Miracle-dz/UniFAR-FTUs huggingface dataset:
A statistical evaluation of associations between reproductive performance and milk composition and animal factors on grazing dairy cows in two New Zealand dairy farms

ABSTRACT Aims To determine if milk composition, milk yield, live weight, live weight change, breed and heterosis are associated with reproductive performance in dairy cows from two dairy farms under New Zealand grazing conditions. Methods Milk composition was determined in herd tests from 205 Holstein-Friesian (F), 77 Jersey (J) and 351 F × J crossbred cows from two Massey University dairy herds in the 2016 and 2017 production seasons. Mating occurred from October to December in each production season. The start of breeding to first service (SBFS), start of breeding to conception (SBCO), submission rate at 21 days (SR21), pregnancy rate at 21 (PR21) and 42 days (PR42) were calculated for each cow. These traits were analysed using mixed linear models that included fixed effects for herd, production season, regression coefficients of deviation from median calving date, lactation number, proportion of F, F × J heterosis, energy-corrected milk yield (ECMY), percentages of fat, protein and lactose, milk urea nitrogen (MUN), live weight and change in live weight, with random effects for cow and residual error. The variables with binomial distribution were analysed using logistic regression. Results Deviation from the herd’s median calving date had a significant effect (p < 0.05) on all reproductive traits. Proportion of F was significant (p = 0.022) on PR21, and F × J heterosis effects were significant on PR21 (p = 0.049) and PR42 (p = 0.046). F cows had 17.8% higher PR21 than J cows, and F × J cows had higher reproductive performance than the mean of the two purebreeds. ECMY was negatively associated with SBFS (p = 0.001) and SBCO (p = 0.001) and positively associated with PR21 (p = 0.002) and PR42 (p = 0.001). Protein percentage was positively associated (p < 0.05) with PR21 and PR42, whereas lactose percentage was negatively associated (p < 0.05) with PR21 and PR42. Cows gaining live weight were more likely to become pregnant within 21 days of the start of breeding (p = 0.020). Milk urea nitrogen was negatively associated (p = 0.042) with SR21. Conclusions and clinical relevance This study confirms that breed, heterosis, ECMY, protein and lactose percentages, live weight change and calving date are associated with the reproductive performance of grazing cows. Results from this study contrast with the historical antagonism between milk production and reproductive performance in dairy cattle, demonstrating that well managed cows can achieve high levels of production and good reproductive performance. MUN was not associated with reproductive performance traits, except with SR21.
Gadolinium chloride pre-treatment reduces the inflammatory response and preserves intestinal barrier function in a rat model of sepsis

The inflammatory response is closely associated with sepsis occurrence and progression. Damage to the function of the intestinal mucosal barrier is considered to be the ῾initiation factor᾿ for the development of multiple organ dysfunction syndrome, which is the most severe progression of sepsis. The aim of the present study was to investigate whether gadolinium chloride (GdCl3) could alleviate the systemic inflammatory response and protect the function of the intestinal mucosal barrier in a rat model of sepsis. The mechanism underlying this protective effect was also explored. Sprague-Dawley rats were divided into four groups: Sham, sham + GdCl3, cecal ligation and puncture (CLP; a model of sepsis) and CLP + GdCl3. In each group, blood was collected from the abdominal aorta, and intestinal tissue was collected after 6, 12 and 24 h of successful modeling. Levels of tumor necrosis factor-α, interleukin (IL)-6 and IL-1β were determined using ELISA. Western blot analysis was used to determine levels of occludin, tight junction protein ZO-1 (ZO-1), myosin light chain kinase 3 (MLCK), NF-κB and caspase-3 in intestinal tissues. Hematoxylin-eosin staining was used to observe the degree of damage to intestinal tissue. The results indicated that in CLP sepsis model rats treated with GdCl3, the release of systemic and intestinal pro-inflammatory factors was reduced and tissue damage was alleviated when compared with untreated CLP rats. Additionally, the expression of occludin and ZO-1 was increased, while that of NF-κB, MLCK, and caspase-3 was reduced in the CLP + GdCl3 rats compared with the CLP rats. GdCl3 may alleviate systemic and intestinal inflammatory responses and reduce the expression of MLCK through inhibition of the activation of NF-kB. The results of the present study also indicated that GdCl3 promoted the expression of occludin and ZO-1. GdCl3 was also demonstrated to reduce cell apoptosis through the inhibition of caspase-3 expression.
Does treatment with antidepressants, antipsychotics, or benzodiazepines hamper allergy skin testing?

