Miracle-dz/UniFAR-FTUs huggingface dataset:
A Study of Seven Asymmetric Kernels for the Estimation of Cumulative Distribution Functions

In this paper, we complement a study recently conducted in a paper of H.A. Mombeni, B. Masouri and M.R. Akhoond by introducing five new asymmetric kernel c.d. f. estimators on the half-line [0,∞), namely the Gamma, inverse Gamma, LogNormal, inverse Gaussian and reciprocal inverse Gaussian kernel c.d. f. estimators. For these five new estimators, we prove the asymptotic normality and we find asymptotic expressions for the following quantities: bias, variance, mean squared error and mean integrated squared error. A numerical study then compares the performance of the five new c.d. f. estimators against traditional methods and the Birnbaum–Saunders and Weibull kernel c.d. f. estimators from Mombeni, Masouri and Akhoond. By using the same experimental design, we show that the LogNormal and Birnbaum–Saunders kernel c.d. f. estimators perform the best overall, while the other asymmetric kernel estimators are sometimes better but always at least competitive against the boundary kernel method from C. Tenreiro.
A Conceptual Model of Biopsychosocial Mechanisms of Transition from Acute to Chronic Postsurgical Pain in Children and Adolescents

Abstract Acute and chronic pain are highly prevalent and impactful consequences of surgery across the lifespan, yet a comprehensive conceptual model encompassing biopsychosocial factors underlying acute to chronic pain transition is lacking, particularly in youth. Building on prior chronic postsurgical pain models, we propose a new conceptual model of biopsychosocial mechanisms of transition from acute to chronic postsurgical pain. This review aims to summarize existing research examining key factors underlying acute to chronic postsurgical pain transition in order to guide prevention and intervention efforts aimed at addressing this health issue in children. As pain transitions from acute nociceptive pain to chronic pain, changes in the peripheral and central nervous system contribute to the chronification of pain after surgery. These changes include alterations in sensory pain processing and psychosocial processes (psychological, behavioral, and social components), which promote the development of chronic pain. Patient-related premorbid factors (eg, demographic factors, genetic profile, and medical factors such as premorbid pain) may further modulate these changes. Factors related to acute injury and recovery (eg, surgical and treatment factors), as well as biological response to surgery (eg, epigenetic, inflammatory, and endocrine factors), may also influence this process. Overall, longitudinal studies examining temporal pathways of biopsychosocial processes including both risk and resiliency factors will be essential to identify the mechanisms involved in the transition from acute to chronic pain. Research is also needed to unravel connections between the acute pain experience, opioid exposure, and sensory pain processing during acute to chronic pain transition. Furthermore, future studies should include larger and more diverse samples to more fully explore risk factors in a broader range of pediatric surgeries. The use of conceptual models to guide intervention approaches targeting mechanisms of transition from acute to chronic pain will significantly advance this field and improve outcomes for children and adolescents undergoing surgery.
Human-Agent Cooperation in Bridge Bidding

We introduce a human-compatible reinforcement-learning approach to a cooperative game, making use of a third-party hand-coded human-compatible bot to generate initial training data and to perform initial evaluation. Our learning approach consists of imitation learning, search, and policy iteration. Our trained agents achieve a new state-of-the-art for bridge bidding in three settings: an agent playing in partnership with a copy of itself; an agent partnering a pre-existing bot; and an agent partnering a human player.
MicroRNA-329-3p alleviates high glucose-induced endothelial cell injury via inhibition of the TLR4/TRAF6/NF-κB signaling pathway

The aim of the current study was to determine the expression of microRNA (miRNA/miR)-329-3p in patients with type 2 diabetes mellitus (T2DM) and to investigate the effect of miR-329-3p on vascular endothelial cell function under high-glucose conditions. A total of 33 healthy individuals and 31 patients with T2DM were enrolled in the present study. Peripheral blood was collected from all participants. Human umbilical vein endothelial cells (HUVECs) were transfected with a miR-329-3p mimic or miR-329-3p inhibitor. Following treatment with 25 mmol/l glucose, a Cell Counting Kit-8 assay and flow cytometry analysis were used to assess cell viability and apoptosis levels, respectively. A dual luciferase reporter assay, western blot analysis and reverse transcription-quantitative PCR were used to assess molecular mechanism of miR-329-3p in HUVECs. The results revealed that plasma miR-329-3p expression was decreased patients with T2DM compared with healthy controls, and in HUVECs treated with high glucose concentrations. In addition, miR-329-3p reduced high glucose-induced damage to HUVEC cells. miR-329-3p directly bound to toll like receptor (TLR)-4 and regulated its expression at the transcriptional and post-transcriptional levels. miR-329-3p was also demonstrated to be involved in the regulation of the TLR4/tumor necrosis factor receptor associated factor 6 (TRAF6)/nuclear factor (NF)-κB signaling pathway and the nuclear translocation of NF-κB under a high glucose environment. In conclusion, the results indicated that miR-329-3p may protect endothelial cells from high glucose-induced apoptosis via inhibition of the TLR4/TRAF6/NF-κB signaling pathway. The present study also demonstrated that miR-329-3p expression in the plasma of patients with T2DM was reduced, suggesting that upregulation of miR-329-3p may alleviate high glucose-induced endothelial cell injury via inhibition of the TLR4/TRAF6/NF-κB signaling pathway.
Predicting cardiovascular risk from national administrative databases using a combined survival analysis and deep learning approach

