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Purpose: To investigate the clinical effect and side effects of early addition of small-dose eltrombopag in initial treatment of primary immune thrombocytopenia (ITP) patients with decreased platelet count and hormone reduction, and patients with persistent ITP. Methods: Thirty ITP patients newly diagnosed with poor hormonal levels were recruited, along with 36 patients with persistent ITP. The ITP patients were given prednisone in combination with eltrombopag. Blood routine tests, blood pressure, blood sugar, liver and kidney function, and serum ion levels were monitored every two weeks. Adverse events were recorded at 2, 4, and 8 weeks after eltrombopag treatment. Results: After treatment for 2, 4, and 8 weeks, there was obvious effectiveness in 45.0, 39.4, and 40.9 % of patients, respectively, while effectiveness was good in 22.7, 37.9, and 50 % of patients, respectively. Treatment was ineffective in 31.8, 22.7, and 9.1 % of patients, respectively. In the 8th week, 15 patients (22.7 %) had to use prednisone at a dose of more than 20 mg/day to keep their platelet level above 30 x 109/L, while 7 patients (10.6 %) stopped using prednisone completely. Moreover, 4 patients (6.1 %) had their platelet counts maintained above 30 x 109/L after stopping the use of eltrombopag for more than 2 weeks. Conclusion: The combined use of eltrombopag and low-dose hormone is an effective treatment for ITP patients. It produces only a slight side effect, and it is affordable. A larger population study will be required to validate these findings of this new therapeutic option.

The purpose of this study is to investigate the entire evolution process of shales with various total organic contents (TOC) in order to build models for quantitative evaluation of oil and gas yields and establish methods for assessing recoverable oil and gas resources from in-situ conversion of organic matters in shale. Thermal simulation experiments under in-situ conversion conditions were conducted on Chang 73 shales from the Ordos Basin in a semi-open system with large capacity. The results showed that TOC and Ro were the key factors affecting the in-situ transformation potential of shale. The remaining oil and gas yields increased linearly with TOC but inconsistently with Ro. Ro ranged 0.75%–1.25% and 1.05%–2.3%, respectively, corresponding to the main oil generation stage and gas generation stage of shale in-situ transformation. Thus a model to evaluate the remaining oil/gas yield with TOC and Ro was obtained. The TOC of shale suitable for in-situ conversion should be greater than 6%, whereas its Ro should be less than 1.0%. Shales with 0.75% (Ro) could obtain the best economic benefit. The results provided a theoretical basis and evaluation methodology for predicting the hydrocarbon resources from in-situ conversion of shale and for the identification of the optimum “sweet spots”. The assessment of the Chang 73 shale in the Ordos Basin indicated that the recoverable oil and gas resources from in-situ conversion of organic matters in shale are substantial, with oil and gas resources reaching approximately 450×108 t and 30×1012 m3, respectively, from an area of 4.27×104 km2.

Supplementary Methods, Figures 1-3, Tables 1-2 from EBV-Induced Human CD8+ NKT Cells Suppress Tumorigenesis by EBV-Associated Malignancies

The underlying mechanism of the protective and suppressive role of NKT cells in human tumor immunosurveillance remains to be fully elucidated. We show that the frequencies of CD8+ NKT cells in patients with EBV-associated Hodgkin's lymphoma or nasopharyngeal carcinoma are significantly lower than those in healthy EBV carriers. These CD8+ NKT cells in tumor patients are also functionally impaired. In human-thymus-severe combined immunodeficient (hu-thym-SCID) chimeras, EBV challenge efficiently promotes the generation of IFN-γ–biased CD8+ NKT cells. These cells are strongly cytotoxic, drive syngeneic T cells into a Th1 bias, and enhance T-cell cytotoxicity to EBV-associated tumor cells. Interleukin-4–biased CD4+ NKT cells are predominately generated in unchallenged chimeras. These cells are noncytotoxic, drive syngeneic T cells into a Th2 bias, and do not affect T-cell cytotoxicity. In humanized xenogeneic tumor-transplanted hu-thym-SCID chimeras, adoptive transfer with EBV-induced CD8+ NKT cells significantly suppresses tumorigenesis by EBV-associated malignancies. EBV-induced CD8+ NKT cells are necessary and sufficient to enhance the T-cell immunity to EBV-associated malignancies in the hu-thym-SCID chimeras. CD4+ NKT cells are synergetic with CD8+ NKT cells, leading to a more pronounced T-cell antitumor response in the chimeras cotransferred with CD4+ and CD8+ NKT cells. Thus, immune reconstitution with EBV-induced CD8+ NKT cells could be a useful strategy in management of EBV-associated malignancies. [Cancer Res 2009;69(20):7935–44]

BackgroundLaparoscopic sleeve gastrectomy (LSG) is a sustainable technique that effectively treats morbid obesity. However, the molecular mechanisms underlying the improvement of metabolic health following this process warrants more investigation. This study investigates LSG-related molecules and uses bulk RNA-sequencing high-throughput analysis to unravel their regulatory mechanisms.MethodsPeripheral blood mononuclear cells (PBMC) were collected from ten obese patients with BMI ≥ 32.5 kg/m2 in the Department of General Surgery of Kunming First People’s Hospital. After LSG, patients were followed up for one month, and blood samples were retaken. Blood samples from ten patients before and after LSG and bulk RNA-Seq data were analyzed in this study. LSG-associated gene expression was detected by weighted gene coexpression network analysis (WGCNA) and differential analysis. Subsequently, essential signature genes were identified using logistic least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE) algorithms. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and single-sample gene set enrichment analysis (ssGSEA) were utilized to reveal the potential functions of the target genes. Furthermore, the Pearson correlation of signature genes with leptin and lipocalin was also explored. Finally, we constructed a robust endogenous RNA (ceRNA) network based on miRWalk and starBase databases.ResultsWe identified 18 overlapping genes from 91 hub genes, and 165 differentially expressed mRNAs (DE-mRNA), which were revealed to be significantly associated with immune cells, immune response, inflammatory response, lipid storage, and localization upon functional enrichment analysis. Three signature genes, IRF1, NFKBIA, and YRDC, were identified from the 18 overlapping genes by LASSO and SVM-REF algorithms. The logistic regression model based on the three signature genes highlighted how robustly they discriminated between samples. ssGSEA indicated these genes to be involved in lipid metabolism and degradation pathways. Moreover, leptin levels were significantly reduced in patients undergoing LSG, and NFKBIA significantly negatively correlated with leptin. Finally, we identified how the long non-coding RNA (lncRNA) ATP2B1-AS1 regulated the expression of the signature genes by competitively binding to six microRNAs (miRNAs), which were hsa-miR-6509-5p, hsa-miR-330-5P, hsa-miR-154-5P, hsa-miR-145-5P, hsa-miR4726-5P and hsa-miR-134-5P.ConclusionThis study identified three critical regulatory genes significantly differentiated between patients before and after LSG treatment and highlighted their potentially crucial role after bariatric surgery. This provides novel insights to increase our understanding of the underlying mechanisms of weight loss and associated metabolic improvement after bariatric surgery.

