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Unsupervised domain adaptation person re-identification (UDA ReID) aims at leveraging knowledge from the source domain to help perform ReID in the unlabeled target domain. Most of existing investigations usually assign target instances identification labels through clustering them into the source person identification patterns. Unfortunately, inaccurate labels are frequently generated in such clustering setting, which undesirably deteriorates the accuracy of UDA ReID. Although a variety of noise label correcting works have been published to attempt addressing these misclustered labels, their effectiveness critically depends on the quality of the generated clustering representations, especially their differentiability, tending to result in limited efficacy. In this work, we design a desirable plug-and-play noise-label correction (PP-NLC) framework to efficiently correct the predicted target domain noise labels. Specifically, in PP-NLC we construct a noise label corrector (NLCr) and treat the predicted target domain noise labels as its probabilistic label variables (PLVs). Consequently, these noise labels are implicitly and automatically corrected through explicitly updating PLVs in the back-propagation process of NLCr, instead of conducting corrections directly on the generated noise pseudo-labels. Notably, the proposed PP-NLC framework enjoys desirable universality and can be deployed to existing UDA ReID approaches. Comprehensive experiments and analyses show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Epigenetic modifiers that accumulate in oocytes, play a crucial role in steering the developmental program of cleavage embryos and initiating life. However, the identification of key maternal epigenetic regulators remains elusive. In the findings, the essential role of maternal Ep400, a chaperone for H3.3, in oocyte quality and early embryo development in mice is highlighted. Depletion of Ep400 in oocytes resulted in a decline in oocyte quality and abnormalities in fertilization. Preimplantation embryos lacking maternal Ep400 exhibited reduced major zygotic genome activation (ZGA) and experienced developmental arrest at the 2‐to‐4‐cell stage. The study shows that EP400 forms protein complex with NFYA, occupies promoters of major ZGA genes, modulates H3.3 distribution between euchromatin and heterochromatin, promotes transcription elongation, activates the expression of genes regulating mitochondrial functions, and facilitates the expression of rate‐limiting enzymes of the TCA cycle. This intricate process driven by Ep400 ensures the proper execution of the developmental program, emphasizing its critical role in maternal‐to‐embryonic transition. [ABSTRACT FROM AUTHOR]

Unsupervised domain adaptation (UDA) aims to explore the knowledge of labeled source domain to help training the model of unlabeled target domain. By now, while most existing UDA approaches typically learn domain-invariant representations by directly matching the distributions across the domains, they pay less attention on respecting the cross-domain similarity and discrimination exploration. To address these issues, this article designs a kind of UDA with dynamic bias alignment and discrimination enhancement (UDA-DBADE). Specifically, in UDA-DBADE we define a dynamic balance factor by the ratio of the normalized cross-domain discrepancy to the discrimination, which decreases gradually in the process of UDA-DBADE. Afterward, we construct domain alignment with adversarial learning as well as distinguishable representations through advancing the discrepancy of multiple classifiers, and dynamically balance them with the defined dynamic factor. In this way, a larger weight is originally assigned on the domain alignment and then gradually on the discrimination enhancement in the learning process of UDA-DBADE. In addition, we further construct a bias matrix to characterize the discrimination alignment between the source and target domain samples. Compared to current state-of-the-art methods, UDA-DBADE achieves an average accuracy of 88.8% and 89.8% on Office-31 dataset and ImageCLEF-DA dataset, respectively. Finally, extensive experiments demonstrate that UDA-DBADE has an excellent performance. [ABSTRACT FROM AUTHOR]

Nowadays, the idea of active learning is gradually adopted to assist domain adaptation. However, due to the existence of domain shift, the traditional active learning methods originating from semi-supervised scenarios can not be directly applied to domain adaptation. To solve the problem, active domain adaptation is proposed as a new domain adaptation paradigm, which aims to improve the performance of the model by annotating a small amount of target domain samples. In this regard, we propose an active domain adaptation method named Boosting Active Domain Adaptation with Exploration of Samples (BADA), dividing Active DA into two related issues: sample selection and sample utilization. We design the instability selection criterion based on predictive consistency and the diversity selection criterion. For the remaining unlabeled samples, we design a self-training framework, which screens out reliable samples and unreliable samples through the sample screening mechanism similar to selection criteria. And we adopt respective loss functions for reliable samples and unreliable samples. Experiments show that BADA remarkably outperforms previous active learning methods and Active DA methods on several domain adaptation datasets. [ABSTRACT FROM AUTHOR]

Floating wheat is a classical herbal with potential efficacy in the treatment of hyperhidrosis. Aiming at revealing the main components and potential mechanisms of floating wheat, a comprehensive and unique phytopharmacology profile study was carried out. First, common wheat was used as a control to look for chemical markers of floating wheat. In the screening analysis, a total of 180 shared compounds were characterized in common wheat and floating wheat, respectively. The results showed that floating wheat and common wheat contain similar types of compounds. In addition, in non-targeted metabolomic analysis, when taking the contents of the constituents into account, it was found that there indeed existed quite a difference between floating wheat and common wheat and 17 potential biomarkers for floating wheat. Meanwhile, a total of seven components targeted for hyperhidrosis were screened out based on network pharmacology. Seven key differential components were screened, among which kaempferol, asiatic acid, sclareol, enoxolone, and secoisolariciresinol had higher degree values than the others. The analysis of interacting genes revealed three key genes, namely, MAP2K1, ESR1, and ESR2. The Kyoto Encyclopaedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses showed that various signaling pathways were involved. Prolactin signaling, thyroid cancer, endocrine resistance, gonadotropin secretion, and estrogen signaling pathways were the main pathways of the intervention of floating wheat in excessive sweating, which was associated with the estrogenic response, hormone receptor binding, androgen metabolism, apoptosis, cancer, and many other biological processes. Molecular docking showed that the screened key components could form good bindings with the target proteins through intermolecular forces. This study reveals the active ingredients and potential molecular mechanism of floating wheat in the treatment of hyperhidrosis and provides a reference for subsequent basic research. [ABSTRACT FROM AUTHOR]

Background: Cancer-associated fibroblasts (CAFs) play pivotal roles in chemoresistance of pancreatic ductal adenocarcinoma (PDAC). However, the underlying mechanisms are poorly understood. Revealing the cross-talk network between tumor stroma and pancreatic cancer and developing effective strategies against oxaliplatin resistance are highly desired in the clinic. Methods: High-throughput sequence was used to screened the key circRNAs transmitted by extracellular vesicles (EVs) from CAFs to pancreatic cancer cells. The associations between EV-packaged circBIRC6 and chemotherapy responsiveness were validated in a cohort of 82 cases of advanced PDAC patients. Then, the effects of EV-packaged circBIRC6 on CAF-induced oxaliplatin resistance were investigated by flow cytometry, colony formation, viability of pancreatic cancer organoids in vitro and by xenograft models in vivo. RNA pulldown, RNA immunoprecipitation, and sites mutation assays were used to reveal the underlying mechanism. Results: We identified a circRNA, circBIRC6, is significantly upregulated in CAF-derived EVs and is positively associated with oxaliplatin-based chemoresistance. In vitro and in vivo functional assays showed that CAF-derived EV-packaged circBIRC6 enhance oxaliplatin resistance of pancreatic cancer cells and organoids via regulating the non-homologous end joining (NHEJ) dependent DNA repair. Mechanistically, circBIRC6 directly binds with XRCC4 and enhanced the interaction of XRCC4 with SUMO1 at the lysine 115 residue, which facilitated XRCC4 chromatin localization. XRCC4K115R mutation dramatically abrogated the EV-packaged circBIRC6 induced effect. Moreover, combination of antisense oligonucleotide inhibitors against circBIRC6 with Olaparib dramatically suppressed chemoresistance in patient-derived xenograft models. Conclusions: Our study revealed that EV-packaged circBIRC6 confer oxaliplatin resistance in PDAC by mediating SUMOylation of XRCC4, introducing a promising predictive and therapeutic target for PDAC on oxaliplatin resistance. [ABSTRACT FROM AUTHOR]

