Miracle-dz/UniFAR-FTUs huggingface dataset:
Interaction of Alcohol Consumption and ABCG2 rs2231142 Variant Contributes to Hyperuricemia in a Taiwanese Population

Background: ABCG2 rs2231142 is an important genetic factor that contributes to the development of gout and hyperuricemia (HUA). Epidemiologic studies have demonstrated that lifestyle risk factors of HUA (e.g., alcohol consumption) and genetic predisposition (e.g., ABCG2 gene) together, contribute to enhanced serum uric acid levels. However, the interaction between ABCG2 rs2231142, alcohol consumption, and HUA in the Taiwanese population is still unclear. Therefore, this study investigated whether the risk of HUA is associated with ABCG2 rs2231142 variants and how this is affected by alcohol consumption. Method: study subjects were selected from the participants of the Taiwan Biobank database. Overall, 114,540 participants aged 30 to 70 years were enrolled in this study. The interaction between ABCG2 rs2231142, alcohol consumption, and serum uric acid (sUA) levels was analyzed by multiple logistic regression models. Results: the prevalence of HUA was 32.7% and 4.4 % in the male and female populations, respectively. In the whole study population, the minor T allele of ABCG2 rs2231142 was significantly associated with HUA risk, and the occurrence of HUA was high in TT genotype and TG genotype. The risk of HUA was significantly increased by the combined association of ABCG2 rs2231142 and alcohol consumption for TG/TT genotype compared to the GG genotype (wild-type genotype), especially among women. Conclusion: the ABCG2 rs2231142 is a crucial genetic locus for sUA levels in the Taiwanese population and our findings revealed that alcohol consumption combined with the ABCG2 rs2231142 risk allele contributes to increased HUA risk.
Estimated Amounts and Rates of Carbon Mobilized by Landsliding in Old‐Growth Temperate Forests of SE Alaska

Landslides, a forest disturbance, mobilize carbon (C) sequestered in vegetation and soils. Mobilized C is deposited either onto hillslopes or into the water, sequestering C from and releasing C to the atmosphere at different time scales. The C‐dense old‐growth temperate forests of SE Alaska are a unique location to quantify C mobilization rate by frequent landslides that often evolve into saturated moving masses known as debris flows. In this study, the amount of C mobilized by debris flows over historic time scales was estimated by combining a landslide inventory with maps of modeled biomass and soil carbon. We analyzed SE Alaskan landslides over a 55‐year period where a total of 4.69 ± 0.21 MtC was mobilized, an average rate of 2.5 tC km−2 yr−1. A single event in August 2015 mobilized 57,651 ± 3,266 tC, an average of 63 tC km−2. Depositional fate was inferred using two methods, a standard stream intersection analysis and a second novel approach using simulated debris flow deposition modeling calibrated to the study area. Approximately 60% of debris flow deposits intersected the stream network (9% into mainstem channels, 91% into small tributaries), consistent with long‐term modeled connectivity, suggesting that debris flows are likely to contribute to globally significant amounts of C buried in local fjord sediments. Our results are consistent with an emerging consensus that landslide disturbances that mobilize organic carbon may play an important role in the global carbon cycle over geologic time, with coastal temperate forests being hotspots of potential carbon sequestration.
THE EFFECTS OF SEEDING RATE AND ROW SPACING ON THE PHOTOSYNTHETIC ACTIVITY OF SOYBEAN (GLYCINE MAX (L.) MERR.)

With an advent of early-maturing soybean varieties that have weaker ability to branch, there is a need to study the increase in seeding rates with different combinations of row spacing, in order to determine the effects of narrowing the area of plant nutrition on photosynthetic plant activity. This is the purpose of our research. The multifactorial experiment was performed by splitting sites in four replications. The research has demonstrated that the Baika variety sown with a row spacing of 15 cm and a seeding rate of 1.2 million pcs. /ha 40.5 ths. m/ha was close to the optimal leaf surface. The Annushka soybean plants were inferior to variety Baika in this regard, with the difference up to 6.0 ths. m/ha, due to differences in the leaf structure of the soybean varieties. The weather conditions have been found to play the dominant role; namely, a strong direct correlation was found between the amount of precipitation and the photosynthetic productivity of plants. For the Annushka variety, it was in the range of r = 0.714. 0.843, and for the Baika variety, r = 0.899. 0.947. Thus, using different combinations of seed sowing rate and row spacing, it is possible to adjust the photosynthetic productivity of soybean plants and, as a result, to provide better conditions for their development and higher yields.
Energy-Based Models in Earth Observation: From Generation to Semisupervised Learning

