Miracle-dz/UniFAR-FTUs huggingface dataset:
Dietary intake restriction during the laying periodMaternal nutrition 2 altered embryonic MYOD 1 , MYF 5 and MYF 6 gene expression in genetically 3 fat and lean lines of chickens 4 5

30 Objective: The objectives of this study were to evaluate the effects of daily feed intake during the laying 31 period on embryonic MYOD1, MYF5 and MYF6 gene expression in genetically fat and lean lines of 32 chickens. 33 Methods: An experiment in a 2 × 2 factorial design was conducted with two dietary intake levels (100% 34 and 75% of nutrition recommendation) and two broiler chicken lines (fat and lean). Two lines of hens 35 (n = 384 for each line) at 23th week of age were randomly divided into 4 treatments with 12 replicates 36 of 16 birds. The experiment started at 27th week of age (5% egg rate) and ended at 54th week of age. 37 Hatched eggs from the medium laying period were collected. Real time PCR analysis was used to 38 analyse the MYOD1, MYF5 and MYF6 mRNA levels of E7, E9, E11, E13 and E15 body tissues and E17, 39 E19 and E21 chest and thigh muscle samples. 40 Results: The results indicated that there were significant effects of line, dietary intake and interactions 41 between them on MYOD1, MYF5 and MYF6 gene mRNA expression levels in embryonic tissues. Low 42 daily feed intake did not change the expression trend of MYOD1 mRNA in either line, but changed the 43 peak values, especially in lean line. Low daily feed intake altered the trend in MYF5 mRNA expression 44 level in both lines and apparently delayed its onset. There was no apparent effect of low daily feed intake 45 on the trends of MYF6 mRNA expression levels in either line, but it significantly changed the values on 46 many embryonic days. 47 Conclusion: Maternal nutrient restriction affects myogenesis and is manifested in the expression of 48 embryonic MYOD1, MYF5, and MYF6 genes. Long term selection for fat deposition in broiler chickens 49 changes the pattern and intensity of myogenesis. 50 51
WP2-GAN: Wavelet-based Multi-level GAN for Progressive Facial Expression Translation with Parallel Generators

Expression translation has received increasing attention from the computer vision community due to its wide applications in the real world. However, expression synthesis is hard because of the non-linear properties of facial skin and muscle caused by different expressions. A recent study showed that the practice of using the same generator for both forward prediction and backward reconstruction as in current conditional GANs would force the generator to leave a potential "noise" in the generated images, therefore hindering the use of the images for further tasks. To eliminate the interference and break the unwanted link between the ﬁrst and second translation, we design a parallel training mechanism with two generators that perform the same ﬁrst translation but work as a reconstruction model for each other. Additionally, inspired by the successful application of wavelet-based multi-level Generative Adversarial Networks(GANs) in face aging and progressive training in geometric conversion, we further design a novel wavelet-based multi-level Generative Adversarial Network (WP2-GAN) for expression translation with a large gap based on a progressive and parallel training strategy. Extensive experiments show the effectiveness of our approach for expression translation compared with the state-of-the-art models by synthesizing photo-realistic images with high ﬁdelity and vivid expression effect.
Genetically Encoded Voltage Indicators Mapping Cardiac Electrical Activity Under a New Light

(1) Institut für Pharmakologie, Universitätsmedizin Göttingen (2) Klinik für Kardiologie und Pulmonologie Germany, Universitätsmedizin Göttingen (3) DZHK (Deutsches Zentrum für Herzkreislaufforschung), Standort Göttingen (4) Technische Universität Hamburg-Harburg (5) Department of Medical Physiology, University Medical Center Utrecht (6) RIKEN Brain Science Institute, Saitama (7) Max-Planck-Institut für Dynamik und Selbstorganisation, Göttingen (8) Max-Planck-Institut für Experimentelle Medizin, Göttingen (9) Transkriptomanalyselabor, Universitätsmedizin Göttingen (10) Klinik für Innere Medizin II, Universitätsmedizin Regensburg (11) Department of Medicine and Centre for Neurotechnology, Imperial College, London
Optimization on Resorting Capacity of Marshalling Station under Transport Capacity Release of Existing Line and Extended Locomotive Routing

