Miracle-dz/UniFAR-FTUs huggingface dataset:
Targeted sequencing of the 9p21.3 region reveals association with reduced disease risks in Ashkenazi Jewish centenarians

Genome‐wide association studies (GWAS) have pinpointed the chromosomal locus 9p21.3 as a genetic hotspot for various age‐related disorders. Common genetic variants in this locus are linked to multiple traits, including coronary artery diseases, cancers, and diabetes. Centenarians are known for their reduced risk and delayed onset of these conditions. To investigate whether this evasion of disease risks involves diminished genetic risks in the 9p21.3 locus, we sequenced this region in an Ashkenazi Jewish centenarian cohort (centenarians: n = 450, healthy controls: n = 500). Risk alleles associated with cancers, glaucoma, CAD, and T2D showed a significant depletion in centenarians. Furthermore, the risk and non‐risk genotypes are linked to two distinct low‐frequency variant profiles, enriched in controls and centenarians, respectively. Our findings provide evidence that the extreme longevity cohort is associated with collectively lower risks of multiple age‐related diseases in the 9p21.3 locus.
No Relationship between Male Pubertal Timing and Depression: New Insights from Epidemiology and Mendelian Randomization

Background: In males, the relationship between pubertal timing and depression is understudied and less consistent than in females, likely for reasons of unmeasured confounding. To clarify this relationship, a combined epidemiological and genetic approach was chosen to exploit the methodological advantages of both approaches. Methods: Data from 2,026 males from a nationwide study (KiGGS) were used to investigate the non-/linear relationship between pubertal timing defined by the age at voice break and major depressive disorder (MDD), considering a multitude of potential confounders and their interactions with pubertal timing. This analysis was complemented by Mendelian Randomization (MR), which is robust to inferential problems inherent to epidemiological studies. We used 71 single nucleotide polymorphisms related to pubertal timing in males as instrumental variable to clarify its causal relationship with MDD based on data from 807,553 individuals (246,363 cases and 561,190 controls) by univariable and multivariable MR, including BMI as pleiotropic phenotype. Results: Univariable MR indicated a causal effect of pubertal timing on MDD risk (inverse-variance weighted: OR=0.93, 95%-CI [0.87-0.99)], p=. 03). However, this was not confirmed by multivariable MR (inverse-variance weighted: OR=0.95, 95%-CI [0.88 to 1.02)], p=. 13), consistent with the epidemiological approach (OR=1.01, 95%-CI [0.81-1.26], p=.93). Instead, the multivariable MR study indicated a causal relationship of BMI with MDD by two of three methods. Conclusions: Pubertal timing is not related to MDD risk in males. Considering the adverse health outcomes of higher BMI levels, our findings support the rationale for preventive measures to address obesity and its related risk factors.
Structural, elastic, and thermodynamic properties of BaXCl3 (X = Li, Na) perovskites under pressure effect: ab initio exploration

In this study, we employed the ab initio pseudopotential plane wave approach, utilizing the GGA-PBEsol exchange-correlation functional, to investigate the structural, elastic, and thermodynamic properties of BaXCl3 (X = Li, Na) perovskites under hydrostatic pressures ranging from 0 to 18 GPa. Apart from utilizing the GGA-PBEsol functional, this study also employed the GGA-PBE, GGA-WC, and LDA functionals to simulate the exchange-correlation interactions for computing the structural parameters. Our results show that the optimized lattice parameters are in good agreement with previously predicted values. Based on the calculated elastic moduli of a single crystal, we found that both BaLiCl3 and BaNaCl3 perovskites retain mechanical stability under hydrostatic pressures of up to 18 GPa. Furthermore, we calculated several other important parameters that describe the polycrystalline aggregates of these compounds, including the modulus of compressibility, the shear modulus, the Poisson’s ratio, Young’s modulus, the speeds of sound, and the Debye temperature. Additionally, we examined the temperature and pressure dependencies of the thermal coefficients of the perovskites using the quasi-harmonic approximation. Notably, all of the results presented in this study are reported for the first time and require further confirmation through experimental investigations. We hope that our findings contribute to a more comprehensive understanding of the structural and thermodynamic properties of BaXCl3 (X = Li, Na) perovskites under pressure.
Efficacy and safety of endoscopic sphincterotomy with balloon dilation versus endoscopic sphincterotomy alone for extraction of common bile duct stones with a maximum diameter of 10 to15 millimeters