Although multi-task deep neural network (DNN) models have computation and storage benefits over individual single-task DNN models, they can be further optimized via model compression. Numerous structured pruning methods are already developed that can readily achieve speedups in single-task models, but the pruning of multi-task networks has not yet been extensively studied. In this work, we investigate the effectiveness of structured pruning on multi-task models. We use an existing single-task filter pruning criterion and also introduce an MTL-based filter pruning criterion for estimating the filter importance scores. We prune the model using an iterative pruning strategy with both pruning methods. We show that, with careful hyper-parameter tuning, architectures obtained from different pruning methods do not have significant differences in their performances across tasks when the number of parameters is similar. We also show that iterative structure pruning may not be the best way to achieve a well-performing pruned model because, at extreme pruning levels, there is a high drop in performance across all tasks. But when the same models are randomly initialized and re-trained, they show better results.

Supplemental material, sj-docx-1-npx-10.1177_1934578X231180076 for Integrated Serum Metabolomics and Network Pharmacology Approach to Reveal the Potential Mechanisms of Diwuyanggan Prescription in the Prevention of Acute Liver Injury by Jinlin Xu, Feng Zou, Yuqi Lin, Zhaoxiang Zeng, Yukun Liu, Lijun Zhang, Rongzeng Huang, Xugui Li, Chengwu Song, Yinping Tang and Shuna Jin in Natural Product Communications

In this paper, we propose an approximating framework for analyzing parametric Markov models. Instead of computing complex rational functions encoding the reachability probability and the reward values of the parametric model, we exploit the scenario approach to synthesize a relatively simple polynomial approximation. The approximation is probably approximately correct (PAC), meaning that with high confidence, the approximating function is close to the actual function with an allowable error. With the PAC approximations, one can check properties of the parametric Markov models. We show that the scenario approach can also be used to check PRCTL properties directly, without synthesizing the polynomial at first hand. We have implemented our algorithm in a prototype tool and conducted thorough experiments. The experimental results demonstrate that our tool is able to compute polynomials for more benchmarks than state of the art tools such as PRISM and Storm, confirming the efficacy of our PAC-based synthesis., Comment: 24 pages, 8 figures

Learning unnormalized statistical models (e.g., energy-based models) is computationally challenging due to the complexity of handling the partition function. To eschew this complexity, noise-contrastive estimation~(NCE) has been proposed by formulating the objective as the logistic loss of the real data and the artificial noise. However, as found in previous works, NCE may perform poorly in many tasks due to its flat loss landscape and slow convergence. In this paper, we study it a direct approach for optimizing the negative log-likelihood of unnormalized models from the perspective of compositional optimization. To tackle the partition function, a noise distribution is introduced such that the log partition function can be written as a compositional function whose inner function can be estimated with stochastic samples. Hence, the objective can be optimized by stochastic compositional optimization algorithms. Despite being a simple method, we demonstrate that it is more favorable than NCE by (1) establishing a fast convergence rate and quantifying its dependence on the noise distribution through the variance of stochastic estimators; (2) developing better results for one-dimensional Gaussian mean estimation by showing our objective has a much favorable loss landscape and hence our method enjoys faster convergence; (3) demonstrating better performance on multiple applications, including density estimation, out-of-distribution detection, and real image generation.

In this paper, we aim to optimize a contrastive loss with individualized temperatures in a principled and systematic manner for self-supervised learning. The common practice of using a global temperature parameter $\tau$ ignores the fact that ``not all semantics are created equal", meaning that different anchor data may have different numbers of samples with similar semantics, especially when data exhibits long-tails. First, we propose a new robust contrastive loss inspired by distributionally robust optimization (DRO), providing us an intuition about the effect of $\tau$ and a mechanism for automatic temperature individualization. Then, we propose an efficient stochastic algorithm for optimizing the robust contrastive loss with a provable convergence guarantee without using large mini-batch sizes. Theoretical and experimental results show that our algorithm automatically learns a suitable $\tau$ for each sample. Specifically, samples with frequent semantics use large temperatures to keep local semantic structures, while samples with rare semantics use small temperatures to induce more separable features. Our method not only outperforms prior strong baselines (e.g., SimCLR, CLIP) on unimodal and bimodal datasets with larger improvements on imbalanced data but also is less sensitive to hyper-parameters. To our best knowledge, this is the first methodical approach to optimizing a contrastive loss with individualized temperatures., Comment: 33 pages, 11 figures, accepted by ICML2023

Hyperspectral images (HSI) with abundant spectral information reflected materials property usually perform low spatial resolution due to the hardware limits. Meanwhile, multispectral images (MSI), e.g., RGB images, have a high spatial resolution but deficient spectral signatures. Hyperspectral and multispectral image fusion can be cost-effective and efficient for acquiring both high spatial resolution and high spectral resolution images. Many of the conventional HSI and MSI fusion algorithms rely on known spatial degradation parameters, i.e., point spread function, spectral degradation parameters, spectral response function, or both of them. Another class of deep learning-based models relies on the ground truth of high spatial resolution HSI and needs large amounts of paired training images when working in a supervised manner. Both of these models are limited in practical fusion scenarios. In this paper, we propose an unsupervised HSI and MSI fusion model based on the cycle consistency, called CycFusion. The CycFusion learns the domain transformation between low spatial resolution HSI (LrHSI) and high spatial resolution MSI (HrMSI), and the desired high spatial resolution HSI (HrHSI) are considered to be intermediate feature maps in the transformation networks. The CycFusion can be trained with the objective functions of marginal matching in single transform and cycle consistency in double transforms. Moreover, the estimated PSF and SRF are embedded in the model as the pre-training weights, which further enhances the practicality of our proposed model. Experiments conducted on several datasets show that our proposed model outperforms all compared unsupervised fusion methods. The codes of this paper will be available at this address: https: //github.com/shuaikaishi/CycFusion for reproducibility.

