Miracle-dz/UniFAR-FTUs huggingface dataset:
Structural and biochemical characterization on the cognate and heterologous interactions of the MazEF-mt9 TA system

The toxin-antitoxin (TA) modules widely exist in bacteria, and their activities are associated with the persister phenotype of the pathogen Mycobacterium tuberculosis (M. tb). M. tb causes Tuberculosis, a contagious and severe airborne disease. There are ten MazEF TA systems in M. tb, which play important roles in stress adaptation. How the antitoxins antagonize toxins in M. tb or how the ten TA systems crosstalk to each other are of interests, but the detailed molecular mechanisms are largely unclear. MazEF-mt9 is a unique member among the MazEF families due to its tRNase activity, which is usually carried out by the VapC family toxins. Here we present the cocrystal structure of the MazEF-mt9 complex at 2.7 Å. By characterizing the association mode between the TA pairs through various characterization techniques, we found that MazF-mt9 not only bound its cognate antitoxin, but also the non-cognate antitoxin MazE-mt1, a phenomenon that could be also observed in vivo. Based on our structural and biochemical work, we proposed that the cognate and heterologous interactions among different TA systems work together to relieve MazF-mt9’s toxicity to M. tb cells, which may facilitate their adaptation to the stressful conditions encountered during host infection. IMPORTANCE Tuberculosis (TB) is one of the most severe contagious diseases. Caused by Mycobacterium tuberculosis (M. tb), it poses a serious threat to human health. Additionally, TB is difficult to cure because of the multipledrug-resistant (MDR) and extensively drug-resistant (XDR) M. tb strains. Toxin-antitoxin (TA) systems have been discovered to widely exist in prokaryotic organisms with diverse roles, normally composed of a pair of molecules that antagonize each other. M. tb has ten MazEF systems, and some of them have been proved to be directly associated with the genesis of persisters and drug-resistance of M. tb. We here report the MazEF-mt9 complex structure, and thoroughly characterized the interactions between MazF-mt9 with MazEs within or outside the MazEF-mt9 family. Our study not only revealed the crosstalks between TA families and its significance to M. tb survival but also offers insights into potential anti-TB drug design.
The Role of Information Technology Innovation in Combating Corruption in SMEs in Developing Countries: A Critical Literature Review

The purpose of this article is to examine the literature on the impact of information technology innovations in combatting corruption in small and medium enterprises in developing countries. This article employed a systematic literature review from a multidisciplinary style to provide research precision and reliability. Corruption affects the performance of the business by stifling their entrepreneurial and innovative initiatives. Small businesses should encourage more ethical conduct by motivating employees to report corruption in their workplaces. However, the finding of this article shows that many employees fear to report corruption due to weak whistleblowing protection policies. In addition, using information technology in whistleblowing, although effective, the results indicate that their usage is limited due to fear of the unknown. The success of anticorruption interventions strategies depends on their relevance to local environmental, cultural, and technical experience. The government should enhance public awareness of the protection of whistleblowers by developing a multiagency framework for streamlining digital whistleblowing channels in public and private enterprises. Policymakers in engineering management should bridge the gap between anticorruption laws enforcement and practical implementation to eradicate corruption. This article contributes to the understanding of whistleblowing as a strategy to fight corruption and its effect on the innovation of small and medium enterprises.
Otimização Multiobjetivo para Alocação de Geradores Distribuídos e Banco de Capacitores Fixos

Resumo—Algumas medidas adotadas para melhorar a performance de sistemas de distribuição são a inserção de geração distribuída (GD) e bancos de capacitores (BC). Este trabalho apresenta um modelo de otimização multiobjetivo para o dimensionamento e posicionamento simultâneo ou não desses recursos. O NSGA-II foi utilizado como ferramenta de busca para minimizar as perdas de potência ativa, melhorar o perfil de tensão, aumentar a capacidade de transferência disponível e reduzir o tamanho do empreendimento. As simulações foram realizadas no MATPOWER no sistema de 33 barras. A escolha de pontos extremos da fronteira de Pareto e a composição max-min foram as ferramentas utilizadas para trade-off e decisão final. O ganho de performance do sistema pós-alocação sugere que a inserção simultânea de GD e BC deve ser priorizada. Keywords—Alocação de Geração Distribuída; Inserção de banco de capacitores; NSGA-II; Pontos extremos; Composição max-min.
Motile curved bacteria are Pareto-optimal

