Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis of the role of collectivism in light of the recent literature on the big triangle: Institutions, culture, and economic development

Why do similar economic and political institutions function differently in various cultures? Do cultural traits, differences in individualism versus collectivism, have a causal impact on economic behavior and development? This article presents a recent survey of the literature on the relationship between culture, institutions, and economic growth. On the one hand, part of the literature indicates that there is a one-way causality from culture to institutions and economic performance. On the other hand, there is an extensive literature that has established causality from institutions to economic growth and culture. However, a growing body of empirical research demonstrates that culture and institutions interact in two ways and complement each other affecting long-term growth. Research documents cultural variables affecting a great deal of economic activity and institutions across the world. Recent dominant discourse on the role of the individualism-collectivism cleavage in the determination of the wealth of nations has attempted to examine the positive effects of individualism rather than collectivism. This paper shows that the advantages of collectivism have been rarely researched within economic literature. Taking into account collectivism can shed light on various puzzles in economics, such as solving collective action problems.
Association of vortical structures and hemodynamic parameters for regional thrombus accumulation in abdominal aortic aneurysms

The intraluminal thrombus (ILT) has been shown to negatively impact the progression of the abdominal aortic aneurysms (AAAs). The formation of this thrombus layer has been connected to the local flow environment within AAAs, but the specific mechanisms leading to thrombus formation are still not fully understood. Our study investigated the association between vortical structures, near‐wall hemodynamic metrics (e.g., time averaged wall shear stress (TAWSS) and oscillatory shear index (OSI)), and ILT accumulation in a longitudinal cohort of 14 AAAs (53 scans total). Vortices and hemodynamic parameters were estimated using hemodynamic simulations performed to each scan of each patient and compared to local 3D changes of ILT thickness between two consecutive scans (ΔILT). Results showed that vortices formed and remained strong and close to the lumen surface in AAAs without an ILT, while in AAAs with ILTs these detached from the lumen surface and dissipated nearby wall region where an increase in ILT thickness was observed. Although low TAWSS was observed in regions with and without ILT accumulation, an inverse correlation between ∆ILT and TAWSS was observed within the regions that experienced a thrombus growth. Our results support the idea that vortical structures might be playing a role modulating ILT accumulation into specific wall regions. Also, it submits the idea that the low TAWSS will be modulating the growth of thrombus within these preferred ILT accumulated regions.
BaGdF5 Nanophosphors Doped with Different Concentrations of Eu3+ for Application in X-ray Photodynamic Therapy

X-ray photodynamic therapy (XPDT) has been recently considered as an efficient alternative to conventional radiotherapy of malignant tissues. Nanocomposites for XPDT typically consist of two components—a nanophosphor which re-emits X-rays into visible light that in turn is absorbed by the second component, a photosensitizer, for further generation of reactive oxygen species. In this study, BaGdF5 nanophosphors doped with different Eu:Gd ratios in the range from 0.01 to 0.50 were synthesized by the microwave route. According to transmission electron microscopy (TEM), the average size of nanophosphors was ~12 nm. Furthermore, different coatings with amorphous SiO2 and citrates were systematically studied. Micro-CT imaging demonstrated superior X-ray attenuation and sufficient contrast in the liver and the spleen after intravenous injection of citric acid-coated nanoparticles. In case of the SiO2 surface, post-treatment core–shell morphology was verified via TEM and the possibility of tunable shell size was reported. Nitrogen adsorption/desorption analysis revealed mesoporous SiO2 formation characterized by the slit-shaped type of pores that should be accessible for methylene blue photosensitizer molecules. It was shown that SiO2 coating subsequently facilitates methylene blue conjugation and results in the formation of the BaGdF5: 10% Eu3+@SiO2@MB nanocomposite as a promising candidate for application in XPDT.
EVALUATING CALCULATED RISK IN GEOTECHNICAL ENGINEERING

