Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring Protective Factors in Wellbeing: How Sensory Processing Sensitivity and Attention Awareness Interact With Resilience

The aim of the study is to analyze the relationship between sensory processing sensitivity (SPS), attention awareness, and resilience and to determine whether attention awareness may moderate the relation between sensitivity and resilience. The sample consisted of 273 adults (239 women; Mage=24.12, SD=6.59years). The highly sensitive person scale, the resiliency assessment scale, and the mindful attention awareness scale were used in the study. The results indicate significant relationships between SPS and resilience; emotional reactivity is especially associated with lower resilience, whereas sensing the subtle is associated with higher resilience. The regression analyses revealed that SPS is a significant predictor of resilience, but diverse aspects of sensitivity explain resilience differently. Attentional awareness was found to be a significant moderator that strengthens the positive relationship between sensing the subtle and tolerance of negative emotions. The consequences of high sensitivity include high levels of distress, anxiety, and a sense of overload; therefore, searching for protective factors is important to maintain the wellbeing of highly sensitive people. As one of the characteristics of highly sensitive persons, sensing the subtle may be an important resource that allows to them to deal effectively with difficult situations. Training on attention awareness and conscious presence may be an important way to deal with negative emotions and develop personal competences. The results indicate that these strategies may be of high significance for improving wellbeing and protecting highly sensitive persons against various stress factors.
Voxel-Wise Adversarial FiboNet for 3D Cerebrovascular Segmentation on Magnetic Resonance Angiography Images

Cerebrovascular segmentation is important in various clinical applications, such as surgical planning and computer-aided diagnosis. In order to achieve high segmentation performance, three challenging problems should be taken into consideration: (1) large variations in vascular anatomies and voxel intensities; (2) severe class imbalance between foreground and background voxels; (3) image noise with different magnitudes. Limited accuracy was achieved without considering these challenges in deep learning-based methods for cerebrovascular segmentation. To overcome the limitations, we propose an end-to-end adversarial model called FiboNet-VANGAN. Specifically, our contributions can be summarized as follows: (1) to relieve the first problem mentioned above, a discriminator is proposed to regularize for voxel-wise distribution consistency between the segmentation results and the ground truth; (2) to mitigate the problem of class imbalance, we propose to use the addition of cross-entropy and Dice coefficient as the loss function of the generator. Focal loss is utilized as the loss function of the discriminator; (3) a new feature connection is proposed, based on which a generator called FiboNet is built. By incorporating Dice coefficient in the training of FiboNet, noise robustness can be improved by a large margin. We evaluate our method on a healthy magnetic resonance angiography (MRA) dataset to validate its effectiveness. A brain atrophy MRA dataset is also collected to test the performance of each method on abnormal cases. Results show that the three problems in cerebrovascular segmentation mentioned above can be alleviated and high segmentation accuracy can be achieved on both datasets using our method.
Comparison of the New Viscoelastic Coagulation Analyzer ClotPro® With ROTEM® Delta and Conventional Coagulation Tests in Critically Ill Patients With COVID-19

Background: Viscoelastic coagulation testing has been suggested to help manage coagulopathy in critically ill patients with COVID-19. However, results from different viscoelastic devices are not readily comparable. ClotPro® is a novel thromboelastometry analyzer offering a wider range of commercially available assays. Methods: We compared the results from ClotPro with results from the well-established ROTEM® Delta device and conventional coagulation tests in critically ill patients with COVID-19. Results: Viscoelastic parameters indicated the presence of a potentially hypercoagulable state in the majority of patients. In up to 95 paired measurements, we found strong correlations between several parameters routinely used in clinical practice: (i) EX test vs. EXTEM CT, A5, A10, MCF, (ii) IN test vs. INTEM A5, A10, MCF, and (iii) FIB test vs. FIBTEM A5, A10, MCF (all R > 0.7 and p < 0.001). In contrast, IN test CT vs. INTEM CT showed only a moderate correlation (R = 0.53 and p < 0.001). Clot strength parameters of both devices exhibited strong correlations with platelet counts and fibrinogen levels (all R > 0.7 and p < 0.001). Divergent correlations of intrinsically activated assays with aPTT and anti-factor Xa activity were visible. Regarding absolute differences of test results, considerable delta occurred in CT, CFT, and clot strength parameters (all p < 0.001) between both devices. Conclusions: Several parameters obtained by ClotPro show strong correlations with ROTEM Delta. Due to weak correlations of intrinsically activated clotting times and considerable absolute differences in a number of parameters, our findings underline the need for device-specific algorithms in this patient cohort.
A Scatter-and-Gather Spiking Convolutional Neural Network on a Reconfigurable Neuromorphic Hardware