Background: Various factors, most notably the stone's features, determine the selection of an appropriate method to extract common bile duct (CBD) stones during endoscopic retrograde cholangiopancreatography. In this study, the efficacy and safety of endoscopic sphincterotomy with balloon dilation (ESBD) versus endoscopic sphincterotomy (EST) for CBD stone extraction with a diameter of 10 to 15 millimeters were compared. Methods: This retrospective cross-sectional study included 154 patients referred to the Rouhani Hospital in Babol, Iran, with CBD stones. Consensus sampling was used. Each individual's demographic information and findings from the procedure were entered into the SPSS software (v. 26). A level of less than 0.05 was considered statistically significant. Results: A total of 154 patients were included in the study, of which 81 (52.6%) were in the EST, and 73 (47.4%) were in the ESBD group. Complete stones removal rate was higher in the ESBD versus the EST group (79.5% versus 46.9%, P<0.001). No significant differences were observed between the two methods' overall side effects rate (P = 0.469). Conclusion: For the complete extraction of CBD stones larger than 10 millimeters, the ESBD method outperforms the EST method.
The Therapeutic Outcome of Sialendoscopy in Patients with Sialoadenitis

Introduction: Recent advances have led to the development of sialendoscopy, an accurate, minimally invasive procedure with high diagnostic and therapeutic capabilities in treating sialolithiasis. This study aimed to evaluate the results and complications of sialendoscopy in patients suffering from sialoadenitis. Materials and Methods: This study was a prospective interventional case series study on patients with sialoadenitis due to sludge or stone formation preoperatively confirmed by sonography or computed tomography (CT) scanning. Diagnostic sialendoscopy was performed, and the presence of stenosis, sludge, or stones inside the gland or duct was examined, and surgery was done. During follow-up time (18.8 ± 7.4 months), recurrence of symptoms, the need for reoperation, and postoperative complications were also assessed. Results: The sialendoscopy was performed in 51 patients, including 55 glands. Forty-five Patients (88.2%) reported pain relief, and 46 patients (90.2%) reported that the treatment using sialendoscopy was better than conservative methods. The duct restenosis also occurred in one patient requiring open surgery. In assessing the main factors predicting the need for reoperation, the site of involvement (parotid versus submandibular glands) and the size of the stone were identified as the main determinants. The best cut-off value for stone size in predicting reoperation requirement was 7.0mm, with a sensitivity of 100% and a specificity of 85.7%. Conclusion: Intraoperative sialendoscopy is a successful diagnostic and therapeutic tool with minimal postoperative complications in salivary gland duct involvement patients.
Microbiological assessment reveals that Salmonella, Shigella and Campylobacter infections are widespread in HIV infected and uninfected patients with diarrhea in Mozambique

Diarrhea is an important cause of hospitalizations in Mozambique. However, little attention has been paid to the impact HIV infection on the prevalence or clinical manifestations of enteric bacterial infections. This study aimed to determine the prevalence of Salmonella spp. , Shigella spp. and Campylobacter spp. in HIV-infected and HIV-uninfected patients with diarrhea, identify risk factors for infection, and explore the association between HIV status, viral load, and bacterial prevalence. We conducted a case-control study at the Centro de Saúde de Mavalane and Centro de Saúde 1° de Maio in Maputo, Mozambique, from November 2021 to May 2022. We recruited 300 patients, including 150 HIV-infected (cases) and 150 HIV-uninfected patients (controls), aged between 0–88 years, presenting with diarrhea. Stool samples were collected for bacterial isolation through culture, and for each HIV-infected patient, 4 ml of venous blood were obtained for viral load detection through PCR. A total of 129 patients (43.0%) had at least one bacterial infection. The prevalence of Salmonella spp. , Shigella spp. and Campylobacter spp. was 33.0% (n = 99), 15.0% (n = 45) and 4.3% (n = 13), respectively. The prevalence of any bacterial infection did not differ significantly between HIV-infected (45.3%, n = 68) and HIV-uninfected patients (40.7%, = 61) (p = 0.414). Overall, having 2–3 symptoms of enteric disease (p = 0.008) and a basic education (p = 0.030) were factors associated with bacterial infection. Of the 148 patients for whom HIV-1 RNA levels were available, 115 had copy numbers ≤ 75. Another 13 had levels between 76 and 1,000 and the remaining 20 had an average of 327,218.45 copies/ml. Bivariate logistic regression found that Shigella spp. were associated with HIV (p = 0.038), although no association was found in the multivariate analysis. Enteric infections are common in both HIV-infected and -uninfected patients. Low schooling influences the occurrence of enteric infections, which highlights the need to raise awareness about their prevention.
Ocular Manifestations of Spondyloarthritis

