Miracle-dz/UniFAR-FTUs huggingface dataset:
Liver Expression of IGF2 and Related Proteins in ZBED6 Gene-Edited Pig by RNA-Seq

Simple Summary Zinc finger BED-type containing 6 (ZBED6), as a regulatory factor, has different regulatory mechanisms in animal development. The intron of insulin-like growth factor 2 (IGF2) regulates the development of animal muscle and adipose by combining with the binding site of ZBED6. As a member of the insulin-like growth factor family, IGF2 plays an important role in embryonic growth and development, cell proliferation, muscle growth and genome imprinting. In order to further study the regulatory mechanism of ZBED6 on IGF2, we detected the expression of IGF2 and related genes in ZBED6 single allele knockout (ZBED6-SKO) pig tissues and analyzed differently expressed genes of the transcriptome of ZBED6-SKO pig liver. The results showed that the partial knockout of ZBED6 could affect the secretion of IGF2 in pig liver but had no significant difference at the protein level. This research provides a new idea for the interaction between IGF2 and ZBED6. Abstract Zinc finger BED-type containing 6 (ZBED6), a highly conservative transcription factor of placental mammals, has conservative interaction of insulin-like growth factor 2 (IGF2) based on the 16 bp binding sites of ZBED6 on the IGF2 sequence. IGF2 is related to embryo growth and cell proliferation. At the same time, its functions in muscle and adipose in mammals have been widely mentioned in recent studies. To further investigate the mechanism of ZBED6 on IGF2, we detected the expression of IGF2 and related genes in ZBED6 single allele knockout (ZBED6-SKO) pig tissues and analyzed the transcriptome of ZBED6-SKO pig liver. Through RNA-seq, we captured nine up-regulated genes and eight down-regulated genes which related to lipid metabolism. The results showed that the mRNA of IGF2 had an upward trend after the partial knockout of ZBED6 in liver and had no significant difference in protein expression of IGF2. In summary, ZBED6-SKO could affect the secretion of IGF2 in pig liver and its own lipid metabolism. Our research has provided basic information for revealing the regulatory mechanism of the interaction between ZBED6 and IGF2 in mammals.
Reversible Neuropsychiatric Disturbances Caused by Nitrous Oxide Toxicity: Clinical, Imaging and Electrophysiological Profiles of 21 Patients with 6–12 Months Follow-up

Purpose Nitrous oxide (N2O) abuse has become an increasingly severe problem in China. The aim of the study was to summarize the features of N2O-induced neurology and enhance the awareness of this disease among physicians. Patients and Methods We retrospectively reviewed the clinical, imaging, electrophysiological characteristics and the prognosis of patients with N2O neurotoxicity in our hospital from January 2016 to August 2019. Results Twenty-one patients (average age: 22.6±4.6 years) were collected. Eighty-six percent (18/21) patients presented with acute or subacute neurological disorders as their initial symptoms. The remaining fourteen percent (3/21) had psychiatric symptoms as the earliest symptoms. With progression, movement dysfunction appeared in ninety percent (19/21) of the patients with fifty-three percent (10/19) presented with weakness limited to both lower extremities. Sixty-two percent (13/21) of the patients presented with subjective sensory deficit. Seventy-one percent (15/21) had vibration sense impairment and positive Romberg’s sign. Sixty-seven percent of the patients had hyporeflexia or areflexia. Fourteen percent (3/21) showed positive Babinski’s sign. Seventy-eight percent (14/18) showed significantly increased homocysteine (HCY) level and only seventeen percent (3/18) showed decreased serum vitamin B12 level. T2 hyperintensity involving the posterior columns and lateral columns with inverted V sign in cervical spinal MRI had been observed in forty-seven percent (8/17) of the patients. Axonal peripheral neuropathy occurred in eighty-five percent (17/20) of the patients. The level of serum vitamin B12 and HCY, as well as imaging findings, were rapidly recovered after supplementation of Vitamin B12. Conclusion The N2O-induced neuropsychiatric disturbances mainly occurred in the young groups and should be recognized by clinicians. The prognosis of N2O intoxication is relatively good.
Rethinking and Designing a High-Performing Automatic License Plate Recognition Approach

