Miracle-dz/UniFAR-FTUs huggingface dataset:
Polyketide synthases mutation in tuberculosis transmission revealed by whole genomic sequence, China, 2011–2019

Introduction: Tuberculosis (TB) is an infectious disease caused by a bacterium called Mycobacterium tuberculosis (Mtb). Previous studies have primarily focused on the transmissibility of multidrug-resistant (MDR) or extensively drug-resistant (XDR) Mtb. However, variations in virulence across Mtb lineages may also account for differences in transmissibility. In Mtb, polyketide synthase (PKS) genes encode large multifunctional proteins which have been shown to be major mycobacterial virulence factors. Therefore, this study aimed to identify the role of PKS mutations in TB transmission and assess its risk and characteristics. Methods: Whole genome sequences (WGSs) data from 3,204 Mtb isolates was collected from 2011 to 2019 in China. Whole genome single nucleotide polymorphism (SNP) profiles were used for phylogenetic tree analysis. Putative transmission clusters (≤10 SNPs) were identified. To identify the role of PKS mutations in TB transmission, we compared SNPs in the PKS gene region between “clustered isolates” and “non-clustered isolates” in different lineages. Results: Cluster-associated mutations in ppsA, pks12, and pks13 were identified among different lineage isolates. They were statistically significant among clustered strains, indicating that they may enhance the transmissibility of Mtb. Conclusion: Overall, this study provides new insights into the function of PKS and its localization in M. tuberculosis. The study found that ppsA, pks12, and pks13 may contribute to disease progression and higher transmission of certain strains. We also discussed the prospective use of mutant ppsA, pks12, and pks13 genes as drug targets.
Nutritional status of hospitalized elderly patients in Ethiopia: a cross-sectional study of an important yet neglected problem in clinical practice

Background Malnutrition is a common geriatric syndrome affecting approximately half of the older population with a more pronounced occurrence rate in those hospitalized. It affects the physiology, and results in poor humanistic and clinical outcomes. In Africa, particularly in Ethiopia, albeit multiple studies are available on malnutrition in non-hospitalized older population, similar studies in inpatient settings are scarce. Therefore, this study was conducted with the intention to quantify the prevalence of malnutrition in older patients on inpatient admission and determine its associated factors. Methods A total of 157 older inpatients aged 60 years and above were included in the present study. The data collection format was developed after an in-depth review of relevant literatures. The full Mini-Nutritional Assessment (MNA) tool was employed to assess the nutritional status on admission. Data completeness was checked thoroughly. Descriptive statistics and logistic regression analysis were conducted using STATA 15.0. The area under the receiver operating characteristic curve (ROC), Hosmer–Lemeshow test, and classification table were computed to evaluate the final model goodness-of-fit. Results Of the total study subjects, 81% were malnourished (MNA score <17) and 17% were at risk for malnutrition (MNA score of 17.5–23.5). However, upon review of the patients’ medical charts, malnutrition diagnosis was recorded in only two patients. Rural residence (AOR = 2.823, 95%CI: 1.088, 7.324), self-reported financial dependence for expenses (AOR = 4.733, 95%CI: 1.011, 22.162), and partial dependence in functional autonomy on admission (AOR = 3.689, 95%CI: 1.190, 11.433) significantly increased the risk of malnutrition. The area under the ROC curve (0.754) and the Hosmer–Lemeshow test (p = 0.7564) indicated that the final model reasonably fits the data. The model`s sensitivity is 96.85%. Conclusion In the present study, an alarmingly high prevalence of malnutrition was identified older inpatients. The problem went undiagnosed in a similar percentage of patients. Several available literatures indicate the presence of an association between nutritional status and patient outcomes, thus strict nutritional screening at inpatient admission and intervention are recommended with special emphasis for those from rural areas, with financial dependence, and with functional impairment on admission.
Assessment of Cognitive Workload During Flight Training by Means of Hybrid NIR/LWIR Imaging