Abstract Background Treatment with commonly used drugs such as antidepressants (ADs), antipsychotics (APs), and benzodiazepines (BDs) may hamper the use of allergy skin testing due to possible antihistaminic effects. Objective To examine the antihistaminic effect of AD, AP, and BD as measured by the ability of these drugs to suppress the normal wheal reaction caused by skin prick test (SPT). Methods Skin prick test was performed in patients receiving treatment with AD, AP, and/or BD. Double SPT was performed with histamine solutions of 10, 30, and 100 mg/ml and mean wheal diameter calculated. Results A total of 313 patients were included. 236 (75%) patients were treated with one of the examined drugs and 77 (25%) patients with more than one of these drugs. Drugs most frequently used was sertraline (n = 65), citalopram (n = 63), mirtazapine (n = 36), venlafaxine (n = 33), and quetiapine (n = 32). Treatment with mirtazapine and/or quetiapine was associated with negative SPTs in 30/36 (83%) and 22/32 (69%), and the antihistaminic effect of these drugs was dose‐dependent. For patients treated with selective serotonin reuptake inhibitors (SSRIs), tricyclic antidepressants (TCAs), serotonin norepinephrine reuptake inhibitors (SNRIs), or BD alone, almost all SPTs were positive (94%, 95%, 100%, and 100%, respectively). Negative SPTs in patients treated with SSRI, TCA, SNRI, or BD and ≥1 other of the examined drugs were associated with simultaneous treatment with mirtazapine or quetiapine in 39/44 (89%) patients. Conclusion Skin testing has little meaning in patients treated with mirtazapine or quetiapine. Treatment with SSRI, SNRI, and BD does not seem to affect the results of SPTs, whereas skin tests in patients treated with TCA should be interpreted with caution.
KIF2C promotes the proliferation of hepatocellular carcinoma cells in vitro and in vivo

Hepatocellular carcinoma (HCC) is one of the most common malignancies with high mortality and morbidity rates. In recent years, HCC targeted therapy has gained increasing attention. Due to the heterogeneity and high metastasis of HCC, more effective therapeutic targets are needed. Kinesin family member 2C (KIF2C), also known as mitotic centromere-associated kinesin, is a microtubule-based motor protein which is involved in a variety of important cellular processes, such as mitosis. The effects of KIF2C on cancer progression and development have been widely studied; however, its potential effects on HCC remains unclear. In the present study, high expression of KIF2C in human HCC tissues was demonstrated using The Cancer Genome Atlas database and immunohistochemistry assays. KIF2C expression was associated with HCC prognosis, including overall survival and disease-free survival. KIF2C expression was also associated with clinical pathological characteristics including the number of tumor nodes (P=0.015) and tumor size (P=0.009). KIF2C knockdown inhibited the proliferation of HCC cells in vitro, confirmed by MTT and colony formation assays, and suppressed tumor growth in mice which was confirmed by a xenograft mouse model. Together, the results suggested that KIF2C may serve as a promising therapeutic target for the treatment of HCC.
Protective effect of parecoxib sodium against ischemia reperfusion-induced intestinal injury