In this paper, we propose a real-time and accurate automatic license plate recognition (ALPR) approach. Our study illustrates the outstanding design of ALPR with four insights: (1) the resampling-based cascaded framework is beneficial to both speed and accuracy; (2) the highly efficient license plate recognition should abundant additional character segmentation and recurrent neural network (RNN), but adopt a plain convolutional neural network (CNN); (3) in the case of CNN, taking advantage of vertex information on license plates improves the recognition performance; and (4) the weight-sharing character classifier addresses the lack of training images in small-scale datasets. Based on these insights, we propose a novel ALPR approach, termed VSNet. Specifically, VSNet includes two CNNs, i.e., VertexNet for license plate detection and SCR-Net for license plate recognition, integrated in a resampling-based cascaded manner. In VertexNet, we propose an efficient integration block to extract the spatial features of license plates. With vertex supervisory information, we propose a vertex-estimation branch in VertexNet such that license plates can be rectified as the input images of SCR-Net. In SCR-Net, we introduce a horizontal encoding technique for left-to-right feature extraction and propose a weight-sharing classifier for character recognition. Experimental results show that the proposed VSNet outperforms state-of-the-art methods by more than 50% relative improvement on error rate, achieving >99% recognition accuracy on CCPD and AOLP datasets with 149 FPS inference speed. Moreover, our method illustrates an outstanding generalization capability when evaluated on the unseen PKUData and CLPD datasets.
The Batyrev-Tschinkel conjecture for a non-normal cubic surface and its symmetric square.

We complete the study of points of bounded height on irreducible non-normal cubic surfaces by doing the point count on the cubic surface $W$ given by $t_0^2 t_2 = t_1^2 t_3$ over any number field. We show that the order of growth agrees with a conjecture by Batyrev and Manin and that the constant reflects the geometry of the variety as predicted by a conjecture of Batyrev and Tschinkel. We then provide the point count for its symmetric square $\mathrm{Sym}^2 W$. Although we can explain the main term of the counting function, the Batyrev--Manin conjecture is only satisfied after removing a thin set. Finally we interpret the main term of the count on $\mathrm{Sym}^2(\mathbb P^2 \times \mathbb P^1)$ done by Le Rudulier using these conjecture.
Why Convolutional Networks Learn Oriented Bandpass Filters: Theory and Empirical Support

It has been repeatedly observed that convolutional architectures when applied to image understanding tasks learn oriented bandpass filters. A standard explanation of this result is that these filters reflect the structure of the images that they have been exposed to during training: Natural images typically are locally composed of oriented contours at various scales and oriented bandpass filters are matched to such structure. We offer an alternative explanation based not on the structure of images, but rather on the structure of convolutional architectures. In particular, complex exponentials are the eigenfunctions of convolution. These eigenfunctions are defined globally; however, convolutional architectures operate locally. To enforce locality, one can apply a windowing function to the eigenfunctions, which leads to oriented bandpass filters as the natural operators to be learned with convolutional architectures. From a representational point of view, these filters allow for a local systematic way to characterize and operate on an image or other signal. We offer empirical support for the hypothesis that convolutional networks learn such filters at all of their convolutional layers. While previous research has shown evidence of filters having oriented bandpass characteristics at early layers, ours appears to be the first study to document the predominance of such filter characteristics at all layers. Previous studies have missed this observation because they have concentrated on the cumulative compositional effects of filtering across layers, while we examine the filter characteristics that are present at each layer.
Revisiting Rainbow: Promoting more insightful and inclusive deep reinforcement learning research

Since the introduction of DQN, a vast majority of reinforcement learning research has focused on reinforcement learning with deep neural networks as function approximators. New methods are typically evaluated on a set of environments that have now become standard, such as Atari 2600 games. While these benchmarks help standardize evaluation, their computational cost has the unfortunate side effect of widening the gap between those with ample access to computational resources, and those without. In this work we argue that, despite the community's emphasis on large-scale environments, the traditional small-scale environments can still yield valuable scientific insights and can help reduce the barriers to entry for underprivileged communities. To substantiate our claims, we empirically revisit the paper which introduced the Rainbow algorithm [Hessel et al., 2018] and present some new insights into the algorithms used by Rainbow.
UWB @ DIACR-Ita: Lexical Semantic Change Detection with CCA and Orthogonal Transformation