Recent years have seen rapidly growing research into applied probability and increased interest in applications to geotechnical engineering practice. Unfortunately, probability still remains a mystery to many engineers, partly because of a language barrier and partly from lack of examples showing how the methodology can be used in the decision‐making process. The following types of applications are described and illustrated in general terms: separating systematic and random errors when evaluating uncertainty in the stability of slopes; safety factors in connection with analysis of liquefaction; optimizing the design of an embankment in the face of uncertainty concerning stability; risk evaluation for an industrial facility built over potentially liquefiable soils; and risk evaluation for earth dams. Even when a precise quantification of probability of failure is not possible, systematic formulation of an analysis aids greatly in understanding the major sources of risk and thus points the way for costeffec.
Polycomb group ring finger protein 6 suppresses Myc-induced lymphomagenesis

Max dimerizes with Mga to form the repressive complex PRC1.6; another PRC1.6 subunit, Pcgf6, suppresses Myc-induced lymphomagenesis but, unexpectedly, does so in a Mga- and PRC1.6-independent manner. Max is an obligate dimerization partner for the Myc transcription factors and for several repressors, such as Mnt, Mxd1-4, and Mga, collectively thought to antagonize Myc function in transcription and oncogenesis. Mga, in particular, is part of the variant Polycomb group repressive complex PRC1.6. Here, we show that ablation of the distinct PRC1.6 subunit Pcgf6–but not Mga–accelerates Myc-induced lymphomagenesis in Eµ-myc transgenic mice. Unexpectedly, however, Pcgf6 loss shows no significant impact on transcriptional profiles, in neither pre-tumoral B-cells, nor lymphomas. Altogether, these data unravel an unforeseen, Mga- and PRC1.6-independent tumor suppressor activity of Pcgf6.
Deep Reinforcement Learning Based Resource Allocation with Radio Remote Head Grouping and Vehicle Clustering in 5G Vehicular Networks

With increasing data traffic requirements in vehicular networks, vehicle-to-everything (V2X) communication has become imperative in improving road safety to guarantee reliable and low latency services. However, V2X communication is highly affected by interference when changing channel states in a high mobility environment in vehicular networks. For optimal interference management in high mobility environments, it is necessary to apply deep reinforcement learning (DRL) to allocate communication resources. In addition, to improve system capacity and reduce system energy consumption from the traffic overheads of periodic messages, a vehicle clustering technique is required. In this paper, a DRL based resource allocation method is proposed with remote radio head grouping and vehicle clustering to maximize system energy efficiency while considering quality of service and reliability. The proposed algorithm is compared with three existing algorithms in terms of performance through simulations, in each case outperforming the existing algorithms in terms of average signal to interference noise ratio, achievable data rate, and system energy efficiency.
An Improved COVID-19 Forecasting by Infectious Disease Modelling Using Machine Learning

The mechanisms of data analytics and machine learning can allow for a profound conceptualization of viruses (such as pathogen transmission rate and behavior). Consequently, such models have been widely employed to provide rapid and accurate viral spread forecasts to public health officials. Nevertheless, the capability of these algorithms to predict outbreaks is not capable of long-term predictions. Thus, the development of superior models is crucial to strengthen disease prevention strategies and long-term COVID-19 forecasting accuracy. This paper provides a comparative analysis of COVID-19 forecasting models, including the Deep Learning (DL) approach and its examination of the circulation and transmission of COVID-19 in the Kingdom of Saudi Arabia (KSA), Kuwait, Bahrain, and the UAE.
Benchmarking Geometric Deep Learning for Cortical Segmentation and Neurodevelopmental Phenotype Prediction