Aircraft pilots are requested to operate complex vehicles under demanding circumstances. Indeed, although current cockpits are designed to be user-friendly, the responsibility of efficiently and consistently operating the modern airplanes is quite challenging, particularly during military emergency scenarios. In this context, the monitoring of pilots’ mental workload (MWL) assumes significant importance as it serves to enhance both the safety and performance of the pilots. The concept of MWL pertains to the amount of cognitive resources needed to fulfill both objective and subjective measures of performance. The evaluation might be conducted by the administration of questionnaires, such as the Bedford Workload Rating Scale (BWRS), or the measurement of physiological signals. Notably, it has been shown that face skin temperature is a reliable indicator of MWL, and the measurement of this parameter may be conducted in a nonintrusive way using Infrared (IR) imaging. This research demonstrates the potential for estimating pilots’ MWL using facial thermography obtained from a compact computational physiological module (CPM) consisting of both co-registered infrared and visible cameras. In order to achieve this objective, a Machine Learning (ML) model based on features derived from the temperature time course of certain face areas was created. Its purpose was to categorize three levels of MWL experienced by pilots during a training session conducted using the Nasa Multi Attribute Tasks Battery II (MATB-II). The classification of MWL levels was based on the BWRS ratings provided by the participants. The ML model achieved a classification accuracy of 73.7%, indicating the potential for estimation of MWL using face thermography in a non-intrusive way without interfering with the pilot’s activity.
Active Power Allocation Method of Doubly Fed Induction Generators Based on Rotor Speed

The integration of wind power into a grid on a large scale results in a reduction of the system’s inertia level, causing an impact on the stability of the system frequency. Doubly fed induction generators (DFIG) can optimize active output but lack inertia support under maximum power point tracking control. To make the wind turbine improve the inertia support ability of the system based on virtual inertia control, a method for active power allocation based on the rotor speed of DFIG is proposed. Firstly, the minimum system inertia requirement based on the frequency change rate of the system is established. Active power allocation assumes that the wind farm inertia meets the minimum system inertia requirement. Secondly, the objective is to enhance the inertia support capability and overall active power output of the wind farm, considering the constraint of the minimum system inertia requirement. Based on the rotor speed to establish the inertia allocation weight factor, the weight of the power command is assigned to a single machine to achieve the wind farm active power allocation. Finally, it is verified that the system’s equivalent inertia meets the minimum inertia requirement of the system. Simulations show that the proposed allocation method can adequately elevate the inertia support capability of DFIGs to the system and the rotor kinetic energy utilization.
Belgium-Luxembourg Income and Capital Tax Treaty (1970) Resolves Double
Taxation on Net Assets of Luxembourg Investment Funds with Belgian
Investors

The Brussels Court of First Instance has decided on two occasions that a Luxembourg SICAV can invoke the Belgium-Luxembourg Income and Capital Tax Treaty (1970) and claim exemption from Belgian tax on its net assets. The judgements also seem to have consequences for Luxembourg contractual funds.
A Compact Quarter-Wavelength Microstrip Filter with Wide Stopband

In this paper, A compact microstrip bandpass filter consisting of quarter-wavelength short-end resonators is proposed. Four conducting bars are used to connect five resonators. The first and second resonators are placed inter-digitally and introduce a hybrid electromagnetic coupling. Then, a transmission zero locates at the lower stopband. A bandpass filter sample with center frequency of 9.85 GHz and bandwidth of 1.3 GHz was fabricated. The measured return loss is better than 20 dB, and the minimum insertion loss is smaller than 2 dB. The filter has a wide upper stopband from 10.74 GHz to 26.95 GHz and a small footprint of 6.7 mm x 4.5 mm.
Predicting disease onset from electronic health records for population health management: a scalable and explainable Deep Learning approach

Introduction The move from a reactive model of care which treats conditions when they arise to a proactive model which intervenes early to prevent adverse healthcare events will benefit from advances in the predictive capabilities of Artificial Intelligence and Machine Learning. This paper investigates the ability of a Deep Learning (DL) approach to predict future disease diagnosis from Electronic Health Records (EHR) for the purposes of Population Health Management. Methods In this study, embeddings were created using a Word2Vec algorithm from structured vocabulary commonly used in EHRs e.g., Systematized Nomenclature of Medicine Clinical Terms (SNOMED CT) codes. This study is based on longitudinal medical data from ~50 m patients in the USA. We introduced a novel method of including binned observation values into an embeddings model. We also included novel features associated with wider determinants of health. Patient records comprising these embeddings were then fed to a Bidirectional Gated Recurrent Unit (GRU) model to predict the likelihood of patients developing Type 2 Diabetes Mellitus, Chronic Obstructive Pulmonary Disorder (COPD), Hypertension or experiencing an Acute Myocardial Infarction (MI) in the next 3 years. SHapley Additive exPlanations (SHAP) values were calculated to achieve model explainability. Results Increasing the data scope to include binned observations and wider determinants of health was found to improve predictive performance. We achieved an area under the Receiver Operating Characteristic curve value of 0.92 for Diabetes prediction, 0.94 for COPD, 0.92 for Hypertension and 0.94 for MI. The SHAP values showed that the models had learned features known to be associated with these outcomes. Discussion The DL approach outlined in this study can identify clinically-relevant features from large-scale EHR data and use these to predict future disease outcomes. This study highlights the promise of DL solutions for identifying patients at future risk of disease and providing clinicians with the means to understand and evaluate the drivers of those predictions.
Predictive value of right heart contrast echocardiography combined with STAF in cardioembolism

