Miracle-dz/UniFAR-FTUs huggingface dataset:
Trade‐off between pre and post‐copulatory traits depends on locomotor activity in male Tribolium castaneum beetles

Locomotor performance is an indicator of dynamic exercise; thus, it is a central trait in many animal behaviours. Although higher locomotor endurance may increase male reproductive success (e.g., in mate searching and male–male contests), investment in other male reproductive traits (e.g., male attractiveness and sperm competition) may be decreased through energy consumption due to higher activity levels. Here, I investigated male attractiveness, mating success, and paternity success using males of the red flour beetle Tribolium castaneum selected for higher (H) and lower (L) locomotor endurance. Although there was no difference in male attractiveness between the selection regimes, H males had significantly higher mating success than L males. Conversely, L males had significantly higher paternity success than H males. Therefore, there was a trade‐off between mating success and paternity success among the selection regimes, suggesting that locomotor endurance affects male reproduction in T. castaneum, and individual variation of locomotor endurance may be maintained within a population.
Ribociclib-induced hepatotoxicity

Introduction Cyclin-dependent kinase (CDK) 4/6 inhibitors have shown a different adverse effect. In this case, persistent grade 3 hepatoxicity was observed after ribociclib. Therefore, ribociclib therapy was stopped, and then palbociclib was introduced. Transaminase levels returned to normal by switching to palbociclib therapy. Case report 71-year-old postmenopausal female patient with luminal subtypes of metastatic breast cancer treated with ribociclib. Management & outcome Grade 3 hepatotoxicity secondary to ribociclib developed. She was successfully treated with palbociclib 125 mg. Discussion In our case, palbociclib was started with a full dose, to increase treatment success. Starting with a 125 mg dose was not cause any toxicity. Nevertheless, laboratory follow-up is required in terms of neutropenia and increased transaminases.
A Critique of Raju and Oshima's Prophecy Formulas for Assessing the Reliability of Item Response Theory-Based Ability Estimates

Raju and Oshima (2005) proposed two prophecy formulas based on item response theory in order to predict the reliability of ability estimates for a test after change in its length. The first prophecy formula is equivalent to the classical Spearman-Brown prophecy formula. The second prophecy formula is misleading because of an underlying false assumption. It can yield negative reliability estimates, especially if the reliability of ability estimates for the old test is low and the new test is shorter. This article identifies the fallacy of the second prophecy formula and demonstrates the scope of its bias in predicting test reliability.
MOMENT METHOD SOLUTION OF A PARTIALLY FILLED PARALLEL PLATE FED THROUGH INFINITE CONDUCTING PLANE (TE CASE)

In this paper, the moment method is used for the solution of the receive case of the partially filled parallel plate fed from a perforated infinite conducting plane. The waveguide is excited by an incident transverse electric (TE) plane wave. An operator equation is first formed using the tangential components of the magnetic fields at the aperture. This equation is used for the computation of the magnetic current on the apertures and the radiation pattern for different cases. Numerical results for the homogeneous case is also given and compared with other available data.
Speech-Encoding Deficits in Neonates Born Large-for-Gestational Age as Revealed With the Envelope Frequency-Following Response

Objectives: The present envelope frequency-following response (FFRENV) study aimed at characterizing the neural encoding of the fundamental frequency of speech sounds in neonates born at the higher end of the birth weight continuum (>90th percentile), known as large-for-gestational age (LGA). Design: Twenty-five LGA newborns were recruited from the maternity unit of Sant Joan de Déu Barcelona Children’s Hospital and paired by age and sex with 25 babies born adequate-for-gestational age (AGA), all from healthy mothers and normal pregnancies. FFRENVs were elicited to the/da/ syllable and recorded while the baby was sleeping in its cradle after a successful universal hearing screening. Neural encoding of the stimulus’ envelope of the fundamental frequency (F0ENV) was characterized through the FFRENV spectral amplitude. Relationships between electrophysiological parameters and maternal/neonatal variables that may condition neonatal neurodevelopment were assessed, including pregestational body mass index (BMI), maternal gestational weight gain and neonatal BMI. Results: LGA newborns showed smaller spectral amplitudes at the F0ENV compared to the AGA group. Significant negative correlations were found between neonatal BMI and the spectral amplitude at the F0ENV. Conclusions: Our results indicate that in spite of having a healthy pregnancy, LGA neonates’ central auditory system is impaired in encoding a fundamental aspect of the speech sounds, namely their fundamental frequency. The negative correlation between the neonates’ BMI and FFRENV indicates that this impaired encoding is independent of the pregnant woman BMI and weight gain during pregnancy, supporting the role of the neonatal BMI. We suggest that the higher adipose tissue observed in the LGA group may impair, via proinflammatory products, the fine-grained central auditory system microstructure required for the neural encoding of the fundamental frequency of speech sounds.
Real-world evidence of nivolumab for non-small-cell lung cancer in a developing country

