Miracle-dz/UniFAR-FTUs huggingface dataset:
tRNAs Are Stable After All: Pitfalls in Quantification of tRNA from Starved Escherichia coli Cultures Exposed by Validation of RNA Purification Methods

We show that E. coli tRNAs are remarkably stable during several days of nutrient starvation, although rRNA is degraded extensively under these conditions. The levels of these two major RNA classes are considered to be strongly coregulated at the level of transcription. ABSTRACT tRNAs and ribosomal RNAs are often considered stable RNAs. In contrast to this view, we recently proposed that tRNAs are degraded during amino acid starvation and drug-induced transcription inhibition. However, reevaluation of our experimental approach revealed that common RNA extraction methods suffer from alarming extraction and size biases that can lead to gross underestimation of RNA levels in starved Escherichia coli populations. Quantification of tRNAs suffers additional biases due to differing fractions of tRNAs with base modifications in growing versus starved bacteria. Applying an improved methodology, we measured tRNA levels after starvation for amino acids, glucose, phosphate, or ammonium and transcription inhibition by rifampicin. We report that tRNA levels remain largely unaffected in all tested conditions, including several days of starvation. This confirms that tRNAs are remarkably stable RNAs and serves as a cautionary tale about quantification of RNA from cells cultured outside the steady-state growth regime. rRNA, conversely, is extensively degraded during starvation. Thus, E. coli downregulates the translation machinery in response to starvation by reducing the ribosome pool through rRNA degradation, while a high concentration of tRNAs available to supply amino acids to the remaining ribosomes is maintained. IMPORTANCE We show that E. coli tRNAs are remarkably stable during several days of nutrient starvation, although rRNA is degraded extensively under these conditions. The levels of these two major RNA classes are considered to be strongly coregulated at the level of transcription. We demonstrate that E. coli can control the ratio of tRNAs per ribosome under starvation by means of differential degradation rates. The question of tRNA stability in stressed E. coli cells has become subject to debate. Our in-depth analysis of RNA quantification methods reveals hidden technical pitfalls at every step of the analysis, from RNA extraction to target detection and normalization. Most importantly, starved E. coli populations were more resilient to RNA extraction than unstarved populations. The current results underscore that the seemingly trivial task of quantifying an abundant RNA species is not straightforward for cells cultured outside the exponential growth regime.
Characterization of a Unique Novel LORF3 Protein of Duck Plague Virus and Its Potential Pathogenesis

DPV is a highly lethal alphaherpesvirus that causes duck plague in birds of the order Anseriformes. The virus has caused huge economic losses to the poultry industry due to high morbidity and mortality and the cost of vaccination. ABSTRACT Duck plague virus (DPV) is a high-morbidity fowl alphaherpesvirus that causes septicemic lesions in various organs. Most DPV genes are conserved among herpesviruses, while a few are specific to fowl herpesviruses, including the LORF3 gene, for which there is currently no literature describing its biological properties and functions. This study first addressed whether the LORF3 protein is expressed by making specific polyclonal antibodies. We could demonstrate that DPV LORF3 is an early gene and encodes a protein involved in virion assembly, mainly localized in the nucleus of DPV-infected DEF cells. To investigate the role of this novel LORF3 protein in DPV pathogenesis, we generated a recombinant virus that lacks expression of the LORF3 protein. Our data revealed that the LORF3 protein is not essential for viral replication but contributes to DPV replication in vitro and in vivo and promotes duck plague disease morbidity and mortality. Interestingly, deletion of the LORF3 protein abolished thymus atrophy in DPV-vaccinated ducks. In conclusion, this study revealed the expression of avian herpesviruses-specific genes and unraveled the role of the early protein LORF3 in the pathogenesis of DPV. IMPORTANCE DPV is a highly lethal alphaherpesvirus that causes duck plague in birds of the order Anseriformes. The virus has caused huge economic losses to the poultry industry due to high morbidity and mortality and the cost of vaccination. DPV encodes 78 open reading frames (ORFs), and these genes are involved in various processes of the viral life cycle. Functional characterization of DPV genes is important for understanding the complex viral life cycle and DPV pathogenesis. Here, we identified a novel protein encoded by LORF3, and our data suggest that the LORF3 protein is involved in the occurrence and development of duck plague.
The DNA Phosphorothioation Restriction-Modification System Influences the Antimicrobial Resistance of Pathogenic Bacteria