The emerging field of geometric deep learning extends the application of convolutional neural networks to irregular domains such as graphs, meshes and surfaces. Several recent studies have explored the potential for using these techniques to analyse and segment the cortical surface. However, there has been no comprehensive comparison of these approaches to one another, nor to existing Euclidean methods, to date. This paper benchmarks a collection of geometric and traditional deep learning models on phenotype prediction and segmentation of sphericalised neonatal cortical surface data, from the publicly available Developing Human Connectome Project (dHCP). Tasks include prediction of postmenstrual age at scan, gestational age at birth and segmentation of the cortical surface into anatomical regions defined by the M-CRIB-S atlas. Performance was assessed not only in terms of model precision, but also in terms of network dependence on image registration, and model interpretation via occlusion. Networks were trained both on sphericalised and anatomical cortical meshes. Findings suggest that the utility of geometric deep learning over traditional deep learning is highly task-specific, which has implications for the design of future deep learning models on the cortical surface. The code, and instructions for data access, are available from https://github.com/Abdulah-Fawaz/Benchmarking-Surface-DL.
Soybean β-conglycinin: structure characteristic, allergenicity, plasma lipid-controlling, prevention of obesity and non-alcoholic fatty liver disease.

Soybean has become an important world commodity because of its low price, good nutritional value and recognized functional health benefits in recent years. β-conglycinin is one of the major storage proteins in soybean. It has captured a growing interest recently because of its allergenicity and potential health benefits, which continues to drive the research and commercial development of β-conglycinin-based food products and ingredients. In this review, the structure, the amino acid composition, extraction methods and electrophoretic pattern of β-conglycinin are briefly summarized. Studies on β-conglycinin by allergenicity, plasma lipid-controlling, obesity and non-alcoholic fatty liver disease are highlighted, critically discussing their main shortcomings and challenges and identifying the research gaps. Studies to date have demonstrated the cultivation of β-conglycinin with health benefits as functional ingredients and foodstuffs. The current research focuses on proteins, mainly challenging the mechanisms of subunit/peptide effects interaction and identifying and characterizing the hidden biological activities in the polypeptide chains. There is much scope for further exploration into various aspects of β-conglycinin, such as the selection of mutant strains and genetic engineering and prospects on targeted β-conglycinin exploitation in the nutraceutical area. In addition, the safety evaluation of β-conglycinin and its stabilized emulsions deserve more attention to food-related applications.
Utility of a Cell-Direct Polymerase Chain Reaction-Based Nucleic Acid Lateral Flow Immunoassay for Detection of Bacteria in Peripheral Blood Leukocytes of Suspected Sepsis Cases

Background The detection of the pathogens in the blood is essential for the management of septic patients; however, conventional blood culture takes 2–3 days. Therefore, rapid and convenient methods may be useful to aid clinical decision-making. Methods Blood samples with sepsis clinically diagnosed in cases that fulfilled the diagnostic criteria were used and analyzed the utility of a novel bacterial nucleic acid identification test using a cell-direct polymerase chain reaction (cdPCR)-based nucleic acid lateral flow immunoassay (NALFIA) which were named as “DiagnoSep” to detect representative bacteria in peripheral blood leukocytes in patients admitted to our hospital and compared the conventional blood culture results simultaneously taken from the patients. Results We analyzed the total 42 samples in the terms of this study and found 18 (42.8%) were positive on cdPCR-NALFIA, and 24 (57.1%) were positive on blood cultures. Although the positive rate was higher with blood cultures, 15 samples showed positive results from both blood cultures and cdPCR-NALFIA, and the identified bacteria agreed for 10 samples. Of the 18 cdPCR-NALFIA-positive cases, the results for 8 samples differed from the results of blood cultures; four of them had an implanted pacemaker or prosthetic joint and were positive for Staphylococcus aureus or Staphylococcus epidermidis on cdPCR-NALFIA. Conclusion Blood culture tests are probably the gold standard in identifying causative organisms in sepsis, but the rapid results from cdPCR-NALFIA simultaneously used with blood culture may make it an important auxiliary diagnostic tool for identifying infecting organisms and lead to the improvement of mortality of the septic patients, because these combined results provide the wide information on the possible pathogens in early phase.
Interaction of a semiconductor sample partly filling a waveguide's window with millimetre wave radiation