Ischemia reperfusion (I/R)-induced intestinal injury is a pathophysiological process leading to oxidative stress and inflammatory responses, and revealing its underlying mechanisms is essential for developing therapeutic strategies. Cyclooxygenase (COX) has been reported to be involved in I/R injury. Parecoxib sodium, a selective inhibitor for COX-2, exerts protective effects, such as reducing I/R-induced injuries in the heart, kidney and brain. However, the potential role of parecoxib sodium in protecting the small intestine against I/R-induced injury has rarely been investigated. Therefore, the aim of the present study was to elucidate the effects and potential mechanisms of parecoxib sodium in I/R-induced intestinal injury. In total, 60 Sprague-Dawley rats were randomly divided into four groups: Control (sham operation) group, intestinal I/R group, 10 mg/kg parecoxib sodium-pre-treated I/R (I/R + Pare/10) group and the 20 mg/kg parecoxib sodium-pre-treated I/R (I/R + Pare/20) group. A regular I/R model was established to induce the intestinal injury in rats. Parecoxib sodium at 10 or 20 mg/kg was intraperitoneally administered into rats in both I/R + Pare groups once daily for 5 consecutive days prior to ischemia. Blood samples and small intestinal tissues were collected at 2 h after reperfusion. Changes in the levels of malondialdehyde, nitric oxide, interleukin (IL)-1β, IL-8, intercellular cell adhesion molecule-1 and IL-10, as well as the total antioxidant capacity were determined using ELISA, as were the activities of superoxidase dismutase and myeloperoxidase. Furthermore, the protein expression levels of total caspase-3, cleaved caspase-3, Bcl-2 and Bax were examined via western blot analysis. In addition, the daily survival rate post-reperfusion was examined for 7 days. It was revealed that parecoxib sodium increased the levels of antioxidants and suppressed the intestinal oxidative injury induced by I/R. Moreover, parecoxib sodium downregulated the expression levels of the proinflammatory factors, but upregulated the expression levels of anti-inflammatory factors. The results also demonstrated that parecoxib sodium attenuated I/R-induced apoptosis and increased the survival rate of rats. Thus, administration of parecoxib sodium prior to intestinal I/R attenuated intestinal injury and increased the rat survival rate by inhibiting I/R-induced inflammation, oxidative stress and apoptosis.
Deliberative process in sharing information with different audiences: Eye-tracking correlates

Research on conversational pragmatics demonstrates how interlocutors tailor the information they share depending on the audience. Previous research showed that, in informal contexts, speakers often provide several alternative answers, whereas in formal contexts, they tend to give only a single answer; however, the psychological underpinnings of these effects remain obscure. To investigate this answer selection process, we measured participants’ eye movements in different experimentally modelled social contexts. Participants answered general knowledge questions by providing responses with either single (one) or plural (three) alternatives. Then, a formal (job interview) or informal (conversation with friends) context was presented and participants decided either to report or withdraw their responses after considering the given social context. Growth curve analysis on the eye movements indicates that the selected response option attracted more eye movements. There was a discrepancy between the answer selection likelihood and the proportion of fixations to the corresponding option—but only in the formal context. These findings support a more elaborate decision-making processes in formal contexts. They also suggest that eye movements do not necessarily accompany the options considered in the decision-making processes.
Targeting focal ischemic and hemorrhagic stroke neuroprotection: Current prospects for local hypothermia

Therapeutic hypothermia (TH) has applications dating back millennia. In modern history, however, TH saw its importation into medical practice where investigations have demonstrated that TH is efficacious in ischemic insults, notably cardiac arrest and hypoxic‐ischemic encephalopathy. As well, studies have been undertaken to investigate whether TH can provide benefit in focal stroke (i.e., focal ischemia and intracerebral hemorrhage). However, clinical studies have encountered various challenges with induction and maintenance of post‐stroke TH. Most clinical studies have attempted to use body‐wide cooling protocols, commonly hindered by side effects that can worsen post‐stroke outcomes. Some of the complications and difficulties with systemic TH can be circumvented by using local hypothermia (LH) methods. Additional advantages include the potential for lower target temperatures to be achieved and faster TH induction rates with LH. This systematic review summarizes the body of clinical and preclinical LH focal stroke studies and raises key points to consider for future LH research. We conclude with an overview of LH neuroprotective mechanisms and a comparison of LH mechanisms with those observed with systemic TH. Overall, whereas many LH studies have been conducted preclinically in the context of focal ischemia, insufficient work has been done in intracerebral hemorrhage. Furthermore, key translational studies have yet to be done in either stroke subtype (e.g., varied models and time‐to‐treat, studies considering aged animals or animals with co‐morbidities). Few clinical LH investigations have been performed and the optimal LH parameters to achieve neuroprotection are unknown.
Single Image 3D Object Estimation with Primitive Graph Networks

