Miracle-dz/UniFAR-FTUs huggingface dataset:
Three Novel Antisense Overlapping Genes in E. coli O157:H7 EDL933

The abundance of long overlapping genes in prokaryotic genomes is likely to be significantly underestimated, since such genes are not allowed in genome annotations. However, ribosome profiling catches mRNA in the moment of being template for protein production. ABSTRACT The abundance of long overlapping genes in prokaryotic genomes is likely to be significantly underestimated. To date, only a few examples of such genes are fully established. Using RNA sequencing and ribosome profiling, we found expression of novel overlapping open reading frames in Escherichia coli O157:H7 EDL933 (EHEC). Indeed, the overlapping candidate genes are equipped with typical structural elements required for transcription and translation, i.e., promoters, transcription start sites, as well as terminators, all of which were experimentally verified. Translationally arrested mutants, unable to produce the overlapping encoded protein, were found to have a growth disadvantage when grown competitively against the wild type. Thus, the phenotypes found imply biological functionality of the genes at the level of proteins produced. The addition of 3 more examples of prokaryotic overlapping genes to the currently limited, yet constantly growing pool of such genes emphasizes the underestimated coding capacity of bacterial genomes. IMPORTANCE The abundance of long overlapping genes in prokaryotic genomes is likely to be significantly underestimated, since such genes are not allowed in genome annotations. However, ribosome profiling catches mRNA in the moment of being template for protein production. Using this technique and subsequent experiments, we verified 3 novel overlapping genes encoded in antisense of known genes. This adds more examples of prokaryotic overlapping genes to the currently limited, yet constantly growing pool of such genes.
Tyrosine Kinase Inhibitors Display Potent Activity against Cryptosporidium parvum

The intestinal parasite Cryptosporidium parvum is a major cause of diarrhea-associated morbidity and mortality in children, immunocompromised people, and young ruminant animals. With no effective drug available to treat cryptosporidiosis in humans and animals, there is an urgent need to identify anti-parasitic compounds and new targets for drug development. ABSTRACT The protozoan parasite Cryptosporidium is a leading cause of diarrheal disease (cryptosporidiosis) and death in young children. Cryptosporidiosis can be life-threatening in individuals with weak immunity such as HIV/AIDS patients and organ transplant recipients. There is currently no effective drug to treat cryptosporidiosis in the pediatric and immunocompromised population. Therefore, there is an urgent need to expedite the drug discovery process in order to develop new and effective therapies to reduce the global disease burden of cryptosporidiosis. In this study, we employed a drug repurposing strategy to screen a library of 473 human kinase inhibitors to determine their activity against Cryptosporidium parvum. We have identified 67 new anti-cryptosporidial compounds using phenotypic screening based on a transgenic C. parvum strain expressing a luciferase reporter. Further, dose-response assays led to the identification of 11 hit compounds that showed potent inhibition of C. parvum at nanomolar concentration. Kinome profiling of these 11 prioritized hits identified compounds that displayed selectivity in targeting specific families of kinases, particularly tyrosine kinases. Overall, this study identified tyrosine kinase inhibitors that hold potential for future development as new drug candidates against cryptosporidiosis. IMPORTANCE The intestinal parasite Cryptosporidium parvum is a major cause of diarrhea-associated morbidity and mortality in children, immunocompromised people, and young ruminant animals. With no effective drug available to treat cryptosporidiosis in humans and animals, there is an urgent need to identify anti-parasitic compounds and new targets for drug development. To address this unmet need, we screened a GSK library of kinase inhibitors and identified several potent compounds, including tyrosine kinase inhibitors, that were highly effective in killing C. parvum. Overall, our study revealed several novel compounds and a new family of kinases that can be targeted for anti-cryptosporidial drug development.
Hierarchical Dense Pattern Detection in Tensors