Deep learning, together with the availability of large amounts of data, has transformed the way we process Earth observation (EO) tasks, such as land cover mapping or image registration. Yet, today, new models are needed to push further the revolution and enable new possibilities. This work focuses on a recent framework for generative modeling and explores its applicability to the EO images. The framework learns an energy-based model (EBM) to estimate the underlying joint distribution of the data and the categories, obtaining a neural network that is able to classify and synthesize images. On these two tasks, we show that EBMs reach comparable or better performances than convolutional networks on various public EO datasets and that they are naturally adapted to semisupervised settings, with very few labeled data. Moreover, models of this kind allow us to address high-potential applications, such as out-of-distribution analysis and land cover mapping with confidence estimation.
Type I Interferon Promotes Antitumor T Cell Response in CRPC by Regulating MDSC

Simple Summary Despite initial tumor regression following androgen blockade treatment, relapse of castration-resistant prostate cancer (CRPC) eventually occurs in most patients. Immunotherapy aims to activate the host immune system to fight against cancer and has achieved significant therapeutic effects in various solid tumors. The purpose of our research was to investigate the mechanisms underlying the immune response during CRPC development and to screen effective immunotherapies against CRPC. We found that interferon-α (IFNα) directly inhibited the progression of CRPC, reduced the accumulation of the immune suppressive granulocytic myeloid-derived suppressor cells (G-MDSCs) in the tumor microenvironment (TME), and impaired the inhibitory function of G-MDSCs on T cell activation. This research provides a potential strategy for the clinical treatment of CRPC. Abstract Background: Metastatic castration-resistant prostate cancer (CRPC) is the leading cause of death among prostate cancer patients. Here, our aim was to ascertain the immune regulatory mechanisms involved in CRPC development and identify potential immunotherapies against CRPC. Methods: A CRPC model was established using Myc-CaP cells in immune-competent FVB mice following castration. The immune cell profile of the tumor microenvironment (TME) was analyzed during CRPC development. Different immunotherapies were screened in the CRPC tumor model, and their efficacies and underlying mechanisms were investigated in vitro and in vivo. Results: During CRPC development, the proportion of granulocytic myeloid-derived suppressor cells (G-MDSCs) in the TME increased. Among the immunotherapies tested, IFNα was more effective than anti-PD-L1, anti-CTLA-4, anti-4-1BB, IL-2, and IL-9 in reducing Myc-CaP CRPC tumor growth. IFNα reduced the number of G-MDSCs both in vitro during differentiation and in vivo in CRPC mice. Furthermore, IFNα reduced the suppressive function of G-MDSCs on T cell proliferation and activation. Conclusion: G-MDSCs are crucial to effective immunotherapy against CRPC. Treatment with IFNα presents a promising therapeutic strategy against CRPC. Besides the direct inhibition of tumor growth and the promotion of T cell priming, IFNα reduces the number and the suppressive function of G-MDSCs and restores T cell activation.
Phylogenetic placement of the spider genus Taczanowskia (Araneae : Araneidae) and description of a new species from Ecuador

Abstract. The genus Taczanowskia Keyserling, 1879 is one of the rarest groups of spiders in the orb-weaving family Araneidae, with only five species described and 17 specimens cited in publications. Our study provides new insights into the evolutionary relationships and diversity of Taczanowskia. Using morphological data, we tested the evolutionary relationships of the genus within the family Araneidae and propose the first phylogenetic hypothesis depicting the relationships among species of Taczanowskia. Our results place Taczanowskia as sister to Mastophora Holmberg, 1876, and confirm the monophyly of Taczanowskia. We describe the first species of Taczanowskia from Ecuador, collected at a Waorani community on the River Curaray basin, Amazonian lowlands of Ecuador. The new species can be easily diagnosed from all other species of Taczanowskia by having two tubercles in the opisthosoma; a distinct patchy dark–light colouration pattern with dark spots concentrated towards the anterior margin and on the lateral tips; small bundles of white setae forming a reticulum across the dorsal part of the opisthosoma, and the first two femora thick but lacking teeth on the margin.
Factors Impacting Users’ Compliance with Information Security Policies: An Empirical Study