Interaction of a semiconductor sample with an electromagnetic wave within the millimetre range has been investigated. A three-dimensional finite-difference time-domain method was applied to simulate wave propagation within a waveguide segment with the semiconductor sample attached to a wide wall of the waveguide. The electric field distribution, voltage standing-wave ratio and the average electric field dependencies in the 78–118 GHz frequency range have been determined for several sets of dimensions and specific resistances of the sample. These investigations have been the basis for the design of the resistive sensor (RS) based on the electron heating effect and devoted to high-power millimetre-wave pulse measurement. By choosing the dimensions and specific resistance of the sample, the decrease in the electron heating effect with frequency as well as the decrease of electric field in the waveguide due to wave dispersion were compensated by an increase in the average electric field. Therefore an RS having practically unchanging sensitivity in a waveguide's frequency range has been proposed.
The Optimal Bandwidth Parameter Selection in GPH Estimation

In this paper, the optimal bandwidth parameter is investigated in the GPH algorithm. Firstly, combining with the stylized facts of financial time series, we generate long memory sequences by using the ARFIMA (1, d, 1) process. Secondly, we use the Monte Carlo method to study the impact of the GPH algorithm on existence test, persistence or antipersistence judgment of long memory, and the estimation accuracy of the long memory parameter. The results show that the accuracy of above three factors in the long memory test reached a relatively high level within the bandwidth parameter interval of 0.5 < a < 0.7. For different lengths of time series, bandwidth parameter a = 0.6 can be used as the optimal choice of the GPH estimation. Furthermore, we give the calculation accuracy of the GPH algorithm on existence, persistence or antipersistence of long memory, and long memory parameter d when a = 0.6.
Transceiver Design for Multiuser MIMO Systems

The use of multiple antennas at both base stations and mobile stations (multipleinput-multiple-output (MIMO)) increases the spectral efficiency and reliability in wireless communications. Multiuser MIMO communication, especially the point-tomultipoint downlink transmission is substantially more complicated compared with a single-user communication environment. The focus of this thesis is the joint design of linear transmitters and receivers (transceivers) for point-to-multipoint transmission in multiuser MIMO systems. Various optimization problems with respect to minimum mean square error (MMSE) and rate will be investigated. The non-convexity of these problems makes the derivation of globally optimal algorithms very difficult. We propose a new duality-based approach for downlink optimization under a total power constraint. Instead of optimizing directly, we set up equivalent uplink/downlink channels and show that under a total power constraint, any MSE point which is achievable in the uplink can be achieved in the downlink as well. As a direct consequence of this MSE duality, the complicated downlink optimization can be carried out efficiently by focusing on the equivalent uplink problem. Additionally, a heuristic alternating optimization strategy is proposed. Since the MSE duality ensures that the same MSEs can be achieved in both links, it is possible to optimize the powers and filters in an alternating manner by switching between the equivalent uplink channel and the downlink channel. This alternating approach is not only applicable to MMSE optimization, but also to rate optimization. To derive an algorithm from this framework, the essential issue is to specify the uplink power allocation according to the concrete optimization problem. New optimal power allocation strategies are derived for the problems considered here. Besides these duality-based approaches, we propose another framework, which performs optimization in the downlink directly. This framework exploits the convexity of certain MMSE-type transmitter optimization problems. We show that these problems can be reformulated as second order cone programs (SOCPs). The optimal linear receiver is known as the linear MMSE filter. We prove that the algorithms derived from this framework converge monotonically. Application examples are studied for the scenarios of multicasting and base station cooperation in network MIMO systems. However, the proposed optimization framework is very general and not restricted to the application examples. Any convex constraints can be included without changing the mathematical structure, e.g., different power constraints, such as a
Signatures of Adaptation in Mitochondrial Genomes of Palearctic Subterranean Voles (Arvicolinae, Rodentia)