The trend for traffic volume after the transport capacity release of the existing line was simulated by setting the virtual volume growth coefficient.Considering the extended locomotive routing,the optimization model for the resorting capacity of marshalling station was constructed,with minimizing the capacity expansion investment and car flow organization cost(the optimal comprehensive effect)as the target.The model was solved by particle swarm optimization algorithm with adaptive mutation,and the capacity allocation demand for each marshalling station of railway network was accurately obtained.Results show that the extended locomotive routing can effectively lower the transit cost without resorting,and the number of through trains will be increased with the growth in traffic volume and the extension of locomotive routing.The rational allocation of the resorting capacity of marshalling station is closely related to through train scheme and car flow resorting scheme.The overall situation must be taken into account for the capacity expansion scheme of marshalling station and the collaborative optimization must be carried out with the optimal comprehensive effect as the target.
Towards an Integrated

The search for a suitable theoretical framework that explains the impacts of information systems in workplaces has been on for over three decades now. Prominent information system researchers have built and tested several models aimed at understanding these underlying interactions. Unfortunately, the dynamic nature of information systems and the different contextual backgrounds continues to throw-up deficiencies with the existing models while calls for a ‘cumulative tradition’ in information systems (IS) research continue to increase. Through a methodological literature review process, previous related works on the various relationships proposed by the technology utilization, satisfaction and performance model (TUSPEM) were extracted from some renowned online databases. This study therefor reports the review of researches on these relationships thereby expanding the body of knowledge on the TUSPEM model by with a view to contributing to theory in information systems research
Security Challenges and Related Solutions in Software Defined Networks: A Survey

– In the current digitalized world, everything is interconnected and accessible from everywhere. Although traditional networks are widely adopted, their management is complicated. Therefore, they are not effective in providing services to the future Internet like a wide range of accessibility, high bandwidth, management, and security. On the other hand, Traditional network architecture relies on manual configurations of proprietary devices that are error-prone and inefficient to utilize the network devices properly. Software-defined Networking (SDN) has drawn massive changes in the traditional network paradigm by decoupling the network operations from the physical hardware and encouraging network control to be logically centralized. It provides network programmability and improves security by enabling a global view of the entire network and issues handled effectively by the centralized controller. As a result, SDN allows networks to monitor the traffic and detect vulnerabilities more effectively. It also simplifies the deployment of new services with more flexibility at a faster pace. On the other hand, the decoupling of control and the data planes introduces security threats such as Distributed Denial of Service (DDoS) attacks, Man in the Middle attacks, Saturation attacks, etc. As a result, SDN has attracted a lot of interest from both academics and industry. In this paper, we study security vulnerabilities on layers of SDN, the security frameworks that protect each layer, and many security methodologies for network-wide security.
Bottom-up and top-down drivers of herbivory on Arabica coffee along an environmental and management gradient

While sustainable agriculture relies on natural pest control, we lack insights into the relative importance of bottom-up and top-down factors on pest levels, especially along broad environmental and management gradients. To this aim, we focused on bottom-up and top-down control of herbivore damage in sixty sites in the centre of origin of Arabica coffee in southwestern Ethiopia, where coffee grows along a management gradient ranging from little or no management in the natural forest to commercial plantations. More specifically, we examined how canopy cover, percentage of surrounding forest and management intensity affected caffeine and chlorogenic acid concentration (bottom-up process) and attack of dummy caterpillars by ants and birds (top-down process), and how these in turn affected pest levels. Caffeine and chlorogenic acid concentrations were negatively related to canopy cover, while ant attack rate was positively related to canopy cover. Both ant and bird attack rate increased with the percentage of surrounding forest. Yet, secondary chemistry and caterpillar attack rates were unrelated to herbivory, and herbivory was only directly and positively affected by management intensity. Our study highlights that canopy cover can have contrasting effects on plant defence and predation, and that changes in bottom-up and top-down factors do unlike often assumed not necessarily translate into reduced pest levels. Instead, direct effects of management on pest levels may be more important than bottom-up or top-down mediated effects. © 2021 The Author(s). Published by Elsevier GmbH on behalf of Gesellschaft für Ökologie. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)
Pc5 magnetic pulsations during the outer electron radiation belt