Quantum correlations as the information resources play an important role in quantum computing and information processing. Most of the previous studies on quantum correlation dynamics are limited to bipartite systems, but only partial solutions are known to detect and quantify of multipartite systems. In this paper, we exploit the notions of tripartite quantum discord D (3) and tripartite negativity τ ABC as a measure of quantum correlations in a model of a three-qubit anisotropic Heisenberg XXZ chain in the presence of an external magnetic field and Dzyaloshinskii–Moriya (DM) interaction. We compare the dynamics of thermal D (3) with that of thermal correlation quantified by τ ABC in thermal equilibrium with external magnetic field and DM interaction. Our results show that the magnetic field, the anisotropic coupling coefficient, and the DM interaction parameter are all efficient control parameters for quantum correlation creation and enhancement. Taking the effect of the intrinsic decoherence into account, the dynamics of D (3) and the tripartite negativity τ ABC are strongly affected by the input configuration of initial states. We find that the tripartite quantum correlations dynamics for the initial GHZ-type states are only related to the external magnetic field parameter. Conversely, for the initial W-type states, the tripartite quantum correlations dynamics is independent of the external magnetic field parameter but depended on the rest parameters of the system. Furthermore, the results also show that the tripartite quantum correlations quantified by D (3) exhibit more robust against the intrinsic decoherence than that of tripartite negativity τ ABC . [ABSTRACT FROM AUTHOR]

Source-free unsupervised domain adaptation (SFUDA) aims to accomplish the task of adaptation to the target domain by utilizing pre-trained source domain model and unlabeled target domain samples, without directly accessing any source domain data. Although many SFUDA works use the pseudo-labeling strategy to improve the accuracy of pseudo-labels in the target domain, these strategies ignore the influence of domain shift on calculating the reference distribution of pseudo-labels. In this article, we propose a novel kind of SFUDA with trusted pseudo samples (SFUDA-TPS), which uses reliable feature reference distribution to solve the SFUDA problem. In SFUDA-TPS, we design a target feature correcting classifier to alleviate the problem of feature reference distribution deviating from target domain samples distribution. On this basis, the more reliable feature reference distribution is calculated by selecting the target domain samples with a high amount of information, i.e., low entropy in the fixed source domain classifier and target feature correcting classifier. The implicit alignment between the source domain and target domain is realized by learning the source domain distributions hidden in the fixed source domain classifier. Experimental evaluations illustrate the effectiveness of our proposed method in solving SFUDA tasks. [ABSTRACT FROM AUTHOR]

During the winter of 2015–2016, an extraordinary warming event occurred in the troposphere in the Arctic region. The above‐normal temperatures extended from the surface to 300 hPa in the Arctic. The number of extremely warm days derived from the surface air temperature revealed that this event was the most significant warming event since 1980. It was found that the tropospheric warming anomaly was not fully dependent on the warming anomalies at lower levels, and it formed earlier than the lower anomalies. In December 2015, there were three obvious warm centers in the troposphere in the mid‐high latitude regions in eastern North America, northern Europe, and western Asia. Albeit the strongest warming anomaly occurred in North America, the prominent warm advection to the Arctic region was transported from northern Europe during the initial stage of the Arctic mid‐upper tropospheric warming. Further investigation revealed that the teleconnection wave train stretching across North America to the North Atlantic and northern Europe contributed significantly to the existence of a poleward transport channel from northern Europe to the Arctic. The wave train spread from west to east and produced a strong southerly wind disturbance over the warm region in northern Europe. As a result, extraordinary poleward heat and water vapour transport occurred across the polar boundary causing substantial warming in the Arctic mid‐upper troposphere. During the extreme Arctic tropospheric warming event, the dynamic processes related to the heat transport over northern Europe directly contributed to the advanced rapid warming in the Arctic mid‐upper troposphere; whereas, the dynamic and thermal processes associated with the transport across the Arctic boundary jointly maintained the late stage of Arctic warming. [ABSTRACT FROM AUTHOR]

The optimal solution is output as the result to the Kmeans algorithm as the initial clustering center, and the proposed linear distance model is used to complete the clustering. Combined with the theory of target management, focusing on the job requirements and responsibilities of the counselors, the counselors' performance appraisal objectives were determined, the counselor performance appraisal system was established, and the first-level indicators and the second-level indicators and their weights were determined by using Del Illegal and Analytic Hierarchy Process (AHP). This paper constructs a performance appraisal system for local undergraduate college counselors based on management by objectives, and has carried out a pilot implementation in a college. The behavior anchoring method is used to determine the scoring standards of each index, which solves the problem of inconsistent scoring standards for different assessment subjects in the past. In the assessment results, the assessment results of the three dimensions are independently evaluated by category. This paper has a certain practical significance and reference value for the optimization research of the counselor's performance appraisal scheme under the background of institutional reform in a university. [ABSTRACT FROM AUTHOR]

[Display omitted] • Cu/γ-Al 2 O 3 and base exhibited a synergetic effect on glycerol conversion. • Dehydration and C-C cleavage were the competing reactions in glycerol conversion. • Base significantly controlled the selectivity of lactic acid/glycolic acid. • NaOH, with strong alkalinity, promoted glycerol dehydration to lactic acid. • NaHCO 3 , enhancing the proton transfer, promoted C2-C3 cleavage to glycolic acid. The numerous parallel reactions in glycerol conversion substantially challenge the effective synthesis of target chemical from oversupplied glycerol. Herein, we intensively studied the control of the complex reaction network in glycerol conversion, and realized the selective production of lactic acid or glycolic acid by integrating the use of different bases with Cu/γ-Al 2 O 3 catalyst. The results indicated that base promoted the oxidation dehydrogenation of glycerol over Cu/γ-Al 2 O 3 catalyst to glyceraldehyde intermediate. Remarkably, base significantly contributed to the control of the competitive reactions in the next glyceraldehyde transformation. DFT calculation revealed that strong base (NaOH) independently promoted the removal of H in C2 of glyceraldehyde, thereby facilitating the dehydration reaction and leading to the selective production of lactic acid. Combing with First-principles calculation, it was revealed that both NaHCO 3 and CuO species contributed to increase the electrophilicity of C2 and C3 in glyceraldehyde, thereby directing the conversion of the resultant glyceraldehyde intermediate towards the cleavage of C2-C3 to glycolal. Cu/γ-Al 2 O 3 then separately catalyzed the oxidation of glycolal selectively to glycolic acid. This work might provide a promising approach and give some clues for the valorization of glycerol in widespread applications, thus affording profitable possibilities for the sustainable biodiesel. [ABSTRACT FROM AUTHOR]