Dense subtensor detection gains remarkable success in spotting anomalies and fraudulent behaviors for multi-aspect data (i.e., tensors), like in social media and event streams. Existing methods detect the densest subtensors flatly and separately, with the underlying assumption that those subtensors are exclusive. However, many real-world tensors usually present hierarchical properties, e.g., the core-periphery structure and dynamic communities in networks. It is also unexplored how to fuse the prior knowledge into dense pattern detection to capture the local behavior. In this article, we propose CatchCore, a novel framework to efficiently find the hierarchical dense subtensors. We first design a unified metric for dense subtensor detection, which can be optimized with gradient-based methods. With the proposed metric, CatchCore detects hierarchical dense subtensors through the hierarchy-wise alternative optimization and finds local dense patterns concerning some items in a query manner. Finally, we utilize the minimum description length principle to measure the quality of detection results and select the optimal hierarchical dense subtensors. Extensive experiments on synthetic and real-world datasets demonstrate that CatchCore outperforms the top competitors in accuracy for detecting dense subtensors and anomaly patterns, like network attacks. Additionally, CatchCore successfully identifies a hierarchical researcher co-authorship group with intense interactions in the DBLP dataset; it can also capture core collaboration and multi-hop relations around some query objects. Meanwhile, CatchCore also scales linearly with all aspects of tensors.
Fine-Grained Runtime Monitoring of Real-Time Embedded Systems

Dynamically ensuring the correctness of the functional behavior of a real-time embedded system is tedious, especially in the autonomous domain. Even though the current real-time task model provides sufficient information to perform basic schedulability tests, it is inadequate to be used in runtime monitoring to assert and guarantee the correctness of a system under hardware/software malfunctions or malicious cyber attacks. In this article, we present a runtime monitoring approach based on a fine-grained model of real-time tasks.
Partial Network Partitioning

We present an extensive study focused on partial network partitioning. Partial network partitions disrupt the communication between some but not all nodes in a cluster. First, we conduct a comprehensive study of system failures caused by this fault in 13 popular systems. Our study reveals that the studied failures are catastrophic (e.g., lead to data loss), easily manifest, and are mainly due to design flaws. Our analysis identifies vulnerabilities in core systems mechanisms including scheduling, membership management, and ZooKeeper-based configuration management. Second, we dissect the design of nine popular systems and identify four principled approaches for tolerating partial partitions. Unfortunately, our analysis shows that implemented fault tolerance techniques are inadequate for modern systems; they either patch a particular mechanism or lead to a complete cluster shutdown, even when alternative network paths exist. Finally, our findings motivate us to build Nifty, a transparent communication layer that masks partial network partitions. Nifty builds an overlay between nodes to detour packets around partial partitions. Nifty provides an approach for applications to optimize their operation during a partial partition. We demonstrate the benefit of this approach through integrating Nifty with VoltDB, HDFS, and Kafka.
A Personality-based Model of Emotional Contagion and Control in Crowd Queuing Simulations

Queuing is a frequent daily activity. However, long waiting lines equate to frustration and potential safety hazards. We present a novel, personality-based model of emotional contagion and control for simulating crowd queuing. Our model integrates the influence of individual personalities and interpersonal relationships. Through the interaction between the agents and the external environment parameters, the emotional contagion model based on well-known theories in psychology is used to complete the agents’ behavior planning and path planning function. We combine the epidemiological SIR model with the cellular automaton model to capture various emotional modelling for multi-agent simulations. The overall formulation involves different emotional parameters, such as patience, urgency, and friendliness, closely related to crowd queuing. In addition, to manage the order of the queue, governing agents are added to prevent the emotional outbreak. We perform qualitative and quantitative comparisons between our simulation results and real-world observations on various scenarios. Numerous experiments show that reasonably increasing the queue channel and adding governing agents can effectively improve the quality of queues.
Using situational analysis to reimagine infrastructure

