Miracle-dz/UniFAR-FTUs huggingface dataset:
An acetylation-mediated chromatin switch governs H3K4 methylation read-write capability

In nucleosomes, histone N-terminal tails exist in dynamic equilibrium between free/accessible and collapsed/DNA-bound states. The latter state is expected to impact histone N-termini availability to the epigenetic machinery. Notably, H3 tail acetylation (e.g., K9ac, K14ac, K18ac) is linked to increased H3K4me3 engagement by the BPTF PHD finger, but it is unknown if this mechanism has broader extension. Here we show that H3 tail acetylation promotes nucleosomal accessibility to other H3K4 methyl readers, and importantly, extends to H3K4 writers, notably methyltransferase MLL1. This regulation is not observed on peptide substrates yet occurs on the cis H3 tail, as determined with fully-defined heterotypic nucleosomes. In vivo, H3 tail acetylation is directly and dynamically coupled with cis H3K4 methylation levels. Together, these observations reveal an acetylation ‘chromatin switch’ on the H3 tail that modulates read-write accessibility in nucleosomes and resolve the long-standing question of why H3K4me3 levels are coupled with H3 acetylation.
The Development of 2-stage Microfermentation Protocols for High Throughput Cell Factory Evaluations

Cell based factories can be engineered to produce a wide variety of products. Advances in DNA synthesis and genome editing have greatly simplified the design and construction of these factories. It has never been easier to generate hundreds or even thousands of cell factory strain variants for evaluation. These advances have amplified the need for standardized, higher throughput means of evaluating these designs. Toward this goal, we have previously reported the development of engineered E. coli strains and associated 2-stage production processes to simplify and standardize strain engineering, evaluation and scale up. This approach relies on decoupling growth (stage 1), from production, which occurs in stationary phase (stage 2). Phosphate depletion is used as the trigger to stop growth as well as induce heterologous expression. Here, we describe in detail the development of optimal protocols used for the evaluation of engineered E. coli strains in 2-stage microfermentations. These protocols are readily adaptable to the evaluation of strains producing a wide variety of protein as well as small molecule products. Additionally, the development approach described is adaptable to additional cellular hosts, as well as other 2-stage processes with various additional triggers.
Why link diverse citizen science surveys? Widespread arboreal habits of a terrestrial amphibian revealed by mammalian tree surveys in Britain

Terrestrial anurans, with their typically short limbs, heavy-set bodies and absent claws or toe pads are incongruous tree climbers, but even occasional arboreal locomotion could offer substantial advantages for evading predators or accessing new shelter or food resources. Despite recent interest, arboreal behaviour remains rarely and unsystematically described for terrestrial amphibians in Europe, likely due to fundamental differences in survey methods and therefore a lack of field data. However, other taxa surveys specifically target trees and tree cavities. We undertook collaborations and large-scale data searches with citizen science projects surveying for arboreal mammals in Britain to investigate potential tree climbing by amphibians at a national scale. Remarkably, we found widespread arboreal usage by amphibians in England and Wales, with occupancy of hazel dormouse (Muscardinus avellenarius) nest boxes, tree cavities investigated as potential bat roosts and even a bird nest by common toads (Bufo bufo), but few additional records of frogs or newts. Toads are potentially attracted to tree cavities and arboreal nests because they provide safe and damp microenvironments which can support an abundance of invertebrate prey but the importance of such tree microhabitats for toad conservation remains unknown. Possible interactions with arboreal mammals are also unclear, but such mammals and even some birds may benefit from the occasional presence of toads if they feed on the mites and other arthropods that frequently infest their nests. We encourage expanding and linking of unrelated monitoring surveys and citizen science initiatives as valuable tools for investigating ecological traits and interactions.
Transposon Mutagenesis Reveals RBMS3 as a Promoter of Malignant Progression of BRAFV600E-Driven Lung Tumorigenesis