Friction squeal is an unstable friction-induced vibration with high frequency and high intensity, which is troublesome for both people and environment. Surface topography is an important influencing factors of friction squeal. In this paper, based on introduction of surface topography, a review on the influence of surface topography on friction squeal is conducted from the aspect of experimental analysis, theoretical research and simulation analysis. For the convenience of scholars’ understanding and carrying out relevant research, the influence of surface topography on friction and contact parameters, and the influence of friction and contact parameters on friction squeal are reviewed. Moreover, the generation methods of rough surface are introduced to facilitate the research. Finally, on the basis of a discussion on difficulties and shortcomings of the former research, recommendations are proposed for future research.

Background: Cardiovascular diseases (CVD) have been reported in 8%-16% of patients with 2019 coronavirus disease (COVID-19). Digoxin is one of the main drugs to treat CVD. Objective: The clinician conducted therapeutic drug monitoring (TDM) of digoxin according to the drug usage on patients to monitor the concentration of digoxin, so as to avoid its toxic and side effects, and provide a theoretical reference for clinical usage of digoxin in patients with COVID-19. Methods: A method for quantifying digoxin concentration in plasma with ultra-performance liquid chromatography/tandem mass spectrometry (UPLC-MS/MS) was developed. After simple protein precipitation of plasma with methanol, digoxin and its internal standard (digoxin-d3) were detected in the positive ion mode using multiple reaction monitoring. Results: Plasma digoxin in the range of 0.2-10 ng/mL had good linearity. The UPLC-MS/MS method was validated with inter-run accuracies ranging from 91.3% to 107.4% and precision less than 13%. Nine plasma samples (5 at valley concentration and 4 at follow-up after stopping dosing) from three patients with COVID-19 were tested. The mean plasma digoxin concentration was 0.73 ng/mL (ranged from 0 to 1.31 ng/mL). Digoxin was detected at the concentration of 0.93 ng/mL after stopping drug administration for 14 days. Conclusion: In this study, we established a simple UPLC-MS/MS method using protein-precipitation to perform TDM of digoxin in patients with COVID-19, and found that about 56% of digoxin plasma concentration was within the treatment window (0.8-2.0 ng/mL). Digoxin can be remained in the body for nearly 14 days in severe patients with COVID-19 after stopping dosing.

Background: Talin-1 is involved in the invasion and synapse development of the Human Immunodeficiency Virus (HIV). We found that talin-1 was cleaved into a 38 KDa fragment (talin-C) in the Peripheral Blood Mononuclear Cells (PBMCs) of HIV patients; however, the underlying mechanisms remain unknown. Objective: This study aimed to determine the relationship between talin-C and HIV infection and to identify the mechanisms underlying the ability of this protein to influence HIV infection. Methods: PBMCs were derived from HIV-infected patients enrolled in this study. N- and C-terminal peptides matching the potential sequence of talin-C were detected in PBMCs by Multiple Reaction Monitoring (MRM) mass spectrometry. TZM-b1 cells were infected with HIV-1 pseudotyped virus (HIVpp) for different durations to detect the talin-C product. Three stable cell lines overexpressing the talin head (TLN1-H) or TLN1-C or with TLN1 knockdown (shTLN1) were created and infected by HIVpp. The HIV marker protein (P24) was then detected by enzyme-linked immunosorbent assay. Finally, an isobaric tag for relative and absolute quantification (iTRAQ)-based proteomics study was performed to detect the TLN1-C-regulated proteins with or without HIVpp infection in TZM-bl cells. The identified proteins were analyzed by R version 4.0.2 and STRING software (Version: 11.0) (https://string-db.org). Results: N- and C-peptides of talin-C were detected to have higher expression in patients with lower HIV load. Talin-C was produced during HIVpp infection. TLN1-C significantly inhibited HIVpp infection in the TZM-b1 cells. Additionally, a proteomic study found that TLN1-C regulated the expression of 99 proteins in TZM-b1 cells with and without HIVpp infection, respectively. According to Gene Ontology (GO) annotation, proteins with cellular metabolic processes and binding function were found to be enriched. Thirty-four proteins have protein-protein interaction, 19 down- and 15 up-regulated proteins, respectively. Conclusion: Talin-C was produced following HIV infection and was inversely proportional to HIV load. A proteomic study indicated that TLN1-C might be involved in HIV infection through regulating metabolic processes.

Personal sound zone systems require to know the acoustic transfer functions (ATFs) between the loudspeakers and matching microphones, which in general have to be measured in advance. As the measurement is affected by such factors as sensors' position mismatches and environmental changes, deviation of the measured ATF-s from the true ones is unavoidable and such perturbation may lead to significant performance degradation in sound zone production. In this work, a method is developed for robust acoustic contrast control, which simultaneously considers the norm-bounded uncertainties and a positive semidefinite constraint to ensure the optimization problem to have a solution. Since the problem is non-convex, the polynomial time difference-of-convex functions (POTDC) algorithm is adopted to identify the optimal solution. Simulations are performed to validate the effectiveness of the proposed method and it demonstrates a performance superior to other existing robust control methods.