Abstract Background Machine learning-based risk prediction models may outperform traditional statistical models in large datasets with many variables, by identifying both novel predictors and the complex interactions between them. This study compared deep learning extensions of survival analysis models with Cox proportional hazards models for predicting cardiovascular disease (CVD) risk in national health administrative datasets. Methods Using individual person linkage of administrative datasets, we constructed a cohort of all New Zealanders aged 30–74 who interacted with public health services during 2012. After excluding people with prior CVD, we developed sex-specific deep learning and Cox proportional hazards models to estimate the risk of CVD events within 5 years. Models were compared based on the proportion of explained variance, model calibration and discrimination, and hazard ratios for predictor variables. Results First CVD events occurred in 61 927 of 2 164 872 people. Within the reference group, the largest hazard ratios estimated by the deep learning models were for tobacco use in women (2.04, 95% CI: 1.99, 2.10) and chronic obstructive pulmonary disease with acute lower respiratory infection in men (1.56, 95% CI: 1.50, 1.62). Other identified predictors (e.g. hypertension, chest pain, diabetes) aligned with current knowledge about CVD risk factors. Deep learning outperformed Cox proportional hazards models on the basis of proportion of explained variance (R2: 0.468 vs 0.425 in women and 0.383 vs 0.348 in men), calibration and discrimination (all P <0.0001). Conclusions Deep learning extensions of survival analysis models can be applied to large health administrative datasets to derive interpretable CVD risk prediction equations that are more accurate than traditional Cox proportional hazards models.
Noise and waves: a unified kinetic theory for stellar systems

The traditional Chandrasekhar picture of the slow relaxation of stellar systems assumes that stars' orbits are only modified by occasional, uncorrelated, two-body flyby encounters with other stars. However, the long-range nature of gravity means that in reality large numbers of stars can behave collectively. In stable systems this collective behaviour (i) amplifies the noisy fluctuations in the system's gravitational potential, effectively 'dressing' the two-body (star-star) encounters, and (ii) allows the system to support large-scale density waves (a.k.a. normal modes) which decay through resonant wave-star interactions. If the relaxation of the system is dominated by effect (i) then it is described by the Balescu-Lenard (BL) kinetic theory. Meanwhile if (ii) dominates, one must describe relaxation using quasilinear (QL) theory, though in the stellar-dynamical context the full set of QL equations has never been presented. Moreover, in some systems like open clusters and galactic disks, both (i) and (ii) might be important. Here we present for the first time the equations of a unified kinetic theory of stellar systems in angle-action variables that accounts for both effects (i) and (ii) simultaneously. We derive the equations in a heuristic, physically-motivated fashion and work in the simplest possible regime by accounting only for very weakly damped waves. This unified theory is effectively a superposition of BL and QL theories, both of which are recovered in appropriate limits. The theory is a first step towards a comprehensive description of those stellar systems for which neither the QL or BL theory will suffice.
Translation-invariant interpolation of parametric dictionaries

In this communication, we address the problem of approximating the atoms of a parametric dictionary, commonly encountered in the context of sparse representations in "continuous" dictionaries. We focus on the case of translation-invariant dictionaries, where the inner product between atoms only depends on the difference between parameters. We investigate the following general question: is there some low-rank approximation of the dictionary $ which interpolates a subset of atoms while preserving the translation-invariant nature of the original dictionary? We derive necessary and sufficient conditions characterizing the existence of such an "interpolating" and "translation-invariant" low-rank approximation. Moreover, we provide closed-form expressions of such a dictionary when it exists. We illustrate the applicability of our results in the case of a two-dimensional isotropic Gaussian dictionary. We show that, in this particular setup, the proposed approximation framework outperforms standard Taylor approximation.
A Model for Ant Trail Formation and its Convergence Properties

We introduce a model for ant trail formation, building upon previous work on biologically feasible local algorithms that plausibly describe how ants maintain trail networks. The model is a variant of a reinforced random walk on a directed graph, where ants lay pheromone on edges as they traverse them and the next edge to traverse is chosen based on the pheromone level; this pheromone decays with time. There is a bidirectional flow of ants: the forward flow proceeds along forward edges from source (e.g. the nest) to sink (e.g. a food source), and the backward flow in the opposite direction. Some fraction of ants are lost as they pass through each node (modeling the loss of ants due to exploration). We initiate a theoretical study of this model. We first consider the linear decision rule, where the flow divides itself among the next set of edges in proportion to their pheromone level. Here, we show that the process converges to the path with minimum leakage when the forward and backward flows do not change over time. When the forward and backward flows increase over time (caused by positive reinforcement from the discovery of a food source, for example), we show that the process converges to the shortest path. These results are for graphs consisting of two parallel paths (a case that has been investigated before in experiments). Through simulations, we show that these results hold for more general graphs drawn from various random graph models. Further, we consider a general family of decision rules, and show that there is no advantage of using a non-linear rule from this family, if the goal is to find the shortest or the minimum leakage path. We also show that bidirectional flow is necessary for convergence to such paths. Our results provide a plausible explanation for field observations, and open up new avenues for further theoretical and experimental investigation.
Learning from Incomplete Data by Simultaneous Training of Neural Networks and Sparse Coding

