Miracle-dz/UniFAR-FTUs huggingface dataset:
Poliovirus induces autophagic signaling independent of the ULK1 complex

ABSTRACT Poliovirus (PV), like many positive-strand RNA viruses, subverts the macroautophagy/autophagy pathway to promote its own replication. Here, we investigate whether the virus uses the canonical autophagic signaling complex, consisting of the ULK1/2 kinases, ATG13, RB1CC1, and ATG101, to activate autophagy. We find that the virus sends autophagic signals independent of the ULK1 complex, and that the members of the autophagic complex are not required for normal levels of viral replication. We also show that the SQSTM1/p62 receptor protein is not degraded in a conventional manner during infection, but is likely cleaved in a manner similar to that shown for coxsackievirus B3. This means that SQSTM1, normally used to monitor autophagic degradation, cannot be used to accurately monitor degradation during poliovirus infection. In fact, autophagic degradation may be affected by the loss of SQSTM1 at the same time as autophagic signals are being sent. Finally, we demonstrate that ULK1 and ULK2 protein levels are greatly reduced during PV infection, and ATG13, RB1CC1, and ATG101 protein levels are reduced as well. Surprisingly, autophagic signaling appears to increase as ULK1 levels decrease. Overexpression of wild-type or dominant-negative ULK1 constructs does not affect virus replication, indicating that ULK1 degradation may be a side effect of the ULK1-independent signaling mechanism used by PV, inducing complex instability. This demonstration of ULK1-independent autophagic signaling is novel and leads to a model by which the virus is signaling to generate autophagosomes downstream of ULK1, while at the same time, cleaving cargo receptors, which may affect cargo loading and autophagic degradative flux. Our data suggest that PV has a finely-tuned relationship with the autophagic machinery, generating autophagosomes without using the primary autophagy signaling pathway. Abbreviations: ACTB - actin beta; ATG13 - autophagy related 13; ATG14 - autophagy related 14; ATG101 - autophagy related 101; BECN1 - beclin 1; CVB3 - coxsackievirus B3; DMV - double-membraned vesicles; EM - electron microscopy; EMCV - encephalomyocarditis virus; EV-71 - enterovirus 71; FMDV - foot and mouth disease virus; GFP - green fluorescent protein; MAP1LC3B/LC3B - microtubule associated protein 1 light chain 3 beta; MOI - multiplicity of infection; MTOR - mechanistic target of rapamycin kinase; PIK3C3 - phosphatidylinositol 3-kinase catalytic subunit type 3; PRKAA2 - protein kinase AMP-activated catalytic subunit alpha 2; PSMG1 - proteasome assembly chaperone 1; PSMG2 - proteasome assembly chaperone 2PV - poliovirus; RB1CC1 - RB1 inducible coiled-coil 1; SQSTM1 - sequestosome 1; ULK1 - unc-51 like autophagy activating kinase 1; ULK2 - unc-51 like autophagy activating kinase 2; WIPI1 - WD repeat domain, phosphoinositide interacting 1
Novel lymphoid enhancer‐binding factor 1‐cytoglobin axis promotes extravasation of osteosarcoma cells into the lungs

Lung metastasis is a major cause of mortality in patients with osteosarcoma (OS). A better understanding of the molecular mechanism of OS lung metastasis may facilitate development of new therapeutic strategies to prevent the metastasis. We have established high‐ and low‐metastatic sublines (LM8‐H and LM8‐L, respectively) from Dunn OS cell line LM8 by using in vivo image‐guided screening. Among the genes whose expression was significantly increased in LM8‐H compared to LM8‐L, the transcription factor lymphoid enhancer‐binding factor 1 (LEF1) was identified as a factor that promotes LM8‐H cell extravasation into the lungs. To identify downstream effectors of LEF1 that are involved in OS lung metastasis, 13 genes were selected based on LM8 microarray data and genomewide meta‐analysis of a public database for OS patients. Among them, the cytoglobin (Cygb) gene was identified as a key effector in promoting OS extravasation into the lungs. CYGB overexpression increased the extravasation ability of LM8‐L cells, whereas knocking out the Cygb gene in LM8‐H cells reduced this ability. Our results showed a novel LEF1‐CYGB axis in OS lung metastasis and may provide a new way of developing therapeutic strategies to prevent OS lung metastasis.
Description of the larva of Oecetis mizrain Malicky & Graf, 2012 (Trichoptera, Leptoceridae) and Lepidostoma scotti (Ulmer, 1930) (Trichoptera, Lepidostomatidae) from Chilimo Forest, Central Ethiopia