Since the discovery of the radiation belt decades ago, there still remain some fundamental questions as to which one is the mechanism responsible for the acceleration of electrons. Groundbased Pc5 magnetic pulsation during the process of increasing of 2-MeV electron fluxes has been analyzed. First, a filter bandpass in the period range of 150-600 seconds has been used to localize the Pc5 waves. Second, we then applied a wavelet transform procedure, whereby the Morlet function as a mother wavelet was selected to analyze Pc5 wave packets. First, we show that dynamic pressure of solar wind controls the power of Pc5 magnetic pulsations. Second, by performing a cross-spectrum analysis of Pc5 wavelet during electron radiation belts we show that the wavelet power of Pc5 magnetic pulsations which is associated with a maximum wavelet cross spectrum show a similar change of Pc5 pulsations occurs during radiation belt events. Increasing of electron fluxes which is initiated by the presence of large power of Pc5 magnetic pulsations has been observed. This indicates that Pc5 magnetic pulsations could play a role in the acceleration and transport mechanism of the electron radiation belt. Also, 4-5 days from the beginning of increasing of electron fluxes we observed globally, a depression in the power of Pc5 magnetic pulsations as well as a monotonically decreasing of the solar wind dynamic pressure. On the other hand, during the end period of the electron belt, we also observed a sudden increasing of Pc5 power. We suggest that during the expansion periode of the outer electron radiation belt outward to interplanetary electron belt pressure that will reduce the solar wind dynamic pressure and consequently a decrease occurs in the power of Pc5 magnetic pulsation. And, in the end period of the electron radiation belt the electron fluxes back to its normal level and consequently a sudden increase of the Pc5 solar wind dynamic pressure occurs and that sudden increase also drives the sudden increasing power of Pc5 magnetic pulsations.
An Experimental Analysis for Classifying Skin Diseases Using Soft Computing Techniques

: The skin is an important part of the human body that cosmetically protects the body from infection. Skin diseases is highly popping up in the society as reported by researchers and medical practitioners (Wiley, 2019). Therefore, there is need to address this issue by introducing efficient treatments with the help of technology. Detecting the skin disease in their early stage will help both the patient and practitioner. To address this issue, we selected some popular machine learning algorithms, this paper focuses on comparing the efficiency of four machine learning techniques such as Logistic Regression, Support Vector Machines (SVM) and Random Forest with Inception V3 model of Transfer Learning for classification of skin diseases into five classes Acne, psoriasis, scabies, rosacea, and fungal infection. We have trained and tested the model using the skin disease images from the Dermnet, Dermis and Dermnet NZ dataset. The results have shown that transfer learning has resulted maximum accuracy as compared to other machine learning algorithms mentioned above.
G-Net: a Recurrent Network Approach to G-Computation for Counterfactual Prediction Under a Dynamic Treatment Regime

Counterfactual prediction is a fundamental task in decision-making. This paper introduces G-Net, a sequential deep learning framework for counterfactual prediction under dynamic time-varying treatment strategies in complex longitudinal settings. G-Net is based upon g-computation, a causal inference method for estimating effects of general dynamic treatment strategies. Past g-computation implementations have mostly been built using classical regression models. G-Net instead adopts a recurrent neural network framework to capture complex temporal and nonlinear dependencies in the data. To our knowledge, G-Net is the first g-computation based deep sequential modeling framework that provides estimates of treat-ment effects under dynamic and time-varying treatment strategies. We evaluate G-Net using simulated longitudinal data from two sources: CVSim, a mechanistic model of the cardiovascular system, and a pharmacokinetic simulation of tumor growth. G-Net outperforms both classical and state-of-the-art counterfactual prediction models in these settings.
Automated Analysis of Time Series Data