Unsupervised domain adaptation (UDA) aims to utilize knowledge from a related but inconsistently distributed source domain for target model training. The challenge of significant domain divergence, known as domain drift, can impact model performance. Although domain drift can lead to a decrease in the performance of the model in the target domain, effectively utilizing the relational information between unlabeled data can alleviate this issue. The new method of UDA, namely, confidence-diffusion instance contrastive learning (CDICL) in proposed in this article mines the deep information of the data in the target data by analyzing the pseudo labels of the target sample pairs. In this article, we propose a novel approach to unsupervised domain adaptation by constructing an information perception space via instance contrastive learning. This space incorporates samples from both source and target domains. Each target sample is assigned a pseudo-label and a corresponding confidence score, which is based on the category prior of the source samples. Throughout the training process, we continuously update the confidence associated with each pseudo-label. The classification task is performed based on the relationship of the target sample with the source domain samples. This method ensures that sample pairs belonging to the same class, regardless of whether they originate from the source or target domain, are drawn closer while those from different classes are simultaneously distanced. This approach offers a fresh perspective on unsupervised domain adaptation. The effectiveness of CDICL in UDA tasks has been demonstrated through experiments on three datasets: Office-31, Office-Home and VisDA-2017. [ABSTRACT FROM AUTHOR]

Source-free unsupervised domain adaptation (SFUDA) aims to achieve target domain predictions through a source model instead of source data. One of the representative ideas for the SFUDA problem is to apply a self-supervised pseudo-labeling strategy (SSPL) to achieve target domain adaptation, but it is prone to being plagued by noisy labels, which can lead to negative transfer. Therefore, many methods attempt to improve the SSPL and leverage confidence scores to weaken the impact of low-confidence samples on the model, which are potentially noisy samples. However, they are unable to completely overcome the problem of noisy labels because the pseudo-labels of high-confidence samples may also be incorrect. Besides, they rarely allow low-confidence samples to be added to training, which can lead to sample selection bias and thus limit the model generalization ability. In this work, we propose information re-exploitation based on confidence scores (RECS) for the SFUDA problem, in which we rethink the information brought by confidence scores and take advantage of them to solve the shortcomings of the improved SSPL. Specifically, we realize cross-domain target adaptation by the symmetric SSPL with dual denoising, and reduce the intra-domain distribution discrepancy by the discriminative class-balanced feature alignment. In this way, the model robustness and generalization are enhanced. Extensive experiments conducted on three standard datasets have demonstrated the effectiveness and superiority of our proposed method. The code is available at https://github.com/lingyuxuan1234/RECS. [ABSTRACT FROM AUTHOR]

• SWAT is modified to simulate NPS NH 4 + pollution in the Jiulong River Watershed. • NPS inputs contribute about 15.3 % to 17.3 % of NH 4 + load at watershed outlet. • NH 4 + enters water mainly via surface runoff and lateral flow, with minimal leaching. • Land-to-water NH 4 + delivery varies spatially, with high levels in agricultural areas. • Reducing fertilizer usage by 30 % lowers the NH 4 + load at outlet by 2.5∼4 %. Watershed water quality modeling is a valuable tool for managing ammonium (NH 4 +) pollution. However, simulating NH 4 + pollution presents unique challenges due to the inherent instability of NH 4 + in natural environment. This study modified the widely-used Soil and Water Assessment Tool (SWAT) model to simulate non-point source (NPS) NH 4 + processes, specifically incorporating the simulation of land-to-water NH 4 + delivery. The Jiulong River Watershed (JRW) is the study area, a coastal watershed in Southeast China with substantial sewage discharge, livestock farming, and fertilizer application. The results demonstrate that the modified model can effectively simulate the NPS NH 4 + processes. It is recommended to use multiple sets of observations to calibrate NH 4 + simulation to enhance model reliability. Despite constituting a minor proportion (5.6 %), point source inputs significantly contribute to NH 4 + load at watershed outlet (32.4∼51.9 %), while NPS inputs contribute 15.3∼17.3 % of NH 4 + loads. NH 4 + primarily enters water through surface runoff and lateral flow, with negligible leaching. Average NH 4 + land-to-water delivery rate is about 2.35 to 2.90 kg N/ha/a. High delivery rates mainly occur at agricultural areas. Notably, proposed NH 4 + mitigation measures, including urban sewage treatment enhancement, livestock manure management improvement, and fertilizer application reduction, demonstrate potential to collectively reduce the NH 4 + load at watershed outlet by 1/4 to 1/3 and significantly enhance water quality standard compliance frequency. Insights gained from modeling experience in the JRW offer valuable implications for NH 4 + modeling and management in regions with similar climates and significant anthropogenic nitrogen inputs. [Display omitted] [ABSTRACT FROM AUTHOR]

Unsupervised domain adaptation person re-identification (UDA Re-ID) aims to leverage the pedestrian knowledge learned from labeled source domain to assist in learning the pedestrian knowledge in the unlabeled target domain. Most of existing investigations typically utilize single-class center clustering algorithms to group unlabeled target domain instances. Unfortunately, single-class center clustering algorithms tend to cluster pedestrian pictures from different identities into the same cluster, leading to inaccurate labels. Training with these noisy labels can undesirably deteriorate the accuracy of UDA Re-ID. Responding to the problem, we propose a multi-class center dynamic contrastive learning (MCC-DCL) for UDA Re-ID, which includes three main parts: multi-center clustering (MCC), dynamic pseudo-labeling (DPL), and dynamic contrastive learning (DCL). In order to reduce noisy labels generated during clustering, we introduce MCC method to generates reliable pseudo-labels for instances. Furthermore, to fully utilize the knowledge learned by the network during each iteration, we propose DPL method to optimizes the pseudo-labels of instances. Finally, for improving the discriminative performance of model and its tolerance to noisy labels, we propose DCL method that utilizes dynamic pseudo-labels and dynamic contrastive loss for supervised training. Comprehensive experiments and analyses demonstrate that MCC-DCL significantly outperforms existing approaches in UDA Re-ID tasks. [ABSTRACT FROM AUTHOR]

Domain adaptation is a fundamental research problem that aims to address the domain shift issue during the transfer of a model from a labeled source domain to an unlabeled target domain. In traditional domain adaptation scenarios, it is assumed that the class spaces of both the source and target domains are identical. However, real-world applications often entail situations where the target domain contains private classes that are absent in the source domain. Forcing the alignment of these two domains may result in negative transfer. This specific concern is addressed by the emerging field of Open-set Domain Adaptation (OSDA). Previous OSDA methods attempted to align the known classes between the source and target domains while separating the unknown samples. However, these methods are inadequate in effectively discerning instances from the unknown classes. Therefore, we propose Enhancing Open-Set Domain Adaptation through Unknown-Filtering Multi-Classifier Adversarial Network (UFMCAN), which leverages multiple classifiers including a weighted auxiliary classifier, an open-set recognizer, a primary classifier and a three-way domain discriminator through adversarial learning to effectively filter instances from the unknown classes present in the target domain while concurrently aligning the source and target-known distribution. Experiments on extensive benchmarks (Office-31, Office-Home, VisDA-2017 and Office-Mix) demonstrate the superior performance of UFMCAN compared to existing state-of-the-art methods. • UFMCAN is proposed to address open set domain adaptation problem. • Multiple classifiers are used to filter unknown samples and align distribution. • Empirical results show the effectiveness of UFMCAN. [ABSTRACT FROM AUTHOR]