One of the main concerns for organizations in today's connected world is to find out how employees follow the information security policy (ISP), as the internal employee has been identified as the weakest link in all breaches of the security policies. Several studies have examined ISP compliance from a dissuasive perspective;however, the results were mixed. This empirical study analyses the impact of organisational security factors and individual non-compliance on users’ intentions toward information security policies. A research model and hypotheses have been developed in this quantitative study. Data from 352 participants was collected through a questionnaire, which then validated the measurement model. The findings revealed that while security system anxiety and non-compliant peer behaviours negatively impact users’ compliance intentions, work impediments positively influence these intentions. Security visibility negatively influences users’ non-compliance, and security education systems positively impact work impediments. This research will help information security managers address the problem of information security compliance because it provides them with an understanding of one of the many factors underlying employee compliance behaviors.
RegHD: Robust and Efficient Regression in Hyper-Dimensional Learning System

Machine learning (ML) algorithms are key enablers to effectively assimilate and extract information from many generated data in the Internet of Things. However, running ML algorithms often results in extremely slow processing speed and high energy consumption. To achieve real-time performance with high energy efficiency and robustness, we proposed RegHD, the first regression solution based on Hyperdimensional computing. RegHD redesign a regression algorithm using strategies that more closely model the ultimate efficient learning machine: the human brain. RegHD performs regression after mapping data points into high-dimensional space using similarity preserving encoding. Due to the encoder’s non-linearity, RegHD learns a regression model in an efficient and linear way. RegHD creates two set of models: Input Model to cluster data points with high similarity, and Regression Model to generate a regression model for each clustered data. During prediction, RegHD computes the output value by the weighted accumulation of all regression models, considering the model confidence obtained during similarity search. To improve RegHD efficiency, we also proposed a framework that enables RegHD model quantization while having no impact on the learning accuracy. Our evaluation shows that RegHD provides 5.6 × and 12.3 × (2.9 × and 4.2 ×) faster and energy efficient training (inference) as compared to state-of-the-art regression algorithms, while providing similar quality of learning.
Fifth Drag Prediction Workshop: Computational Fluid Dynamics Studies Carried Out at ONERA

This article presents the computational studies that have been performed at ONERA–The French Aerospace Lab in the context of the 5th AIAA Drag Prediction Workshop. A grid-convergence process and a buffet-onset study have been carried out using a Reynolds-averaged Navier–Stokes solver and far-field methods. In this work, the multiblock grids of the Common Research Model provided by the 5th AIAA Drag Prediction Workshop Committee are employed, and a new meshing strategy involving overset techniques and Cartesian grids is tested. All of these meshes are computed with the ONERA elsA code using the Spalart–Allmaras turbulence model and far-field drag analyses are performed with the ONERA ffd72 software. The grid-convergence study led with the common multiblock grids gives an extrapolated drag value close to 250 drag counts, which corresponds to the numerical median of all 5th AIAA Drag Prediction Workshop participants. The meshing approach using Cartesian grids also provides promising results. Comparisons with.
Ferromagnetic 1H-LaBr2 monolayer: a promising 2D piezoelectric