Abstract The Ethiopian caddisfly fauna comprises 85 species, including 10 Oecetis species and three Lepidostoma species. In this context we provide the first species-level descriptions of Ethiopian caddisfly larvae. We describe and illustrate the larvae of O. mizrain and L. scotti, with additional notes on their habitats and distribution.
Factors affecting the enjoyment of ERP software learning for non-IT learners

The learning enjoyment was previously discovered to have an influence on the perceived ease of use and usage intention of ERP software contributing to the learning achievement of non-IT learners based on Technology Acceptance Model (TAM) approach. The present study was extended from the previous one for the purpose of examining the factors affecting the learning enjoyment. The samples were collected using the questionnaire in the ERP class of business students (non-IT learners) at Thai-Nichi Institute of Technology. The structural model was tested by the method of Structural Equation Modelling (SEM). Three constructs of learning enjoyment are derived from three perspectives, which are the learners' self-efficacy, the learner-ability-based teaching style of the instructors and the relaxed learning environment. From the data analysis, the valid model reveals that only the learners' self- efficacy has significantly a direct effect on the learning enjoyment while the other two constructs don't have. However, the teaching style of the instructors has a significant effect on the learners' self-efficacy, indicating that the ability-based teaching style has an indirect effect on the learning enjoyment through the learners' self-efficacy as a mediator. Consequently, the non-IT learners can perceive the learning enjoyment as they can build their self- efficacy in using ERP software while the instructors play as supportive roles to assist the non-IT learners. The significant factors found in the study can be practically considered to enhance the software training effectiveness in the company.
Generalized Benford's Law for Blind Detection of Morphed Face Images

A morphed face image in a photo ID is a serious threat to image-based user verification enabling that multiple persons could be matched with the same document. The application of machine-readable travel documents (MRTD) at automated border control (ABC) gates is an example of a verification scenario that is very sensitive to this kind of fraud. Detection of morphed face images prior to face matching is, therefore, indispensable for effective border security. We introduce the face morphing detection approach based on fitting a logarithmic curve to nine Benford features extracted from quantized DCT coefficients of JPEG compressed original and morphed face images. We separately study the parameters of the logarithmic curve in face and background regions to establish the traces imposed by the morphing process. The evaluation results show that a single parameter of the logarithmic curve may be sufficient to clearly separate morphed and original images.
Fall assessment and its injury prevention using a wearable airbag technology

Falls are common among the older people. Fall cause major injuries like broken hip, which are very serious. Elder people who fall once are more likely to fall again. Hence a wearable airbag which incorporates a fall detection system which uses acceleration and angular velocity signals to trigger the inflation of the airbag. Here Gyro sensor and Tri-axial accelerometer sensors are used to detect acceleration and angular velocity. The fall detection algorithm was devised using the thresholding technique. When the value of acceleration and angular velocity signals reaches the fixed threshold value (±3m/s2, 0.5rad/s), the fall will be detected, the relay gets switched ON and the solenoid valve opens and activates the compressor, which will send the compressed air to inflate the airbag and finally the relay is switched OFF soon after the inflation of the airbag.
A tandem repeat array in IG-DMR is essential for imprinting of paternal allele at the Dlk1–Dio3 domain during embryonic development