Nivolumab is a human programmed death receptor-1 blocking antibody, used as treatment option in patients with advanced non-small-cell lung cancer (NSCLC). We assessed the nivolumab efficacy in terms of survival and response to treatment as second-line (2L) or third-line (3L) therapy in patients with advanced NSCLC. This is a multicentric observational study. Data of patients with advanced NSCLC who received nivolumab as 2L or 3L treatment were analyzed retrospectively. Information regarding patient demographics and clinical backgrounds, treatment patterns from diagnosis to post-nivolumab treatment, effectiveness, and safety of nivolumab treatment were collected. The outcomes evaluated were overall survival (OS), progression-free survival (PFS), and objective response rate (ORR) to treatment. OS and PFS were estimated with the Kaplan–Meier method and the differences were evaluated through the log-rank test. Data of 178 patients were included. The median follow-up was 26.8 months (interquartile range (IQR): 20.3–40.4). Nivolumab was commonly used as a 2L treatment (77.5%). The outcomes in this setting (2L) were as follows: ORR was 21.0%, and the median PFS and OS were 5.5 months (95% confidence interval (CI): 4.5–6.5) and 12.4 months (95% CI: 10.8–14.0), respectively. In 3L, the ORR with nivolumab was 15.0%, the median PFS and OS were 4.1 months (95% CI: 3.1–5.1) and 10.1 months (95% CI: 9.4–10.6), respectively. Three patients (1.7%) required discontinuation due to toxicity. Nivolumab effectiveness and safety in this scenario was consistent with that reported by previous trials and other real-world data.
A new proof of the existence of a trace in a finite von Neumann algebra

The construction of the dimension function for projections in the various types of factor, and the definition of the trace in a factor of type Hi first appeared in a classical paper of Murray and von Neumann [7]. The proof of the additivity and weak continuity of the trace appeared in [8]. Subsequent authors [2], [5], [6] have demonstrated the existence of traces on a larger class of von Neumann algebras, but all have employed some variant of the Murray-von Neumann method of proof. The purpose of the present paper is to provide a short and independent proof of the following theorem.
The Indentation of an Anisotropic Half Space by a Rigid Punch

Eshelby et al. [1] and Stroh [2] have developed the theory of anisotropic elasticity for a three dimensional state of stress in which the stress is independent of one of the Cartesian coordinates. Various problems involving dislocations in an infinite anisotropic medium are solved in the first paper, while Stroh considers dislocation problems as well as determining the stresses round a crack subjected to an arbitrary non-uniform applied stress. In this note, which follows their treatment, we consider the problem of determining the stresses produced by the indentation of the plane surface of an anisotropic half space by a rigid punch. Problems of this type have been solved by Green and Zerna [3], Lekhnitskii [4], Brilla [5], Gallin [6] and Milne-Thomson [7] but if we take Cartesian coordinates x1, x2, x3 and let the stress be independent of x3, then these authors all assume the x1x2 plane to be one of elastic symmetry. The solution presented in this paper does not require this assumption so that it has a more general application than has been the case with previous solutions to problems of this type. The first part of the analysis given here is for general anisotropy, but in order to obtain a solution to the punch problem by the method of this paper, it is necessary to consider only a particular class of anisotropic materials. The indentation of such materials by a circular block is discussed in section 4 and the results are used in section 5 to examine the case when the circular block is on a transversely isotropic half space.
Intelligent Discrete Deep Learning Based Classification Methodology in Chemometrics

In recent years, deep learning models have attracted much attention for classification purposes in chemometrics. The popularity of deep learning models in this field comes from their unique features like universal approximation capability with the desired accuracy. Deep learning classifiers use several intelligent processing layers to model mixed, complex, and nonlinear patterns in the underlying data sets, which is why the development of deep learning based models has never been stopped in the chemometrics literature. Despite the variety of deep learning classification models used in this field, they all use a continuous distance-based cost function in their learning processes. Although using a continuous cost function for learning deep classifiers is a common approach, it conflicts with the discrete nature of the classification problem. In fact, applying a continuous cost function for inherently discrete classification problems can reduce the performance of the classification. In this research, a novel discrete learning based classification approach is proposed and implemented on a deep feed-forward neural network as one of the most commonly used deep learning models to develop a different learning process for deep classification models. The basis of the proposed learning approach is maximizing a discrete matching function of the actual and fitted values instead of minimizing a continuous distance-based cost function. The proposed classification approach is evaluated on five benchmark data sets in the chemistry field. The empirical results indicated the superiority of the proposed discrete deep learning approach over its classic continuous form. The results of this study demonstrate the important effect of discrete learning processes on the performances of deep learning classification models. Therefore, the proposed methodology can be a powerful alternative to common classification approaches to analyze chemical data in the chemometrics field.
Small Area Forecasting of Opioid-Related Mortality: Bayesian Spatiotemporal Dynamic Modeling Approach