Abstract The discovery of two dimensional (2D) materials that have excellent piezoelectric response along with intrinsic magnetism is promising for nanoscale multifunctional piezoelectric or spintronic devices. Piezoelectricity requires non-centrosymmetric structures with an electric band-gap, whereas magnetism demands broken time-reversal symmetry. Most of the well-known 2D piezoelectric materials – e.g., 1H-MoS2 monolayer – are not magnetic. Being intrinsically magnetic, semiconducting 1H-LaBr2and 1H-VS2 monolayers can combine magnetism and piezoelectricity. We compare piezoelectric properties of 1H-MoS2, 1H-VS2 and 1H-LaBr2 using density functional theory. Our results show that ferromagnetic 1H-LaBr2 2D monolayer displays a larger piezoelectric strain co-efficient (d_{11}= -4.527 pm/V, which is close to d_{11}= 4.104 pm/V of 1H-VS2 monolayer) compared to that of well-known 1H-MoS2 monolayer (d_{11}= 3.706 pm/V), while 1H-MoS2 monolayer has a larger piezoelectric stress co-efficient (e_{11}= 370.675 pC/m) than the 1H-LaBr2 monolayer (e_{11}= -94.175 pC/m, which is also lower than e_{11}= 298.100 pC/m of 1H-VS2 monolayer). These in-plane piezoelectric d_{11} coefficients are quite comparable with piezo-response of bulk wurtzite nitrides – e.g., d_{33} of GaN is about 3.1 pm/V. The large d_{11} for 1H-LaBr2 monolayer originates from the low elastic constants, C_{11}= 30.338 N/m and C_{12} = 9.534 N/m. Interestingly, the sign of the piezoelectric co-coefficients for 1H-LaBr2 monolayer is different to that of the 1H-MoS2 or 1H-VS2 monolayers. The negative sign arises from the negative ionic contribution of e_{11}, which dominates in the 1H-LaBr2 monolayer, whereas the electronic part of e_{11} dominates in 1H-MoS2 and 1H-VS2. Furthermore, we explain the origin of this large ionic contribution of e_{11} for 1H-LaBr2 in terms of the Born effective charges (Z_{11}) and the sensitivity of the atomic positions to the strain (\frac{du}{d\eta}). Surprisingly, we observe a sign reversal in the Z_{11} of Mo and S compared to the nominal oxidation states, which makes both the electronic and ionic parts of e_{11} positive, and results in the high value of e_{11}. Additionally, our interatomic bond analysis using crystal orbital Hamilton populations indicates that the weaker covalent bond in 1H-LaBr2 monolayer is responsible for large \frac{du}{d\eta} and elastic softening (lower elastic constants).
COMPARING DRIVER INFORMATION SYSTEMS IN A DYNAMIC MODELING FRAMEWORK

A modeling framework is developed to assess the performance of driver information systems. The two main components of the framework are a route choice module and a traffic simulation module. In the route choice module, three types of driver information systems are studied: the pretrip information service, the in-car route guidance system, and the variable message signs. The traffic simulation module controls the dynamics in a road network as traffic evolves over time in response to driver information. The dynamic properties of the framework are demonstrated in a test network and a simplified Singapore road network. The results suggest that there could be an optimal level of subscription to driver information or market penetration. Subscription to real-time driver information services is recommended, but a high level of subscription may result in extra delay. Variable message signs are not as effective as either pretrip information or in-car route guidance at their respective optimal levels of market penetration.
Smoking Cessation Apps: A Systematic Review of Format, Outcomes, and Features

Smoking cessation interventions are effective, but they are not easily accessible for all treatment-seeking smokers. Mobile health (mHealth) apps have been used in recent years to overcome some of these limitations. Smoking cessation apps can be used in combination with a face-to-face intervention (FFSC-Apps), or alone as general apps (GSC-Apps). The aims of this review were (1) to examine the effects of FFSC-Apps and GSC-Apps on abstinence, tobacco use, and relapse rates; and (2) to describe their features. A systematic review was conducted following the internationally Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement. Of the total 6016 studies screened, 24 were included, of which nine used GSC-Apps and 15 FFSC-Apps. Eight studies reported significant differences between conditions in smoking cessation outcomes, with three of them being in favor of the use of apps, and two between different point-assessments. Concerning Apps features, most GSC-Apps included self-tracking and setting a quit plan, whereas most of the FFSC-Apps included self-tracking and carbon monoxide (CO) measures. Smartphone apps for smoking cessation could be promising tools. However, more research with an adequate methodological quality is needed to determine its effect. Nevertheless, smartphone apps’ high availability and attractiveness represent a great opportunity to reach large populations.
Modelling novelty detection in the thalamocortical loop