Genomic imprinting is a phenomenon that causes parent-origin-specific monoallelic expression of a small subset of genes, known as imprinted genes, by parentally inherited epigenetic marks. Imprinted genes at the delta-like homolog 1 gene (Dlk1)-type III iodothyronine deiodinase gene (Dio3) imprinted domain, regulated by intergenic differentially methylated region (IG-DMR), are essential for normal development of late embryonic stages. Although the functions of IG-DMR have been reported by generating knockout mice, molecular details of the regulatory mechanisms are not fully understood as the specific sequence(s) of IG-DMR have not been identified. Here, we generated mutant mice by deleting a 216 bp tandem repeated sequence in IG-DMR, which comprised seven repeats of 24 bp motifs, by genome editing technologies. The mutant mice showed that paternal transmission of the deletion allele, but not maternal transmission, induces severe growth retardation and perinatal lethality, possibly due to placental defects. Embryos with a paternally transmitted deletion allele showed biallelic expression of maternally expressed genes and repression of paternally expressed genes. DNA methylation status also showed loss of methylation at IG-DMR and Gtl2-DMR, indicating that the tandem repeat sequence of IG-DMR is one of the functional sequences of IG-DMR, which is required for maintaining DNA methylation imprints of paternal allele at IG-DMR.
Comparative study and analysis of different level modular multilevel inverter with various load

In this paper simulation and study of different levels modular multilevel inverter (MMI) and their modulation techniques are presented. Different modulation techniques used for chopper cell based MMI are reviewed and are used for controlling the output voltage of a MMI. Simulation study has been carried out using MATLAB/SIMULINK software for finding the Total Harmonic Distortion (THD) of different level modular multilevel inverter with different load. Finally it is concluded that, middle level MMI are best suitable for low voltage applications.
Blind forensics tool of falsification for RAW images

This paper presents a novel method for blind forgery detection of natural image in RAW format. The approach is based on a statistical noise model of natural RAW images. This model is characterized by two parameters which are used as a fingerprint to falsification identification. The identification is cast in the framework of the hypothesis testing theory. For practice use, the Generalized Likelihood Ratio Test (GLRT) is presented and its performance is theoretically established in case of unknown parameters where an estimation of those parameters is designed. Experiments with simulated and real images highlight the relevance of the proposed approach.
Enhancing the lifetime of STT-RAM with MRU replacement algorithm

Recent trends on Emerging Technologies of Non-Volatile Memory (NVM) were considered as better alternatives for SRAM. Spin-Transfer Torque Random Access Memory (STT-RAM) is a type of NVM having less leakage power and high cell density. Besides its advantages, NVM has low write endurance and high error rates. This paper proposes a mechanism of enhancing the lifetime of STT-RAM. The Sliding mode based ECC architecture has been proposed to evenly distribute the write of ECC bits across data bits. This is necessary because the ECC part which is used to maintain reliability of NVM will wear out early as compared to the data part, since the write are unevenly distributed. In the proposed method, the MRU replacement algorithm has been implemented to improve the lifetime of the cache block and it was observed that the improvement was 4x when compared to the LRU replacement algorithm.
The Correlation of CSR and Consumer Behavior: A Study of Convenience Store

In recent years, issues related to corporate social responsibility (CSR) have received more and more attention, it also as the goal of total quality management (TQM). Since CSR can influence enterprise performance. This study explores the impact of the consumer’s perception of a company’s CSR on brand trust, brand satisfaction, brand attachment, and current and future buying behavior as well as probe into the differences between different generational groups. A consumer survey produced a total of 753 valid questionnaires which indicate a significant difference in the two correlation paths between the X and Y generational groups. This shows that consumers belonging to different generation groups exhibit slightly different correlation patterns as far as the perception of CSR is concerned. Trust in a certain brand on the part of X-generation consumers does not affect their emotional attachment to this brand. Y-generation consumers, on the other hand, are more attached to brands that they trust.
Energy efficient cross-layer transmission design for two-user wireless systems with imperfect channel state information

In this paper, we study a cross-layer transmission design problem in a class of multi-user wireless network. We consider the case that two independent information sources both desire to send two types of delay-sensitive message to a common destination. One type is generated periodically, and is expected to be delivered with sufficient transmission reliability. The other type is generated randomly, and is expected to have finite transmission delay. Utilizing the Lyapunov optimization theory, we design a transmission strategy to achieve high energy efficiency based on both imperfect channel state information (CSI) at the physical layer and queue state information (QSI) at the Media Access Control layer. Simulation results show that the performance of the proposed strategy can be balanced through tuning optimization parameters. The capability of being adaptive to both CSI and QSI also permits the proposed method to outperform the transmission design based on only CSI.
Testing Mood-Activated Psychological Markers for Suicidal Ideation