In this paper, we describe our method for detection of lexical semantic change (i.e., word sense changes over time) for the DIACR-Ita shared task, where we ranked $1^{st}$. We examine semantic differences between specific words in two Italian corpora, chosen from different time periods. Our method is fully unsupervised and language independent. It consists of preparing a semantic vector space for each corpus, earlier and later. Then we compute a linear transformation between earlier and later spaces, using CCA and Orthogonal Transformation. Finally, we measure the cosines between the transformed vectors.
Effects of Single and Repeated Oral Doses of Ochratoxin A on the Lipid Peroxidation and Antioxidant Defense Systems in Mouse Kidneys

Ochratoxin-A (OTA) is a carcinogenic and nephrotoxic mycotoxin, which may cause health problems in humans and animals, and it is a contaminant in foods and feeds. The purpose of the present study is to evaluate the effect of oral OTA exposure on the antioxidant defense and lipid peroxidation in the kidney. In vivo administration of OTA in CD1, male mice (1 or 10 mg/kg body weight in a single oral dose for 24 h and repeated daily oral dose for 72 h or repeated daily oral dose of 0.5 mg/kg bodyweight for 21 days) resulted in a significant elevation of OTA levels in blood plasma. Some histopathological alterations, transcriptional changes in the glutathione system, and oxidative stress response-related genes were also found. In the renal cortex, the activity of the glutathione-system-related enzymes and certain metabolites of the lipid peroxidation (conjugated dienes, trienes, and thiobarbituric reactive substances) also changed.
Layer Pruning via Fusible Residual Convolutional Block for Deep Neural Networks

In order to deploy deep convolutional neural networks (CNNs) on resource-limited devices, many model pruning methods for filters and weights have been developed, while only a few to layer pruning. However, compared with filter pruning and weight pruning, the compact model obtained by layer pruning has less inference time and run-time memory usage when the same FLOPs and number of parameters are pruned because of less data moving in memory. In this paper, we propose a simple layer pruning method using fusible residual convolutional block (ResConv), which is implemented by inserting shortcut connection with a trainable information control parameter into a single convolutional layer. Using ResConv structures in training can improve network accuracy and train deep plain networks, and adds no additional computation during inference process because ResConv is fused to be an ordinary convolutional layer after training. For layer pruning, we convert convolutional layers of network into ResConv with a layer scaling factor. In the training process, the L1 regularization is adopted to make the scaling factors sparse, so that unimportant layers are automatically identified and then removed, resulting in a model of layer reduction. Our pruning method achieves excellent performance of compression and acceleration over the state-of-the-arts on different datasets, and needs no retraining in the case of low pruning rate. For example, with ResNet-110, we achieve a 65.5%-FLOPs reduction by removing 55.5% of the parameters, with only a small loss of 0.13% in top-1 accuracy on CIFAR-10.
The Optical Luminosity–Time Correlation for More than 100 Gamma-Ray Burst Afterglows

Gamma-ray bursts (GRBs) are fascinating events due to their panchromatic nature. Their afterglow emission is observed from sub-TeV energies to radio wavelengths. We investigate GRBs that present an optical plateau, leveraging on the resemblance with the X-ray plateau shown in many GRB light curves (LCs). We comprehensively analyze all published GRBs with known redshifts and optical plateau observed mostly by the Neil Gehrels Swift Observatory (Swift). We fit 267 optical LCs and show the existence of the plateau in 102 cases, which is the largest compilation so far of optical plateaus. For 56 Swift GRBs with optical and X-ray plateaus, we compare the rest-frame end time at both wavelengths ( , ), and conclude that the plateau is achromatic between and . We also confirm the existence of the two-dimensional relations between and the optical luminosity at the end of the plateau emission, which resembles the same luminosity–time correlation in X-rays (Dainotti et al. ). The existence of this optical correlation has been demonstrated for the largest sample of optical plateaus in the literature to date. The squared scatter in this optical correlation is smallest for the subset of the Gold GRBs with a decrease in the scatter equivalent to 52.4% when compared to the scatter of the entire GRB sample.
Clinical Features and Short-Term Outcomes in COVID-19-Infected Patients with Cancer