China's Loess Plateau regions have experienced severe soil erosion for many decades due to fragmented landscapes, steep slopes, and concentrated rainfall storm-events. Restoring sub-optimal or marginal farming fields, mostly on steep, hilly terrain, to plantation forests has been a long-standing strategic policy in China aimed at rehabilitating degraded environments and reducing soil and water erosion. While there are numerous studies that have focused on the effects of forests at controlling soil erosion at relatively short time scales, few have addressed longer-term effects of plantation forests on reducing runoff and the mechanisms that inhibit erosion. Chinese pine (Pinus tabulaeformis) has been widely planted in abandoned or reclaimed lands that were formerly farmed in Northwest China; however, there is limited knowledge about the effectiveness of the tree species at reducing soil and water erosion. In this study, we examined reduction rates of runoff and erosion by Chinese pine plantation in comparison with agricultural land as a control (i.e., wheat, a dominant agricultural commodity in the region), based on long-term monitoring of modified standard erosion plots with slopes of 10°, 15°, and 20°. Results showed that as the slope of the land increased, rates of erosion increased for both plantation and agricultural land use. However, the runoff and soil erosion rates under Chinese pine plantation forest were about 11% and 60% lower, respectively, than those under agricultural land use of the same slope. Scaling with the slope, the highest reduced runoff and erosion rates by Chinese pine forest were 17% and 72%, respectively, on 20° slope. Also, it was found that runoff rates from the forested land were positively related to erosive rainfall (i.e., rainfall when runoff generated), and varied with forest canopy coverage. The rates of runoff and erosion can be well model led with multiple regression models. Taken together, this study provides insight into the importance and potential of Chinese pine plantations in the conservation of soil and water in China's Loess Plateau. [ABSTRACT FROM AUTHOR]

With rapid development of modern social economic and people's living standards, modern landscape, as the main place of adjusting urban life, can not only provide recreation places for the fast pace of urban life, but also make people's body and mind get relaxed. Therefore, the modern landscape construction and economic management is an issue worth thinking and exploration, which attracting more and more people's attention, but its economy management lacks of scientific guidance. In order to improve the quality of modern landscape economy management, by analyzing the modern landscape construction methods and economic management, the main deficiencies of the modern landscape economic management are analyzed in this paper. And big data technology is studied to improve the quality of modern landscape economic management, a correlation analysis method under the big data fusion of modern landscape economy management mode is put forward which can provide theory for improving as well as economic management pointed out the direction of the future development of modern landscape. [ABSTRACT FROM AUTHOR]

In the software development life cycle, the quality and reliability of software are critical to software developers. Poor quality and reliability not only cause the loss of customers and sales but also increase the operational risk due to unreliable codes. Therefore, software developers should try their best to reduce such potential software defects by undertaking a software testing project. However, to pursue perfect and faultless software is unrealistic since the budget, time, and testing resources are limited, and the software developers need to reach a compromise that balances software reliability and the testing cost. Using the model presented in this study, software developers can devise multiple alternatives for a software testing project, and each alternative has its distinct allocation of human resources. The best alternative can therefore be selected. Furthermore, the allocation incorporates debuggers' learning and negligent factors, both of which influence the efficiency of software testing in practice. Accordingly, the study considers both human factors and the nature of errors during the debugging process to develop a software reliability growth model to estimate the related costs and the reliability indicator. Additionally, the issue of error classification is also extended by considering the impacts of errors on the system, and the expected time required to remove simple or complex errors can be estimated based on different truncated exponential distributions. Finally, numerical examples are presented and sensitivity analyses are performed to provide managerial insights and useful directions to inform software release strategies. [ABSTRACT FROM AUTHOR]

In this study, an imperfect debugging software reliability growth model (SRGM) with Bayesian analysis was proposed to determine an optimal software release in order to minimize software testing costs and also enhance the practicability. Generally, it is not easy to estimate the model parameters by applying MLE (maximum likelihood estimation) or LSE (least squares estimation) with insufficient historical data. Therefore, in the situation of insufficient data, the proposed Bayesian method can adopt domain experts' prior judgments and utilize few software testing data to forecast the reliability and the cost to proceed with the prior analysis and the posterior analysis. Moreover, the debugging efficiency involves testing staff's learning and negligent factors, and therefore, the human factors and the nature of debugging process are taken into consideration in developing the fundamental model. Based on this, the estimation of the model's parameters would be more intuitive and can be easily evaluated by domain experts, which is the major advantage for extending the related applications in practice. Finally, numerical examples and sensitivity analyses are performed to provide managerial insights and useful directions for software release strategies. [ABSTRACT FROM AUTHOR]

Brassica napus L. provides high-quality edible oil and clean energy for humans. For a long time, rapeseed breeders have tried to breed improved varieties through traditional breeding strategies. However, B. napus is an allotetraploid species containing many repetitive sequences. It is very inefficient to change traits through traditional genetic methods such as hybridization and random mutagenesis due to gene redundancy. Today, the burgeoning CRISPR/Cas9 technology has been applied in polyploid rapeseed for gene function research and targeted genetic improvement because of its unique advantages of high efficiency and simplicity. This review summarizes current reports about the application of CRISPR/Cas9 system for gene function research and genetic improvement in rapeseed, involving important agronomic traits such as yield, oil content, and fatty acid composition. The application status of emerging precise genome editing technology in plants and several potential limitations and technical bottlenecks in rapeseed gene editing is discussed, which will provide confidence for researchers in rapeseed gene function research and genetic improvement through genome editing technology. [ABSTRACT FROM AUTHOR]

Background: Embryo implantation into the uterus is a crucial step for human reproduction. A hypothesis has been proposed that the molecular circuit invented by trophoblasts for invasive embryo implantation during evolution might be misused by cancer cells to promote malignancy. Unfortunately, our current understanding of the molecular mechanism underlying embryo implantation is far from complete. Results: Here we used the mouse as an animal model and generated a single-cell transcriptomic atlas of the embryo implantation site of mouse uterus at the invasion phase of embryo implantation on gestational day 6. We revealed 23 distinct cell clusters, including 5 stromal cell clusters, 2 epithelial cell clusters, 1 smooth muscle cell cluster, 2 pericyte clusters, 4 endothelial cell clusters, and 9 immune cell clusters. Through data analysis, we identified differentially expression changes in all uterine cell types upon embryo implantation. By integrated with single-cell RNA-seq data from E5.5 embryos, we predicted cell–cell crosstalk between trophoblasts and uterine cell types. Conclusions: Our study provides a valuable resource for understanding of the molecular mechanism of embryo implantation. [ABSTRACT FROM AUTHOR]

If a surgeon wishes to use TXA (or is contemplating using it), that surgeon should get the patient's specific informed consent. Safety Concerns About Intrathecal TXA Extend Beyond Complications Beyond the potential complications associated with topical TXA in spine surgery, there have been several published reports of safety issues related to topical TXA. We believe that this is a dangerous practice, and surgeons should not use topical TXA in routine spine surgery until or unless its safety has been demonstrated in studies large enough to provide precise estimates of its potential risks and harms. I From the Column Editor: The goal of "On Patient Safety" column is to highlight key safety messages about clinically relevant topics. [Extracted from the article]