In this article, we ask what it means to think of infrastructure discursively through situational analysis. First, we consider how policymakers have historically used writing and rhetoric to redefine, reframe, and resituate what infrastructure can be in technical documents. Second, we address the impact of policymakers' discursive practices on the spaces and material realities of communities. We view the infrastructural function of writing "as a conceptual foundation for revealing structures and foundations of organizations that affect people" (Read, 2019, p. 237). We use three texts as the space of our discourse mapping: President Franklin Roosevelt's "Fireside Chat on the Recovery Program," the Green New Deal, and President Joseph Biden's recently proposed American Jobs Plan. Through these three cases, we argue that infrastructure has always been defined in relation to environment. Any definition of infrastructure is rooted in environment or seeks to change environment. These shifts in definition have been used strategically to bring more visibility to marginalized communities and make their concerns central to the concerns of the United States' socio-economic agenda. We close with implications for both communities and policymakers.
Experimental study of pure bitumens in tension, compression, and shear

Two nominally identical pure bitumens, commonly employed for pavement construction in the United Kingdom, were tested in uniaxial tension, compression, and shear, over a wide range of temperatures, stresses, and strain rates. The bitumens were found to exhibit linear viscous behavior at low stress levels, and power-law creeping behavior at higher stress levels. The temperature dependence was found to follow the Arrhenius relationship at temperatures immediately above the glass transition and the Williams, Landel, and Ferry (WLF) equation at higher temperatures. Below the glass transition temperature, the Eyring plasticity model was found to hold. Constitutive models that reflect the physical mechanisms of steady state and transient deformation are proposed. The fracture properties of the bitumens are also discussed.
Self-relevance diminishes the effectiveness of importance and trustworthiness cues in consumer response to online product-related messages

Abstract The existing literature suggests that people rely less on norms and conventions when the context is more relevant to them. On that account, the paper proposes that the self-relevance of response to an online product offering diminishes the effectiveness of message-related cues regarding the importance of presented product attributes and the source trustworthiness. Three experiments (Study 1: N = 222, Study 2: N = 174, Study 3: N = 79) manipulated self-relevance by asking participants to imagine that they buy products for themselves (vs. the participants merely evaluated products) (Studies 1–2) and exposing participants to a product-related narrative (Study 3). Additionally, message-related cues were presented to the participants (attribute-importance cues in Studies 1–2, source-trustworthiness cues in Study 3). Product preferences (Studies 1–2) and perceived message trustworthiness (Study 3) were measured. The results indicate that in the high self-relevance condition, the effect of attribute = importance cues on consumer product preference is weaker (Studies 1–2), and the effect of source-trustworthiness cues on perceived trustworthiness (Study 3). The paper presents a novel perspective linking the concept of consumer self-relevance with the effectiveness of message-related cues in product offerings. The results suggest to online marketers when to communicate cues regarding attribute importance and source trustworthiness and provide valuable guidelines for policymakers and consumers about how to resist those cues.
“Be authentic: Be the most authentic ‘you’ you can be”: Consejos from Latina administrative leaders in public higher education

ABSTRACT Despite the increasing entry of Latinas into higher education, this group continues to be underrepresented at the level of leadership. Previous studies have indicated Latinas that have achieved academic administrative leadership roles can play an important role in helping others to aspire to and attain such positions by offering guidance based on their own personal experiences and observations. However, to date, there have been limited efforts to collate and share that advice. Accordingly, drawing on conversations with Latina leaders in academia, this paper provides insight into the consejos that these leaders would offer others interested in pursuing leadership roles. The paper contributes to the growing literature on experiences of Latina leaders in higher education, while providing practical advice that will help Latinas to achieve a critical mass at the leadership level in US institutions of higher education.
Evaluations of Closure Correlations for the Simulation of Two-Phase Flows in Condensers

The effects of different closure correlations on numerical simulations of vapor-liquid two-phase flow and heat transfer in steam surface condensers are critically assessed in this study. A modified k-ϵ turbulence model for two-phase flows is used in the simulation. The closure correlations are those for condensation vapor shear, interphase drag forces, non-condensable air, tube-side fluid flow, inundation, and hydraulic resistance due to the tube bundle. Numerical simulations of a steam surface condenser are carried out using different closure correlations, and the numerical results are compared with the experimental data. Recommendations are given for different closure correlations.
There is only one po