Background Opioid-related overdose mortality has remained at crisis levels across the United States, increasing 5-fold and worsened during the COVID-19 pandemic. The ability to provide forecasts of opioid-related mortality at granular geographical and temporal scales may help guide preemptive public health responses. Current forecasting models focus on prediction on a large geographical scale, such as states or counties, lacking the spatial granularity that local public health officials desire to guide policy decisions and resource allocation. Objective The overarching objective of our study was to develop Bayesian spatiotemporal dynamic models to predict opioid-related mortality counts and rates at temporally and geographically granular scales (ie, ZIP Code Tabulation Areas [ZCTAs]) for Massachusetts. Methods We obtained decedent data from the Massachusetts Registry of Vital Records and Statistics for 2005 through 2019. We developed Bayesian spatiotemporal dynamic models to predict opioid-related mortality across Massachusetts’ 537 ZCTAs. We evaluated the prediction performance of our models using the one-year ahead approach. We investigated the potential improvement of prediction accuracy by incorporating ZCTA-level demographic and socioeconomic determinants. We identified ZCTAs with the highest predicted opioid-related mortality in terms of rates and counts and stratified them by rural and urban areas. Results Bayesian dynamic models with the full spatial and temporal dependency performed best. Inclusion of the ZCTA-level demographic and socioeconomic variables as predictors improved the prediction accuracy, but only in the model that did not account for the neighborhood-level spatial dependency of the ZCTAs. Predictions were better for urban areas than for rural areas, which were more sparsely populated. Using the best performing model and the Massachusetts opioid-related mortality data from 2005 through 2019, our models suggested a stabilizing pattern in opioid-related overdose mortality in 2020 and 2021 if there were no disruptive changes to the trends observed for 2005-2019. Conclusions Our Bayesian spatiotemporal models focused on opioid-related overdose mortality data facilitated prediction approaches that can inform preemptive public health decision-making and resource allocation. While sparse data from rural and less populated locales typically pose special challenges in small area predictions, our dynamic Bayesian models, which maximized information borrowing across geographic areas and time points, were used to provide more accurate predictions for small areas. Such approaches can be replicated in other jurisdictions and at varying temporal and geographical levels. We encourage the formation of a modeling consortium for fatal opioid-related overdose predictions, where different modeling techniques could be ensembled to inform public health policy.
A riboswitch separated from its ribosome-binding site still regulates translation

Abstract Riboswitches regulate downstream gene expression by binding cellular metabolites. Regulation of translation initiation by riboswitches is posited to occur by metabolite-mediated sequestration of the Shine-Dalgarno sequence (SDS), causing bypass by the ribosome. Recently, we solved a co-crystal structure of a prequeuosine1-sensing riboswitch from Carnobacterium antarcticum that binds two metabolites in a single pocket. The structure revealed that the second nucleotide within the gene-regulatory SDS, G34, engages in a crystal contact, obscuring the molecular basis of gene regulation. Here, we report a co-crystal structure wherein C10 pairs with G34. However, molecular dynamics simulations reveal quick dissolution of the pair, which fails to reform. Functional and chemical probing assays inside live bacterial cells corroborate the dispensability of the C10–G34 pair in gene regulation, leading to the hypothesis that the compact pseudoknot fold is sufficient for translation attenuation. Remarkably, the C. antarcticum aptamer retained significant gene-regulatory activity when uncoupled from the SDS using unstructured spacers up to 10 nucleotides away from the riboswitch—akin to steric-blocking employed by sRNAs. Accordingly, our work reveals that the RNA fold regulates translation without SDS sequestration, expanding known riboswitch-mediated gene-regulatory mechanisms. The results infer that riboswitches exist wherein the SDS is not embedded inside a stable fold.
Novel Somatic UBA1 Variant in a Patient With VEXAS Syndrome

Somatic mutations in UBA1 have recently been causally linked to a severe adult‐onset inflammatory condition referred to as VEXAS (vacuoles, E1 enzyme, X‐linked, autoinflammatory, somatic) syndrome. Ubiquitin‐activating enzyme E1 (UBA‐1) is of fundamental importance to the modulation of ubiquitin homeostasis and to the majority of downstream ubiquitylation‐dependent cellular processes. Direct sequencing analysis of exon 3 containing the prevalent variants p.Met41Leu, p.Met41Val, and/or p.Met41Thr is usually used to confirm the disease‐associated mutations.
Evidence for unusual dynamical effects in the halo of NGC 5128 (Centaurus A)

