby exhibiting potential for extensive use in the study of electrochemical dynamic processes.

Background: Formaldehyde (FA) has been implicated in Alzheimer's disease (AD) pathology as an age-related factor and as a protein cross-linker known to aggregate Amyloid-Beta (Aβ) and tau protein in vitro. Higher levels of FA have also been found in patients with greater cognitive impairment and in AD patient brains. Objective: To directly evaluate the effect of chronically elevated FA levels on the primate brain with respect to AD pathological markers. Method: Young rhesus macaques (5-8 yrs, without AD related mutations) were given chronic intracerebroventricular (i.c.v.) injections of FA or vehicle over a 12-month period. Monkeys were monitored for changes in cognitive ability and evaluated post-mortem for common AD pathological markers. Results: Monkeys injected with FA were found to have significant spatial working memory impairments. Histopathological analysis revealed the presence of amyloid-β+ neuritic-like plaques, neurofibrillary tangle-like formations, increased tau protein phosphorylation, neuronal loss and reactive gliosis in three memory (and AD) related brain areas (the hippocampus, entorhinal cortex and prefrontal cortex (PFC)) of monkeys receiving i.c.v. injections of FA. ELISA assays revealed that the amounts of pT181 and Aβ42 were markedly higher in the PFC and hippocampus of FA treated monkeys. Conclusion: FA was found to induce major AD-like pathological markers and cognitive impairments in young rhesus monkeys independent of genetic predispositions. This suggests FA may play a significant role in the initiation and progression of the disease.

Photodetectors (PDs) have great potential in applications of imaging, telecommunication, and biological sensing. In this article, state-of-the-art achievements on typical low-dimensional nanostructured PDs and hybrid PDs are reviewed. In the 2D nanostructured PDs part, 2D transition metal dichalcogenides have a natural gap, which promise high sensitivity of photodetection. Graphene and black phosphorus can also stand for 2D nanostructured PDs due to their broadband absorption and tunable direct bandgap, respectively. In the 1D nanostructured PDs part, owing to its high photoconductive characteristic, ZnO nanowire film is a promising material for ultraviolet PDs. Carbon nanotubes show potential in infrared (IR) detection due to its unique physical properties. In the 0D nanostructured PDs part, lead sulfide has a small bandgap and large Bohr exciton radius, which collectively give it a wide spectral tunability in the IR. In the hybrid PDs part, electrical and chemical doping is applied to combine different nanomaterials to realize PDs with high performance. In each part, the present situation and major challenges are overviewed. Then, the evolutions of the methods to overcome these challenges and the tremendous research breakthroughs are demonstrated. At last, future directions that could improve the performance of PDs are discussed.

New techniques are needed to distinguish between leakage of methane (CH4) into surface waters from gas wells and natural sources. Here, scientists worked with >50 citizen scientists in a hydrocarbon‐rich basin (Pennsylvania, U.S.A.) to measure methane concentrations ([CH4]) in streams. These measurements were combined with published observations to form a reconnaissance dataset. The dataset was then used to categorize sites as background or as impacted by other sources of gas. For 479 samples at 131 sites, 470 were supersaturated with respect to the atmosphere (>0.08 µg/L). Sites with the lowest concentrations generally were located in low‐productivity, sandstone‐underlain upland streams, while other streams contained CH4from sources in addition to atmospheric. The median of 63 sites not located near wetland habitats and not affected by known thermogenic influxes yielded an estimate of background [CH4] in the streams, 0.5 µg/L. The highest individual measurements (∼70 µg/L) in the stream dataset were observed in one site near a wetland and one site near a putatively leaking gas well. Inspection of the dataset revealed that values of [CH4] above a threshold for non‐wetland sites, 4 μg/L, signals gas is likely deriving from sources such as leaking gas wells, shallow organic‐rich shales, coal, or landfills. Using historical and local volunteer knowledge, we discovered 12 non‐wetland sites above the threshold that are potentially contaminated by such sources. Although sources of CH4cannot be proven from such surveys of [CH4], stream sampling with nonscientists nonetheless allows discovery of sites of potential contamination that can be further investigated.