This study evaluates signatures of selection in the evolution of the mitochondrial DNA of voles, subfamily Arvicolinae, during the colonization of subterranean environments. The comparative sequence analysis of mitochondrial protein-coding genes of eight subterranean vole species (Prometheomys schaposchnikowi, three species of the genus Ellobius: Ellobius talpinus, Ellobius fuscocapillus and Ellobius lutescens, two species of the genus Terricola: Terricola subterraneus and Terricola daghestanicus, Lasiopodomys mandarinus, and Hyperacrius fertilis) and their closest aboveground relatives was applied using codon-substitution models. The highest number of selection signatures was detected in genes ATP8 and CYTB. The relaxation of selection was observed in most mitochondrial DNA protein-coding genes for subterranean species. The largest amount of relaxed genes is discovered in mole voles (genus Ellobius). The number of selection signatures was found to be independent of the evolutionary age of the lineage but fits the degree of specialization to the subterranean niche. The common trends of selective pressures were observed among the evolutionary ancient and highly specialized subterranean rodent families and phylogenetically young lineages of voles. It suggests that the signatures of adaptation in individual mitochondrial protein-coding genes associated with the colonization of the subterranean niche may appear within a rather short evolutionary timespan.
Mutational Switch-Backs Can Accelerate Evolution of Francisella to a Combination of Ciprofloxacin and Doxycycline

Combination antimicrobial therapy has been considered a promising strategy to combat the evolution of antimicrobial resistance. Francisella tularensis is the causative agent of tularemia and in addition to being found in the nature, is recognized as a threat agent that requires vigilance. We investigated the evolutionary outcome of adapting the Live Vaccine Strain (LVS) of Francisella to two non-interacting drugs, ciprofloxacin and doxycycline, individually, sequentially, and in combination. Despite their individual efficacies and independence of mechanisms, evolution to the combination appeared to progress faster than evolution to the two drugs sequentially. We conducted a longitudinal mutational analysis of the populations evolving to the drug combination, genetically reconstructed the identified evolutionary pathway, and carried out biochemical validation. We discovered that, after the appearance of an initial weak generalist mutation (FupA/B), each successive mutation alternated between adaptation to one drug or the other. In combination, these mutations allowed the population to more efficiently ascend the fitness peak through a series of evolutionary switch-backs. Clonal interference, weak pleiotropy, and positive epistasis also contributed to combinatorial evolution. This finding suggests that, under some selection conditions, the use of non-interacting drug pairs as a treatment strategy may result in a more rapid ascent to multi-drug resistance and serves as a cautionary tale. Author summary The antimicrobial resistance crisis requires the use of novel treatment strategies to prevent or delay the emergence of resistance. Combinations of drugs offer one strategy to delay resistance, but the efficacy of such drug combinations depends on the evolutionary response of the organism. Using experimental evolution, we show that under some conditions, a potential drug combination does not delay the onset of resistance in bacteria responsible for causing tularemia, Francisella. In fact, they evolve resistance to the combination faster than when the two drugs are applied sequentially. This result is surprising and concerning: using this drug combination in a hospital setting could lead to simultaneous emergence of resistance to two antibiotics. Employing whole genome sequencing, we identified the molecular mechanism leading to evolution of resistance to the combination. The mechanism is similar to the switch-back route used by hikers while scaling steep mountains i.e., instead of simultaneously acquiring mutations conferring resistance to both drugs, the bacteria acquire mutations to each drug in alternating manner. Rather than scaling the steep mountain directly, the bacteria ascend the mountain by a series of evolutionary switch-backs to gain elevation and in doing so, they get to the top more efficiently.
Reacting to a Generalised Crisis. A Theoretical Approach to the Consumption of Slack Resources in Family Firms

Research to date has shown that companies can accumulate resources over those strictly needed in order to overcome the uncertainty associated to a crisis. But the usage, redeployment, or consumption of this excess of resources when facing an adverse environment is yet underexplored. As suggested by the literature, the condition of family business can exert an important effect in such behaviour. This paper proposes a theoretical framework, focused on family business, about how firms manage the different slack resources when facing a general crisis. We make a call on family business scholars to leverage our propositions and the existing literature on slack resources to develop a guidance for family owners when facing an economic downturn.