To what extent are death- and life-oriented psychological processes among suicidal individuals activated by mood? According to Teasdale’s (1988) Differential Activation Hypothesis, we would expect that negative mood-activated psychological processes are maladaptive among suicide ideators (vs. non-ideators) and predictive of subsequent suicidal ideation. This, however, has never been prospectively studied. To address this knowledge gap, we conducted a prospective study assessing psychological risk factors via the Death/Life Implicit Association Test (IAT) and the Suicide Stroop task before and after a temporary negative mood induction. Suicidal ideation was assessed one and six months later. Results based on Death/Life IAT performance largely supported hypotheses, such that suicide ideators demonstrated significantly weaker implicit identification with life after (vs. before) the negative mood induction. Non-ideators demonstrated no significant change, maintaining strong identification with life irrespective of mood. Of note, this baseline interaction may have been accounted for by depressive symptoms. Identification with death (vs. life) predicted greater likelihood of suicidal ideation one month later, controlling for depressive symptoms and baseline suicidal ideation. Only negative mood-activated identification with death predicted suicidal ideation six months later. Suicide Stroop scores did not change as a function of mood or predict subsequent suicidal ideation. Death/Life IAT findings support the Differential Activation Hypothesis and suggest that suicide ideators’ identification with life is more variable and easily weakened by negative mood relative to non-ideators. We encourage future work to consider the potential role of transient mood and the importance of measuring psychological processes that pertain to both death and life.
Identifying and Removing , Irrelevant and Redundant Features in High Dimension Data Using Feature Subset

Feature subset selection can be analyzed as the practice of identifying and removing as lot of inappropriate and unnecessary features as achievable. This if for the reason that, irrelevant features do not contribute to the predictive accuracy and redundant features do not redound to receiving a better analysis for that they provide typically information which is previously present in other features of all the existing feature subset selection algorithms, most of them can effectively eliminate irrelevant features but fail to handle redundant features. The improved FAST algorithm is evaluated using various types of data like text data, micro-array data and image data to represent its performance. Fast clustering algorithm work can be done in two steps. The first step is to moving out irrelevant features from the dataset, for irrelevant features are removed by the features having the value above the predefined threshold. And the second step is to eliminate the redundant features from the dataset, the redundant features is removed by constructing the Minimum Spanning Tree and separate the tree having the edge distance more than its neighbour to form the separate clusters, from the clusters features that are strongly associated with the target features are selected to form the subset of features. The Fast clustering Algorithm is more efficient than the existing feature subset selection algorithms. These can be formed in well equipped format and the time taken to retrieve the information will be short time and the Fast algorithm calculates the retrieval time of the data from the dataset. This algorithm formulates as per the data available in the dataset. Ghazi Fuad Khamis Rabi Abunqara Master of Science (Information System), Nizam College (Autonomous),O.U, Basheer Bagh, Hyderabad. T. Ramdas Naik Assistant Professor Dept, Computer Science (PG) Nizam College (Autonomous),O.U, Basheer Bagh, Hyderabad. By analyzing the efficiency of the proposed work and existing work, the time taken to retrieve the data will be better in the proposed by removing all the irrelevant features which gets analyzed. To ensure the efficiency of FAST, we adopt the efficient minimum-spanning tree (MST) clustering method. The efficiency and effectiveness of the FAST algorithm are evaluated through an empirical study. Extensive experiments are carried out to compare FAST and several representative feature selection algorithms, namely, FCBF, Relief, CFS, Consist, and FOCUSSF, with respect to four types of wellknown classifiers, namely, the probability based Naive Bayes, the tree-based C4.5, the instance-based IB1, and the rule-based RIPPER before and after feature selection.
CRC Platform: A Colorectal Cancer Domain-specific Chemogenomics Knowledgebase for Polypharmacology and Target Identification Research