De nouvelles observations ont porte a 35 le nombre d'amas globulaires spectroscopiquement confirmes dans NGC 5128. Pour les rayons inferieurs a ∼11' la vitesse moyenne de l'ensemble des amas semble depasser de ∼4 σ, la vitesse systematique de la galaxie, alors qu'aux plus grands rayons les deux valeurs se rejoignent
A two-dimensional hybrid simulation of the magnetotail reconnection layer

Two-dimensional (2-D) hybrid simulations are carried out to study the structure of the reconnection layer in the distant magnetotail. In the simulation an initial current sheet separates the two lobes with antiparallel magnetic field components in the x direction. The current sheet normal is along the z direction. It is found that a leading bulge-like magnetic configuration and a trailing, quasi-steady reconnection layer are formed in a magnetic reconnection. If the duration of the reconnection is sufficiently long, the trailing reconnection layer will dominate the plasma outflow region. For the symmetric lobes with By = 0, two pairs of slow shocks are present in the quasi-steady reconnection layer. The slow shocks are expected to be fully developed at a sufficient distance from the X line, where the separation between the two shocks is greater than a few tens of the lobe ion inertial length. The Rankine-Hugoniot jump conditions of the slow shock are found to be better satisfied as the distance from the X line along the x axis increases. For the cases with By ≠ 0 in the two lobes, two rotational discontinuity-like structures appear to develop in the reconnection layer. On the other hand, in the leading bulge region of a magnetic reconnection, no steady MHD discontinuities are found. Across the plasma sheet boundary layer the increase of the flow velocity appears to be much smaller than that predicted from the Rankine-Hugoniot jump conditions for a steady discontinuity, and the increase in the ion number density is much larger. In addition, a large increase in the parallel ion temperature is found in the plasma sheet boundary layer. The 2-D simulation results are also compared with the one-dimensional hybrid simulations for the Riemann problem associated with the magnetotail reconnection. It is found that the 2-D effects may lead to the presence of the non-switch-off slow shocks and thus the lack of coherent wave trains in slow shocks.
Anterior versus posterior retractor reinsertion with a lateral tarsal strip for involutional entropion repair: A multicentric experience

Purpose The aim was the comparison of two different approaches to re-insert the inferior eyelid retractors within addition to lateral tarsal strip at lower eyelid involutional entropion (LEIE) surgical correction. Method This multicentric retrospective case series involved 233 consecutive patients (195 eyelids) who underwent LEIE repair. All the lids had a lateral tarsal strip (LTS) in addition to the reinsertion of retractors onto the tarsal plate via the anterior approach (group 1) or the posterior approach (group 2). The desired normal position of the eyelids at 6-month follow-up was considered ‘surgical successes, while entropion recurrence and overcorrection (ectropion) were considered ‘surgical failures’. Results One-hundred ninety-one (82%) surgeries were included in group 1 and 42 (18%) in group 2. The success rate was 92.1% (176 lids) in group 1 and 85.7% (36 lids) in group 2 (p = 0.188). The recurrence rate was statistically higher for group 2 (14.3%) than for group 1 (3.7%) (p = 0.016). Overcorrection only described in group 1 (3.1%). Both groups had a similar complication rate (p = 0.268), with trichiasis being the most frequent (14, 6%). Ten eyelids (47.6%) from the 21 overall failures were satisfactorily reoperated, and the remaining ones were treated conservatively. Conclusion The anterior or posterior approach to reinsert lower eyelid retractors to tarsal plate in addition to LTS to correct LEIE can provide a similar outcome. However, the anterior approach achieves a slightly higher success rate with fewer recurrences but with a higher overcorrection rate.
Stimulus classification with electrical potential and impedance of living plants: comparing discriminant analysis and deep-learning methods

The physiology of living organisms, such as living plants, is complex and particularly difficult to understand on a macroscopic, organism-holistic level. Among the many options for studying plant physiology, electrical potential and tissue impedance are arguably simple measurement techniques that can be used to gather plant-level information. Despite the many possible uses, our research is exclusively driven by the idea of phytosensing, that is, interpreting living plants’ signals to gather information about surrounding environmental conditions. As ready-to-use plant-level physiological models are not available, we consider the plant as a blackbox and apply statistics and machine learning to automatically interpret measured signals. In simple plant experiments, we expose Zamioculcas zamiifolia and Solanum lycopersicum (tomato) to four different stimuli: wind, heat, red light and blue light. We measure electrical potential and tissue impedance signals. Given these signals, we evaluate a large variety of methods from statistical discriminant analysis and from deep learning, for the classification problem of determining the stimulus to which the plant was exposed. We identify a set of methods that successfully classify stimuli with good accuracy, without a clear winner. The statistical approach is competitive, partially depending on data availability for the machine learning approach. Our extensive results show the feasibility of the blackbox approach and can be used in future research to select appropriate classifier techniques for a given use case. In our own future research, we will exploit these methods to derive a phytosensing approach to monitoring air pollution in urban areas.