Mutationally-activated BRAFV600E is detected in ~2% of all human non-small cell lung cancers (NSCLC), and serves as a predictive biomarker for treatment of patients with FDA-approved pathway-targeted therapies that inhibit signaling by the BRAFV600E oncoprotein kinase. In genetically engineered mouse (GEM) models, expression of BRAFV600E in alveolar type 2 (AT2) pneumocytes initiates the development of benign lung tumors that, without additional genetic alterations, rarely progress to malignant lung adenocarcinomas. To identify genes that might cooperate with BRAFV600E for malignant lung cancer progression we employed Sleeping Beauty (SB)-mediated transposon mutagenesis, which dramatically accelerated the onset of lethal lung adenocarcinomas. Amongst the diverse group of genes identified by this in vivo screen was Rbms3 (RNA binding motif single-stranded interacting protein 3), an RNA-binding protein implicated as a possible tumor suppressor. Using CRISPR/CAS9 gene editing we confirmed that RBMS3 silencing cooperated with BRAFV600E to promote progression of malignant lung cancer with a distinct micropapillary architecture. Moreover, RBMS3 silencing also cooperated with BRAFV600E to promote the growth of lung organoids in vitro. BRAFV600E/RBMS3Null lung tumors displayed elevated expression of b-catenin (CTNNB1), suggesting that RBMS3 silencing may result in elevated signaling through the WNT>CTNNB1>c-MYC pathway. Finally, analyses of patient samples in The Cancer Genome Atlas (TCGA) revealed that the region of chromosome 3 encompassing RBMS3 is frequently lost in NSCLC and correlates with poor patient prognosis. Collectively, SB-mediated transposon mutagenesis has revealed the ability of a novel tumor suppressor, RBMS3, to cooperate with BRAFV600E to promote lung carcinogenesis, and suggests that RBMS3 silencing may contribute to malignant progression of numerous human lung cancers. SIGNIFICANCE The BRAFV600E oncoprotein kinase is a potent initiator of benign lung tumorigenesis, but is insufficient to elicit malignant lung adenocarcinoma without additional cooperating alterations. Sleeping Beauty-mediated transposon mutagenesis has revealed a number of genes that cooperate with BRAFV600E to promote lung cancer progression, in particular Rbms3, which encodes an RNA binding protein. Hence, this genetic screen provides a deeper understanding of the molecular mechanisms underlying BRAFV600E-driven lung carcinogenesis, and is an important step improving our ability to successfully target this disease.
Maturation of cortical endoplasmic reticulum clusters in the mouse oocyte: changes at fertilization

Oocytes from many invertebrate and vertebrate species exhibit unique endoplasmic reticulum specializations (cortical ER clusters) thought be essential for egg activation. In examination of cortical ER clusters, we observed they were tethered to previously unreported fenestrae within the cortical actin layer. Further, studies demonstrated sperm preferentially bind to plasma membrane overlying the fenestrae, establishing close proximity to underlying ER clusters. Moreover, following sperm-oocyte fusion, cortical ER clusters undergo a previously unrecognized global maturational change in volume, shape, and calreticulin content that persists through sperm incorporation, before dispersing at the pronuclear stage. These changes did not occur in oocytes from females mated with Izumo1 -/- males demonstrating that gamete fusion plays an important role in ER cluster maturation. In addition to these global changes seen at sites distant to the sperm, highly localized ER modifications were noted at the sperm binding site as cortical ER clusters surround the sperm head during incorporation, then form a diffuse cloud surrounding the decondensing sperm nucleus. This study provides the first evidence that cortical ER clusters interact with the fertilizing sperm, indirectly through a previous unknown lattice work of actin fenestrae, then directly during sperm incorporation. These observations raise the possibility that oocyte ER cluster-sperm interactions provide a competitive advantage to the oocyte, which may not occur during assisted reproductive technologies such as intracytoplasmic sperm injection. Summary Statement Sperm-oocyte interactions stimulate global changes in cortical endoplasmic reticulum cluster structure as well as localized responses at the sperm binding site.
EphB1 controls long-range cortical axon guidance through a cell non-autonomous role in GABAergic cells

