Miracle-dz/UniFAR-FTUs huggingface dataset:
Circulating cytokines allow for identification of malignant intraductal papillary mucinous neoplasms of the pancreas

Intraductal papillary mucinous neoplasms (IPMNs) are the precursor lesions of pancreatic cancers, requiring active surgical intervention during cancer development. However, the current criteria for predicting malignant IPMNs remain challenging and limited. Hence, this study aimed to assess the discriminatory performance of circulating cytokines, including TNF‐α, IL‐2R, IL‐6, and IL‐8, then build a novel predictive model to improve the diagnostic accuracy.
Cerebellum neuropathology and motor skill deficits in fragile X syndrome

Fragile X syndrome (FXS) is a leading form of inherited intellectual disability and single‐gene cause of autism spectrum disorder (ASD) and is characterized by core deficits in cognitive flexibility, sensory sensitivity, emotion, and social interactions. Motor deficits are a shared feature of FXS and autism. The cerebellum has emerged as one of the target brain areas affected by neurodevelopmental diseases. Alterations in the cerebellar structure, circuits, and function may be the key drivers of impaired fine and gross motor skills in FXS and fragile X‐associated tremor/ataxia syndrome (FXTAS). In this review, we briefly examined recent findings in FXS and present a discussion on the literature supporting motor skill deficits in FXS. Subsequently, we focused on neuropathological alterations in the cerebellum in FXS and FXTAS. We highlight studies that have directly examined the function of fragile X mental retardation protein and related epigenetic variations in the cerebellum. Overall, we obtained considerable supporting evidence for the hypothesis that cerebellar dysfunction is evident in FXS and FXTAS; however, compared with studies on other ASD models, studies on motor skills related to fragile X disorders are particularly rare and inconclusive. Hence, future research should address FXS‐related motor and behavioral trajectories and examine the underlying mechanisms at both the cell and circuit levels.
HDAC1 and PRC2 mediate combinatorial control in SPI1/PU.1-dependent gene repression in murine erythroleukaemia

Abstract Although originally described as transcriptional activator, SPI1/PU.1, a major player in haematopoiesis whose alterations are associated with haematological malignancies, has the ability to repress transcription. Here, we investigated the mechanisms underlying gene repression in the erythroid lineage, in which SPI1 exerts an oncogenic function by blocking differentiation. We show that SPI1 represses genes by binding active enhancers that are located in intergenic or gene body regions. HDAC1 acts as a cooperative mediator of SPI1-induced transcriptional repression by deacetylating SPI1-bound enhancers in a subset of genes, including those involved in erythroid differentiation. Enhancer deacetylation impacts on promoter acetylation, chromatin accessibility and RNA pol II occupancy. In addition to the activities of HDAC1, polycomb repressive complex 2 (PRC2) reinforces gene repression by depositing H3K27me3 at promoter sequences when SPI1 is located at enhancer sequences. Moreover, our study identified a synergistic relationship between PRC2 and HDAC1 complexes in mediating the transcriptional repression activity of SPI1, ultimately inducing synergistic adverse effects on leukaemic cell survival. Our results highlight the importance of the mechanism underlying transcriptional repression in leukemic cells, involving complex functional connections between SPI1 and the epigenetic regulators PRC2 and HDAC1.
Potential negative effect of total parenteral nutrition on the human circadian clock

When patients cannot eat on their own, total parenteral nutrition (TPN) is a clinically beneficial method of maintaining nutrition. However, many animal studies have demonstrated that circadian rhythms are strongly affected by feeding time, raising the concern that continuous TPN around the clock may have an unexpected negative impact on the circadian clock of patients. To investigate this concern, we compared clock gene expression of aged subjects with or without TPN using hair follicle cells and found that while none of the non‐TPN subjects showed any obvious defects in circadian rhythms of peripheral clock gene expression, a portion of aged subjects receiving continuous TPN showed abnormal circadian rhythms in peripheral clocks. Continuous TPN around the clock may therefore potentially perturb peripheral circadian rhythms, giving rise to the proposal that TPN needs to be administered with consideration to time factors.
Réécriture et Détection d’Implication Textuelle