Artificial neural networks (ANNs), like convolutional neural networks (CNNs), have achieved the state-of-the-art results for many machine learning tasks. However, inference with large-scale full-precision CNNs must cause substantial energy consumption and memory occupation, which seriously hinders their deployment on mobile and embedded systems. Highly inspired from biological brain, spiking neural networks (SNNs) are emerging as new solutions because of natural superiority in brain-like learning and great energy efficiency with event-driven communication and computation. Nevertheless, training a deep SNN remains a main challenge and there is usually a big accuracy gap between ANNs and SNNs. In this paper, we introduce a hardware-friendly conversion algorithm called “scatter-and-gather” to convert quantized ANNs to lossless SNNs, where neurons are connected with ternary {−1,0,1} synaptic weights. Each spiking neuron is stateless and more like original McCulloch and Pitts model, because it fires at most one spike and need be reset at each time step. Furthermore, we develop an incremental mapping framework to demonstrate efficient network deployments on a reconfigurable neuromorphic chip. Experimental results show our spiking LeNet on MNIST and VGG-Net on CIFAR-10 datasetobtain 99.37% and 91.91% classification accuracy, respectively. Besides, the presented mapping algorithm manages network deployment on our neuromorphic chip with maximum resource efficiency and excellent flexibility. Our four-spike LeNet and VGG-Net on chip can achieve respective real-time inference speed of 0.38 ms/image, 3.24 ms/image, and an average power consumption of 0.28 mJ/image and 2.3 mJ/image at 0.9 V, 252 MHz, which is nearly two orders of magnitude more efficient than traditional GPUs.
Reproductive Performance and Culling Rate of Purebred Holstein Cows and Their Crosses With Fleckvieh and Brown Swiss Cows Under Subtropical Conditions

This study aimed to elucidate the reproductive performance of purebred Holstein (HO) cows with their crosses with Fleckvieh (FV) and Brown Swiss (BS) cows under subtropical conditions. A total of 677 cows [487 HO, 104 HO × FV (HFV); 50% FV and 50% HO and 86 HO × BS (HB); 50% BS and 50% HO] were enrolled in this study. Pure HO cows had significantly greater service per conception (S/C; 3.69), days open (147.9 days), and calving interval (449.6 days), than the HFV (2.89, 116.7, and 407.4 days, respectively) and HB (3.07, 134.3, and 434.2 days, respectively) crossbred cows. At day 28, the conception percentage was significantly greater among HFV crossbred cows vs. pure HO cows [crude odds ratios (COR) = 2.16], but embryonic loss, abortion percentage, calving difficulty, and retained placenta percentage were similar (p > 0.05) among pure HO cows and their crosses. HFV crossbreds had significantly lower incidence of endometritis (COR = 0.70, p = 0.035), mastitis (COR = 0.69, p = 0.015), and ketosis (COR = 0.53, p = 0.004) vs. other cows. HB and pure HO cows had a similar incidence of mastitis, lameness, and ketosis (COR = 0.76, 0.75, and 0.81; p = 0.223, 0.468, and 0.492, respectively). HFV crossbred cows had a lower risk of culling rate than HB crossbred cows. In summary, HFV cows demonstrated the best reproductive performance in terms of S/C, days open, calving interval, conception at 28 days, mastitis percentage, ketosis percentage, and endometritis.
Epstein–Barr Virus+ B Cells in Breast Cancer Immune Response: A Case Report