EphB1 is required for proper guidance of cortical axon projections during brain development, but how EphB1 regulates this process remains unclear. We show here that EphB1 conditional knockout (cKO) in GABAergic cells (Vgat-Cre or Dlx1/2-Cre), but not in cortical excitatory neurons (Emx1-Cre), reproduced the cortical axon guidance defects observed in global EphB1 KO mice. Interestingly, in EphB1 cKOVgat mice, the misguided axon bundles contained comingled striatal GABAergic and somatosensory cortical glutamatergic axons. In wildtype mice, somatosensory axons also co-fasciculated with striatal axons notably in the globus pallidus, suggesting that a subset of glutamatergic cortical axons normally follows long-range GABAergic axons to reach their targets. Surprisingly, the ectopic axons in EphB1 KO mice were juxtaposed to major blood vessels. However, conditional loss of EphB1 in endothelial cells (Tie2-Cre), or in mural and oligodendrocyte precursor cells (Cspg4-Cre) did not produce the axon guidance defects, suggesting that EphB1 in GABAergic neurons normally promotes avoidance of these ectopic axons from following the developing vasculature. Together, our data reveal a new role for EphB1 in GABAergic neurons to influence proper cortical glutamatergic axon guidance during brain development.
Tighter MA/1 Circuit Lower Bounds From Verifier Efficient PCPs for PSPACE

We prove that for some constant a > 1, for all k ≤ a , MATIME [ n k + o (1) ] / 1 ̸⊂ SIZE [ O ( n k )] , for some specific o (1) function. This is a super linear polynomial circuit lower bound. Previously, Santhanam [29] showed that there exists a constant c > 1 such that for all k > 1: MATIME [ n ck ] / 1 ̸⊂ SIZE [ O ( n k )] . Inherently to Santhanam’s proof, c is a large constant and there is no upper bound on c . Using ideas from Murray and Williams [26], one can show for all k > 1: MATIME [ n 10 k 2 ] / 1 ̸⊂ SIZE [ O ( n k )] . To prove this result, we construct the first PCP for SPACE [ n ] with quasi-linear verifier time: our PCP has a ˜ O ( n ) time verifier, ˜ O ( n ) space prover, O (log( n )) queries, and polynomial alphabet size. Prior to this work, PCP s for SPACE [ O ( n )] had verifiers that run in Ω( n 2 ) time. This PCP also proves that NE has MIP verifiers which run in time ˜ O ( n ).
Performance analysis of inner sensors used for partial discharge detection in cable accessories

The structure of high voltage cross-linked polyethylene(XLPE) cable and the characteristics of partial discharge(PD) mean that such discharges are very likely to happen in cable accessories due to insulation defects.The authors designed a wideband inner capacitive coupler for PD detection in cable accessories,and analyzed the characteristics of the coupler using an equivalent circuit model.The frequency response characteristics of the coupler were measured by square wave response.In addition,the measured results of the surface discharge produced along the dielectric interface in joints are given in 110 kV XLPE cable accessories.It is shown that the couplers work well in bands up to 500 MHz and can detect nanosecond transient pulse signals effectively.The couplers are available for ultra-high frequency(UHF) measurement of PD in cables and cable accessories.
Ray Tracing Analysis of Sub-6 GHz and mmWave Indoor Coverage with Reflecting Surfaces

Indoor coverage and channel modelling is crucial for network planning purposes at mmWave bands. In this paper, we analyzed received power patterns and connectivity in an indoor office environment for sub-6 GHz and mmWave bands using ray tracing simulations and theoretical models over different scenarios. We discussed the effect of using metallic walls instead of regular drywall, base station (BS) location, and open/shut doors. Our results showed that ray tracing solutions are consistent with theoretical calculations, and using reflective walls significantly improves average received power and connectivity at mmWave bands, e.g., for the given floor plan, coverage increases from 86% to 97.5% at 60 GHz band.
Target Detection using U-Net for a DTV-based Passive Bistatic Radar System

Digital Television (DTV) has a wider bandwidth than a radio signal, and the signal power is stronger than Wi-Fi or LTE, so it is advantageous in exploiting a Passive Bistatic Radar (PBR) signal for drone detection. Most of the PBR systems detect a target using a Constant False Alarm Rate (CFAR) detector. However, CFAR detection suffers from multi-clutters, noise, and sidelobes. To overcome the limitation of CFAR, we propose a target detector exploiting semantic segmentation. The proposed detector is based on U-Net, a model for semantic segmentation. Training datasets for the proposed detector are generated by synthesized DTV signals. The performances of the CFAR and the proposed detector were compared using actual drone measurement data. The proposed detector detects drones better than the CFAR one while reducing the number of false alarms.
A Ka-Band Iris-Loaded Waveguide Slot Antenna With Enhanced Out-of-Band Suppression