BackgroundThis study aims to screen out differentially expressed genes (DEGs) regulated by BRCA1-associated protein 1 (BAP1) in osteosarcoma cells, and to analyze their biological functions.MethodsThe microarray dataset GSE23035 of BAP1-knockdown osteosarcoma cells was obtained from Gene Expression Omnibus (GEO) database, consisting of shControl, shBAP1#1 and shBAP1#2 samples. The DEGs between the BAP1-knockdown osteosarcoma cells and the untreated osteosarcoma cells were screened with limma package, and then subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Gene Set Enrichment Analysis (GSEA) was also performed for the three groups of samples. Hub genes in a protein-protein interaction (PPI) network of DEGs was filtered, and then subjected to prognostic analysis and correlation analysis with BAP1 in Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. Besides, the correlation between BAP1 and biological processes/pathways was analyzed by Gene Set Variation Analysis (GSVA) method and the correlation between BAP1 and immune infiltration by CIBERSORT and ESTIMATE methods. The roles of BAP1 in regulating proliferation and epithelial-mesenchymal transition (EMT) were validated by CCK-8 and western blot.Results58 upregulated DEGs and 81 downregulated DEGs were obtained with |logFC| ≥ 1 and adj.p < 0.05. Cell cycle, DNA repair, and focal adhesion were associated with BAP1 in datasets. Further, BAP1 was negatively correlated with naïve CD4 T cells infiltration. In vitro, BAP1 inhibited proliferation and EMT.ConclusionBAP1 might be a tumor suppressor in osteosarcoma and a promising therapeutic target.

The dataset is composed of the theoretically optimized structures, energetics and electronic structures information of more than two hundreds of tetrahedral-bonding hybrid heterostructured semiconductors (HHSs). We have given the related information to reproduce the calculations by using the plane-wave pseudopotential methods within the density functional theory with the Perdew-Burke-Ernzerhof exchange-correlation functional as implemented in the Vienna Ab-initio Simulation Package. The file directory is organized as below: |-.Input-files |-.HHSs-CIF |-.Tetrahedral-Bonding HHSs |-.Inorganic_Semiconductors |-.Organic_Molecules |-.HHSs-INCAR |-.HHSs-POTCAR |-.a-[CaAs]1en ->POTCAR …… |-.Output-data |- Formation-energy_bandgap_information.docx |-Relaxed_structure_information.xlsx We expect this database will serve as a valuable resource for the scientific community to further explorethe tetrahedral-bonding hybrid heterostructured semiconductors

Based on 991 groups of analysis data of shale samples from the Lower Member of the Cretaceous Eagle Ford Formation of 1317 production wells and 72 systematic coring wells in the U.S. Gulf Basin, the estimated ultimate recovery (EUR) of shale oil and gas of the wells are predicted by using two classical EUR estimation models, and the average values predicted excluding the effect of engineering factors are taken as the final EUR. Key geological factors controlling EUR of shale oil and gas are fully investigated. The reservoir capacity, resources, flow capacity and fracability are the four key geological parameters controlling EUR. The storage capacity of shale oil and gas is directly controlled by total porosity and hydrocarbon-bearing porosity, and indirectly controlled by total organic carbon (TOC) and vitrinite reflectance (Ro). The resources of shale oil and gas are controlled by hydrocarbon-bearing porosity and effective shale thickness etc. The flow capacity of shale oil and gas is controlled by effective permeability, crude oil density, gas-oil ratio, condensate oil-gas ratio, formation pressure gradient, and Ro. The fracability of shale is directly controlled by brittleness index, and indirectly controlled by clay content in volume. EUR of shale oil and gas is controlled by six geological parameters: it is positively correlated with effective shale thickness, TOC and fracture porosity, negatively correlated with clay content in volume, and increases firstly and then decreases with the rise of Ro and formation pressure gradient. Under the present upper limit of horizontal well fracturing effective thickness of 65 m and the lower limit of EUR of 3×104 m3, when TOC

Approximate matrix multiplication with limited space has received ever-increasing attention due to the emergence of large-scale applications. Recently, based on a popular matrix sketching algorithm---frequent directions, previous work has introduced co-occuring directions (COD) to reduce the approximation error for this problem. Although it enjoys the space complexity of $O((m_x+m_y)\ell)$ for two input matrices $X\in\mathbb{R}^{m_x\times n}$ and $Y\in\mathbb{R}^{m_y\times n}$ where $\ell$ is the sketch size, its time complexity is $O\left(n(m_x+m_y+\ell)\ell\right)$, which is still very high for large input matrices. In this paper, we propose to reduce the time complexity by exploiting the sparsity of the input matrices. The key idea is to employ an approximate singular value decomposition (SVD) method which can utilize the sparsity, to reduce the number of QR decompositions required by COD. In this way, we develop sparse co-occuring directions, which reduces the time complexity to $\widetilde{O}\left((\nnz(X)+\nnz(Y))\ell+n\ell^2\right)$ in expectation while keeps the same space complexity as $O((m_x+m_y)\ell)$, where $\nnz(X)$ denotes the number of non-zero entries in $X$. Theoretical analysis reveals that the approximation error of our algorithm is almost the same as that of COD. Furthermore, we empirically verify the efficiency and effectiveness of our algorithm.

To analyse local robustness properties of deep neural networks (DNNs), we present a practical framework from a model learning perspective. Based on black-box model learning with scenario optimisation, we abstract the local behaviour of a DNN via an affine model with the probably approximately correct (PAC) guarantee. From the learned model, we can infer the corresponding PAC-model robustness property. The innovation of our work is the integration of model learning into PAC robustness analysis: that is, we construct a PAC guarantee on the model level instead of sample distribution, which induces a more faithful and accurate robustness evaluation. This is in contrast to existing statistical methods without model learning. We implement our method in a prototypical tool named DeepPAC. As a black-box method, DeepPAC is scalable and efficient, especially when DNNs have complex structures or high-dimensional inputs. We extensively evaluate DeepPAC, with 4 baselines (using formal verification, statistical methods, testing and adversarial attack) and 20 DNN models across 3 datasets, including MNIST, CIFAR-10, and ImageNet. It is shown that DeepPAC outperforms the state-of-the-art statistical method PROVERO, and it achieves more practical robustness analysis than the formal verification tool ERAN. Also, its results are consistent with existing DNN testing work like DeepGini.