EBV-specific T cells have been recently described to be involved in fatal encephalitis and myocarditis in cancer patients after immune checkpoint therapies. Here, we report the study of a human triple-negative breast cancer tumor (TNBC) and EBV-transformed B cells obtained from a patient-derived xenograft (PDX) that progressed into a lymphocytic neoplasm named xenograft-associated B-cell lymphoma (XABCL). T-cell receptor (TCR) high-throughput sequencing was performed to monitor the T-cell clonotypes present in the different samples. Forty-three T-cell clonotypes were found infiltrating the XABCL tissue after three passes in mice along 6 months. Eighteen of these (42%) were also found in the TNBC biopsy. TCR infiltrating the XABCL tissue showed a very restricted T-cell repertoire as compared with the biopsy-infiltrating T cells. Consequently, T cells derived from the TNBC biopsy were expanded in the presence of the B-cell line obtained from the XABCL (XABCL-LCL), after which the TCR repertoire obtained was again very restricted, i.e., only certain clonotypes were selected by the B cells. A number of these TCRs had previously been reported as sequences involved in infection, cancer, and/or autoimmunity. We then analyzed the immunopeptidome from the XABCL-LCL, to identify putative B-cell-associated peptides that might have been expanding these T cells. The HLA class I and class II-associated peptides from XABCL-LCL were then compared with published repertoires from LCL of different HLA typing. Proteins from the antigen processing and presentation pathway remained significantly enriched in the XABCL-LCL repertoire. Interestingly, some class II-presented peptides were derived from cancer-related proteins. These results suggest that bystander tumor-infiltrating EBV+ B cells acting as APC may be able to interact with tumor-infiltrating T cells and influence the TCR repertoire in the tumor site.
The Surgeon’s Proficiency Affected Survival Outcomes of Minimally Invasive Surgery for Early-Stage Cervical Cancer: A Retrospective Study of 851 Patients

Purpose To compare the clinical outcomes of patients with early-stage cervical cancer who underwent minimally invasive surgery (MIS) by surgeons in different phases and evaluate whether the proficiency of surgeons affects the survival outcomes. Materials and Methods A total of 851 patients with early-stage cervical cancer who underwent radical hysterectomy between January 2008 and June 2018 (every year from January to June) at a tertiary hospital were retrospectively analyzed. We categorized patients into four phases according to their sequence (phase one, 1-10 cases; phase two: 11-20 cases; phase three: 21-30 cases; phase four: > 30 cases). Demographics and clinical and pathological data were collected and analyzed. Results There were no statistical differences between the open surgery and MIS groups regarding three- and five-year overall survival (OS) and disease-free survival (DFS). The OS and DFS of patients in the MIS group in phase one were significantly lower than those in later phases and those in the open surgery group after adjustment (OS, P = 0.009; HR, 2.896; 95%CI, 1.303-6.435; DFS, P = 0.009; HR, 2.712; 95%CI, 1.289-5.706). Survival outcomes were not statistically significant when comparing different surgeons. Conclusion The phase one cases of MIS had lower OS and DFS than those in later phases and those in the open surgery group. Thus, we suggest that the proficiency of surgeons is associated with survival outcomes of MIS. Favorable outcomes can be obtained after a certain number of MIS cases.
Contextual Graph Representation Learning in Text Disambiguation

Word representations derived by neural language models have been shown to effectively carry useful semantic information to improve the final results of various Natural Language Processing tasks. The information provided by these representations encodes the subtle distinction that might occur between different meanings of the same word. However, these representations do not include the input text’s information, as the context, and a semantic knowledge base network. This integration of context and semantic network is helpful in NLP tasks, specifically in the lexical ambiguity problem. In this paper, we first analyzed the defects of current state-of-the-art representations learning approaches, and second, we present a word representation learning method, named ContextBERT, that is aware of the semantic knowledge base network and the context. ContextBERT is a novel approach to producing sense embeddings for the lexical meanings within a lexical knowledge base, using pre-trained BERT model The novel difference in our representation is the integration of the knowledge base information and the input text. Our representations enable a simple 1 -Nearest-Neighbour algorithm to perform state-of-the-art models in the English Word Sense Disambiguation task.
Mining the Cause of Political Decision-Making from Social Media: A Case Study of COVID-19 Policies across the US States