Nous présentons un système de normalisation de la variation syntaxique qui permet de mieux reconnaître la relation d’implication textuelle entre deux phrases. Le système est évalué sur une suite de tests comportant 2 520 paires test et les résultats montrent un gain en précision par rapport à un système de base variant entre 29.8 et 78.5 points la complexité des cas considérés.
ScholarWorks ScholarWorks School-based alcohol education School-based alcohol education

Abstract The purpose of school-based alcohol education is to reduce the number of alcohol-related problems and deaths among young people. Effective educational efforts, hopefully, will lessen children's and adolescents' chances of developing alcohol related problems as they mature and become adults. This paper will review the literature concerning school-based alcohol education in primary and secondary schools in the United States.
Supplementary Material for Do What You Can, With What You Have: Scale-aware and High Quality Monocular Depth Estimation Without Real World Labels

As the main focus of this work is to develop a training methodology, we adopt the light-weight network architecture from Monodepth2 [6] as our MDE and pose estimation network. More specifically, we use ResNet18 [8] as our depth as well as pose encoder, whereas a DispNet [11] based decoder is used for the MDE model. The architecture of our depth and pose decoders are explained in Tab. 1 and Tab. 2 respectively, these tables are taken directly from Monodepth2 [6] and added here to make the material selfcontained.
Depthwise Convolution For Compact Object Detector In nighttime Images

Despite thermal imaging primarily used for nighttime surveillance, uniform temperature of object and background makes it difficult to acquire details in the scene being observed and thereby object detection. Further, thermal images collected over long distances degrade the spatial resolution of the acquired objects and so do the moving objects leading to noisy features. We present a computationally efficient object detection approach using Depthwise Deep Convolutional Neural Network (DDCNN) for detecting and classifying objects in nighttime images under low resolution. The Depthwise Convolution (DC) employed in the proposed approach minimises the network’s computational complexity resulting in the lowest number of training parameters (i.e., 3M) as compared to the other existing state-of-the-art methods such as FRCNN (52M), SSD (24M) and YOLO-v3 (61M) parameters. Further, by introducing novel Tversky and Intersection over Union (IoU) loss functions into the compact architectural design, we improve nighttime object detection accuracy. The validity of the proposed model is assessed on numerous datasets such as FLIR, KAIST, MS, and our internal dataset having multiple objects in each image. The experimental results from the proposed method indicate both quantitative and qualitative improvements over the recent state-of-the-art methods for nighttime imaging. The proposed approach achieves a mean Average Precision (mAP) of 52.39% and a highest individual object detection accuracy of 72.70% accuracy for cars in nigh-time situations suggesting applications in real-time use cases.
Optimising rPPG Signal Extraction by Exploiting Facial Surface Orientation

Remote photoplethysmography (rPPG) is a contactless method to measure human vital signs by detecting subtle skin color changes through a camera. Although many studies have used region of interest (ROI) selection tools to improve rPPG signal extraction, no study has investigated the influence of the ROI’s surface orientation. We propose a novel ‘angle map’ representation of the face to study the effects of the surface orientation on the extracted rPPG signal. The angle map is generated by mapping each facial pixel to an angle of reflection (angle between the skin surface and the camera) calculated from the surface normal of the facial landmarks and the camera axis. Our results show that surface orientation significantly affects the correlation between the extracted rPPG signal and ground truth blood volume pulse (BVP). Regions with small angles of reflection contained stronger signals, which explains why areas near the cheeks and forehead are often chosen for rPPG signal extraction. Moreover, we applied a thresholding method to the angle map and demonstrated its potential for dynamic ROI selection, thereby optimising the rPPG signal extraction process.
Deep Learning Classifier for Advancing Video Monitoring of Atrial Fibrillation