A previously published study showed that stress may interfere with associative aversive learning and facilitate mood-related disorders. However, whether emotional stress alone affects aversive learning is unknown. Using three chamber-vicarious social defeat stress (3C-VSDS) model mice, we investigated the effect of emotional stress on aversive learning. An important origin of dopamine (DA) neurons, the zona incerta (ZI), is expected to be a novel target for the modulation of aversive learning. However, less is known about the circuit mechanism of ZIDA neurons in aversive learning. Here, we subjected mice to a fear-conditioning system (FCS) and observed an increased calcium activity of ZI TH+ neurons in aversive expectation during the conditioning phase, especially during the late stage of the conditional stimulus (CS) when CS and unconditional stimulus (US) pairings were used. Optogenetic inhibition of ZI TH+ neurons at the late stage of CS disrupted conditioned fear learning in mice. We further identified a TH+ projection from the ZI to the basomedial amygdala (BMA) and found that optogenetic inhibition of the ZI-BMA circuit could also block aversive learning. Finally, we used 3C-VSDS mice as a model of emotional stress. We found that the 3C-VSDS model mice demonstrated reduced aversive expectation associated with ZI TH+ neurons in the late stage of CS and impaired aversive learning in FCS. Optogenetic activation of ZI-BMA TH+ projections in the late stage of CS significantly reversed the aversive FCS learning disability of 3C-VSDS model mice. These data suggest that a TH+ circuit from the ZI to the BMA is required for aversive expectation, both at baseline and in 3C-VSDS-induced aversive learning deficits and that this circuit is a potential target for the modulation of aversive learning. Low activity of ZI-BMA TH+ projections is one reason for 3C-VSDS-induced aversive learning deficits.

The Permian Lucaogou Formation of the Jimsar Sag in the Junggar Basin, NW China, is one of the largest areas of oil exploration and exploitation in the lacustrine shale sequence in China. Oil is commercially extracted from this markedly heterogeneous formation, which is characterized by frequently interbedded shale and tight reservoirs, although producible intervals within the Lucaogou Formation remain unresolved. This study focused on the Jimsar Sag reservoirs to investigate petrological and mineralogical characteristics, source rock and reservoir physical properties, and the molecular biogeochemistry of core extracts and crude oils. The i-C18/n-C18 and Pr/n-C19 ratios of two-step ultrasonic extracts were applied to infer whether the oil is produced from shale or tight reservoirs, taking into account solvent polarity, molecular characteristics of n-alkanes and isoprenoids, and physical properties of the reservoir. The experimental results indicated that the shallower pay zone is mainly produced from tight reservoirs, while in deeper zone with organic-matter maturity above 1.0%, some of the oil is produced from shale. The reservoir properties in organic-rich shale with vitrinite reflectance (Ro) exceeding 1.0% are improved by pore interconnectivity, and oil mobility is enhanced by high gas/oil ratios, which favors production of free-phase hydrocarbons. Such zones may become major prospects for shale oil exploration and production. The results of the present study can potentially apply to sweet-spot identification and development optimization for the Lucaogou shale and other lacustrine shale sequences.

Since 2008, many academics have increasingly paid attention to blockchain technology from different perspectives. In general, researchers desire to achieve global blockchain systems within a sustainable manufacturing domain; however, a number of technical challenges have come to exist in the recent decade, for instance, consensus algorithms and computing paradigms that can meet the privacy protection requirements of manufacturing systems. Therefore, an integrated decision-making framework called Pythagorean fuzzy-entropy-rank sum-Combined Compromise Solution (PF-entropy-RS-CoCoSo) is developed in this study, including two main phases. In the first phase, the PF-entropy-RS method is applied to obtain the subjective and objective weights of criteria to evaluate the technical challenges of transforming blockchain technology for a sustainable manufacturing paradigm in the Industry 4.0 era. The PF-CoCoSo model is then utilized in the second phase to assess the preferences of organizations over different technical challenges of the blockchain technology transformation for the sustainable manufacturing paradigm in the Industry 4.0 era. An empirical case study is taken to assess the main technical challenges of blockchain technology transformation for the sustainable manufacturing paradigm. Furthermore, a comparison analysis and a sensitivity investigation are made to demonstrate the superiority of the developed framework. © 2023 Elsevier Inc.

Yan Song &ast;, Lijun Zhang &ast;, Wenhui Wang Department of Nursing, The Second Hospital of Nanjing, Nanjing University of Chinese Medicine, Nanjing, 210000, People’s Republic of China&ast; These authors contributed equally to this workCorrespondence: Yan Song, Department of Nursing, The Second Hospital of Nanjing University of Chinese Medicine, Nanjing, 210000, People’s Republic of China, Tel +86025-83626512, Fax +86025-83626060, Email songyan_sy01@outlook.comObjective: The aim of this study is to improve the effectiveness of training in putting on and removing personal protective equipment (PPE) during COVID-19.Methods: An information–motivation–behavioral skills (IMB) model intervention team was established with the adoption of the IMB model to intervene in putting on and removing PPE by medical staff during COVID-19. Specifically, the information intervention was strengthened through the formulation of the hospital manual for PPE application. In the present study’s five-stage motivational interview, the personal motivation and social motivation of medical staff were deeply understood, helping the medical staff to improve their belief in the importance of correct PPE application and to establish the correct attitude toward PPE application. In terms of behavioral skills, there are numerous issues that could interfere with the establishment of proper PPE application, including material supply, double duty, simultaneous supervision of entering and leaving, continuous supervision, video supervision, and nosocomial infection inspection. The scores relating to PPE application knowledge and self-efficacy as well as the PPE usage qualification rate were calculated in the control group and the IMB intervention group and subsequently compared.Results: For the control group and the IMB intervention group, the scores for PPE application knowledge were 87.78 ± 10.46 and 95.56 ± 9.06 points, respectively. For self-efficacy, the scores were 25.19 ± 0.97 and 33.79 ± 2.05 points, and the PPE usage qualification rates were 64.8% and 90.0%, respectively. The differences in all the scores were statistically significant between the two groups.Conclusion: The application of the IMB model could improve the relevant knowledge relating to PPE application, strengthen the belief in the importance of and motivation for correct PPE application, improve the qualification rate for PPE application, and provide a theoretical and practical basis for reducing the occurrence of nosocomial infection.Keywords: information–motivation–behavioral skills model, COVID-19, personal protective equipment