In this study, we demonstrated the effect of DNA PT modification-based R-M systems on horizontal gene transfer of AMR genes in pathogenic bacteria. We show that there is no apparent association between the genetic background of the strains harboring PT R-Ms and the number of AMR genes or the kinds of gene families. ABSTRACT Bacterial defense barriers, such as DNA methylation-associated restriction-modification (R-M) and the CRISPR-Cas system, play an important role in bacterial antimicrobial resistance (AMR). Recently, a novel R-M system based on DNA phosphorothioate (PT) modification has been shown to be widespread in the kingdom of Bacteria as well as Archaea. However, the potential role of the PT R-M system in bacterial AMR remains unclear. In this study, we explored the role of PT R-Ms in AMR with a series of common clinical pathogenic bacteria. By analyzing the distribution of AMR genes related to mobile genetic elements (MGEs), it was shown that the presence of PT R-M effectively reduced the distribution of horizontal gene transfer (HGT)-derived AMR genes in the genome, even in the bacteria that did not tend to acquire AMR genes by HGT. In addition, unique gene variation analysis based on pangenome analysis and MGE prediction revealed that the presence of PT R-M could suppress HGT frequency. Thus, this is the first report showing that the PT R-M system has the potential to repress HGT-derived AMR gene acquisition by reducing the HGT frequency. IMPORTANCE In this study, we demonstrated the effect of DNA PT modification-based R-M systems on horizontal gene transfer of AMR genes in pathogenic bacteria. We show that there is no apparent association between the genetic background of the strains harboring PT R-Ms and the number of AMR genes or the kinds of gene families. The strains equipped with PT R-M harbor fewer plasmid-derived, prophage-derived, or integrating mobile genetic element (iMGE)-related AMR genes and have a lower HGT frequency, but the degree of inhibition varies among different bacteria. In addition, compared with Salmonella enterica and Escherichia coli, Klebsiella pneumoniae prefers to acquire MGE-derived AMR genes, and there is no coevolution between PT R-M clusters and bacterial core genes.
Ant biodiversity in a temperate urban environment

Abstract. The southeastern United States is experiencing rapid environmental changes associated with urbanization. An ongoing question is understanding how such rapid changes impact biodiversity. Insects are recognized as serving important roles as indicators of overall ecological health. Specifically, the diversity and presence of ants can serve as indicators of environmental quality due to their ubiquitous presence across many terrestrial environments and their key role in numerous ecosystem processes. The aim of this study is to measure changes in ant biodiversity across an urban gradient in a temperate habitat. Food baits were set out across nine locations representing various degrees of urbanization as measured by percent impervious surface. Results show the abundance of ants significantly increased with increasing urbanization, with more generalist species found in great abundance in highly urbanized areas. However, a significant difference in ant species diversity across the urban gradient was not detected. The findings are consistent with other urban studies that showed an increase in the abundance of generalist species in urban areas. As the southeast United States continue to undergo urbanization to meet the demands of a growing human population, it is important for developers and ecologists to consider ways in which urban design can facilitate biodiversity, as levels of biodiversity will ultimately impact urban ecosystem function and the subsequent health of all occupants.
Examining the Competitive Capabilities of Manufacturing Firms