In the present paper I argue that, in Czech, all occurrences of po- that contain some notion of small quantity are in fact instantiations of a single prefix, so called delimitative po- (analyzed as containing an extensive measure function, following Filip (e.g. 2000)), though it might not seem to be the case at first sight. In order to do that, I first demonstrate that there is no need to have more po-'s (hence, the unifying analysis is superior); and second, I try to show that the po- that attaches to quantized and the one attaching to non-quantized predicates can be unified, too (i.e. there is no need of any homogeneity requirement for extensive measure functions).
A large sample of southern IRAS galaxies: spectral classes and superclustering

Results of an extensive spectroscopic survey of IRAS galaxies, in three Southern Hemisphere fields totalling about 0.5 sr, are presented. Spectroscopic observations have been made of 527 galaxies which are candidates for 474 IRAS sources. Together with published data, there are now measured redshifts for almost 850 of the 1178 selected IRAS galaxies in these fields. This measured sample includes nearly all galaxies at high redshifts, and hence high infrared luminosities, within these fields. Classification of more than 350 of these galaxies using emission-line ratios and line widths shows that about 90% have starburst characteristics.
Spectral Super-Resolution via Coupled Sparse Dictionary Learning

—High-spectral resolution imaging systems provide critical information enabling a better identiﬁcation and characterization of the objects in a scene of interest. Nevertheless, multiple factors may impair spectral resolution, as in the case of modern snapshot spectral imagers that associate each “hyper-pixel” with a speciﬁc spectral band. In this work, we propose a novel post-acquisition computational technique aiming to enhance the spectral dimensionality of imaging systems by exploiting the mathematical frameworks of Sparse Representations and Dictionary Learning. The key contribution of this work is a novel coupled sparse dictionary learning model which considers coupled feature spaces, composed of low and high spectral resolution hypercubes, in order to address the spectral super-resolution problem. We formulate our spectral coupled dictionary learning technique within the context of the Alternating Direction Method of Multipliers, optimizing each variable via closed-form expressions. Experimental results demonstrate the ability of the proposed approach to synthesize high-spectral resolution three-dimensional hypercubes, achieving better performance compared to state-of-the-art resolution enhancement methods.
The Influence of Digital Reading Habits on Student Performance at Vision College: A Conceptual Framework

Since the advent of digital media and the proliferation of digital resources, reading has altered tremendously. Regardless of digital reading, several concerns or variables, such as demographic characteristics, the effect of digital media and ICT, a lack of reading enthusiasm, and new reading norms during the reading COVID-19 epidemic, contribute to bad reading habits among individuals, particularly among students. The impact of digital reading habits on undergraduate achievement, especially among Vision College students, will be investigated in this study. Based on earlier research, this study has also established a conceptual framework.
Long QT syndrome with mutations in three genes: A rare case (cid:2)

Congenital long QT syndrome (LQTS) is a rare hereditary disease, with an incidence of 1 in 2000, characterized by prolonged ventricular repolarization and malignant ventricular tachyarrhythmias. We report the case of a 30-year-old woman, previously diagnosed with neurocardiogenic syncope, in whom LQTS was identiﬁed. The patient received an implantable cardioverter-deﬁbrillator due to polymorphic ventricular tachycardia under beta-blocker therapy. Molecular genetic testing identiﬁed three mutations in heterozygosity in the KCNH2 , KCNQ1 and SCN5A genes, which is a rare ﬁnding and is associated with worse prognosis
Analysis of opinion spreading in homogeneous networks with signed relationships

Recently, significant attention has been devoted to opinion dynamics in social networks, in which all the relationships between individuals are assumed as positive ones (i.e. friend, altruism or trust). However, many realistic social networks include negative relationships (i.e. enemy or distrust) as well as positive ones. In order to find the dynamical behavior of opinion spreading in signed networks, we propose a model taking into account the impacts of positive and negative relationships. Based on this model, we analyze the dynamical process and provide a detailed mathematical analysis for identifying the threshold of opinion spreading in homogeneous networks with signed relationships. By performing numerical simulations for the threshold in three different signed networks, we find that the theoretical and numerical results are in good agreement, confirming the correctness of our exact solution.
Electromagnetic Steering of a Magnetic Cylindrical Microrobot Using Optical Feedback Closed-Loop Control