Significance Bacteria exhibit a bewildering diversity of morphologies, but despite their impact on nearly all aspects of life, they are frequently classified into a few general categories, usually just “spheres” and “rods. ” Curved-rod bacteria are one simple variation observed in many environments, particularly the ocean. However, why so many species have evolved this shape is unknown. We show that curvature can increase swimming efficiency, revealing a widely applicable selective advantage. Furthermore, we show that the distribution of cell lengths and curvatures observed across bacteria in nature is predicted by evolutionary trade-offs between three tasks influenced by shape: efficient swimming, the ability to detect chemical gradients, and reduced cost of cell construction. We therefore reveal shape as an important component of microbial fitness. Curved rods are a ubiquitous bacterial phenotype, but the fundamental question of why they are shaped this way remains unanswered. Through in silico experiments, we assessed freely swimming straight- and curved-rod bacteria of a wide diversity of equal-volume shapes parameterized by elongation and curvature, and predicted their performances in tasks likely to strongly influence overall fitness. Performance trade-offs between these tasks lead to a variety of shapes that are Pareto-optimal, including coccoids, all straight rods, and a range of curvatures. Comparison with an extensive morphological survey of motile curved-rod bacteria indicates that the vast majority of species fall within the Pareto-optimal region of morphospace. This result is consistent with evolutionary trade-offs between just three tasks: efficient swimming, chemotaxis, and low cell construction cost. We thus reveal the underlying selective pressures driving morphological diversity in a widespread component of microbial ecosystems.
Comparison Study of DAC Realizations in Current Input CTΣΔ Modulators

In this brief, a comparison between five different DAC realizations is presented for an equivalent multi-<inline-formula> <tex-math notation="LaTeX">$\text{M}\Omega $ </tex-math></inline-formula> range resistance in a digitizing current sensor, namely resistor, current source, pseudo-resistor, T-network resistor, and switched-capacitor-resistor (SCR). These DACs are needed to realize current input continuous-time sigma-delta modulator (C-CT<inline-formula> <tex-math notation="LaTeX">$\Sigma \Delta \text{M}$ </tex-math></inline-formula>) readouts to sense currents in the pA range. All DAC realizations are discussed concerning disadvantages and advantages and are implemented, laid out, and simulated on transistor level in a 180nm CMOS technology. The comparison is carried out on the area consumption, required circuit overhead, noise performance, inherent anti-aliasing filter (AAF) quality, and two sources of non-idealities (clock jitter and ISI). It is shown that the SCR-DAC - due to its decaying waveform and its inverse proportionality of the equivalent resistance to its capacitor size - shows the best compromise over the other methods and achieves the best performance in the smallest area.
Modeling the urban climate of Budapest using the SURFEX land surface model driven by the ALADIN-Climate regional climate model results

⎯ Urbanized areas modify the local climate due to the physical properties and morphology of surface objects. The urban impacts on local scale interact with the regional climate resulting in an amplification of certain climate aspects in the cities (e.g., higher maximum air temperature), which may be further enhanced with climate change. Regional climate models provide adequate tool for assessing the regional characteristics of global changes, however, they are incapable for describing the local impacts, e.g., in cities, due to their relatively low resolution (usually 10–25 km horizontally) and due to the lack of detailed description of relevant physical processes. To investigate the future climate change in cities, surface models provide a scientifically sound and cutting-edge solution for the previous problem. In this study, the behavior of SURFEX externalized land surface model including the TEB urban canopy scheme and coupled to the ALADIN-Climate regional climate model in offline mode is investigated. A 10-year-long simulation for 2001–2010 was achieved on 1 km resolution for Budapest. The main goals of our investigation are i) to assess how the biases of the regional climate model inherited and modified by SURFEX, ii) what is the added value of SURFEX to ALADIN-Climate, and iii) what are the capabilities of SURFEX in terms of describing urban and suburban seasonal temperature cycle and daily urban heat island (UHI) evolution in Budapest. Quantified validation is conducted using the measurements of two stations located in the city center and in the suburban area. It was found that SURFEX overestimates the 2 m temperature in both locations throughout the year, in spite of the too cold ALADIN forcings. The strength of nocturnal UHI is overestimated from autumn till spring, while it is slightly too weak in summer. Moreover, the evolution and collapse of daily UHI is imperfectly simulated, namely some delay and slower daily dynamics occur, which might be caused by the method applied for deriving the atmospheric forcings. Key-words: urban climate change, urban heat island, land surface modelling, SURFEX, TEB, ALADIN-Climate, validation
Autonomy Support and Academic Stress: A Relationship Mediated by Self-Regulated Learning and Mastery Goal Orientation.