Manufacturers compete in a complex and uncertain environment with growing global competition, changing and emerging markets, and increasing levels of manufacturing technology. Order winning hinges on their ability to achieve a set of competitive capabilities that have an external, customer orientation and manifest the relative strength of the individual firm against its competitors. This study proposes a framework for research on competitive capabilities, reports on the development of a set of constructs for measuring those capabilities, and tests relations among them. The constructs measure flexible product innovation, quality, delivery dependability, competitive price, and premium price. The constructs are reliable across industries. Tests of a structural model suggest significant relations among the competitive capabilities and significant, positive, and direct-indirect relations between the competitive capabilities and profitability. Results are based on a sample of 244 firms across 4 industries.
Tutorial on Automated Trading using API

Automation has played a significant role in many domains, and stock market trading is not an exception. Even retail traders can automate his/her trading strategy using API. A computer program doing trading is known as Algorithmic Trading. It eliminates inefficiency due to human emotions. Trading logic that decides when to buy and sell a stock is generally term as a trading strategy. The availability of large trading data has made it possible to automate the generation of dynamic trading strategies. Speed and accuracy are very vital aspects of profitable stock market trading. Algorithmic trading is far superior to manual trading in terms of speed and accuracy. In this tutorial, we demonstrate the automation of predefined trading strategies using Python API. We will also demonstrate the generation of trading strategies using Reinforcement learning, Deep learning, and various other domains in computer science. Validating the performance of any predefined trading strategy on historical data plays a significant role in its live performance and it is known as backtesting. Backtesting is the key feature of Algorithmic Trading. We will demonstrate backtesting of the trading strategies using a computer program on the Indian and American stock market data.
Linking Landscape Pattern and Human Disturbance on a Regional Level: A Case Study in Beira Interior Region, Portugal

The land use and land cover pattern of landscapes are key elements of basic landscape structure; accordingly, this pattern has an important role in landscape management, nature conservation and preservation. In the other hand the human impact on the ecological environment has attracted a significant amount of attention. In this study, hemeroby index (HI) was used to quantify the degree of human disturbance, and the relationship between HI and landscape pattern metrics was explored in a region of Central Portugal. The main objectives of this study were to analyze the temporal and spatial characteristics of landscape pattern, and its relationship with different degrees of human disturbance. The Spearman’s correlations were determined to reveal the statistical connections between the landscape metric parameters and the HI values. At a landscape level the Mean Perimeter-Area Ratio (MPAR) showed a significant statistical connection with the HI. At a class level most of the metrics can be used to estimate the spatial changes of the hemeroby level, with Mean Patch Size (MPS) and Mean Perimeter-Area Ratio (MPAR) showing signiﬁcant statistical connection with the hemeroby index, for agricultural areas, pastures and seminatural areas.
A Galois connection between partial formal contexts and attribute sets

This paper establishes an ordering between partial formal contexts, which are trivalued contexts. We use three values to represent the presence or absence of a certain property or its unknown value. We establish a Galois connection between this ordered set and the Boolean algebra of attribute sets. Finally, we discuss the interpretation of this Galois connection.
Armed conflict and fisheries in the Lake Victoria basin

Civil conflict is the most prevalent form of armed conflict in the world today, but this significant driver of food and income security has been largely missing from studies of fisheries. Fisheries conflict is an example of complex dynamics operating in social-ecological systems. We theorize and document the existence of such a feedback loop between conflict in Uganda and fisheries in Lake Victoria. Civil war in northern Uganda resulted in mass human population displacement, which corresponded in time with increases in fishing effort in Lake Victoria. Subsequent changes in catch of Nile perch, the dominant commercial fishery, sparked armed conflict in the lake itself, at Migingo Island, between Uganda and Kenya. From this case study, we draw seven main conclusions. First, these correlation-based relationships are illustrative but not conclusive and we call for further empirical investigation. Second, the couplings between conflict and fishing subsystems are spatially asymmetric: conflict effects are diffuse in their links to broad changes in the fishery, whereas fishery effects may produce more localized conflict events. Third, and most relevant to conflict scholars, the drivers of fishing effort and catch may originate in different subsystems, but their changes and effects must be analyzed in concert. Fourth, the complex and path-dependent impacts of conflicts on natural resources in general, and fisheries in particular, highlights the urgent need for targeted surveys and more mechanistic understanding. Fifth, the open access nature of fisheries in Lake Victoria may exacerbate instabilities not present in other systems. Sixth, the diffuse and context-specific effects of conflicts on fisheries means models of fisheries management (e.g., stock assessment) should not incorporate conflict as a driver at this time. Finally, countries and their stakeholders should focus on diversification in employment for short term coping mechanisms during conflict as a means of shortcircuiting the conflict-fisheries feedback loop.
A nonlinear parking problem