Cardioembolism (CE) is a prevalent cerebrovascular disease in clinical practice. This study aims to assess the predictive efficacy of right heart contrast echocardiography combined with the Score for Targeting Atrial Fibrillation (STAF) for diagnosing CE. A total of 149 patients with CE and 93 patients with non-CE (NCE) were classified into the study group and control group, respectively, and their data were retrospectively analyzed. All patients underwent right heart contrast echocardiography and STAF assessment, and comparative analyses between the groups were performed. In addition, the predictive potential of combining right heart contrast echocardiography with STAF for CE was evaluated, and the CE patients underwent a one-year follow-up to assess survival and prognostic factors. The results of this study showed that patients in the CE group had higher incidences of grade 1 + 2 + 3 right-to-left shunt, patent foramen ovale (PFO) positivity, greater foramen ovale length, larger shunt inner diameter, and higher STAF score compared to the NCE group (p < 0.05). Receiver operating characteristic curve analysis revealed that the predictive values for CE, in terms of right-to-left shunt grade, PFO positivity, foramen ovale length, shunt inner diameter, and area under the curve (AUC) for STAF, were 0.582, 0.570, 0.679, 0.808 and 0.750, respectively. The combined AUC value for all these parameters was 0.905. Univariate and multiple logistic regression analysis indicated that atrial fibrillation, total cholesterol and fibrinogen were not prognostic factors in CE patients (p > 0.05), whereas National Institute of Health stroke scale (NIHSS) score, PFO positivity, foramen ovale length and STAF at admission were prognostic factors (p < 0.05). Therefore, combining right heart contrast echocardiography with STAF may enhance the predictive efficacy for CE.
Employee Engagement Mediation: Employee Performance Through Employee Loyalty and Organizational Commitment

The automotive parts highly value the quality of production results, so it requires employee involvement to mediate employee loyalty and organizational commitment to improve employee performance. The quality demands of automotive parts production must meet customer standards. Therefore, employee performance determines the desired quality of work. Researchers will investigate the relationship between employee loyalty and organizational commitment by examining how employee engagement influences employee performance. The results of this study can provide input to management for future follow-up. Researchers used descriptive statistics using Smart PLS 4.0 to analyze data and identify factors related to employee loyalty and organizational commitment, which increase company productivity by mediating employee engagement to employee performance. Questionnaires were distributed to 158 employees using Google Forms. Engagement and performance are strongly and positively influenced by employee loyalty. While organizational dedication has a positive and significant effect on employee performance. What significantly mediates the relationship between employee loyalty, organizational commitment, and employee performance is employee engagement, which is strongly correlated with it. Employee loyalty and organisational commitment indirectly affect employee performance through employee engagement, suggesting that these factors may indirectly improve performance through employee engagement.
Approach of Item-Based Collaborative Filtering Recommendation Using Energy Distance

—The current collaborative filtering recommendation method using energy distance only focuses on the relationship between the user and the user, between the user group and the user group. This method has not yet considered the relationship between the item and the item. In this article, we mainly focus on proposing an item-based collaborative filtering model using the energy distance. The proposed model is evaluated on two popular datasets Jester5k and MovieLens100k. Besides, the proposed model is also compared with two item-based collaborative filtering models using the Cosine and Pearson measures. The experimental results have shown that the proposed model is better than two compared models.
Exploring Q-Learning in Social Robots for English-Slovak Vocabulary Learning

The study explores the use of social robots in the context of teaching new vocabulary to students. We employ reinforcement learning, specifically Q-learning, as our base algorithm for selecting which area of words should be learned by each student individually, so the students can improve areas where they have gaps. Experiments were designed to compare students with a personalized selection of words to learn versus those without it. This study incorporated words from both English and Slovak languages. We prove that the use of Q-learning has a positive impact on learning outcomes with an observed improvement of average grade points. We have also interviewed students and teachers to discover their thoughts about using the NAO robot as an assistant for vocabulary learning.
Effect of structural variation in the promoter region of RsMYB1.1 on the skin color of radish taproot