Sepsis is a disease with a high morbidity and mortality rate. At present, there is a lack of ideal biomarker prognostic models for sepsis and promising studies using prognostic models to predict and guide the clinical use of medications. In this study, 71 differentially expressed genes (DEGs) were obtained by analyzing single-cell RNA sequencing (scRNA-seq) and transcriptome RNA-seq data, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment pathway analyses were performed on these genes. Then, a prognosis model with CCL5, HBD, IFR2BP2, LTB, and WFDC1 as prognostic signatures was successfully constructed after univariate LASSO regression analysis and multivariate Cox regression analysis. Kaplan–Meier (K-M) survival analysis, receiver operating characteristic (ROC) time curve analysis, internal validation, and principal component analysis (PCA) further validated the model for its high stability and predictive power. Furthermore, based on a risk prediction model, gene set enrichment analysis (GSEA) showed that multiple cellular functions and immune function signaling pathways were significantly different between the high- and low-risk groups. In-depth analysis of the distribution of immune cells in healthy individuals and sepsis patients using scRNA-seq data revealed immunosuppression in sepsis patients and differences in the abundance of immune cells between the high- and low-risk groups. Finally, the genetic targets of immunosuppression-related drugs were used to accurately predict the potential use of clinical agents in high-risk patients with sepsis.

Additional file 1: Table S1. Analysis of Deviance Table (Type II Wald chi-square tests). Fig S1. Mean (±SE) phoresy proportion in different part of C. formosanus. Fig S2. Mean (±SE) proportion and number of phoresy on two castes of C. formosanus. Fig S3. Location of A. farris attachment on the head of two C. formosanus castes (left: soldier; right: worker; red arrow: mite).

Additional file 3. Presents supplementary tables S1-S3. Related literature references are contained within the note.

In this paper, we investigate an online prediction strategy named as Discounted-Normal-Predictor (Kapralov and Panigrahy, 2010) for smoothed online convex optimization (SOCO), in which the learner needs to minimize not only the hitting cost but also the switching cost. In the setting of learning with expert advice, Daniely and Mansour (2019) demonstrate that Discounted-Normal-Predictor can be utilized to yield nearly optimal regret bounds over any interval, even in the presence of switching costs. Inspired by their results, we develop a simple algorithm for SOCO: Combining online gradient descent (OGD) with different step sizes sequentially by Discounted-Normal-Predictor. Despite its simplicity, we prove that it is able to minimize the adaptive regret with switching cost, i.e., attaining nearly optimal regret with switching cost on every interval. By exploiting the theoretical guarantee of OGD for dynamic regret, we further show that the proposed algorithm can minimize the dynamic regret with switching cost in every interval.

Tree-structured multi-task architectures have been employed to jointly tackle multiple vision tasks in the context of multi-task learning (MTL). The major challenge is to determine where to branch out for each task given a backbone model to optimize for both task accuracy and computation efficiency. To address the challenge, this paper proposes a recommender that, given a set of tasks and a convolutional neural network-based backbone model, automatically suggests tree-structured multi-task architectures that could achieve a high task performance while meeting a user-specified computation budget without performing model training. Extensive evaluations on popular MTL benchmarks show that the recommended architectures could achieve competitive task accuracy and computation efficiency compared with state-of-the-art MTL methods. Our tree-structured multi-task model recommender is open-sourced and available at https://github.com/zhanglijun95/TreeMTL., Comment: 12 pages, 2 figures; Accepted by AutoML-Conf 2022

In this paper, we present EPMC, an extendible probabilistic model checker. EPMC has a small kernel, and is designed modularly. It supports discrete probabilistic models such as Markov chains and Markov decision processes. Like PRISM, it supports properties specified in PCTL*. Two central advantages of EPMC are its modularity and extendibility. We demonstrate these features by extending EPMC to EPMC-PETL, a model checker for probabilistic epistemic properties on multi-agent systems. EPMC-PETL takes advantage of EPMC to provide two model checking algorithms for multi-agent systems with respect to probabilistic epistemic logic: an exact algorithm based on SMT techniques and an approximated one based on UCT. Multi-agent systems and epistemic properties are given in an extension of the modelling language of PRISM, making it easy to model this kind of scenarios.

Additional file 4. Presents a theoretic model for the generation of false species by misclassifying true species in a population survey. Characterizes the influence of false species accumulation on the observed and estimated unobserved richness components underlying standard richness estimators. Related figures and literature references are contained within the note.

The determinization of a nondeterministic Büchi automaton (NBA) is a fundamental construction of automata theory, with applications to probabilistic verification and reactive synthesis. The standard determinization constructions, such as the ones based on the Safra-Piterman's approach, work on the whole NBA. In this work we propose a divide-and-conquer determinization approach. To this end, we first classify the strongly connected components (SCCs) of the given NBA as inherently weak, deterministic accepting, and nondeterministic accepting. We then present how to determinize each type of SCC independently from the others; this results in an easier handling of the determinization algorithm that takes advantage of the structure of that SCC. Once all SCCs have been determinized, we show how to compose them so to obtain the final equivalent deterministic Emerson-Lei automaton, which can be converted into a deterministic Rabin automaton without blow-up of states and transitions. We implement our algorithm in a our tool COLA and empirically evaluate COLA with the state-of-the-art tools Spot and OWL on a large set of benchmarks from the literature. The experimental results show that our prototype COLA outperforms Spot and OWL regarding the number of states and transitions., This is an extended version of our CAV'22 paper

Critical scenario generation requires the ability of sampling critical combinations from the infinite parameter space in the logic scenario. Existing solutions aim to explore the correlation of action parameters in the initial scenario rather than action sequences. How to model action sequences so that one can further consider the effects of different action parameters in the scenario is the bottleneck of the problem. In this paper, we attack the problem by proposing the ECSAS framework. Specifically, we first propose a description language, BTScenario, allowing us to model action sequences of the scenarios. We then use reinforcement learning to search for combinations of critical action parameters. To increase efficiency, we further propose several optimizations, including action masking and replay buffer. We have implemented ECSAS, and experimental results show that it is more efficient than native approaches such as random and combination testing in various nontrivial scenarios.

supplemental-document

Additional file 7. Review history.