Control of small magnetic machines in viscous fluids may enable new medical applications of microrobots. Small-scale viscous environments lead to low Reynolds numbers, and although the flow is linear and steady, the magnetic actuation introduces a dynamic response that is nonlinear. We account for these nonlinearities, and the uncertainties in the dynamic and magnetic properties of the microrobot, by using time-delay estimation. The microrobot consists of a cylindrical magnet, 1 mm long and 500 µm in diameter, and is tracked using a visual feedback system. The microrobot was placed in silicone oil with a dynamic viscosity of 1 Pa.s, and followed step inputs with rise times of 0.45 s, 0.51 s, and 1.77 s, and overshoots of 37.5%, 33.3%, and 34.4% in the x, y, and z directions, respectively. In silicone oil with a viscosity of 3 Pa.s, the rise times were 1.04 s, 0.72 s, and 2.19 s, and the overshoots were 47.8%, 48.5%, and 86.8%. This demonstrates that closed-loop control of the magnetic microrobot was better in the less viscous fluid.
Flowers Classiﬁcation via Deep Learning

Due to time restrictions or computational restraints, its not always possible to build a model from scratch about plant classiﬁcation problem in real life, we will compare the performance of methods and try to ﬁnd a pre-trained way for this kind of problem. The problem we are trying to solve is given an image of ﬂowers, we want to determine which type it is (daisy, dandelion, rose, sunﬂower, tulip) in 4323 pictures. To solve this classiﬁcation problem, we plan to use different kinds of approaches including machine learning (Support vector machine[1], Random Forest[2], K-nearest Neighbors[3]) and deep learning methods (Custom CNN, VGG [4], ResNet[5] ,DenseNet[6]). We found in this classi-ﬁcation problem, deep learning method works much better.
Correlations in Ising Ferromagnets. I

An inequality relating binary correlation functions for an Ising model with purely ferromagnetic interactions is derived by elementary arguments and used to show that such a ferromagnet cannot exhibit a spontaneous magnetization at temperatures above the mean-field approximation to the Curie or “critical” point. (As a consequence, the corresponding “lattice gas” cannot undergo a first order phase transition in density (condensation) above this temperature. ) The mean-field susceptibility in zero magnetic field at high temperatures is shown to be an upper bound for the exact result.
Velocity control of an Yb beam by a frequency-doubled mode-locked laser

An ytterbium atomic beam has been decelerated with the use of a multimode light beam based on a frequency-doubled mode-locked Ti:sapphire laser. Compression of the atomic velocity distribution was achieved by the addition of a copropagating single-mode light beam. The contribution of each isotope to the observed compressed peaks of the velocity distribution is explained.
Low-energy neutron direct capture by {sup 12}C in a dispersive optical potential

A dispersive optical potential for the interaction between low-energy neutrons and {sup 12}C nuclei is derived from a dispersion relation based on the Feshbach generalized optical model. The potential reproduces completely neutron total cross sections below 1.0 MeV and substantially reproduces the energy of the 3090keV(1/2{sup +}) level in {sup 13}C which is of nearly pure 2s{sub 1/2} single-particle character. It is found that direct-capture model calculations with this potential explain quite successfully the observed off-resonance capture transitions to the ground (1/2{sup {minus}}), 3090keV(1/2{sup +}), 3685keV(3/2{sup {minus}}), and 3854keV(5/2{sup +}) levels in {sup 13}C at neutron energies of 20{endash}600 keV. Special emphasis is laid on the fact that in these model analyses, account should be taken of the spatial nonlocality of the neutron-nucleus interaction potential, in particular for negative energies. {copyright} {ital 1998} {ital The American Physical Society}