Video-based non-contact monitoring of cardiac conditions offers an attractive alternative to contact-based monitoring using sensors attached to the skin. Specifically, video monitoring can significantly improve the monitoring of atrial fibrillation; a prevalent and growing cardiac disease affecting millions around the world. We propose and investigate the performance of a deep learning classifier for the detection of atrial fibrillation. We compare the performance of the proposed classifier with a benchmark of five existing classifiers based on traditional signal processing and machine learning. In addition, we compare performance across various sensing modalities, including a high-end camera, a webcam, an earlobe oximeter, and an electrocardiogram holter. To this end, we conduct a clinical study with 55 atrial fibrillation patients in a hospital setting. Results show that the proposed classifier outperforms the benchmark, especially when using a low-cost webcam, and provides consistently accurate detection when applied to an electrocardiogram, a photo plethysmography sensor, and two video camera sensors, thereby placing video monitoring on par with its contract-based counterparts.
What’s in a Caption? Visual Description Linguistic Patterns and Their Effects on Models and Metrics

MSR-VTT: The MSR-VTT (MSR Video to Text) [XMYR16] dataset is a medium-scale open domain benchmark for visual description. It was originally collected using 257 YouTube search queries across 20 categories, with 118 videos collected for each query (41.2 Hours). The dataset is annotated with 20 captions per video by 1,327 Amazon Mechanical Turk workers. Each video has a duration between 10 and 30 seconds, with an average of two shots per clip.
Analyzing water monitoring data with RCA-based approaches

This paper is a short feedback on a collaborative research work by computer scentists and hydroecologists. We have applied Relational Concept Analysis on complex data about running water characteristics (physical, biological and chemical parameters), to answer various questions. Two approaches are presented and discussed: the ﬁrst one extracts patterns from temporal data, the second one extracts rules from a multi-relational dataset
Seed Dormancy and Germination Response of Aegilops tauschii to Exogenous Application of GA 3 and Warm Water

The invasion of Aegilops tauschii has seriously threatened the wheat production in China. The seeds of A. tauschii display obvious dormancy. In present study, seed dormancy and germination of A. tauschii were investigated by treating seeds with exogenous GA 3 and different hot water treatments. The results revealed that exogenous GA 3 and warm water soaking treatments were beneficial to dormancy breaking and germination of seeds. Among all treatments, 500 mg·L -1 GA 3 for 24 h and hot water treatments with 45ºC warm for 5 min significantly improved germination. During germination, SOD activity in seeds treated with GA 3 (500 mg·L -1 ) and warm water (45ºC) increased significantly. Increased SOD activity reduced the degree of oxidative damage of plasma membrane and resulted a continuous decrease in thiobarbituric acid reactive substance assay (TBARS) content. Thereby, seeds were prompted to develop in a direction conducive to germination. In addition, GA 3 (500 mg L -1 ) and warm water (45ºC) treatments increased the endogenous levels of gibberellin (GA 3 ), auxin (IAA), and zeatin riboside (ZR), and decreased the endogenous ABA in seeds. This had resulted significantly higher ratios of GA 3 /ABA, IAA/ABA and ZR/ABA than those ratios of distilled water (room temperature) treated plants. The effect eventually promoted the germination of E. ulmoides seeds. Based on the changes in enzyme activity and endogenous hormones during seed germination, it is concluded that 500 mg·L -1 GA 3 for 24 h and hot water treatments with 45ºC warm for 5 min significantly improved germination of A. tauschii seeds. © 2020 Friends Science Publishers
Inequality in the household: neonatal health eﬀects on education outcomes and parents’ compensations among siblings.