The problem of minimizing Egc(Zt) is considered asymptotically, where Z0,Z1,… is a perturbed random walk and gc are convex functions which are minimized at values that approach ∞ as c ↓ 0. It is shown that a first passage time is asymptotically optimal, and the boundary for this time is characterized in terms of gc and the limiting distribution of the excess over the boundary. Applications to change point problems and power one tests are presented.
A Multi-Dimensional Matrix Product - A Natural Tool for Parameterized Graph Algorithms

We introduce the concept of a k-dimensional matrix product D of k matrices A1,…,Ak of sizes n1×n,…,nk×n, respectively, where D[i1,…,ik] is equal to ∑ℓ=1nA1[i1,ℓ]×…×Ak[ik,ℓ]. We provide upper bounds on the time complexity of computing the product and solving related problems of computing witnesses and maximum witnesses of the Boolean version of the product in terms of the time complexity of rectangular matrix multiplication. The multi-dimensional matrix product framework is useful in the design of parameterized graph algorithms. First, we apply our results on the multi-dimensional matrix product to the fundamental problem of detecting/counting copies of a fixed pattern graph in a host graph. The recent progress on this problem has not included complete pattern graphs, i.e., cliques (and their complements, i.e., edge-free pattern graphs, in the induced setting). The fastest algorithms for the aforementioned patterns are based on a reduction to triangle detection/counting. We provide an alternative simple method of detection/counting copies of fixed size cliques based on the multi-dimensional matrix product. It is at least as time efficient as the triangle method in cases of K4 and K5. Next, we show an immediate reduction of the k-dominating set problem to the multi-dimensional matrix product. It implies the W[2] hardness of the problem of computing the k-dimensional Boolean matrix product. Finally, we provide an efficient reduction of the problem of finding the lowest common ancestors for all k-tuples of vertices in a directed acyclic graph to the problem of finding witnesses of the Boolean variant of the multi-dimensional matrix product. Although the time complexities of the algorithms resulting from the aforementioned reductions solely match those of the known algorithms, the advantage of our algorithms is simplicity. Our algorithms also demonstrate the versatility of the multi-dimensional matrix product framework.
Physics-Inspired Compressive Sensing: Beyond deep unrolling

As an emerging paradigm for signal acquisition and reconstruction, compressive sensing (CS) achieves high-speed sampling and compression jointly and has found its way into many applications. With the fast growth of deep learning in computer vision, various methods of applying neural networks (NNs) in CS imaging tasks have been proposed. One category of them, named the deep unrolling network, is inspired by the physical sampling model and combines the merits of both optimization model- and data-driven methods, becoming the mainstream of this realm. In this review article, we first review the inverse imaging model and optimization algorithms encountered in the CS research and then provide the recent representative developments of CS networks, which are grouped into deep physics-free and physics-inspired approaches with respect to the utilization of sampling matrix and measurement information. Following this, we analyze the conceptual connections and relationships among various existing methods and present our perspectives on recent advances and trends for future research.
Genetic and phenotypic relationships between coronary atherosclerotic heart disease and electrocardiographic traits