Accumulation of anthocyanins in the taproot of radish is an agronomic trait beneficial for human health. Several genetic loci are related to a red skin or flesh color of radish, however, the functional divergence of candidate genes between non-red and red radishes has not been investigated. Here, we report that a novel genetic locus on the R2 chromosome, where RsMYB1.1 is located, is associated with the red color of the skin of radish taproot. A genome-wide association study (GWAS) of 66 non-red-skinned (nR) and 34 red-skinned (R) radish accessions identified three nonsynonymous single nucleotide polymorphisms (SNPs) in the third exon of RsMYB1.1. Although the genotypes of SNP loci differed between the nR and R radishes, no functional difference in the RsMYB1.1 proteins of nR and R radishes in their physical interaction with RsTT8 was detected by yeast-two hybrid assay or in anthocyanin accumulation in tobacco and radish leaves coexpressing RsMYB1.1 and RsTT8. By contrast, insertion- or deletion-based GWAS revealed that one large AT-rich low-complexity sequence of 1.3–2 kb was inserted in the promoter region of RsMYB1.1 in the nR radishes (RsMYB1.1nR ), whereas the R radishes had no such insertion; this represents a presence/absence variation (PAV). This insertion sequence (RsIS) was radish specific and distributed among the nine chromosomes of Raphanus genomes. Despite the extremely low transcription level of RsMYB1.1nR in the nR radishes, the inactive RsMYB1.1nR promoter could be functionally restored by deletion of the RsIS. The results of a transient expression assay using radish root sections suggested that the RsIS negatively regulates the expression of RsMYB1.1nR , resulting in the downregulation of anthocyanin biosynthesis genes, including RsCHS, RsDFR, and RsANS, in the nR radishes. This work provides the first evidence of the involvement of PAV in an agronomic trait of radish.
Crustal Imaging with Noisy Teleseismic Receiver Functions Using Sparse Radon Transforms

The receiver function (RF) is a widely used crustal imaging technique. In principle, it assumes relatively noise-free traces that can be used to target receiver-side structures following source deconvolution. In practice, however, mode conversions and reflections may be severely degraded by noisy conditions, hampering robust estimation of crustal parameters. In this study, we use a sparsity-promoting Radon transform to decompose the observed RF traces into their wavefield contributions, that is, direct conversions, multiples, and incoherent noise. By applying a crustal mask on the Radon-transformed RF, we obtain noise-free RF traces with only Moho conversions and reflections. We demonstrate, using a synthetic experiment and a real-data example from the Sierra Nevada, that our approach can effectively denoise the RFs and extract the underlying Moho signals. This greatly improves the robustness of crustal structure recovery as exemplified by subsequent H−κ stacking. We further demonstrate, using a station sitting on loose sediments in the Upper Mississippi embayment, that a combination of our approach and frequency-domain filtering can significantly improve crustal imaging in reverberant settings. In the presence of complex crustal structures, for example, dipping Moho, intracrustal layers, and crustal anisotropy, we recommend caution when applying our proposed approach due to the difficulty of interpreting a possibly more complicated Radon image. We expect that our technique will enable high-resolution crustal imaging and inspire more applications of Radon transforms in seismic signal processing.
Longtermist Education Interventions Increase Concern for and Action to Protect Future Generations

In a contemporary landscape fraught with unprecedented challenges, it is imperative to forge strategies that transcend present concerns and equally prioritize future generations. This research, anchored in the philosophy of longtermism, seeks to bridge this temporal divide. Across three pre-registered and highly-powered studies, we scrutinize the potential of philosophical arguments underpinning longtermism to foster alignment with its principles, thereby catalyzing attitudes and actions that resonate with a more future-oriented approach to global welfare. Leveraging scalable educational interventions through text and video media formats, we discern a noticeable resonance of these philosophical arguments among individuals, influencing their beliefs, policy support, donation behaviors, and cognitive investment directed toward the betterment of future generations. Our findings illuminate the critical mediating role of longtermism beliefs between the interventions and favorable future-focused outcomes, establishing the promising potential of philosophical discourse as a pragmatic tool in mobilizing collective efforts to safeguard our long-term future.