Mining the causes of political decision-making is an active research area in the ﬁeld of political science. In the past, most studies have focused on long-term policies that are collected over several decades of time, and have primarily relied on surveys as the main source of predictors. However, the recent COVID19 pandemic has given rise to a new political phenomenon, where political decision-making consists of frequent short-term decisions, all on the same controlled topic—the pandemic. In this paper, we focus on the ques-tion of how public opinion inﬂuences policy decisions, while controlling for confounders such as COVID-19 case increases or unemployment rates. Using a dataset consisting of Twitter data from the 50 US states, we classify the sentiments toward governors of each state, and conduct controlled studies and com-parisons. Based on the compiled samples of sentiments, policies, and confounders, we conduct causal inference to discover trends in political decision-making across different states.
Proteomic Studies on the Mechanism of Myostatin Regulating Cattle Skeletal Muscle Development

Myostatin (MSTN) is an important negative regulator of muscle growth and development. In this study, we performed comparatively the proteomics analyses of gluteus tissues from MSTN+/− Mongolian cattle (MG.MSTN+/−) and wild type Mongolian cattle (MG.WT) using a shotgun-based tandem mass tag (TMT) 6-plex labeling method to investigate the regulation mechanism of MSTN on the growth and development of bovine skeletal muscle. A total of 1,950 proteins were identified in MG.MSTN+/− and MG.WT. Compared with MG.WT cattle, a total of 320 differentially expressed proteins were identified in MG.MSTN cattle, including 245 up-regulated differentially expressed proteins and 75 down-regulated differentially expressed proteins. Bioinformatics analysis showed that knockdown of the MSTN gene increased the expression of extracellular matrix and ribosome-related proteins, induced activation of focal adhesion, PI3K-AKT, and Ribosomal pathways. The results of proteomic analysis were verified by muscle tissue Western blot test and in vitro MSTN gene knockdown test, and it was found that knockdown MSTN gene expression could promote the proliferation and myogenic differentiation of bovine skeletal muscle satellite cells (BSMSCs). At the same time, Co-Immunoprecipitation (CO-IP) assay showed that MSTN gene interacted with extracellular matrix related protein type I collagen α 1 (COL1A1), and knocking down the expression of COL1A1 could inhibit the activity of adhesion, PI3K-AKT and ribosome pathway, thus inhibit BSMSCs proliferation. These results suggest that the MSTN gene regulates focal adhesion, PI3K-AKT, and Ribosomal pathway through the COL1A1 gene. In general, this study provides new insights into the regulatory mechanism of MSTN involved in muscle growth and development.
Type 1 Innate Lymphoid Cells Limit the Antitumoral Immune Response

Natural killer (NK) cells are known to be able to kill established tumor cell lines, but important caveats remain regarding their roles in the detection and elimination of developing primary tumors. Using a genetic model of selective ILC1 and NK cell deficiency, we showed that these cells were dispensable for tumor immunosurveillance and immunoediting in the MCA-induced carcinogenesis model. However, we were able to generate primary cell lines derived from MCA-induced tumors with graded sensitivity to NK1.1+ cells (including NK cells and ILC1). This differential sensitivity was associated neither with a modulation of intratumoral NK cell frequency, nor the capacity of tumor cells to activate NK cells. Instead, ILC1 infiltration into the tumor was found to be a critical determinant of NK1.1+ cell-dependent tumor growth. Finally, bulk tumor RNAseq analysis identified a gene expression signature associated with tumor sensitivity to NK1.1+ cells. ILC1 therefore appear to play an active role in inhibiting the antitumoral immune response, prompting to evaluate the differential tumor infiltration of ILC1 and NK cells in patients to optimize the harnessing of immunity in cancer therapies.
Knowledge-Interactive Network with Sentiment Polarity Intensity-Aware Multi-Task Learning for Emotion Recognition in Conversations