This study examines the relationship between autonomy support from instructors, self-regulated learning, mastery goal orientation and academic stress. College and graduate students in China ( N =366) participated in this study. Mediation analysis was used to examine the relationships between the constructs. The results indicate that academic stress is a multidimensional construct with four subconstructs: pressures to perform, stress related to teacher expectation, perception of workload and stress related to academic self-perceptions. These sub-constructs were studied in relation to autonomy support, self-regulated learning, and mastery goal orientation. Direct effects of autonomy support were found on self-regulated learning and mastery goal orientation, as well as on stress related to academic self-perception. Mediation effects of self-regulated learning and mastery goal orientation were found between autonomy support and some of the subconstructs of academic stress. square error of approximation (RMSEA); (5) Chi-square statistic and degrees of freedom; (6) the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC). Acceptable fit in the current study was defined as CFI and TLI values of 0.90 or greater (Schumacker & Lomax, 1996), an SRMR of 0.08 or less, an RMSEA value 0.08 or less (Hu & Bentler, 1999; Joreskog & Sorbom, 1996). Degrees of freedom divided by chi-square with values smaller than 3 indicate acceptable fit (Kline, 1998). Lower AIC and BIC values indicate better fit. Local fit indices used to examine the models include standardized loadings, regression coefficients and R-squares.
Joint Optimization of Wind Turbine Micrositing and Cabling in an Offshore Wind Farm

Wind farms (WFs) are important components of smart grid. The modeling and optimal planning of the WF is preliminary before its construction. In this article, a bi-level multi-objective optimization framework is presented, with the aim of simultaneously designing the configuration of wind turbines (WTs) as well as the topology of electrical collector system in an offshore WF. The installation capacity of the WF, the positioning of the WTs and the planning scheme of the electrical system are balanced to achieve a better performance of the WF. In this proposal, there is an outer layer along with two inner layers. The objectives of the outer-layer model are the maximization of the WF’s daily profit rate, the daily average capacity factor, and power quality. It is tackled by the Non-dominated Sorting Genetic Algorithm-III (NSGA-III). The objectives of the two inner layer models are to determine the topology of the electrical system and the generation schedule of other generators, and are solved by means of the Binary Particle Swarm Optimization (BPSO) algorithm and the quadratic programming (QP) method respectively. The WF is assumed to be connected to the IEEE-24 bus test system. The simulation results validate the adaptability and effectiveness of the proposed approach with the main factors that affect the WF layout being analyzed.
Lipid rafts increase to facilitate ectoderm lineage specification of differentiating embryonic stem cells

Lipid rafts are packed nanoscopic domains on plasma membrane and essential signalling platforms for transducing extracellular stimuli into cellular responses. Although depletion of raft component glycoshpingolipids causes abnormality particularly in ectoderm layer formation, it remains unclear whether rafts play a role in lineage determination, a critical but less-known stage in lineage commitment. Here, inducing mouse embryonic stem cell (mESC) differentiation with retinoic acid (RA), we observed lipid rafts increased since early stage, especially in ectoderm-like cells. Stochastic optical reconstruction microscopy characterized at super-resolution the distinct raft features in mESCs and the derived differentiated cells. Furthermore, RA-induced commitment of ectoderm-like cells was significantly diminished not only by genetic ablation of rafts but by applying inhibitor for glycosphingolipids or cholesterol at early differentiation stages. Meanwhile, raft inhibition delayed RA-induced pluripotency exit, an early step required for differentiation. Therefore, lipid rafts increase and facilitate ectoderm lineage specification as well as pluripotency exit during mESC differentiation.
Effect of Health Care Provider Role and Social Supporters Role in Diabetic Patient Self-Care: Systematic Review Study

Most of the research findings have stressed the significance of social support derived from Health Care providers, family, spouses, and friends of Type 2 Diabetes mellitus patients in enhancing participation in self-care activities. Nevertheless, relevant data on the issue of diabetes self-care practices within a Saudi Arabian context is particularly limited, while literature exploring the relationship between self-care and social support is absent. Consequently, research was not available on social support within a Saudi Arabian context, resulting in a lack of clarity surrounding its impact on diabetes self-care in this country. This evidence gap has reinforced the need for research to analyse the effect of Health Care Providers roles and social supports roles as determinants of self-care among Saudi Arabian Diabetes mellitus patients. Currently, few research studies have sought to resolve the problem of non-adherence to self-care activities in Saudi Arabia. It appears that diabetes self-care activities and social support may play an important role in managing Diabetes mellitus. The present study seeks to address this critical evidence gap. Reviewing literature can support Saudi Arabiabased Health Care Providers to develop healthcare services and social support systems that include health education programs on individual patient needs and conditions. In conclusion, social support strategies must be considered in relation to potential individual barriers to self-care activities. The study recommends that great emphasis should be placed on the development of diabetes patient care that supports the patients in implementing effective self-management care.
Global Optimization: Combining Local Loss With Result Ranking Loss in Remote Sensing Image Retrieval