This paper presents a Ka-band waveguide slot array antenna with a simple structural upgrade that is capable of enhancing out-of-band suppression for mitigation of potential strong interference signals. The enhanced suppression is enabled by employing rectangular irises that are uniformly distributed along the propagation direction of the waveguide. A prototype of the waveguide slot array antenna is designed and experimentally demonstrated. Measured result of the engineered antenna shows good in-band radiation performance, i.e., a reflection coefficient of less than -10 dB from 31.6 to 34.28 GHz (a relative bandwidth of 8.1%) and corresponding realized gains of 13–14.5 dBi. Compared to the conventional waveguide slot antenna without loading any iris, the implemented antenna achieves a reduction of 11.3–18.5 dB in the out-of-band realized gains at 38.25–40 GHz.
Implementation and Analysis of Rolling Bearing Faults Caused by Shaft Currents

Bearing failures make the greatest proportion of damages affecting electrical machines. At the same time, there are various types of the bearing failures but the most common are related to damages caused by shaft currents in the machines leading to problems in industry. In this case, diagnostics and timely prediction of potential faults are important. The paper gives an overview of the problem describing main bearing damages. Particularly, different shaft currents related faults are discussed in detail. Implementation of these faults in laboratory environment is introduced. Analysis of rolling bearing faults caused by shaft currents is presented.
Software Bug Number Prediction Based on Complex Network Theory and Panel Data Model

Accurate software bug number prediction makes software test resource allocation, maintenance, and release time cost efficient. However, it is a challenge to accurately predict the number of software bugs when there fluctuations caused by many uncertain factors faced by the complex software. Considering this, a new method for software bug number prediction based on a panel data model from the perspective of complex networks is proposed in this article. Using complex network theory, we constructed the software code network and calculated the static metrics of the network structure, and the percolation threshold of change in the network structure based on percolation theory as a dynamic metric. These network metrics were then normalized as inputs and a panel data model was used for bug prediction. The proposed method can predict the number of bugs for both within-project and cross-project. Empirical studies were performed on data obtained from 120 releases of eight open-source software projects (Lua, SQLite, Redis, Linux kernel, ant, jmeter, poi, and tomcat), the experimental results indicated that network metrics are effective bug indicators, and the proposed method outperformed ten baseline methods (with an average improvement of 28.05%). This article is expected to provide insights into more smart software quality and reliability assurance.
Parametric Design of Cycloidal Gears Based on UG/OPEN API

Parameterized equations of inner cycloid and outer cycloid are established through analyzing the character of cycloidal gear.Under the environment of UG NX/OPEN API, the method of parameterized designing for cycloidal gears is introduced, the three-dimensional model can be created by changing number of gear teeth and radius of rolling circle parameters of gear.Product design cycle is reduced as the result of visualization and automation in design process ,so cycloidal gears are designed in high efficiency.
Social Media Rumour Detection Through Graph Attention Networks

Rumours are unverified statements or news that spread quickly across the Internet. The global ubiquity of social media platforms provides the perfect conditions for the spread of rumours. Such rumours can have global consequences. Tools for detection of rumours are therefore needed. Diverse methods have been applied to discover rumours through approaches based on text mining, propagation patterns and user networks and their interactions. Such approaches treat user interactions in discussions equally. In this paper, we propose a model to extract information from user interactions based on Graph Attention Networks. In the propagation graph, the nodes represent the user text content and the edges represent the reply interactions. The attention mechanism is implemented to determine the edge weights between node pairs. We conduct experiments using Twitter15, Twitter16, and PHEME datasets and achieve state of the art results.
Dual-Branch Difference Amplification Graph Convolutional Network for Hyperspectral Image Change Detection