Projection-free online learning, which eschews the projection operation via less expensive computations such as linear optimization (LO), has received much interest recently due to its efficiency in handling high-dimensional problems with complex constraints. However, previous studies assume that any queried gradient is revealed immediately, which may not hold in practice and limits their applications. To address this limitation, we generalize the online Frank-Wolfe (OFW) algorithm and the online smooth projection-free (OSPF) algorithm, which are state-of-the-art LO-based projection-free online algorithms for non-smooth and smooth functions respectively, into a delayed setting where queried gradients can be delayed by arbitrary rounds. Specifically, the main idea of our generalized OFW is to perform an update similar to the original OFW after receiving any delayed gradient, and play the latest decision for each round. Moreover, the essential change on OSPF is to replace the sum of queried gradients, which is originally utilized in each update, with the sum of available gradients. Despite their simplicities, our novel analysis shows that under a relatively large amount of delay, the generalized OFW and OSPF enjoy the same regret bound as OFW and OSPF in the non-delayed setting, respectively.

Additional file 1. Presents the theoretic models for substitution errors introduced during amplification and sequencing, incorporating back mutations. The influence of recovered abundance-dependent accumulation of false sequencing read classifications is characterized. Related figures and literature references are contained within the note.

In this paper, we study contrastive learning from an optimization perspective, aiming to analyze and address a fundamental issue of existing contrastive learning methods that either rely on a large batch size or a large dictionary of feature vectors. We consider a global objective for contrastive learning, which contrasts each positive pair with all negative pairs for an anchor point. From the optimization perspective, we explain why existing methods such as SimCLR require a large batch size in order to achieve a satisfactory result. In order to remove such requirement, we propose a memory-efficient Stochastic Optimization algorithm for solving the Global objective of Contrastive Learning of Representations, named SogCLR. We show that its optimization error is negligible under a reasonable condition after a sufficient number of iterations or is diminishing for a slightly different global contrastive objective. Empirically, we demonstrate that SogCLR with small batch size (e.g., 256) can achieve similar performance as SimCLR with large batch size (e.g., 8192) on self-supervised learning task on ImageNet-1K. We also attempt to show that the proposed optimization technique is generic and can be applied to solving other contrastive losses, e.g., two-way contrastive losses for bimodal contrastive learning. The proposed method is implemented in our open-sourced library LibAUC (www.libauc.org)., Comment: Accepted by ICML2022

Additional file 6. Presents details on smoothing spline model construction. Related literature references are contained within the note.

Additional file 5. Presents simulation details for studying the confounding introduced by between-sample differential abundance of detected taxa in between-sample differential richness.

Trapezoidal shaping is frequently used in nuclear digital pulse processing to realize high-resolution and high-throughput spectroscopy. However, this algorithm is susceptible to parameter drifts of a front-end detector and a circuit. To examine how parameter drifts influence the performance of trapezoidal shaping, we keep the shaping parameters unchanged while varying the input signals. From shaping results, we find that the trapezoidal shapes are distorted when uni- and bi-exponential signal parameters deviating from predetermined values. While for ramp reset signal, the effects of parameter drifts can be eliminated by subtracting the baseline. Exploiting the gradient of the trapezoid top, a parameter correction method is introduced. Simulation results validate the feasibility of the algorithm and it is helpful to enhance the system robustness.

Background Positron emission tomography–computed tomography (PET–CT) and bone marrow biopsy are currently the common clinical examination of lymphoma infiltration. The aim of this research is to evaluate the value of PET–CT in diagnosis of bone marrow infiltration, clinical staging and pathological typing of lymphoma. Methods 153 cases were analyzed retrospectively to compare the consistency of PET–CT and bone marrow biopsy. We analyzed the sensitivity, accuracy and specificity of PET–CT in different clinical pathology of lymphoma. Results The PET–CT sensitivity in detecting bone marrow infiltration is 54.3% with a specificity of 80.5% and accuracy of 74.5%. In aggressive B-cell lymphoma (DLBCL, HG-BL) and MZL, PET–CT results of bone marrow infiltration showed high accuracy of 88.1% and 83.3% respectively. The median value of SUVmax in the patients detected to have bone marrow infiltration by BMB was significantly higher than patients with BMB negative results among subgroups of aggressive B-cell lymphoma, MZL and T-NHL (p Conclusion PET–CT is significant in detecting bone marrow infiltration in certain pathological types of lymphoma. However pathological inconsistencies still exist between bone marrow biopsy and PET–CT, thus PET–CT cannot completely replace biopsy.

A polyester filament winding spindle is the most complex winding rotor system, due to its high speed, heavy load, and frequency-dependent parameters; furthermore, the spindle's rotating speed constantly changes and it is continually crossing the critical speed points. This paper presents an approach to establish the finite element model of the winding spindle to predict its dynamic behavior characteristics during start-up. Firstly, three finite element models of the discrete single component were developed based on the Timoshenko beam theory. The bending, transverse shear effect, and gyroscopic moment were considered in these models. The flexible supporting system, which consists of a deep groove ball bearing and several rubber O-rings, is simplified by a nonlinear spring and damper. Its frequency-dependent dynamic supporting parameters are identified by experiment. Secondly, a fully dynamic model of the polyester winding spindle system, which consists of the cantilever supporting arm, shaft, and sleeve, as well as the flexible and rigid coupling elements, was established. Thirdly, the Newmark method was used to develop a program for solving the dynamic equations of the spindle system in MATLAB®. Based on the model of the spindle system and the computation program, the effects of the supporting stiffness, damping, and start-up time on the spindle's unbalanced response were investigated. The results indicate that the model of the spindle system presented in this paper is suitable for the prediction of the dynamic performance during its start-up.