Background Coronavirus disease 2019 (COVID-19) is an infectious disease that has been spreading very fast worldwide. Up to now, there is scarce information regarding the clinical features and short-term outcomes of infected patients with cancer. Methods We performed a retrospective study in Wuhan Union Hospital from Feb 14, 2020, to Mar 15, 2020, China. Data were retrieved including demographic and clinical features, laboratory findings, and outcome data. Patients were classified into the discharged group and undischarged group by the 4-week outcomes from admission. Difference analysis and correlation analysis were performed between the two groups. Results A total of 37 patients were enrolled in the study, including 27 cancer survivors in routine follow-up. Breast cancer (18.9%) was the most frequent cancer type, and common symptoms included cough (54.1%), fever (48.6%), and fatigue (27%). Lymphocytopenia and hypoproteinemia were much frequent in patients who had received chemotherapy, radiotherapy, or surgery within the past month. However, the concentration of D-dimer (median: 3.75 vs 0.43, P =0.010) and fibrin degradation products (median: 23.60 vs 1.80, P =0.002) were evidently increased in this population compared with cancer survivors. At the end of follow-up, 83.8% of the enrolled patients were discharged. Among the discharged, women (48.6%) and cancer survivors (67.6%) showed better short-term outcomes. The elevated level of FDP was significantly higher in the undischarged group (median: 21.85 vs 2.00, P =0.049). The proportion of CD3-positive lymphocyte cells and CD4-positive lymphocytes was correlated with short-term outcomes. Conclusion Peripheral lymphocyte subset (CD3-positive and CD4-positive) on admission as a novel biomarker had a potential association with early efficacy. Cancer survivors in routine follow-up would achieve better short-term outcomes. COVID-19 patients with cancer should gain more attention and close monitoring.
High Failure Rates of Locking Compression Plate Osteosynthesis with Transverse Fracture around a Well-Fixed Stem Tip for Periprosthetic Femoral Fracture

This study investigated the incidence of failure after locking compression plate (LCP) osteosynthesis around a well-fixed stem of periprosthetic femoral fractures (PFFs). We retrospectively evaluated outcomes of 63 Vancouver type B1 and C PFFs treated with LCP between May 2001 and February 2018. The mean follow-up duration was 47 months. Only patients with fracture fixation with a locking plate without supplemental allograft struts were included. We identified six periprosthetic fractures of proximal Vancouver B1 fractures with spiral pattern (Group A). Vancouver B1 fractures around the stem tip were grouped into seven transverse fracture patterns (Group B) and 38 other fracture patterns such as comminuted, oblique, or spiral (Group C). Vancouver C fractures comprised 12 periprosthetic fractures with spiral, comminuted, or oblique patterns (Group D). Fracture healing without complications was achieved in all six cases in Group A, 4/7 (57%) in Group B, 35/38 (92%) in Group C, and 11/12 (92%) in Group D, respectively. The failure rates of transverse Vancouver type B1 PFFs around the stem tip were significantly different from those of Vancouver type B1/C PFFs with other patterns. For fracture with transverse pattern around the stem tip, additional fixation is necessary because LCP osteosynthesis has high failure rates.
The Effects of Live-Fire Drills on Visual and Auditory Cognitive Performance among Firefighters

Introduction: The nature of firefighters’ work is characterized by its unexpected emergencies, sleep deprivation, night shift schedules, and smoke exposure during firefighting. Methods: Eighteen firefighters were involved in simulated live-fire activities. Firefighters’ health status in terms of cardiovascular and mental conditions was checked by a physician and through reviewing their medical records. Firefighters’ cognitive functions were evaluated by visual and auditory continuous performance tests (VCPT and ACPT) and paced visual and auditory serial addition tests (PVSAT and PASAT). Results: VCPT and ACPT scores were lower after the activity relative to baseline. The results revealed that ACPT appears to be more difficult than VCPT. Also, PVSAT and PASAT scores decreased significantly after the experiment. PASAT scores following firefighting tasks experienced a more significant decline compared to those of the PVSAT. Conclusions: These findings suggest that firefighters have visual and auditory cognitive function problems following firefighting activities. In conclusion, auditory cognitive function was more influenced than the visual ability as a result of the experiment.
Self-Expressive Graph Neural Network for Unsupervised Community Detection