Observational studies have revealed that Coronary Atherosclerotic Heart Disease (CAHD) is associated with abnormal electrocardiogram (ECG) traits. However, it remains unclear whether there are genetic correlations between ECG and CAHD. Here, we explored genetic correlations and putative causal relationships between CAHD and ECG by performing Mendelian randomization (MR) and Polygenic risk score (PRS) analyses on the summary statistics from a large-scale genome-wide association study (GWAS) for CAHD (FinnGen: Ncase 23363, Ncontrol 187840) and ECG traits (UK Biobank: Ncase=1137, Ncontrol=40823). Results showed a causal genetic relationship between CAHD and six ECG traits in the lead V6. These ECG traits combining with age and gender have predicted CAHD risk with an AUC of 0.76. Further summary data-based Mendelian randomization (SMR) analysis identified 11 risk genes associated with the causality between CAHD and ECG. Thus, the revealed putative causal effects of CAHD on ECG traits provide genetic evidence to support the importance of monitoring CAHD risk through the ECG.
The extended medium sensitivity survey distant cluster sample - X-ray data and interpretation of the luminosity evolution

The X-ray properties of a cluster of galaxies subsample of the Einstein Extended Medium Sensitivity Survey is described. A summary of this sample and its implication has been presented previously; this paper gives the full details. The cluster subsample is 98.4 percent identified and contains 93 X-ray-selected clusters to a redshift of 0.58. The cluster X-ray luminosity function at three cosmic epochs is derived. While the present luminosity function agrees with previous determinations at the lowest redshifts, it is found that the volume density of high-luminosity clusters is greater now than it was in the past. The normalization, shape, and time dependence of the luminosity function can be described by a simple hierarchical formation model with parameters which also describe the temperature function of an independent sample of low-redshift clusters. In this model the comoving hot gas density remains constant with time at least to redshifts of order 0.35.
EnsembleDroid: A Malware Detection Approach for Android System based on Ensemble Learning

In recent years, mobile devices such as smartphones and tablets have become one of the most popular digital devices of choice in our daily lives when it comes to functionality and convenience. With only ever increasing popularity, mobile devices with Android operating systems have become a common target for malware especially through third-party markets. To make things worse, the emergence of obfuscation and adversarial example attacks enables malware to evade traditional security methods and steal a user’s private information. In this paper, we propose an ensemble learning-based framework for detecting malware by using risky permissions as features to train a classifier and determine whether a mobile application (a.k.a. app) is malware or not. To evaluate the performance of the proposed malware detection approach, we have conducted a series of experiments using real world Android app datasets that are composed of both malicious and benign apps. Experimental results clearly show that the proposed malware detection approach can effectively detect malware with a high accuracy.
Detection of Hardware Trojan using CNN with Residual Network

Due to very high demand of low cost IoTs and smart devices, a lot of company outsources their design for fabrication to third party. This raises a deep security concern of malicious modification of integrated circuits (IC) which is known as Hardware Trojan (HT). By lowering the effective entropy of a random-number generator or revealing information on the internal operation of the processor to an attacker, very simple but difficult-to-detect trojans could be used to weaken the security of a crypto processor. The purpose of side-channel measurements for identifying fraudulent IC tampering has been intensively investigated during the previous decade. This paper proposes a deep convolutional neural network (DCNN) model with residual network for hardware trojan detection, with the goal of high detection accuracy. The accuracy of the model in detecting malicious hardware was then assessed against cutting-edge algorithms. A convolutional neural network model was trained using power traces from an FPGA-based implementation (HT-free) of a symmetric Cryptographic algorithm (AES). In this phase, the convolutional neural network with residual unit has been implemented, and evaluation of the trojan detection indicates a high accuracy, with an average percentage accuracy of 90.48%.
Determining Fruit Set Density and Spatial Arrangement of Fruit Trees for Fruit Picking Operations

Decreasing numbers of farmers and an aging farming population pose challenges for Japanese agriculture. This has led to issues such as difficulties maintaining crop quality and ensuring productivity. In addition, the cultivation of fruit trees, such as apples requires maintenance labor for tasks such as thinning. These tasks are difficult to quantify and therefore are mainly performed by experienced agricultural workers based on their intuition. In this study, we used a general object detection algorithm and an RGB-D camera to visualize fruit cultivation work and determine the fruiting density of apple trees.