Reconstructing 3D object from a single image (RGB or depth) is a fundamental problem in visual scene understanding and yet remains challenging due to its ill-posed nature and complexity in real-world scenes. To address those challenges, we adopt a primitive-based representation for 3D object, and propose a two-stage graph network for primitive-based 3D object estimation, which consists of a sequential proposal module and a graph reasoning module. Given a 2D image, our proposal module first generates a sequence of 3D primitives from input image with local feature attention. Then the graph reasoning module performs joint reasoning on a primitive graph to capture the global shape context for each primitive. Such a framework is capable of taking into account rich geometry and semantic constraints during 3D structure recovery, producing 3D objects with more coherent structure even under challenging viewing conditions. We train the entire graph neural network in a stage-wise strategy and evaluate it on three benchmarks: Pix3D, ModelNet and NYU Depth V2. Extensive experiments show that our approach outperforms the previous state of the arts with a considerable margin.
AStitchInLanguageModels: Dataset and Methods for the Exploration of Idiomaticity in Pre-Trained Language Models

Despite their success in a variety of NLP tasks, pre-trained language models, due to their heavy reliance on compositionality, fail in effectively capturing the meanings of multiword expressions (MWEs), especially idioms. Therefore, datasets and methods to improve the representation of MWEs are urgently needed. Existing datasets are limited to providing the degree of idiomaticity of expressions along with the literal and, where applicable, (a single) non-literal interpretation of MWEs. This work presents a novel dataset of naturally occurring sentences containing MWEs manually classified into a fine-grained set of meanings, spanning both English and Portuguese. We use this dataset in two tasks designed to test i) a language model's ability to detect idiom usage, and ii) the effectiveness of a language model in generating representations of sentences containing idioms. Our experiments demonstrate that, on the task of detecting idiomatic usage, these models perform reasonably well in the one-shot and few-shot scenarios, but that there is significant scope for improvement in the zero-shot scenario. On the task of representing idiomaticity, we find that pre-training is not always effective, while fine-tuning could provide a sample efficient method of learning representations of sentences containing MWEs.
KELM: Knowledge Enhanced Pre-Trained Language Representations with Message Passing on Hierarchical Relational Graphs

Incorporating factual knowledge into pre-trained language models (PLM) such as BERT is an emerging trend in recent NLP studies. However, most of the existing methods combine the external knowledge integration module with a modified pre-training loss and re-implement the pre-training process on the large-scale corpus. Re-pretraining these models is usually resource-consuming, and difficult to adapt to another domain with a different knowledge graph (KG). Besides, those works either cannot embed knowledge context dynamically according to textual context or struggle with the knowledge ambiguity issue. In this paper, we propose a novel knowledge-aware language model framework based on fine-tuning process, which equips PLM with a unified knowledge-enhanced text graph that contains both text and multi-relational sub-graphs extracted from KG. We design a hierarchical relational-graph-based message passing mechanism, which can allow the representations of injected KG and text to mutually update each other and can dynamically select ambiguous mentioned entities that share the same text. Our empirical results show that our model can efficiently incorporate world knowledge from KGs into existing language models such as BERT, and achieve significant improvement on the machine reading comprehension (MRC) task compared with other knowledge-enhanced models.
PowerGym: A Reinforcement Learning Environment for Volt-Var Control in Power Distribution Systems

We introduce PowerGym, an open-source reinforcement learning environment for Volt-Var control in power distribution systems. Following OpenAI Gym APIs, PowerGym targets minimizing power loss and voltage violations under physical networked constraints. PowerGym provides four distribution systems (13Bus, 34Bus, 123Bus, and 8500Node) based on IEEE benchmark systems and design variants for various control difficulties. To foster generalization, PowerGym offers a detailed customization guide for users working with their distribution systems. As a demonstration, we examine state-of-the-art reinforcement learning algorithms in PowerGym and validate the environment by studying controller behaviors. The repository is available at \url{https://github.com/siemens/powergym}.
TimeTraveler: Reinforcement Learning for Temporal Knowledge Graph Forecasting