Introduction: Ocular manifestations are seen in several autoimmune disorders including spondyloarthritis (SpAs). Though acute anterior uveitis (AAU) is the hallmark of SpAs, episcleritis and scleritis are also seen. Genetic and geographical factors impact the prevalence of AAU; however, available evidence support that HLA-B27 positivity is closely associated with it. Objective: The current narrative review is focused on clinical features of AAU and its management. Methods: For the purpose of this narrative review; literature search covered MEDLINE, Google scholar and EMBASE databases and included articles published in English language from January 1980 till April 2022 using the following keywords: “Ankylosing spondylitis”, “Spondyloarthritis”, “Eye manifestations”, “Ocular”, “Uveitis” and “Arthritis”. Conclusion: Patients with SpA may suffer from different ocular complications with uveitis being the most common one. Biological therapy is a promising medical strategy enabling in achieving therapeutic goals with minimal adverse effects. An effective management strategy for patients with AAU associated with SpA could be formulated by the collaboration between ophthalmologists and rheumatologists.
Early Career Materials Researcher Prospective

Dense suspensions of fine particles are significant in numerous biological, industrial, and natural phenomena. They are often out-of-equilibrium and display various rheological features including liquid–solid phase transition under external deformation being one of the most interesting one. Developing an understanding of the mechanisms that drive these phenomena has over the last several years led to a surge in research activity at the intersection of fluid mechanics, granular materials, driven disordered systems, tribology, and soft condensed matter physics. One central aspect that emerged is that these phenomena occur due to a shear-activated or deactivated network of contacts between particles. The perspective briefly presents the current state of understanding and challenges associated with relating the flow of material at the bulk scale with the microscopic physics at the particle scale.
An Analytically Solvable Model of Firing Rate Heterogeneity in Balanced State Networks

Distributions of neuronal activity within cortical circuits are often found to display highly skewed shapes with many neurons emitting action potentials at low or vanishing rates, while some are active at high rates. Theoretical studies were able to reproduce such distributions, but come with a lack of mathematical tractability, preventing a deeper understanding of the impact of model parameters. In this study, using the Gauss-Rice neuron model, we present a balanced-state cortical circuit model for which the firing rate distribution can be exactly calculated. It offers selfconsistent solutions to recurrent neuronal networks and allows for the combination of multiple neuronal populations, with single or multiple synaptic receptors (e.g. AMPA and NMDA in excitatory populations), paving the way for a deeper understanding of how firing rate distributions are impacted by single neuron or synaptic properties.
Multimodal Sensor Fusion for Real-Time Location-Dependent Defect Detection in Laser-Directed Energy Deposition

Real-time defect detection is crucial in laser-directed energy deposition (L-DED) additive manufacturing (AM). Traditional in-situ monitoring approach utilizes a single sensor (i.e., acoustic, visual, or thermal sensor) to capture the complex process dynamic behaviors, which is insufficient for defect detection with high accuracy and robustness. This paper proposes a novel multimodal sensor fusion method for real-time location-dependent defect detection in the robotic L-DED process. The multimodal fusion sources include a microphone sensor capturing the laser-material interaction sound and a visible spectrum CCD camera capturing the coaxial melt pool images. A hybrid convolutional neural network (CNN) is proposed to fuse acoustic and visual data. The key novelty in this study is that the traditional manual feature extraction procedures are no longer required, and the raw melt pool images and acoustic signals are fused directly by the hybrid CNN model, which achieved the highest defect prediction accuracy (98.5 %) without the thermal sensing modality. Moreover, unlike previous region-based quality prediction, the proposed hybrid CNN can detect the onset of defect occurrences. The defect prediction outcomes are synchronized and registered with in-situ acquired robot tool-center-point (TCP) data, which enables localized defect identification. The proposed multimodal sensor fusion method offers a robust solution for in-situ defect detection.
Quantum state complexity meets many-body scars