Emotion Recognition in Conversation (ERC) has gained much attention from the NLP community recently. Some models concentrate on leveraging commonsense knowledge or multi-task learning to help complicated emotional reasoning. However, these models neglect direct utterance-knowledge interaction. In addition, these models utilize emotion-indirect auxiliary tasks, which provide limited affective information for the ERC task. To address the above is-sues, we propose a Knowledge-Interactive Network with sentiment polarity intensity-aware multi-task learning, namely KI-Net, which leverages both commonsense knowledge and sentiment lexicon to augment semantic information. Speciﬁcally, we use a self-matching module for internal utterance-knowledge interaction. Considering correlations with the ERC task, a phrase-level Sentiment Polarity Intensity Prediction (SPIP) task is devised as an auxiliary task. Experiments show that all knowledge introduction, self-matching and SPIP modules improve the model performance respectively on three datasets. Moreover, our KI-Net model shows 1.04% performance improvement over the state-of-the-art model on the IEMOCAP dataset.
Changes in Dry-Season Water Availability and Attributions in the Yellow River Basin, China

Global warming will significantly change patterns of precipitation (P) and evapotranspiration (E) and thus the surface water availability (P minus E, P–E). Changes in P–E will challenge freshwater supply, food security, and sustainability of the ecosystems. Therefore, understanding the spatiotemporal change in P–E and its drivers is key for water resources management. Here, we quantified the changes in water availability during the driest month of the year and identified its drivers in the Yellow River Basin (YRB), China, during 1982–2016. Our results showed that 89.6% of the YRB showed declining dry-season water availability in 2000–2016 compared with 1982–1999, although the total dry-season water resources (defined as the proportion of the sum of monthly P–E to the P) remained nearly unchanged due to the increased P. Changes in seasonal P and E contributed to 87.0 and 99.0% declines in dry-season water availability, respectively, demonstrating the key role of E in net seasonal water fluxes. Increased air temperature (41.8%), vegetation greening (30.8%), and vapor pressure deficit (19.2%) were the main factors driving changes in E in the YRB during the study period. Our study highlighted a drier dry season in the YRB during 1982–2016 and illustrated that climate and vegetation changes played important roles in driving changes in dry-season water availability. Seasonal water fluxes must be considered in future water resources management in the YRB, especially in the context of climate warming and revegetation programs.
Epidemiological Analysis From 2018 to 2020 in China and Prevention Strategy of Porcine Circovirus Type 2

Porcine circovirus type 2 (PCV2) is one of the smallest known animal viruses and is the main pathogen of PCV-associated diseases (PCVAD). Epidemiological surveillance results have shown that the PCV2 infection rate is on the rise in China, thus, PCV2 disease prevention and control has become a huge challenge for the Chinese swine industry. We collected clinical samples from multiple different provinces in China from 2018 to 2020 and found that the positive rate of PCV2 was 53% (3619/6872), identity between the cloned 62 ORF2 genes was 84.4–100% and identity between the cloned 62 ORF2 sequences and reference sequence was 72.9–99.8%. Genetic evolution analysis found that PCV2d accounted for 79% (49/62 samples), PCV2a for 12.9% (8/62 samples), PCV2b for 8% (5/62 samples), and PCV2c and PCV2e genotypes were not found. However, most commercial PCV2 subunit vaccines are based on the PCV2a genotype, and there are very few vaccines based on PCV2b or PCV2d. Therefore, the homologous and heterologous protection ability of PCV2b and PCV2d Cap proteins based on the baculovirus against the PCV2b and PCV2d infections was evaluated, which is expected to design and develop excellent PCV2 protein vaccine candidates. This study found that both PCV2b and PCV2d Cap proteins can increase the level of humoral immunity and cellular immune response in mice. Importantly, both PCV2b and PCV2d cap proteins can provide homologous and heterologous protection against the PCV2b and PCV2d viruses. Overall, this study provides a reference for the prevention and control of PCVAD in mainland China and the development of PCV2 vaccines.