AbstractContext:Prostate cancer (PCa) is one of the most commonly diagnosed malignancy in men in the western world.Objective:We aim to investigate the biological role of long non-coding RNA FENDRR and its mechanism in PCa.Materials and methods:We determined the expression of FENDRR and miR-18a-5p in PCa tissues and examined the regulatory mechanism in PCa cell lines.Results:FENDRR transcripts in human PCa tissues were significantly decreased compared with the normal controls. Reduced expression of FENDRR was correlated with the increase of pathological degree and poor prognosis in PCa patients. Upregulation of FENDRR inhibited cell proliferation, increased apoptosis and decreased invasion and migration ability, which was inhibited by miR-18a-5p mimic. Knockdown of FENDRR resulted in a significant increase of PCa cell proliferation and decrease of apoptosis and this effect was inhibited miR-18a-5p inhibitor. FENDRR and RUNX1 contain potential target sites for miR-18a-5p. miR-18a-5p mimic inhibited RUNX1 expression and luciferase activity. FENDRR could increase RUNX1 expression, which was inhibited by miR-18a-5p. The effect of FENDRR on cell proliferation, apoptosis and invasion and migration ability was suppressed by silence of RUNX1.Discussion and conclusion:These results position FENDRR/miR-18a-5p/RUNX1 as a potential therapeutic target and biomarker for PCa.

Eleven thousand groundwater samples collected in the 2010s in an area of Marcellus shale-gas development are analyzed to assess spatial and temporal patterns of water quality. Using a new data mining technique, we confirm previous observations that methane concentrations in groundwater tend to be naturally elevated in valleys and near faults, but we also show that methane is also more concentrated near an anticline. Data mining also highlights waters with elevated methane that are not otherwise explained by geologic features. These slightly elevated concentrations occur near 7 out of the 1,385 shale-gas wells and near some conventional gas wells in the study area. For ten analytes for which uncensored data are abundant in this 3,000 km2rural region, concentrations are unchanged or improved as compared to samples analyzed prior to 1990. Specifically, TDS, Fe, Mn, sulfate, and pH show small but statistically significant improvement, and As, Pb, Ba, Cl, and Na show no change. Evidence from this rural area could document improved groundwater quality caused by decreased acid rain (pH, sulfate) since the imposition of the Clean Air Act or decreased steel production (Fe, Mn). Such improvements have not been reported in groundwater in more developed areas of the U.S.

Genotype imputation is a powerful technique employed by next-generation sequencing (NGS) and genotyping arrays, which can significantly enhance the cost-effectiveness and efficiency of genomic selection. The accuracy of imputation is largely determined by the choice of reference panel, with previous studies generally demonstrating that a closely related population as a reference panel leads to greater accuracy than a more distantly-related population. Various strategies have been proposed for selecting desirable individuals via targeted resequencing, but their efficiencies need further improvement. In this study, we present a practical broiler selection methodology for a local Chinese chicken line that integrates established methods based on pedigree, genomics, and random sampling, and leverages genotype and pedigree information from the yellow-plumage dwarf chicken line. The efficacy of these selection strategies was assessed by evaluating their ability to accurately impute masked genotypes from data obtained using a 600K chip. Our findings reveal that the pedigree-based method yields superior accuracy in genotype imputation, whereas the haplotype-based method exhibits greater stability. Nonetheless, the impact of these targeted methods for selecting key individuals is slightly different when initiating a new sequencing project in a production context. Overall, this study highlights the advantages of using the pedigree-based approach as the preferred method for optimizing genotype imputation in broiler chickens.