Conventional unsupervised domain adaptation (UDA) methods often presuppose the existence of labeled source domain samples while adapting the source model to the target domain. Nevertheless, this premise is not always tenable in the context of source-free UDA (SFUDA) attributed to data privacy considerations. Some existing methods address this challenging SFUDA problem by self-supervised learning. But inaccurate pseudo-labels are always unavoidable to degrade the performance of the target model among these methods. Therefore, we propose a promising SFUDA method, namely Generation, Division and Training (GDT) which aims to promote the reliability of pseudo-labels for self-supervised learning and encourage similar features to have closer predictions than dissimilar ones by contrastive learning. Specifically in our GDT method, we first refine pseudo-labels with deep clustering for target samples and then split them into reliable samples and unreliable samples. After that, we adopt self-supervised learning and information maximization for reliable samples training. And for unreliable samples, we conduct contrastive learning via the perspective of similarity and disparity to attract similar samples and repulse dissimilar samples, which helps pull the similar features closed and push the dissimilar features away, leading to efficient feature clustering. Thorough experimentation on three benchmark datasets substantiates the excellence of our proposed approach. [ABSTRACT FROM AUTHOR]

Survival of mesenchymal stem cells in the bone marrow is essential for bone marrow microenvironment homeostasis, but the molecular mechanisms remain poorly understood. RIPK1 has emerged as a critical molecule of programmed cell death in tissue homeostasis. However, little is known about the regulation of RIPK1 on bone marrow mesenchymal stem cells (MSCs). Here, we have investigated for the first time the role of RIPK1 in bone marrow MSCs. We have found that RIPK1 knockdown suppressed proliferation, differentiation, and migration in bone marrow MSCs. Furthermore, RIPK1 knockdown resulted in the opening of mitochondrial permeability transition pore (mPTP) and mtDNA damage, leading to mitochondrial dysfunction, and consequently induced apoptosis and necroptosis in bone marrow MSCs. Moreover, we identified that the p53-PUMA axis pathway was involved in mitochondrial dysfunction in RIPK1-deficient bone marrow MSCs. Together, our findings highlighted that RIPK1 was indispensable for bone marrow MSC survival. [ABSTRACT FROM AUTHOR]

Recently, Zhang et al. constructed one family of orthogonal product states which cannot be perfectly distinguished by local operations and classical communication (LOCC) in the 2 m ⊗ 2 n quantum system with m , n ≥ 2 (Sci Rep 6: 28864, 2016). However, it is an interesting question that what entanglement resources are necessary and/or sufficient for this task to be possible with LOCC. In this paper, we study the local distinguishability of mutually orthogonal product states with quantum entanglement as an auxiliary resource. Specifically, we put forward that the locally indistinguishable orthogonal product states in a low-dimensional system can be locally distinguished with certainty merely by utilizing an additional 2 ⊗ 2 maximally entangled state. Then, we generalize the distinguishing method to the states in 2 m ⊗ 2 n . These results reveal the phenomenon of less nonlocality with more entanglement. And they enable us to better understand the role of quantum entanglement in the local discrimination of quantum states and the relationship between entanglement and nonlocality. [ABSTRACT FROM AUTHOR]

Currently, glycated hemoglobin A1c (HbA1c) has been widely used to assess the glycemic control of patients with diabetes. However, HbA1c has certain limitations in describing both short-term and long-term glycemic control. To more accurately evaluate the glycemic control of diabetes patients, the continuous glucose monitoring (CGM) technology has emerged. CGM technology can provide robust data on short-term glycemic control and introduce new monitoring parameters such as time in range, time above range, and time below range as indicators of glycemic fluctuation. These indicators are used to describe the changes in glycemic control after interventions in clinical research or treatment modifications in diabetes patient care. Recent studies both domestically and internationally have shown that these indicators are not only associated with microvascular complications of diabetes mellitus but also closely related to cardiovascular disease complications and prognosis. Therefore, this article aims to comprehensively review the association between CGM-based glycemic parameters and cardiovascular disease complications by analyzing a large number of domestic and international literature. The purpose is to provide scientific evidence and guidance for the standardized application of these indicators in clinical practice, in order to better evaluate the glycemic control of diabetes patients and prevent the occurrence of cardiovascular disease complications. This research will contribute to improving the quality of life for diabetes patients and provide important references for clinical decision-making. [ABSTRACT FROM AUTHOR]

In the environment of smart cities, human facial age estimation has become an important research topic due to its wide applications. Although a variety of methods are proposed to depict facial biologic attributes, the underlying relationships between facial appearance and its biological aging process have not been fully explored. Although relationship learning methods have been constructed in terms of modeling either the facial representations or the facial attributes, none of them model the relationships simultaneously. In this paper, we propose a unified model to explore these relationships simultaneously, coined as JREAE. In JREAE, two covariance matrices are symmetrically constructed to capture the underlying correlations from both aspects of input facial features and output age labels. In this way, the potential relationships of both feature and label are not only modeled definitely, but also explored adaptively from the training data, which is significantly different from those methods that define the relationships manually. Then, we extend the JREAE model with deep convolutional architecture (deep-JREAE) for more powerful discrimination. In addition, we present optimization algorithms to solve the proposed models with theoretical convergence and complexity proof. Finally, through extensive experiments, we not only validate the effectiveness and superior of the proposed methods in performance, but also visualize and analyze the resulting relationships. [ABSTRACT FROM AUTHOR]

Recently, Zhang et al. raised two classes of orthogonal product states which cannot be exactly discriminated by local operations and classical communication (LOCC) in the quantum system of (2k + 1) ⊗ 2l and (3k + i) ⊗ (3l + j) (Sci. Rep. 6, 28864, 2016). However, it remains a meaningful question that what entanglement resources are necessary and/or sufficient for this task to be possible with LOCC. In this paper, we present a method of using only an auxiliary 2 ⊗ 2 maximally entangled state to complete the local discrimination. Specifically, by utilizing a two-qubit maximally entangled state as an ancillary resource, we propose a concrete protocol respectively to locally identify orthogonal product states in 5 ⊗ 6 and 5 ⊗ 5. Then we generalize the distinguishing method respectively to the states in the quantum system of (2k + 1) ⊗ 2l and (3k + i) ⊗ (3l + j). These results can make us have a better understanding of the relationship between quantum entanglement and quantum nonlocality. [ABSTRACT FROM AUTHOR]

Background: Deep vein thrombosis (DVT) significantly influences the prognosis of patients. It's necessary to analyze the risk factors for postoperative DVT in patients undergone gynecological laparoscopic surgery.Methods: Patients who underwent gynecological laparoscopic surgery from January 1, 2018 to May 31, 2020 were included. The characteristics and clinical data of DVT and non DVT patients were collected and analyzed. Logistic regression analysis was performed to identify the risk factors of DVT in patients undergone gynecological laparoscopic surgery.Results: A total of 355 patients undergone gynecological laparoscopic surgery were included, the incidence of postoperative DVT was 11.55%. There were significant differences in the age, hypertension, D-dimer, duration of surgery, intraoperative pneumoperitoneum pressure, duration of days in bed between DVT and non-DVT groups (all p < 0.05), and there were no significant difference in the BMI, diabetes, hyperlipidemia, ASA classification and intraoperative blood transfusion between DVT and non-DVT groups (all p > 0.05). Age > 50 years (OR 4.246, 95% CI 1.234-7.114), hypertension (OR 2.219, 95% CI 1.153-4.591), D-dimer > 0.5 mg/L (OR 3.914, 95% CI 1.083-5.229), duration of surgery ≥ 60 min (OR 2.542, 95% CI 1.101-4.723), intraoperative pneumoperitoneum pressure ≥ 15 mmHg (OR 3.845, 95% CI 1.119-5.218), duration of days in bed > 3 days (OR 1.566, 95% CI 1.182-1.994) was the independent risk factors for DVT in patients undergone gynecological laparoscopic surgery (all p < 0.05).Conclusions: The incidence of postoperative DVT in patients undergone gynecological laparoscopic surgery is high, and those high-risk factors should be targeted to intervene in order to reduce the postoperative DVT. [ABSTRACT FROM AUTHOR]