This study estimates neonatal health associations with future health and education outcomes and explores parents’ reactions to low health endowments using a 9-year panel of Mexican siblings. We contribute to the literature by providing results on different aspects of the uterine environment in poorer settings and offering a more dynamic picture of how initial health influences education and parents’ compensations among siblings, from childhood to adulthood. Our results are robust to different family fixed-effects models suggesting that unhealthy children at birth have worse adult health, a lower height, and fewer years of schooling at any age between 5 and 22. We offer evidence of reinforcing and compensating patterns among siblings: less-educated parents spend on average 15% fewer economic resources on their less-healthy children’s education, while wealthier parents invest 14% more. Notably, the compensating pattern in richer settings starts early in life and remains consistent across all ages. A, B, and C show the average results of our neonatal measures on years of schooling, if the child is attending school, and grade repetition. Once we introduce different sets of fixed-effects the results are not clearly identified. The signs of the coeﬀicients are also not consistent across neonatal measures.
An Behavioral Finance Analysis Using Learning Vector An Behavioral Finance Analysis Using Learning Vector Quantization in the Taiwan Stock Market Index Future Quantization in the Taiwan Stock Market Index Future

There are various types of trading behavior in the stock market. And the buying or selling activities in many investment strategies are influenced by numerous factors respectively, such as fundamental analysis, macroeconomic analysis, and news analysis. Consequently, various factors will reflect on market price. Random Walk in financial engineering is not the focus in this paper. Otherwise, the importance of the technique analysis about Taiwan Stock Index Futures will be emphasized in this research. It is the intention of this paper to investigate the information content of Open, High, Low, Close prices in the previous trading day and relative higher and lower points in the prior period of the current trading day, as well as their prices in analyzing Taiwan Stock Index Future. The predictability of Learning Vector Quantizationl Network can clearly be seen from the empirical result.
PerturbNet predicts single-cell responses to unseen chemical and genetic perturbations

Small molecule treatment and gene knockout or overexpression induce complex changes in the molecular states of cells, and the space of possible perturbations is too large to measure exhaustively. We present PerturbNet, a deep generative model for predicting the distribution of cell states induced by unseen chemical or genetic perturbations. Our key innovation is to use high-throughput perturbation response data such as Perturb-Seq to learn a continuous mapping between the space of possible perturbations and the space of possible cell states. Using Sci-Plex and LINCS datasets, PerturbNet can accurately predict the distribution of gene expression changes induced by unseen small molecules given only their chemical structures. PerturbNet also accurately predicts gene expression changes induced by shRNA, CRISPRi, or CRISPRa perturbations using a perturbation network trained on gene functional annotations. Furthermore, self-supervised sequence embeddings allow PerturbNet to predict gene expression changes induced by missense mutations. We also use PerturbNet to attribute cell state shifts to specific perturbation features, including atoms and functional gene annotations. Finally, we leverage PerturbNet to design perturbations that achieve a desired cell state distribution. PerturbNet holds great promise for understanding perturbation responses and ultimately designing novel chemical and genetic interventions.
Signals Recognition by CNN Based on Attention Mechanism

: Automatic modulation recognition is a key technology in non-collaborative communication. However, it is affected by complex electromagnetic environments, leading to low recognition accuracy. To address this problem, this paper develops a ResNext signal recognition model based on an attention mechanism. Firstly, a channel, including additive Gaussian white noise (AWGN), Rician multipath fading
Porous TiO2/Carbon Dot Nanoflowers with Enhanced Surface Areas for Improving Photocatalytic Activity

Electron–hole recombination and the narrow-range utilization of sunlight limit the photocatalytic efficiency of titanium oxide (TiO2). We synthesized carbon dots (CDs) and modified TiO2 nanoparticles (NPs) with a flower-like mesoporous structure, i.e., porous TiO2/CDs nanoflowers. Among such hybrid particles, the CDs worked as photosensitizers for the mesoporous TiO2 and enabled the resultant TiO2/CDs nanoflowers with a wide-range light absorption. Rhodamine B (Rh-B) was employed as a model organic pollutant to investigate the photocatalytic activity of the TiO2/CDs nanoflowers. The results demonstrated that the decoration of the CDs on both the TiO2 nanoflowers and the (commercially available AEROXIDE TiO2) P25 NPs enabled a significant improvement in the photocatalytic degradation efficiency compared with the pristine TiO2. The TiO2/CDs nanoflowers, with their porous structure and larger surface areas compared to P25, showed a higher efficiency to prevent local aggregation of carbon materials. All of the results revealed that the introduced CDs, with the unique mesoporous structure, large surface areas and loads of pore channels of the prepared TiO2 NPs, played important roles in the enhancement of the photocatalytic efficiency of the TiO2/CDs hybrid nanoflowers.
EFFECT OF POINT MUTATIONS ON THE TOXICITY OF SHIGA TOXIN A1 SUBUNIT