Temporal knowledge graph (TKG) reasoning is a crucial task that has gained increasing research interest in recent years. Most existing methods focus on reasoning at past timestamps to complete the missing facts, and there are only a few works of reasoning on known TKGs to forecast future facts. Compared with the completion task, the forecasting task is more difficult that faces two main challenges: (1) how to effectively model the time information to handle future timestamps? (2) how to make inductive inference to handle previously unseen entities that emerge over time? To address these challenges, we propose the first reinforcement learning method for forecasting. Specifically, the agent travels on historical knowledge graph snapshots to search for the answer. Our method defines a relative time encoding function to capture the timespan information, and we design a novel time-shaped reward based on Dirichlet distribution to guide the model learning. Furthermore, we propose a novel representation method for unseen entities to improve the inductive inference ability of the model. We evaluate our method for this link prediction task at future timestamps. Extensive experiments on four benchmark datasets demonstrate substantial performance improvement meanwhile with higher explainability, less calculation, and fewer parameters when compared with existing state-of-the-art methods.
Improving Video-Text Retrieval by Multi-Stream Corpus Alignment and Dual Softmax Loss

Employing large-scale pre-trained model CLIP to conduct video-text retrieval task (VTR) has become a new trend, which exceeds previous VTR methods. Though, due to the heterogeneity of structures and contents between video and text, previous CLIP-based models are prone to overfitting in the training phase, resulting in relatively poor retrieval performance. In this paper, we propose a multi-stream Corpus Alignment network with single gate Mixture-of-Experts (CAMoE) and a novel Dual Softmax Loss (DSL) to solve the two heterogeneity. The CAMoE employs Mixture-of-Experts (MoE) to extract multi-perspective video representations, including action, entity, scene, etc., then align them with the corresponding part of the text. In this stage, we conduct massive explorations towards the feature extraction module and feature alignment module. DSL is proposed to avoid the one-way optimum-match which occurs in previous contrastive methods. Introducing the intrinsic prior of each pair in a batch, DSL serves as a reviser to correct the similarity matrix and achieves the dual optimal match. DSL is easy to implement with only one-line code but improves significantly. The results show that the proposed CAMoE and DSL are of strong efficiency, and each of them is capable of achieving State-of-The-Art (SOTA) individually on various benchmarks such as MSR-VTT, MSVD, and LSMDC. Further, with both of them, the performance is advanced to a big extend, surpassing the previous SOTA methods for around 4.6\% R@1 in MSR-VTT.
OSSR-PID: One-Shot Symbol Recognition in P&ID Sheets using Path Sampling and GCN

In this paper, we focus on recognition of line-drawn symbols in engineering drawings with only one prototypical example per symbol available for training. In particular, Piping and Instrumentation Diagrams (P&ID) are ubiquitous in several manufacturing, oil and gas enterprises for representing engineering schematics and equipment layout. There is an urgent need to extract and digitize information from P&IDs without the cost of annotating a varying set of symbols for each new use case. A robust one-shot learning approach for symbol recognition i.e., localization followed by classification, would therefore go a long way towards this goal. Our method works by sampling pixels sequentially along the different contour boundaries in the image. These sampled points form paths which are used in the prototypical line diagram to construct a graph that captures the structure of the contours. Subsequently, the prototypical graphs are fed into a Dynamic Graph Convolutional Neural Network (DGCNN) which is trained to classify graphs into one of the given symbol classes. Further, we append embeddings from a Resnet-34 network which is trained on symbol images containing sampled points to make the classification network more robust. Since, many symbols in P&ID are structurally very similar to each other, we utilize Arcface loss during DGCNN training which helps in maximizing symbol class separability by producing highly discriminative embeddings. During inference time, a similar line based sampling procedure is adopted for generating sampled points across P&ID image. The images consist of components attached on the pipeline (straight line). The sampled points segregated around the symbol regions are used for the classification task. The proposed pipeline, named OSSR-PID, is fast and gives outstanding performance for recognition of symbols on a synthetic dataset of 100 P&ID diagrams. We also compare our method against prior-work that uses full supervision (not one-shot) on a real-world private dataset of 12 P&ID sheets and obtain comparable/superior results. Remarkably, it is able to achieve such excellent performance using only one prototypical example per symbol.