Depression is a highly prevalent comorbidity arising in patients with Parkinson's disease (PD). However, depression in patients with PD is poorly treated. Hydrogen sulfide (H 2 S), a neuromodulator, has the potential to relieve depression. To investigate whether H 2 S attenuates depression-like behaviours in a rat model of PD and examine the underlying mechanisms. We utilised rotenone to develop a PD model with subcutaneous injections in the dorsal cervical region of Sprague-Dawley rats. The depression-like behaviours in the rotenone-induced PD model rats were assessed through forced swimming, tail suspension, open field, novelty-suppressed feeding, and elevated plus-maze tests. The expression of postsynaptic density protein-95 and synapsin-1, related to synaptic plasticity, was detected using Western blot in the hippocampus. The hippocampal ultrastructure, including the synaptic density, length of the synaptic active zone, postsynaptic density thickness, and synaptic gap width, was detected using transmission electron microscopy. We proved that sodium hydrosulfide (NaHS; a donor of H 2 S) significantly attenuated the depression-like behaviours and disorders of hippocampal synaptic plasticity in rotenone-induced PD rats. Furthermore, inhibition of the hippocampal Warburg effect by 2-deoxyglucose abolished NaHS-enhanced hippocampal synaptic plasticity and reversed NaHS-attenuated depression-like behaviours in the rotenone-induced PD rats. H 2 S attenuates PD-associated depression by improving the hippocampal synaptic plasticity in a hippocampal Warburg effect-dependent manner. • H 2 S attenuates the depression-like behaviors in PD rats. • H 2 S prevents the disorders of hippocampal synaptic plasticity in PD rats. • Inhibited Warburg effect abolishes H 2 S-enhanced hippocampal synaptic plasticity. • Inhibited Warburg effect reverses H 2 S-attenuated depression-like behaviors. [ABSTRACT FROM AUTHOR]

The objective of unsupervised domain adaptation (UDA) for person re-identification (re-ID) is to associate person in images captured from heterogeneous camera perspectives. Currently, mainstream UDA methods for person re-ID are mainly conducted in single-source and single-target domain scenarios. Moreover, most of these methods do not take the repercussions of pseudo-label noise on model performance into consideration. Therefore, we put forward an unsupervised multi-source domain adaptation (UMDA) method for person re-ID via feature fusion and pseudo-label refinement. Firstly, our method is designed for scenarios where there exist several source domains and only one target domain. We suggest using feature fusion techniques to minimize the domain disparity among the source domains, and employ pseudo-label refinement techniques to ameliorate the ramifications of label noise on model predictions. To substantiate the effectiveness of the recommended methodology, we carry out a succession of experiments on multiple datasets. The advantage and preeminence of our proposed method can be manifested by the experimental outcomes. [ABSTRACT FROM AUTHOR]

• Foamed concrete was prepared mainly by sulphoaluminate cement (SAC) and recycled waste concrete powder (RP). • The effects of water to solid ratio (W/S), RP content and foam dosage on the properties of foamed concrete were discussed. • The pore structure and reaction mechanism were analyzed. Foamed concrete was prepared mainly by recycled waste concrete powder (RP) using wet foam mixing method. By measuring the stability time of preformed foam and setting process of paste, as well as the compressive strength, dry density, porosity and thermal conductivity of specimen, the effects of water to solid ratio (W/S), RP content and foam dosage on the properties of foamed concrete were discussed. The pore structure and reaction mechanism were analyzed by means of image analysis and X-ray diffraction. The results indicate that with the increase of W/S, the viscosity of paste decreased and the setting time increased gradually. The paste and preformed foam achieved a good matching state when W/S was 0.8. Thus the specimen had a largest porosity. With the increase of RP content, the compressive strength decreased obviously. The maximum amount of RP can reach up to 70%. Foamed concrete with different density grades can be prepared by changing preformed foam content. With the increase of foam content, compressive strength and thermal inertia were obviously decreased, and the pore distribution was gradually coarsening. Due to its inert properties, RP mainly played a filling role in the foamed concrete, but it can still have a significant effect on the setting process of paste, as well as the compressive strength and thermal conductivity of specimen. [ABSTRACT FROM AUTHOR]

Unsupervised domain adaptation (UDA) person re-identification (Re-ID) is to enhance the discriminability of Re-ID tasks in the target domain by leveraging labeled source domain data and unlabeled target domain data. Employing clustering as a mean to generate pseudo labels has emerged as a successful approach for addressing UDA Re-ID tasks. However, clustering may be disturbed by data noise when generating pseudo labels, resulting in unstable quality of pseudo labels. In addition, persons with similar colored clothing can also affect the recognition accuracy of the model. To address these aforementioned issues in UDA Re-ID, we propose a Cluster-based Dual-branch Contrastive Learning (CDCL) approach. In CDCL, we design partially grayed images as input and utilize two-branch network structure to reduce the effect of upper-body clothing color on model recognition. Based on the two-branch network architecture, we additionally propose a cluster refinement method to improve the confidence of pseudo labels, which selects samples with high similarity, i.e., samples with weighted distance between subclass samples less than a threshold, and thus makes pseudo labels more reliable. The proposed CDCL has shown its effectiveness through experimental results, achieving an mAP accuracy of 81.5%. [ABSTRACT FROM AUTHOR]

Foamed concrete is a primary choice for building envelopes due to its good thermal inertia, controllable density and excellent fire resistance. Because of the high carbon emissions and energy consumption, it has become an increasingly urgent need to find a material that can replace cement. In particular, how to effectively use solid waste is an important trend and a significant challenge. In this study, foamed concrete was prepared by recycled waste concrete powder (RP) and ground granulated blast furnace slag (GGBS) using wet foam mixing method. By measuring the stability time of preformed foam and setting process of paste, as well as the compressive strength, dry density, porosity and thermal conductivity of specimen, the effects of alkali activator dosage, CaCl 2 ·6H 2 O dosage and foam content on the properties of foamed concrete were discussed. The pore structure and reaction mechanism were analyzed by means of image analysis and X-ray diffraction. The results indicate that the compressive strength of specimen increased and then decreased with the increase of alkali content, and reached the maximum value at 5% of alkali activator. But the porosity and dry density were less affected by the amount of alkali activator. The addition of CaCl 2 ·6H 2 O significantly shortened the setting process of paste, which made the paste and preformed foam achieve a better matching state. Thus the compressive strength and thermal inertia were markedly improved, and the pores were effectively refined. Foamed concrete with different density grades can be prepared by changing preformed foam content. With the increase of foam content, compressive strength and thermal inertia were obviously decreased, and the pore distribution was gradually coarsening. Furthermore, the thermal conductivity and porosity conformed to ME2 model. The activity of both RP and GGBS can be effectively activated, the two played a coordinating and complementary role to achieve the effect of synergistic utilization of solid waste. [ABSTRACT FROM AUTHOR]