Shiga toxin (Stx)-producing Escherichia coli (STEC) infections can lead to life-threatening complications, including hemorrhagic colitis (HC) and hemolytic-uremic syndrome (HUS), which is the most common cause of acute renal failure in children in the United States. Previous studies have shown that STEC strains producing Stx2 are more commonly associated with HUS than those producing Stx1. Our lab has shown that the A1 subunit of Stx2 (Stx2A1) is more active than the A1 subunit of Stx1 (Stx1A1). The purpose of my study is to understand the basis for the higher toxicity of Stx2A1 by interchanging its residues with Stx1A1. Initially, the activity of wild type Stx1A1 and Stx2A1 was compared in yeast in order to establish a base line to study the effect of new mutations. We used site directed mutagenesis to exchange residues that contribute to surface charge differences between Stx1A1 and Stx2A1. Point mutations were made by interchanging Stx2A1 residues with Stx1A1 residues. If these residues contributed to the higher toxicity of Stx2A1 its toxicity would be reduced and the toxicity of Stx1A1 would be increased. The plasmids containing the mutants were transformed into yeast. The transformants were then grown in dextrose in a double overnight culture and expression was induced in
Diffusion-based location-aware recommender systems

The recommendation is now part of our daily life. As the years pass by, companies collect more and more information about the users of their platforms. One question which could arise is: are the data collected useful for better predictions? In this paper, we investigate the performance impact on adding geographical positions on the performance of the prediction of users’ behavior using an existing diffusion-based recommender system. We show how we can improve the accuracy of the diffusion algorithm using the geographical position of users. The accuracy of the improved algorithm is compared with the state of art similar recommender algorithms. Moreover, we design a general framework to infer the position location of users based on the position of their activities.
Analysis of the wireless power transfer system using electromagnetic topology method

In this article, a new approach is proposed to analyze a wireless power transfer (WPT) system using the electromagnetic topology (EMT) method. Characteristic parameters for the junction and tube in the EMT method are obtained from the equivalent circuit of the WPT system. The load voltage is computed using the Baum–Liu–Tesche equation, which is the solver for the EMT method. A simple WPT system is analyzed to confirm the validity of the proposed method by comparing it with the conventional full wave analysis method. The analysis results obtained by the proposed method are in good agreement with the results of the conventional method.
Exchange-correlation functionals from ab initio potentials

An accurate description of the exchange-correlation energy is central to density functional theory (DFT). In this thesis a series of exchange-correlation functionals are developed using new approaches based on exchange-correlation potentials and enhancement factors. The functionals are assessed for a wide range of molecular properties. Chapter 1 describes Hartree Fock theory, introducing the key concepts of exchange and correlation. Chapter 2 describes the fundamental ideas of DFT and the implementation of Kohn-Sham theory. Chapter 3 describes a new approach for determining exchange-correlation functionals solely from ab initio potentials. A series of functionals are developed and assessed. Chapter 4 investigates the performance of one of these functionals, which provides particularly high quality structural predictions. A challenging benchmark of sulfur-containing compounds and a new benchmark of diatomic molecules are considered. Results are assessed in terms of the enhancement factor. In Chapter 5, the enhancement factor is used to develop new functionals that satisfy several exact physical conditions. The potential energy curve of the Helium dimer is investigated, since this is known to be sensitive to the enhancement factor. In Chapter 6 a series of hybrid functionals are determined. Particular attention is paid to their performance for chemical reactions and the relationship of these results to self-interaction errors. Chapter 7 investigates a new definition of the exchange-correlation charge (hole), which is directly related to the exchange-correlation potential. The first such calculations on molecular systems are presented. Concluding remarks are presented in Chapter 8.