— Time series data is used in many areas of research and various ways to represent this kind of data is available. However not a lot of research has been done to determine which kind of data representations leads to the best results when performing automated analysis of time series data. Five diﬀerent neural networks have been tested with three diﬀerent types of data representation as input in order to determine which of them leads to the best results. In order to further improve network performance four diﬀerent kinds of data augmentations have also been tested in order to show which of these is most eﬀective in improving network performance. The dataset used contains data from various earthquakes in the Groningen seismological region. Using spectrograms as input leads to a small performance increase in all cases when compared to the other data representations. Thus, spectrograms should be the ﬁrst data representation to look at when deciding which data representation to use for analysing time series data.
Attempt to replicate voxel-based morphometry analysis in fibromyalgia: Detection of below threshold differences framed by contributions of variable clinical presentation to low reproducibility.

Background: Fibromyalgia is a prevalent chronic pain condition characterized by widespread pain and sensory hypersensitivity. While much remains unknown about the neurobiological underpinnings of fibromyalgia, central nervous system alterations appear to be heavily implicated in its pathophysiology. Previous research examining brain structural abnormalities associated with fibromyalgia has yielded inconsistent findings. Thus, we followed previous methods to examine brain gray matter differences in fibromyalgia. We hypothesized that, relative to healthy controls, participants with fibromyalgia would exhibit lower gray matter volume in regions consistently implicated in fibromyalgia: the anterior cingulate cortex and medial prefrontal cortex. Methods: This study used magnetic resonance imaging to evaluate regional and whole brain differences in gray matter among females with and without fibromyalgia. Group differences were analyzed with two-sample t-tests, controlling for total intracranial volume. Results: No significant differences in regional or whole brain gray matter volumes were detected between fibromyalgia and healthy controls. Conclusions: Results add to an existing body of disparate findings regarding brain gray matter volume differences in fibromyalgia, and suggest structural differences previously detected in fibromyalgia should be examined for reproducibility. Absent significant differences may also suggest that functional, but not structural, brain adaptations are primarily associated with fibromyalgia.
Mining for Health: A Comparison of Word Embedding Methods for Analysis of EHRs Data

Electronic health records (EHRs), routinely collected as part of healthcare delivery, offer great promise for advancing precision health. At the same time, they present significant analytical challenges. In EHRs, data for individual patients are collected at irregular time intervals and with varying frequencies; they include both structured and unstructured data. Advanced statistical and machine learning methods have been developed to tackle these challenges, for example, for predicting diagnoses earlier and more accurately. One powerful tool for extracting useful information from EHRs data is word embedding algorithms, which represent words as vectors of real numbers that capture semantic and syntactic similarities. Learning embeddings can be viewed as automated feature engineering, producing features that can be used for predictive modeling of medical events. Methods such as Word2Vec, BERT, FastText, ELMo, and GloVe have been developed for word embedding, but there has been little work on re-purposing these algorithms for the analysis of structured medical data. Our work seeks to fill this important gap. We extended word embedding methods to embed (structured) medical codes from a patient medical history, and used the resultant embeddings to build prediction models for diseases. We assessed the performance of multiple embedding methods in terms of predictive accuracy and computation time using the Medical Information Mart for Intensive Care (MIMIC) database. We found that using Word2Vec, Fast- Text, and GloVe algorithms yield comparable models, while more recent contextual embeddings provide marginal further improvement. Our results provide insights and guidance to practitioners regarding the use of word embedding methods for the analysis of EHR data.
Exploring Potential Causal Genes for Uterine Leiomyomas: A Summary Data-Based Mendelian Randomization and FUMA Analysis