The effects of cholamine, a raw material for synthesis of some active lipids, are unknown in poultry. To address this, 180 52-wk-old Hyline laying hens were randomly divided into 3 groups (20 replicates per group with three hens per replicate). The control group and the treatment groups (treatment 1 and 2) were fed basal diet and the diet supplemented with 500 or 1,000 mg of cholamine per kilogram of the diet for 35 d, respectively. The data showed that supplementary cholamine significantly lowered egg production, daily feed intake, serum high-density lipoprotein cholesterol level, liver index, and the percentages of C15:0 and C20:0 in fatty acid composition of liver, significantly elevated hepatic triglyceride content, the ratio of villus height to crypt depth (P< 0.05), and the percentage of C18:2n−6 and the ratio of n−6 to n−3 polyunsaturated fatty acids in liver fat (P< 0.10). Moreover, supplementary cholamine altered the relative abundance of some intestinal bacteria with a decrease in the alpha biodiversity (P< 0.10). Additionally, transcriptome analysis on the livers of the treatment vs. the control groups identified 1,151 up- and 914 down-regulated differentially expressed genes (DEGs), and pathway analysis revealed that the suppressed Notch signaling pathway and the enhanced Oxidative phosphorylation pathway were enriched with DEGs. Particularly, fat absorption, transport and oxidative phosphorylation-related DEGs (e.g., FABP1, APOA4, and PCK1) were significantly induced, but fatty acid synthesis, and lipid package and secretion-related DEGs (e.g., FASN, SCD, and MTTP) were not. In conclusion, supplementary cholamine may lower egg production by promoting hepatic lipid deposition and reducing abundances of beneficial intestinal bacteria and microfloral biodiversity in laying hens.

With the maturity and wide availability of GPS, wireless, telecommunication, and Web technologies, massive amounts of object movement data have been collected from various moving object targets, such as animals, mobile devices, vehicles, and climate radars. Analyzing such data has deep implications in many applications, such as, ecological study, traffic control, mobile communication management, and climatological forecast. In this article, we focus our study on animal movement data analysis and examine advanced data mining methods for discovery of various animal movement patterns. In particular, we introduce a moving object data mining system, MoveMine, which integrates multiple data mining functions, including sophisticated pattern mining and trajectory analysis. In this system, two interesting moving object pattern mining functions are newly developed: (1) periodic behavior mining and (2) swarm pattern mining. For mining periodic behaviors, a reference location-based method is developed, which first detects the reference locations, discovers the periods in complex movements, and then finds periodic patterns by hierarchical clustering. For mining swarm patterns, an efficient method is developed to uncover flexible moving object clusters by relaxing the popularly-enforced collective movement constraints. In the MoveMine system, a set of commonly used moving object mining functions are built and a user-friendly interface is provided to facilitate interactive exploration of moving object data mining and flexible tuning of the mining constraints and parameters. MoveMine has been tested on multiple kinds of real datasets, especially for MoveBank applications and other moving object data analysis. The system will benefit scientists and other users to carry out versatile analysis tasks to analyze object movement regularities and anomalies. Moreover, it will benefit researchers to realize the importance and limitations of current techniques and promote future studies on moving object data mining. As expected, a mastery of animal movement patterns and trends will improve our understanding of the interactions between and the changes of the animal world and the ecosystem and therefore help ensure the sustainability of our ecosystem.