Scar eigenstates in a many-body system refers to a small subset of non-thermal finite energy density eigenstates embedded into an otherwise thermal spectrum. This novel non-thermal behaviour has been seen in recent experiments simulating a one-dimensional PXP model with a kinetically-constrained local Hilbert space realised by a chain of Rydberg atoms. We probe these small sets of special eigenstates starting from particular initial states by computing the spread complexity associated to time evolution of the PXP hamiltonian. Since the scar subspace in this model is embedded only loosely, the scar states form a weakly broken representation of the Lie algebra. We demonstrate why a careful usage of the forward scattering approximation (FSA), instead of any other method, is required to extract the most appropriate set of Lanczos coefficients in this case as the consequence of this approximate symmetry. Only such a method leads to a well defined notion of a closed Krylov subspace and consequently, that of spread complexity. We show this using three separate initial states, namely |Z2⟩,|Z3⟩ and the vacuum state, due to the disparate classes of scar states hosted by these sectors. We also discuss systematic methods of remedying the imperfections in the FSA setup stemming from these approximate symmetries.
Quasipolynomial-time algorithms for Gibbs point processes

We demonstrate a quasipolynomial-time deterministic approximation algorithm for the partition function of a Gibbs point process interacting via a stable potential. This result holds for all activities $\lambda$ for which the partition function satisfies a zero-free assumption in a neighbourhood of the interval $[0,\lambda ]$ . As a corollary, for all finiterange stable potentials, we obtain a quasipolynomial-time deterministic algorithm for all $\lambda \lt 1/(e^{B + 1} \hat C_\phi )$ where $\hat C_\phi$ is a temperedness parameter and $B$ is the stability constant of $\phi$ . In the special case of a repulsive potential such as the hard-sphere gas we improve the range of activity by a factor of at least $e^2$ and obtain a quasipolynomial-time deterministic approximation algorithm for all $\lambda \lt e/\Delta _\phi$ , where $\Delta _\phi$ is the potential-weighted connective constant of the potential $\phi$ . Our algorithm approximates coefficients of the cluster expansion of the partition function and uses the interpolation method of Barvinok to extend this approximation throughout the zero-free region.
Metric Diophantine approximation with two restricted variables II: A prime and a square-free integer

In this paper we continue the investigation begun in [6] concerning the number of solutions of the inequality for almost all α (in the sense of Lebesgue measure on ℝ), where β is a given real number, , and both m and n are confined to sets of numbertheoretic interest. Our aim is to extend existing results ([7], [8], [5] for example), where only n is restricted. Here we shall prove the following result where, as elsewhere in this paper, p denotes a prime, and a square-free integer may be positive or negative.
Positive indiscernibles

We generalise various theorems for finding indiscernible trees and arrays to positive logic: based on an existing modelling theorem for s-trees, we prove modelling theorems for str-trees, str$_0$-trees (the reduct of str-trees that forgets the length comparison relation) and arrays. In doing so we prove stronger versions for basing -- rather than locally basing or EM-basing -- str-trees on s-trees and str$_0$-trees on str-trees. As an application we show that a thick positive theory has $k$-TP$_2$ iff it has $2$-TP$_2$.
L-SA: Learning Under-Explored Targets in Multi-Target Reinforcement Learning