In complex natural environments, sensory systems are constantly exposed to a large stream of inputs. Novel or rare stimuli, which are often associated with behaviorally important events, are typically processed differently than the steady sensory background, which has less relevance. Neural signatures of such differential processing, commonly referred to as novelty detection, have been identified on the level of EEG recordings as mismatch negativity and the level of single neurons as stimulus-specific adaptation. Here, we propose a multi-scale recurrent network with synaptic depression to explain how novelty detection can arise in the whisker-related part of the somatosensory thalamocortical loop. The architecture and dynamics of the model presume that neurons in cortical layer 6 adapt, via synaptic depression, specifically to a frequently presented stimulus, resulting in reduced population activity in the corresponding cortical column when compared with the population activity evoked by a rare stimulus. This difference in population activity is then projected from the cortex to the thalamus and amplified through the interaction between neurons of the primary and reticular nuclei of the thalamus, resulting in spindle-like, rhythmic oscillations. These differentially activated thalamic oscillations are forwarded to cortical layer 4 as a late secondary response that is specific to rare stimuli that violate a particular stimulus pattern. Model results show a strong analogy between this late single neuron activity and EEG-based mismatch negativity in terms of their common sensitivity to presentation context and timescales of response latency, as observed experimentally. Our results indicate that adaptation in L6 can establish the thalamocortical dynamics that produce signatures of SSA and MMN and suggest a mechanistic model of novelty detection that could generalize to other sensory modalities. Author summary Cortical sensory neurons have been shown to be capable of novelty detection, that is they respond more vigorously when a novel, unexpected stimulus is presented, and less so when the stimulus is part of a predictable sequence. However, the neural mechanism underlying this capability is not yet fully understood. Here, we developed a thalamocortical network model that accounts for novelty detection and reproduces physiologically observed neural response patterns in the somatosensory cortex. Specifically, our results demonstrate that the novelty signal arises from the complex recurrent interplay between thalamic neurons and cortical neurons in layers 4 and 6. This work therefore provides a concrete mechanism that can serve as a starting point for further investigating the neural circuit mechanisms underlying novelty detection.
Wake Vortex Alleviation Using Rapidly Actuated Segmented Gurney Flaps

A study to assess the potential for using rapidly actuated segmented Gurney flaps, also known as miniature trailing-edge effectors, for active wake vortex alleviation is conducted. Experiments are performed using a half-span model wing with NACA 0012 shape and an aspect ratio of 4.1. The wing is at an 8.9-deg angle of attack and the chord-based Reynolds number is around 350,000. It is equipped with an array of miniature trailing-edge effectors that extend 0.015 chord lengths perpendicular to the freestream on the pressure side of the wing when deployed and sit just behind the blunt trailing edge when retracted. Measurements of section lift coefficient and the velocity field in the intermediate wake using dynamic particle image velocimetry and a five-hole probe delineate the time-varying perturbation imparted by the miniature trailing-edge effectors upon the trailing vortex while keeping the lift nearly constant. The data are used to form a complete initial condition for a vortex filament computation of the far-wake evolution. Cases at the model-scale and full-scale configurations are computed
Exercise and Nutritional Strategies to Promote Weight Loss: A Narrative Review

Obesity has historically been thought to simply be related to an imbalance between energy intake and expenditure. Weight loss recommendations have traditionally focused on reducing energy intake, maintaining either a high-carbohydrate low-fat diet or vice versa, and increasing physical activity typically through low to moderate intensity exercise. More recently, genetic, physiological, and behavioral factors have also been found to play a significant role in the etiology of obesity. For this reason, the prevention and management of obesity through implementation of different types of exercise and diet intervention programs, behavioral interventions, and/or medical interventions are at the forefront of obesity research. As a result, we are beginning to see a paradigm shift from traditional weight loss and management approaches to personalized diet and exercise strategies. This purpose of this narrative review was to overview: 1. ) the effects of physical activity on weight loss; 2. ) the effects of diet and cardiovascular exercise on weight loss; 3. ) the effects of high protein diet with resistance-exercise on weight loss; and, 4. ) behavioral factors that contribute weight loss and maintenance.