Whole cell patch-clamp recordings were obtained from supraoptic neurons to investigate the effects of histamine on excitatory postsynaptic currents evoked by electrical stimulation of areas around the posterior supraoptic nucleus. When cells were voltage-clamped at −70 mV, evoked excitatory postsynaptic currents had amplitudes of 88.4 ± 9.6 pA and durations of 41.1 ± 3.0 ms (mean ± SE;n= 43). With twin stimulus pulses (20 Hz) used, paired-pulse facilitation ratios were 1.93 ± 0.12. Bath application of 6-cyano-7-nitroquinoxalene-2,3-dione (CNQX) abolished synaptic currents. Histamine at concentrations ∼0.1–10 μM reversibly suppressed excitatory postsynaptic currents in all supraoptic neurons tested. Within 2 min after application of (10 μM) histamine, current amplitudes and durations decreased by 61.5 and 31.0%, respectively, with little change in the paired-pulse facilitation ratio. Dimaprit or imetit (H2or H3receptor agonists) did not reduce synaptic currents, whereas pyrilamine (H1receptor antagonist) blocked histamine-induced suppression of synaptic currents. When patch electrodes containing guanosine 5′-O-(2-thiodiphosphate) (GDP-β-S) were used to record cells, histamine still suppressed current amplitudes by 49.1% and durations by 41.9%. Similarly, intracellular diffusion of bis-(o-aminophenoxy)-N,N,N′,N′-tetraacetic acid (BAPTA) and H7did not abolish histamine-induced suppression of synaptic currents, either. Bath perifusion of 8-bromo-quanosine 3′,5′-cyclic monophosphate reduced current amplitudes by 32.3% and durations by 27.9%. After bath perfusion of slices with Nω-nitro-l-arginine methyl ester (L-NAME), histamine injection decreased current amplitudes only by 31.9%, much less than the inhibition rate in control (P< 0.01). In addition, histamine induced little change in current durations and paired-pulse facilitation ratios, representing a partial blockade of histamine effects on synaptic currents by L-NAME. In supraoptic neurons recorded using electrodes containing BAPTA and perifused with L-NAME, the effects of histamine on synaptic currents were completely abolished. Norepinephrine injection reversibly decreased current amplitudes by 39.1% and duration by 64.5%, with a drop in the paired-pulse facilitation ratio of 47.9%. Bath perifusion of L-NAME, as well as intracellular diffusion of GDP-β-S, , 1-(5-isoquinolinylsulfonyl)-2-methyl-piperazine , or BAPTA, failed to block norepinephrine-induced suppression of evoked synaptic currents. The present results suggest that histamine suppresses non-N-methyl-d-aspartate synaptic currents in supraoptic neurons through activation of H1receptors. It is possible that histamine first acts at supraoptic cells (perhaps both neuronal and nonneuronal) and induces the production of nitric oxide, which then diffuses to nearby neurons and modulates synaptic transmission by a postsynaptic mechanism.

ABSTRACTComputerized image analysis for whole-slide images has been shown to improve efficiency, accuracy, and consistency in histopathology evaluations. We aimed to assess whether immunohistochemistry (IHC) image quantitative features can reflect the immune status and provide prognostic information for colorectal cancer patients. A fully automated pipeline was designed to extract histogram features from IHC digital images in a training set (N = 243). A Hist-Immune signature was generated with selected features using the LASSO Cox model. The results were validated using internal (N = 147) and external (N = 76) validation sets. The five-feature-based Hist-Immune signature was significantly associated with overall survival in training (HR 2.72, 95% CI 1.68–4.41, P< .001), internal (2.86, 1.28–6.39, 0.010), and external (2.30, 1.02–6.16, 0.044) validation sets. The full model constructed by integrating the Hist-Immune signature and clinicopathological factors had good discrimination ability (C-index 0.727, 95% CI 0.678–0.776), confirmed using internal (0.703, 0.621–0.784) and external (0.756, 0.653–0.859) validation sets. Our findings indicate that the Hist-Immune signature constructed based on the quantitative features could reflect the immune status of patients with colorectal cancer, which might advocate change in risk stratification and consequent precision medicine.

Responses of 42 neurosecretory neurons in the supraoptic nucleus (SON) to human recombinant interleukin-1β (IL-1β) were examined during intracellular recordings in rat brain slices. IL-1β (10-9-10-8) depolarized the membrane and caused increased firing in 25 neurons (59.5). In 11 other neurons (26.6), depolarization was also seen, followed by membrane hyperpolarization. The IL-induced depolarizing effect remained in the presence of tetrodotoxin (TTX) but was abolished by sodium salicylate. The results suggest that IL-1β mainly exerts a direct excitatory effect on SON neurons and further, that prostaglandins may be involved in such an effect.

WE have previously reported that human recombinant interleukin (IL)-1β depolarizes membrane potentials in most rat hypothalamic supraoptic (SON) neurones in vitro.In the present study intracellular recordings have been made to examine contributions of local GABAergic interneurones to the effects of IL-1β on SON neurones in rat brain slice preparations. The results suggest that local GABAergic inputs modulate IL-induced excitatory responses, and are involved in tonic inhibition of SON neuronal activities.