Objective: To explore potential causal genetic variants and genes underlying the pathogenesis of uterine leiomyomas (ULs). Methods: We conducted the summary data-based Mendelian randomization (SMR) analyses and performed functional mapping and annotation using FUMA to examine genetic variants and genes that are potentially involved in the pathogenies of ULs. Both analyses used summarized data of a recent genome-wide association study (GWAS) on ULs, which has a total sample size of 244,324 (20,406 cases and 223,918 controls). We performed separate SMR analysis using CAGE and GTEx eQTL data. Results: Using the CAGE eQTL data, our SMR analysis identified 13 probes tagging 10 unique genes that were pleiotropically/potentially causally associated with ULs, with the top three probes being ILMN_1675156 (tagging CDC42, PSMR = 8.03 × 10−9), ILMN_1705330 (tagging CDC42, PSMR = 1.02 × 10−7) and ILMN_2343048 (tagging ABCB9, PSMR = 9.37 × 10−7). Using GTEx eQTL data, our SMR analysis did not identify any significant genes after correction for multiple testing. FUMA analysis identified 106 independent SNPs, 24 genomic loci and 137 genes that are potentially involved in the pathogenesis of ULs, seven of which were also identified by the SMR analysis. Conclusions: We identified many genetic variants, genes, and genomic loci that are potentially involved in the pathogenesis of ULs. More studies are needed to explore the exact underlying mechanisms in the etiology of ULs.
An algorithm to regularize static spherically symmetric black hole spacetimes

A simple algorithm to transform a singular, asymptotically ﬂat, static and spherically symmetric black hole into a regular black hole spacetime is presented. The regular black hole is a solution of General Relativity theory coupled to nonlinear electrodynamics (NLED), even if the original metric is not a vacuum, or electro-vacuum, solution of General Relativity.
Just Rank: Rethinking Evaluation with Word and Sentence Similarities

Word and sentence embeddings are useful feature representations in natural language processing. However, intrinsic evaluation for embeddings lags far behind, and there has been no significant update since the past decade. Word and sentence similarity tasks have become the de facto evaluation method. It leads models to overfit to such evaluations, negatively impacting embedding models’ development. This paper first points out the problems using semantic similarity as the gold standard for word and sentence embedding evaluations. Further, we propose a new intrinsic evaluation method called EvalRank, which shows a much stronger correlation with downstream tasks. Extensive experiments are conducted based on 60+ models and popular datasets to certify our judgments. Finally, the practical evaluation toolkit is released for future benchmarking purposes.
Analysis of Open Magnetic Field Characteristic of Permanent Drum Magnetic Separator

Permanent magnetic drum separator is widely used in the field of ferrous metal ore dressing.The structure of magnetic system and the character of magnetic field are the key factors affecting magnetic performance,which plays an important role in the ore dressing efficiency.Based on the elaboration of design of permanent magnetic drum separator and characteristics of magnetic field,the paper focused on the analysis of the impact factors,such as the structural parameters of magnetic unit,assist magnetic poles matching and etc,which can be of good guiding function for permanent drum magnetic separator designing and industry model selection.
A new p‐control chart with measurement error correction

Control charts are important tools to monitor quality of products. One of useful applications is to monitor the proportion of non‐conforming products. However, in practical applications, measurement error is ubiquitous and may occur due to false records or misclassification, which makes the observed proportion different from the underlying true proportion. It is also well‐known that ignoring measurement error effects provides biases, and is expected that the resulting control charts may incur wrong detection. In this paper, we study this important problem and propose a valid method to correct for measurement error effects and obtain error‐eliminated control chart for the proportion of non‐conforming products. In addition, unlike traditional approaches, the corrected EWMA p‐control chart provides asymmetric control limits and is flexible to handle the data with small sample size. Numerical results are conducted to justify the validity of the corrected EWMA p‐control chart and verify the necessity of measurement error correction.