Tasks that involve interaction with various targets are called multi-target tasks. When applying general reinforcement learning approaches for such tasks, certain targets that are difficult to access or interact with may be neglected throughout the course of training - a predicament we call Under-explored Target Problem (UTP). To address this problem, we propose L-SA (Learning by adaptive Sampling and Active querying) framework that includes adaptive sampling and active querying. In the L-SA framework, adaptive sampling dynamically samples targets with the highest increase of success rates at a high proportion, resulting in curricular learning from easy to hard targets. Active querying prompts the agent to interact more frequently with under-explored targets that need more experience or exploration. Our experimental results on visual navigation tasks show that the L-SA framework improves sample efficiency as well as success rates on various multi-target tasks with UTP. Also, it is experimentally demonstrated that the cyclic relationship between adaptive sampling and active querying effectively improves the sample richness of under-explored targets and alleviates UTP.
Differentiable Stripe Patterns for Inverse Design of Structured Surfaces

Stripe patterns are ubiquitous in nature and everyday life. While the synthesis of these patterns has been thoroughly studied in the literature, their potential to control the mechanics of structured materials remains largely unexplored. In this work, we introduce Differentiable Stripe Patterns---a computational approach for automated design of physical surfaces structured with stripe-shaped bi-material distributions. Our method builds on the work by Knöppel and colleagues [2015] for generating globally-continuous and equally-spaced stripe patterns. To unlock the full potential of this design space, we propose a gradient-based optimization tool to automatically compute stripe patterns that best approximate macromechanical performance goals. Specifically, we propose a computational model that combines solid shell finite elements with XFEM for accurate and fully-differentiable modeling of elastic bi-material surfaces. To resolve non-uniqueness problems in the original method, we furthermore propose a robust formulation that yields unique and differentiable stripe patterns. We combine these components with equilibrium state derivatives into an end-to-end differentiable pipeline that enables inverse design of mechanical stripe patterns. We demonstrate our method on a diverse set of examples that illustrate the potential of stripe patterns as a design space for structured materials. Our simulation results are experimentally validated on physical prototypes.
Layer-wise Adaptive Step-Sizes for Stochastic First-Order Methods for Deep Learning

We propose a new per-layer adaptive step-size procedure for stochastic first-order optimization methods for minimizing empirical loss functions in deep learning, eliminating the need for the user to tune the learning rate (LR). The proposed approach exploits the layer-wise stochastic curvature information contained in the diagonal blocks of the Hessian in deep neural networks (DNNs) to compute adaptive step-sizes (i.e., LRs) for each layer. The method has memory requirements that are comparable to those of first-order methods, while its per-iteration time complexity is only increased by an amount that is roughly equivalent to an additional gradient computation. Numerical experiments show that SGD with momentum and AdamW combined with the proposed per-layer step-sizes are able to choose effective LR schedules and outperform fine-tuned LR versions of these methods as well as popular first-order and second-order algorithms for training DNNs on Autoencoder, Convolutional Neural Network (CNN) and Graph Convolutional Network (GCN) models. Finally, it is proved that an idealized version of SGD with the layer-wise step sizes converges linearly when using full-batch gradients.
Squared Neural Families: A New Class of Tractable Density Models

Flexible models for probability distributions are an essential ingredient in many machine learning tasks. We develop and investigate a new class of probability distributions, which we call a Squared Neural Family (SNEFY), formed by squaring the 2-norm of a neural network and normalising it with respect to a base measure. Following the reasoning similar to the well established connections between infinitely wide neural networks and Gaussian processes, we show that SNEFYs admit closed form normalising constants in many cases of interest, thereby resulting in flexible yet fully tractable density models. SNEFYs strictly generalise classical exponential families, are closed under conditioning, and have tractable marginal distributions. Their utility is illustrated on a variety of density estimation, conditional density estimation, and density estimation with missing data tasks.
IdEALS: Idiomatic Expressions for Advancement of Language Skills

Although significant progress has been made in developing methods for Grammatical Error Correction (GEC), addressing word choice improvements has been notably lacking and enhancing sentence expressivity by replacing phrases with advanced expressions is an understudied aspect. In this paper, we focus on this area and present our investigation into the task of incorporating the usage of idiomatic expressions in student writing. To facilitate our study, we curate extensive training sets and expert-annotated testing sets using real-world data and evaluate various approaches and compare their performance against human experts.