Hyperspectral image (HSI) change detection aims to identify the differences in multi-temporal HSIs. Recently, graph convolutional network (GCN) has attracted increasing attention in the field of remote sensing due to its advantages in processing irregular data. In comparison with convolutional neural network (CNN) that can only perform convolution operations on data with the assumption of Euclidean structure, GCN adopts a graph structure to flexibly capture the characteristics and structure information of non-Euclidean data. In this paper, we propose a novel dual-branch difference amplification GCN (D2AGCN) for HSI change detection with limited samples, which allows the network to fully extract and effectively amplify the difference features of multi-temporal HSIs for change detection. The dual-branch structure can effectively extract sufficient different features to facilitate the detection of the changed areas. As far as we know, this is the first time that GCN has been introduced into HSI change detection. A difference magnification module is designed to suppress similar regions and highlight the feature differences between the multi-temporal HSIs in the dual-branch structure, which increases the distinction between change and non-change classes. The visual and quantitative experimental results on three real hyperspectral datasets (i.e., China, Bay Area, and Santa Barbara) show that the proposed D2AGCN outperforms most of the state-of-the-art methods in HSI change detection with limited training samples.
Detection of Epileptic Seizure using Discrete Wavelet Transform on Gamma band and Artificial Neural Network

Electroencephalography (EEG) is a valuable instrument for acquiring brain signals from the scalp surface area that correlate to many states, one of which is epilepsy, which is defined as a central nervous system disorder that generates periods of abnormal brain activity, often known as seizures. Based on the EEG signal, frequencies are ranging from 0.1 Hz to more than 100 Hz, these signals are classified as delta, theta, alpha, beta, and gamma. This paper uses the four features extracted from EEG signal to detect Epileptic Seizures, by using a combination of both Discrete Wavelet Transform (DWT) and Artificial Neural Network (ANN) mainly using MATLAB. Bonn University EEG database has been used. The data was obtained using 128 channels and is divided into five different data classes: Z, N, O, F, and S. Each dataset class contains 100 segments taken from individual channels with a period of 23.6 seconds, or in other words, each dataset class contains 4097 pulses/samples with a sampling frequency of 173.61 Hz. Important statistical features were computed such as mean, standard deviation, skewness, and kurtosis. In this work only the gamma-band of the decomposed signal is used to extract the four statistical features, two classifiers are applied one to detect epilepsy and one to detect the seizure. the epilepsy classifier is 90.3 % accurate and the seizure classifier is 98.7% accurate.
Improvement of Credit Scoring by LSTM Autoencoder Model

One of the most important causes of the financial crisis of 2007–2008 is the risk management systems. Credit scoring is an essential part of these risk management systems in financial institutions. While the effectiveness of feature extraction by artificial intelligence on credit scoring is well known, the effect of historical repayment data needs to be studied. The performance of machine learning models depends on the features and representation of data. The features extracted by deep learning models in an end-to-end manner have more information than hand engineered features. In this paper, we extract features from customer’s previous loan repayments in two ways: manual and by LSTM autoencoder, and their effects evaluated on the credit scoring problem. Using proposed methods to feature extraction, shows improvement in the performance metrics of the models.
A machine learning approach for detecting and categorizing evasion sources in Android malware

Evasion techniques are used by some Android malware to hide their malicious behavior and to hinder their execution during the dynamic analysis process. Many tools tackle such evasions by using a manually created list of API functions (as sources of evasions) to detect these evasions. As an important consequence, no matter how good the tool is, it can only guarantee to defeat these evasions and extract the real behavior of the malware if its list of evasion sources is complete. This way, if some evasion sources are missing from the list or when similar API functions are used, the dynamic analysis can be hindered. In this paper, we propose a machine learning approach to detect and categorize various evasion sources in Android malware. The proposed approach uses a manually collected training dataset to train two classifiers. The first classifier is used to detect the evasion nature of the Android API methods, while the second classifier is used to categorize the detected evasion sources into predefined categories. We applied the proposed approach to a large number of methods extracted from Android API 27. The proposed approach could detect hundreds of evasions with accuracy of 92.8% for the first classifier and 90.5% for the second classifier. The evaluation for 500 real-world samples showed that many of the evasions are detected by our approach, are not considered by the state-of-the-art dynamic analysis frameworks that are indeed used by malware samples.