Recently, active domain adaptation (ADA) has become an emerging paradigm, as it significantly boosts the performance of the model with additional supervision. Despite the impressive performance of ADA methods, they are assumed that the source domain can be available during model adaptation. In many scenarios, the source domain can be unavailable due to issues such as data privacy. Consequently, ADA methods fail to achieve the expected results or even become invalid. To solve the problem, we design an effective framework named Feature Mixing and Self-Training (FMAS) for source-free active domain adaptation. Specifically, we adopt Clustering-based Alignment to make the classification boundary clearer to be beneficial to the subsequent sample selection, which can also achieve initial domain adaptation. When reaching active sample selection, we utilize feature mixing to explore the uncertainty and diversity of the sample. We construct the sample candidate pool by Mixup-based Uncertainty Selection and then consider the diversity to overcome the redundancy among the pool by Entropy-based Diversity Selection. Finally, for the remaining unlabeled samples, we adopt the self-training framework, which screens out high-confidence samples to explore the information of unlabeled samples. Thorough experiments show that FMAS improves average accuracy by at least 1 % compared to other methods on benchmark datasets OfficeHome and MiniDomainNet. And FMAS can also achieve better results with ADA methods without the source domain data, which demonstrates FMAS can outperform previous ADA methods. [ABSTRACT FROM AUTHOR]

We present a teaching-and-learning case study of a 2018–2019 university-based summer institute in Beijing, China that concerned environmental education in support of global citizenship, involving Chinese and international faculty from education, social work, and related disciplines. The case study identifies: (1) the context of the summer institute; (2) its goals, design, and basic content; and (3) lessons learned for social work education. Specific attention is paid to: the importance of connecting university-based ecosocial work education with community-based practice in a Chinese context; addressing global social and environmental justice concerns through rural collaborative problem-solving; and navigating disciplinary boundaries involving social work education and other disciplines and professions. [ABSTRACT FROM AUTHOR]

We recently reported that necroptosis contributed to compression-induced nucleus pulposus (NP) cells death. In the current study, we investigated the regulative effect of necroptosis inhibitor Necrostatin-1 on NP cells apoptosis and autophagy. Necrostatin-1, autophagy inhibitor 3-Methyladenine and apoptosis inhibitor Z-VAD-FMK were employed, and NP cells were exposed to 1.0 MPa compression for 0, 24 and 36 h. Necroptosis-associated molecules were measured by Western blot and RT-PCR. Autophagy and apoptosis levels were evaluated by Western blot and quantified by flow cytometry after monodansylcadaverine and Annexin V-FITC/propidium iodide staining, respectively. The cell viability and cell death were also examined. Furthermore, we measured mitochondrial membrane potential (MMP), mitochondrial permeability transition pore (MPTP) and indices of oxidative stress to assess mitochondrial dysfunction. The results established that Necrostatin-1 blocked NP cells autophagy, and 3-Methyladenine had little influence on NP cells necroptosis. The Necrostatin-1+3-Methyladenine treatment exerted almost the same role as Necrostatin-1 in reducing NP cells death. Necrostatin-1 restrained NP cells apoptosis, while Z-VAD-FMK enhanced NP cells necroptosis. The Necrostatin-1+Z-VAD-FMK treatment provided more prominent role in blocking NP cells death compared with Necrostatin-1, consistent with increased MMP, reduced opening of MPTP and oxidative stress. In summary, the synergistic utilization of Necrostatin-1 and Z-VAD-FMK is a very worthwhile solution in preventing compression-mediated NP cells death, which might be largely attributed to restored mitochondrial function. [ABSTRACT FROM AUTHOR]

We recently reported that necroptosis contributed to compression-induced nucleus pulposus (NP) cells death. In the current study, we investigated the regulative effect of necroptosis inhibitor Necrostatin-1 on NP cells apoptosis and autophagy. Necrostatin-1, autophagy inhibitor 3-Methyladenine and apoptosis inhibitor Z-VAD-FMK were employed, and NP cells were exposed to 1.0 MPa compression for 0, 24 and 36 h. Necroptosis-associated molecules were measured by Western blot and RT-PCR. Autophagy and apoptosis levels were evaluated by Western blot and quantified by flow cytometry after monodansylcadaverine and Annexin V-FITC/propidium iodide staining, respectively. The cell viability and cell death were also examined. Furthermore, we measured mitochondrial membrane potential (MMP), mitochondrial permeability transition pore (MPTP) and indices of oxidative stress to assess mitochondrial dysfunction. The results established that Necrostatin-1 blocked NP cells autophagy, and 3-Methyladenine had little influence on NP cells necroptosis. The Necrostatin-1+3-Methyladenine treatment exerted almost the same role as Necrostatin-1 in reducing NP cells death. Necrostatin-1 restrained NP cells apoptosis, while Z-VAD-FMK enhanced NP cells necroptosis. The Necrostatin-1+Z-VAD-FMK treatment provided more prominent role in blocking NP cells death compared with Necrostatin-1, consistent with increased MMP, reduced opening of MPTP and oxidative stress. In summary, the synergistic utilization of Necrostatin-1 and Z-VAD-FMK is a very worthwhile solution in preventing compression-mediated NP cells death, which might be largely attributed to restored mitochondrial function. [ABSTRACT FROM AUTHOR]

We recently reported that necroptosis contributed to compression-induced nucleus pulposus (NP) cells death. In the current study, we investigated the regulative effect of necroptosis inhibitor Necrostatin-1 on NP cells apoptosis and autophagy. Necrostatin-1, autophagy inhibitor 3-Methyladenine and apoptosis inhibitor Z-VAD-FMK were employed, and NP cells were exposed to 1.0 MPa compression for 0, 24 and 36 h. Necroptosis-associated molecules were measured by Western blot and RT-PCR. Autophagy and apoptosis levels were evaluated by Western blot and quantified by flow cytometry after monodansylcadaverine and Annexin V-FITC/propidium iodide staining, respectively. The cell viability and cell death were also examined. Furthermore, we measured mitochondrial membrane potential (MMP), mitochondrial permeability transition pore (MPTP) and indices of oxidative stress to assess mitochondrial dysfunction. The results established that Necrostatin-1 blocked NP cells autophagy, and 3-Methyladenine had little influence on NP cells necroptosis. The Necrostatin-1+3-Methyladenine treatment exerted almost the same role as Necrostatin-1 in reducing NP cells death. Necrostatin-1 restrained NP cells apoptosis, while Z-VAD-FMK enhanced NP cells necroptosis. The Necrostatin-1+Z-VAD-FMK treatment provided more prominent role in blocking NP cells death compared with Necrostatin-1, consistent with increased MMP, reduced opening of MPTP and oxidative stress. In summary, the synergistic utilization of Necrostatin-1 and Z-VAD-FMK is a very worthwhile solution in preventing compression-mediated NP cells death, which might be largely attributed to restored mitochondrial function. [ABSTRACT FROM AUTHOR]