With the explosive growth of remote sensing big data, large-scale remote sensing image retrieval (RSIR) has become one of the most challenging tasks in data mining, attracting more and more attention from researchers. In recent years, deep learning has achieved fruitful results in RSIR. However, there are three major problems. The first problem is due to the imbalanced categories in RSIR data sets. Accordingly, a novel sample mining method is proposed. This method is based on top-<inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> “misplaced” samples (Top-<inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> MS) along the top-<inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> nearest neighbor decision boundary, rather than setting boundary threshold of sample mining and reducing the impact of artificial and objective factors on network training as usual. The second problem comes from the inconsistency between the loss reduction direction and the optimization direction. In response, we additionally propose a novel result ranking loss (RRL) according to the retrieval results in a batch. RRL is used as a plug-in and combined with other local losses. As a result, the retrieval accuracy improved significantly. For the third problem, processing a large number of high-resolution images will increase training time and cause network freeze. To solve the problem, this article proposes a global optimization model based on feature space and retrieval results (FSRR). More specifically, FSRR consists of local loss based on feature space and RRL based on retrieval results, and can be optimized in an end-to-end manner. Experimental results show that our proposed algorithm achieves the best performance when compared with other state-of-the-art deep RSIR algorithms on two benchmark data sets.
Evaluation of the XylS/Pm Expression Cassette in Corynebacterium glutamicum

For bacteria, the most common expression system for recombinant protein production is Escherichia coli (E. coli). However, a large fraction of recombinant proteins produced in E. coli are in an insoluble form which sometimes makes them irrelevant for therapeutic applications. There could be several advantages in producing recombinant proteins in other bacteria, such as Corynebacterium glutamicum (C. glutamicum), especially when it comes to proteins that are difficult to secrete or dependent on effective downstream processes. C. glutamicum has an ability to secrete proteins into the growth medium, and has a different intercellular environment which could result in soluble protein expression when E. coli fails. The aim of this thesis was, therefore, to test and adapt the expression technology of Vectron Biosolutions, the XylS/Pm expression cassette, to the production of recombinant proteins in C. glutamicum. Two C. glutamicum/E. coli shuttle vectors were constructed. Both of them, pXMJ19mCherry and pVB-4A0E1-mCherry harboring the XylS/Pm expression cassette, were found to be functional in E. coli, producing mCherry in high amounts when induced with IPTG and m-toluate respectively, yielding pink cultures. For both of them, most of the mCherry protein was found in a soluble state. Both vectors were successfully transferred into C. glutamicum. However, none of them resulted in mCherry production. To further investigate why no mCherry protein was obtained, and to possibly figure out if the bottleneck was at xylS or mCherry level and also if transcription or translation of these proteins were the main issue, the transcript levels of mCherry and xylS were evaluted. For C. glutamicum harboring pXMJ19-mCherry, mCherry transcriptis could be identified and transcript level increased 12 times when the culture was induced with IPTG. However, the amount of mCherry transcript was significantly less than for E. coli. For C. glutamicum harboring pVB-4A0E1-Cherry, no mCherry transcript could be identified. From this, it could not be confirmed whether mCherry is a suitable reporter gene in C. glutamicum. The results also showed that when compared to E. coli, the amount of xylS transcript from the XylS/Pm expression cassette in C. glutamicum was very low. Even though no functional shuttle vector expressing recombinant proteins from the XylS/Pm expression cassette has been verified, xylS transcription has been identified as a bottleneck for protein expression from the XylS/Pm expression cassette in C. glutamicum. This work is a contribution to the ongoing research into developing C. glutamicum as an alternative bacterial host.
Deep Learning Empowered Traffic Offloading in Intelligent Software Defined Cellular V2X Networks

The ever-increasing and unbalanced traffic load in cellular vehicle-to-everything (C-V2X) networks have increased the network congestion and led to user dissatisfaction. To relieve the network congestion and improve the traffic load balance, in this paper, we propose an intelligent software defined C-V2X network framework to enable flexible and low-complexity traffic offloading by decoupling the network data plane from the control plane. In the data plane, the cellular traffic offloading and the vehicle assisted traffic offloading are jointly performed. In the control plane, deep learning is deployed to reduce the software defined network (SDN) control complexity and improve the traffic offloading efficiency. Under the proposed framework, we investigate the traffic offloading problem, which can be formulated as a multi-objective optimization problem. Specifically, the first objective maximizes the cellular access point (AP) throughput with consideration of the load balance by associating the users with the APs. The second objective maximizes the vehicle throughput with consideration of the vehicle trajectory by associating the delay-insensitive users with the vehicles. The two objectives are coupled by the association between the cellular APs and the vehicles. A deep learning based online-offline approach is proposed to solve the multi-objective optimization problem. The online stage decouples the optimization problem into two sub-problems and utilizes the ‘Pareto optimal’ to find the solutions. The offline stage utilizes deep learning to learn from the historical optimization information of the online stage and helps predict the optimal solutions with reduced complexity. Numerical results are provided to validate the advantages of our proposed traffic offloading approach via deep learning in C-V2X networks.