Colorectal cancer (CRC) is the third most common cancer, causing more than 600,000 deaths worldwide annually. Due to the involvement of complicated signaling pathways, epigenetic changes and genetic/genomic alterations, it is still challenging to develop effective treatments to reverse CRC progression. In order to facilitate developing new drugs for CRC treatment and revealing the mechanisms of CRC drug action at molecular level, we have constructed a computational CRC Platform (http://www.cbligand.org/CRC/), a domain-specific chemogenomics knowledgebase. The CRC platform consists of four database modules, e.g. 762 CRC related genes and proteins, 411 known CRC drugs and chemicals, 168383 CRC related bioassays, and 269 CRC pathways, as well as searching tools for multi-function retrieval. It is also featured with powerful cloud computation technologies and computational tools to expedite target identification, polypharmacology and drug synergy analysis for CRC research. We have also demonstrated the application of the CRC platform in the case studies: (1) computational exploration of FDA-approved CRC drugs for polypharmacology and drug synergy analysis; (2) in silico target identification of small chemical molecules from natural products with anti-CRC bioactivity; and (3) target identification and experimental validation for our in-house compounds. CRC platform will not only enrich our knowledge of CRC target identification, polypharmacology analysis, and biomarkers investigation, but also enhance the CRC chemogenomics data sharing and information exchange globally, and assist new drug design discovery and development for CRC treatment.
Escherichia coli β-clamp slows down DNA polymerase I dependent nick translation while accelerating ligation

The nick translation property of DNA polymerase I (Pol I) ensures the maturation of Okazaki fragments by removing primer RNAs and facilitating ligation. However, prolonged nick translation traversing downstream DNA is an energy wasting futile process, as Pol I simultaneously polymerizes and depolymerizes at the nick sites utilizing energy-rich dNTPs. Using an in vitro assay system, we demonstrate that the β-clamp of the Escherichia coli replisome strongly inhibits nick translation on the DNA substrate. To do so, β-clamp inhibits the strand displacement activity of Pol I by interfering with the interaction between the finger subdomain of Pol I and the downstream primer-template junction. Conversely, β-clamp stimulates the 5’ exonuclease property of Pol I to cleave single nucleotides or shorter oligonucleotide flaps. This single nucleotide flap removal at high frequency increases the probability of ligation between the upstream and downstream DNA strands at an early phase, terminating nick translation. Besides β-clamp-mediated ligation helps DNA ligase to seal the nick promptly during the maturation of Okazaki fragments.
Bias Based General Framework for Delay Reduction in Backpressure Routing Algorithm

In queueing networks, it is well known that the throughput-optimal backpressure routing algorithm results in poor delay performance for light and moderate traffic loads. To improve delay performance of backpressure routing algorithm, available works exploit various information of queueing networks, such as queue length, shortest path and packet delay, to direct packets to shorter routes to their destinations. Despite different forms of these works, they share the common characteristic: using bias to help backpressure routing to reduce packet delay. From this observation, we propose in this paper a bias based general framework to reduce packet delay of backpressure routing algorithm. Our framework is general in the sense that it not only covers many bias based variants of backpressure routing algorithm as special cases but also enables machine learning based methods to be adopted to further improve delay performance. Based on the general framework, we propose a specific Q-learning based backpressure routing algorithm to reduce packet delay.
Emotion analysis and speech signal processing

In recent times there have been notable advancements in the field of Automatic Speech Recognition (ASR) in terms of the technology used as well as its applications. But still the performance of ASR systems is much inferior as compared to Human Speech Recognition (HSR). One reason being that the ASR systems are not much developed when it comes to the recognition of emotions carried in the speech signal which is done involuntarily in HSR. This paper discusses the series of steps to be followed in order to analyze the speech signal for the recognition of its emotions highlighting some of the best available techniques at present for each step. The disturbances such as background noises incurred during the transmission makes speech processing complex and difficult. In this paper, we present a mechanism to perform the classification of the speech for different emotions.