Selective autophagy is an evolutionarily conserved mechanism that removes excess protein aggregates and damaged intracellular components. Most eukaryotic cells, including neurons, rely on proficient mitophagy responses to fine-tune the mitochondrial number and preserve energy metabolism. In some circumstances (such as the presence of pathogenic protein oligopolymers and protein mutations), dysfunctional mitophagy leads to nerve degeneration, with age-dependent intracellular accumulation of protein aggregates and dysfunctional organelles, leading to neurodegenerative disease. However, when pathogenic protein oligopolymers, protein mutations, stress, or injury are present, mitophagy prevents the accumulation of damaged mitochondria. Accordingly, mitophagy mediates neuroprotective effects in some forms of neurodegenerative disease (e.g., Alzheimer's disease, Parkinson’s disease, Huntington's disease, and Amyotrophic lateral sclerosis) and acute brain damage (e.g., stroke, hypoxic–ischemic brain injury, epilepsy, and traumatic brain injury). The complex interplay between mitophagy and neurological disorders suggests that targeting mitophagy might be applicable for the treatment of neurodegenerative diseases and acute brain injury. However, due to the complexity of the mitophagy mechanism, mitophagy can be both harmful and beneficial, and future efforts should focus on maximizing its benefits. Here, we discuss the impact of mitophagy on neurological disorders, emphasizing the contrast between the positive and negative effects of mitophagy.

Background: This study aimed to compare the early outcomes between closed reduction (CR) and open reduction (OR) in children aged 6–24 months with developmental dysplasia of the hip (DDH) who could be reduced safely and stably by the closed method.Methods: We retrospectively reviewed the medical records of 77 patients who underwent CR or OR treatment for DDH from August 2012 to October 2017. Fifty-one patients (56 hips) underwent CR, while 26 (29 hips) underwent OR. The demographic data, International Hip Dysplasia Institute classification, and acetabular index (AI) before reduction were analysed. The centre-edge angle (CEA), AI, Alsberg angle (AA), Reimer’s migration index (RMI), and height-to-width index (HWI) of the epiphysis were compared between the CR and OR groups at the final follow-up. The percentages of avascular necrosis (AVN), residual acetabular dysplasia (RAD), HWI < 0.357, RMI > 33%, AA > 81˚, and coxa magna > 15% of the normal side were calculated.Results: At the final follow up, the mean AA in the CR and OR groups were 77.66˚ (60˚–89˚) and 81.97˚ (73˚–91˚) (p=0.001), respectively, and there were 32.14% and 58.62% of the hips with an AA > 81˚ (p=0.019). The frequency of coxa magna > 15% of the normal side was higher in the OR group (60.9%) than in the CR group (6.5%) (p 33%, and AVN were not statistically different.Conclusion: In children aged 6–24 months with DDH, if a stable and safe CR can be obtained but with a widening joint space, an OR may not benefit acetabular remodelling more than a CR procedure, and thus, CR should be attempted.

This paper reports on recent research advances in supervisor synthesis, as well as industrial applications and future research challenges, especially in the context of a research project funded by Rijkswaterstaat, responsible for the construction and maintenance of infrastructure in the Netherlands.

The Lucaogou Formation in the Jimsar Sag is a typical lacustrine shale oil development area where the first large-scale shale oil field in China was discovered, and a large number of studies have been conducted on the genesis and distribution of the shale oil in this formation. However, few detailed studies have been conducted on the comparison between the characteristics of the hydrocarbons in the shale and the adjacent tight reservoirs in the same shale sequence package. In this study, the total scanning fluorescence (TSF) quantitative fluorescence technique, which has been rarely applied to and studied in the Lucaogou Formation in the Jimsar Sag, was used to conduct systematic quantitative fluorescence analysis of the shale, tight reservoirs, and crude oils in the Lucaogou Formation. The geochemical analysis was also carried out for all samples. The results revealed that the shale, tight reservoirs, and oils have similar single-peak TSF spectrograms, and different values of the TSF parameters in the Lucaogou Formation. The TSF parameters R1and R2values of the blocky shale, fractured shale, tight reservoirs, and produced oils are decreased successively. The TSF parameters can be used as indicators of hydrocarbon composition, physical properties, thermal maturity, migration, and oil-producing layers. The values of TSF parameters R1and R2are generally smaller in the upper section of the Lucaogou Formation (P2l2) than in the lower section (P2l1), indicating that the hydrocarbons in the upper section have better physical properties and higher thermal maturity. The distribution of TSF parameters R1and R2in the upper section of the Lucaogou Formation is relatively scattered, indicating that the hydrocarbons have undergone obvious lateral migration. Based on the TSF parameter comparison of core and oil samples, it was preliminarily determined that the shale oil in the upper and lower sections of the Lucaogou Formation was mainly produced from the tight reservoirs in the shale sequence. The results of this study provide new data and ideas for fine studies of the shale oil in the Lucaogou Formation in the Jimsar Sag.

Volatile profiles of oat-based foods are mainly analyzed on the oat flakes and porridge as snack or breakfast, whereas the volatile characteristics of the traditional Chinese oat meal (TCOM), a popular main food in some regions of northern China, with special strong aroma, are not known. Here, we compared the volatile profiles from headspace solid phase microextraction gas chromatography-mass spectrometry analysis of oat porridge (OP) and TCOM, which were of different processing and cooking methods, from those of different cultivars, and analyzed the effect of cultivation locations on oat volatile features. Apart from the 35 volatiles shared by OP and TCOM, there were 23 and 24 volatiles specific to OP and TCOM, respectively, with the later showing more toasting and frying-related volatiles due to the dry frying process of the grains before milling. Principle component analysis of the volatiles of OP and TCOM from 16 cultivars showed that they were clustered into two groups, and four cultivars were clustered together, independent of processing and cooking methods. The oat volatile profiles of cultivars grown in three regions of north China were dependent on the cultivation locations rather than cultivars, regardless of OP or TCOM, with those from Datong of Shanxi Province and Zhangjiakou of Hebei Province clustered together. The location effect could be due to significant less precipitation in the two regions above than the other region Ulanqab of Inner Mongolia. PRACTICAL APPLICATION: The volatile compounds in oat are closely related to cultivation regions, which could be applied as a key factor by oat producers for marketing. The four cultivars showed similar and stable volatile profiles, which could be used as reference cultivars for breeding of high-quality oat with better flavor.