Endoscopy is widely used as a clinical diagnosis and treatment method for certain hepatobiliary and pancreatic diseases. However, due to the distinctive epidemiological characteristics of severe acute respiratory syndrome coronavirus 2, the virus causing coronavirus disease‐2019 (COVID‐19), healthcare providers are exposed to the patient's respiratory and gastrointestinal fluids, rendering endoscopy a high risk for transmitting a nosocomial infection. This article introduces preventive measures for endoscopic treatment enacted in our medical center during COVID‐19, including the adjustment of indications, the application of endoscope protective equipment, the design and application of endoscopic masks and splash‐proof films, and novel recommendations for bedside endoscope pre‐sterilization. [ABSTRACT FROM AUTHOR]

Background and aims: The relationship between wheat root exudates and watermelon rhizosphere microbial communities in wheat/watermelon cropping system is well documented now, however, less is known about the activity of allelochemicals in wheat root exudates against watermelon Fusarium oxysporum f. sp. niveum (FON), and how it may influence belowground plant-microbe interactions for disease control. Methods: TLC board and UPLC-Q-TOF-MS techniques were used to isolate and analyze the composition of wheat root exudates, and responses of watermelon rhizosphere community against phytosphingosine were analyzed by high-throughput sequencing and quantitative PCR (qPCR). Results: Total of 15 inhibitory compounds were identified in wheat root exudates and most of them were long chain alcohols. Due to highest mass spectral fragment ion matching with already known compounds, we selected phytosphingosine as a model component to study the influence of allelochemical compounds of wheat root exudates on FON and overall rhizosphere microbial community composition of watermelon. Treatment with phytosphingosine increased the diversity of total fungi and bacteria, and altered the compositions of their communities particularly at lower concentration. The abundances and relative abundances of certain potentially plant beneficial bacteria and pathogen-antagonistic fungi were stimulated at lower concentration, while potentially pathogenic fungi including FON were inhibited at different degree. Conclusions: Phytosphingosine inhibited the FON directly, regulated the microbial community structure in watermelon rhizosphere and formed an environment not conducive to pathogen invasion, eventually alleviating the incidence of watermelon Fusarium wilt in wheat/watermelon companion cropping system. [ABSTRACT FROM AUTHOR]

Introduction: To evaluate the efficacy and safety of dipeptidyl peptidase 4 inhibitors (DPP4i) used in combination with insulin in patients with type 2 diabetes mellitus (T2DM). Methods: We searched the MEDLINE, Embase, and Cochrane library databases for randomized controlled trials (RCTs) published through June 2018. Studies with at least a 12-week treatment period were included to compare the addition of DPP4i to insulin with insulin control therapy. Meanwhile, groups on a stable insulin dosage (insulin-stable subgroup) or titrating insulin dosage (insulin-flexible subgroup) were analyzed separately. Results: Twenty-one RCTs with 3697 patients randomized to a DPP4i/insulin treatment arm and 3538 to an insulin control arm were included. DPP4i, when added to insulin therapy, led to a significantly greater reduction in HbA1c (− 0.57%, 95% CI − 0.66, − 0.48) and provided significantly greater odds of achieving the HbA1c target < 7% (OR 3.45; 95% CI 2.58, 4.63). These effects were achieved in the context of a decrease in the daily insulin requirement, without increases in hypoglycemia risk and body weight, compared with the control treatment. Subgroup analysis showed control-adjusted reductions in HbA1c from baseline in the insulin-stable subgroup (− 0.64%; 95% CI − 0.74, − 0.53) and the insulin-flexible subgroup (− 0.43%; 95% CI − 0.56, − 0.30). Other results occurred similarly in both subgroups. Conclusions: The addition of DPP4i to insulin is associated with a statistically significant reduction in glycemic control as measured by HbA1c, fasting plasma glucose, and 2-h postprandial glucose, without increasing the risk of hypoglycemia and weight gain. These conclusions were also observed in both stable-dose and flexible-dose insulin subgroups. [ABSTRACT FROM AUTHOR]

Canonical correlation analysis (CCA) is a typical and useful learning paradigm in big data analysis for capturing correlation across multiple views of the same objects. When dealing with data with additional ordinal information, traditional CCA suffers from poor performance due to ignoring the ordinal relationships within the data. Such data is becoming increasingly common, as either temporal or sequential information is often associated with the data collection process. To incorporate the ordinal information into the objective function of CCA, the so-called ordinal discriminative CCA has been presented in the literature. Although ordinal discriminative CCA can yield better ordinal regression results, its performance deteriorates when data is corrupted with noise and outliers, as it tends to smear the order information contained in class centers. To address this issue, in this article we construct a robust manifold-preserved ordinal discriminative correlation regression (rmODCR). The robustness is achieved by replacing the traditional (l2-norm) class centers with lp-norm centers, where p is efficiently estimated according to the moments of the data distributions, as well as by incorporating the manifold distribution information of the data in the objective optimization. In addition, we further extend the robust manifold-preserved ordinal discriminative correlation regression to deep convolutional architectures. Extensive experimental evaluations have demonstrated the superiority of the proposed methods. [ABSTRACT FROM AUTHOR]

Background: As the lethal bone tumor, osteosarcoma often frequently occurs in children and adolescents with locally destructive and high metastasis. Distinctive kinds of nanoplatform with high therapeutical effect and precise diagnosis for osteosarcoma are urgently required. Multimodal optical imaging and programmed treatment, including synergistic photothermal-chemodynamic therapy (PTT-CDT) elicits immunogenetic cell death (ICD) is a promising strategy that possesses high bio-imaging sensitivity for accurate osteosarcoma delineating as well as appreciable therapeutic efficacy with ignorable side-effects. Methods and results: In this study, mesoporous Cu and Ce based oxide nanoplatform with Arg-Gly-Asp (RGD) anchoring is designed and successfully constructed. After loading with indocyanine green, this nanoplatform can be utilized for precisely targeting and efficaciously ablating against osteosarcoma via PTT boosted CDT and the closely following ICD stimulation both in vitro and in vivo. Besides, it provides off-peak fluorescence bio-imaging in the second window of near-infrared region (NIR II, 1000-1700 nm) and Magnetic resonance signal, serves as the dual-mode contrast agents for osteosarcoma tissue discrimination. Conclusion: Tumor targeted Cu&Ce based mesoporous nanoplatform permits efficient osteosarcoma suppression and dual-mode bio-imaging that opens new possibility for effectively diagnosing and inhibiting the clinical malignant osteosarcoma. [ABSTRACT FROM AUTHOR]

With the increasing number of metros, the comfort and safety of crew and passengers in metro stations have been paid great attention. The environment forecasting has become very important for decision-making. The outputs of the traditional point prediction methods are some exact values in the future. However, it might be closer to the real conditions that the predicted variables are given a probability range with a different confidence rather than exact values. This paper proposes a probabilistic forecasting method of metro station environment based on autoregressive Long Short Term Memory (LSTM) network. It has a good performance to quantify the uncertainty of environment trend in a metro station. Seven-day field tests were carried out to obtain the measured data of 7 internal environmental parameters in a metro station and 8 external environment parameters. In order to ensure the prediction performance, the random forest algorithm is used to select the input variables for the proposed probabilistic forecasting method. The selected input variables and the previous predicted values are as the input variables to build the probabilistic forecasting model. The proposed method can realize to predict the probabilistic distribution of internal environmental parameters in a metro station. This work may contribute to prevent emergency events and regulate environment control system reasonably. [ABSTRACT FROM AUTHOR]