Graph neural networks are able to achieve promising performance on multiple graph downstream tasks such as node classification and link prediction. Comparatively lesser work has been done to design graph neural networks (GNNs) which can operate directly for community detection in a graph. Traditionally, GNNs are trained on a semi-supervised or self-supervised loss function and then clustering algorithms are applied to detect communities. However, such decoupled approaches are inherently sub-optimal. Design of an unsupervised community detection loss function to train a GNN is a fundamental challenge to propose an integrated solution. To tackle this problem, we combine the principle of self-expressiveness with the framework of self-supervised graph neural network for unsupervised community detection for the first time in the literature. To improve the scalability of the approach, we propose a randomly sampled batch-wise training and use the principle of self-expressiveness to generate a subset of strong node similarity / dissimilarity values. These values are used to regularize the node communities obtained from a self-supervised graph neural network. Our solution is trained in an end-to-end fashion. We are able to achieve state-of-the-art community detection results on multiple publicly available datasets.
Blinded sample size recalculation in multiple composite population designs with normal data and baseline adjustments

The increasing interest in subpopulation analysis has led to the development of various new trial designs and analysis methods in the fields of personalized medicine and targeted therapies. In this paper, subpopulations are defined in terms of an accumulation of disjoint population subsets and will therefore be called composite populations. The proposed trial design is applicable to any set of composite populations, considering normally distributed endpoints and random baseline covariates. Treatment effects for composite populations are tested by combining p‐values, calculated on the subset levels, using the inverse normal combination function to generate test statistics for those composite populations while the closed testing procedure accounts for multiple testing. Critical boundaries for intersection hypothesis tests are derived using multivariate normal distributions, reflecting the joint distribution of composite population test statistics given no treatment effect exists. For sample size calculation and sample size, recalculation multivariate normal distributions are derived which describe the joint distribution of composite population test statistics under an assumed alternative hypothesis. Simulations demonstrate the absence of any practical relevant inflation of the type I error rate. The target power after sample size recalculation is typically met or close to being met.
COVID-19 Knowledge, Attitudes, and Prevention Practices Among People with Hypertension and Diabetes Mellitus Attending Public Health Facilities in Ambo, Ethiopia

Background To confirm effective preventive practice and reduce the risk of COVID-19 data on knowledge, attitude, and preventive practices (KAP) are essential. Therefore, the current study was designed to evaluate the KAP of COVID-19 among people with hypertension (HTN) and/or diabetes mellitus (DM) attending public health facilities in Ambo town. Patients and Methods Institutional-based cross-sectional study design was done among patients with HTN and/or DM from June 2020 to September 2020 at Ambo University Referral Hospital (AURH) and Ambo General Hospital (AGH). To identify the associated factors with poor practice and knowledge, logistic regression analyses were used. Results The mean age of the study respondents was 44.6 years (± 9.84) of which the majority were male 235 (55.6%) and 159 (37.59%) of the participants had good knowledge. Concerning attitude, 335 (79.2%) have strongly believed that DM and HTN patients were more at risk of death because of COVID-19. Only 44 (10.4%) of them had a good level of COVID-19 prevention practice measures. Patients who use the source of information daily were 54.4% less likely to have poor knowledge about COVID-19 than those who use it weekly. Participants with no formal education were 3 times more likely to have poor COVID-19 prevention practice than those who were with formal education, and participants who have poor knowledge about COVID-19 were 2 times more likely to have poor COVID-19 prevention practice than those who have knowledge. Conclusion The prevalence of poor knowledge about COVID-19 was low, and only less than ¾ of the participants strongly believed COVID-19 as a serious disease. A small percentage of participants had a good level of COVID-19 prevention practice. Good knowledge had an association with a good level of prevention practice. So, health sectors should work to increase accessibility of COVID-19 information.