Handling correctly incomplete datasets in machine learning is a fundamental and classical challenge. In this paper, the problem of training a classifier on a dataset with missing features, and its application to a complete or incomplete test dataset, is addressed. A supervised learning method is developed to train a general classifier, such as a logistic regression or a deep neural network, using only a limited number of features per sample, while assuming sparse representations of data vectors on an unknown dictionary. The pattern of missing features is allowed to be different for each input data instance and can be either random or structured. The proposed method simultaneously learns the classifier, the dictionary and the corresponding sparse representation of each input data sample. A theoretical analysis is provided, comparing this method with the standard imputation approach, which consists of performing data completion followed by training the classifier with those reconstructions. Sufficient conditions are identified such that, if it is possible to train a classifier on incomplete observations so that their reconstructions are well separated by a hyperplane, then the same classifier also correctly separates the original (unobserved) data samples. Extensive simulation results on synthetic and well-known reference datasets are presented that validate our theoretical findings and demonstrate the effectiveness of the proposed method compared to traditional data imputation approaches and one state of the art algorithm.
Modified Clavien–Dindo Classification and Outcome Prediction in Free Flap Reconstruction among Patients with Head and Neck Cancer

Because of limitations caused by unique complications of free flap reconstruction, the Clavien–Dindo classification was modified to include grade “IIIc” for “partial or total free flap failure. ” From 2013 to 2018, 116 patients who had undergone free flap reconstruction for head and neck cancer with grade III or higher postoperative complications were grouped using the “Modified” Clavien–Dindo classification. Alcoholism displayed significant predictive effects between grade IIIb and IIIc (72.7% vs. 50%, p = 0.028). Significant differences were observed between grade IIIb and IIIc in the duration of hospital stay (23.1 ± 10.1 vs. 28.6 ± 11.9 days, p = 0.015), duration of intensive care unit stay (6.0 ± 3.4 vs. 8.7 ± 4.3 days, p = 0.001), reoperation times during the current hospitalization (1.4 ± 0.8 vs. 2.0 ± 1.0 times, p < 0.001), and wound infection rate (29.9% vs. 62.5%, p = 0.002). The severity levels were significantly positively correlated with reoperation times during the current hospitalization (p < 0.001), ICU stay (p = 0.001), and hospital stay (p < 0.001). The modified Clavien–Dindo classification with grade IIIc describes the perioperative complications of head and neck free flap reconstruction to predict clinical outcomes based on severity.
Does Circular Stapler Size in Surgical Management of Esophageal Cancer Affect Anastomotic Leak Rate? 4-Year Experience of a European High-Volume Center

Simple Summary One of the most severe postoperative complications after a transthoracic esophagectomy for esophageal cancer is a leakage of the anastomosis created between the remnant esophagus and the stomach. There is substantial debate on which surgical technique and which stapler are the best. The aim of this study was to retrospectively analyze whether the stapler diameter had an impact on postoperative anastomotic leak rates during a 4-year time frame from 2016 to 2020. A total of 632 patients (open, hybrid, and totally minimally invasive esophagectomy) met the inclusion criteria. A total of 214 patients underwent an anastomosis with a 25 mm stapler vs. 418 patients with a 28 mm stapler. Anastomotic leak rates were 15.4% vs. 10.8%, respectively. Stapler size should be chosen according to the individual anatomical situation of the patient and may be of higher relevance in patients undergoing totally minimally invasive reconstruction. Abstract Anastomotic leak is one of the most severe postoperative complications and is therefore considered a benchmark for the quality of surgery for esophageal cancer. There is substantial debate on which anastomotic technique is the best for patients undergoing Ivor Lewis esophagectomy. Our standardized technique is a circular stapled anastomosis with either a 25 or 28 mm anvil. The aim of this study was to retrospectively analyze whether the stapler diameter had an impact on postoperative anastomotic leak rates during a 4-year time frame from 2016 to 2020. A total of 632 patients (open, hybrid, and totally minimally invasive esophagectomy) met the inclusion criteria. A total of 214 patients underwent an anastomosis with a 25 mm stapler vs. 418 patients with a 28 mm stapler. Anastomotic leak rates were 15.4% vs. 10.8%, respectively (p = 0.0925). Stapler size should be chosen according to the individual anatomical situation of the patient. Stapler size may be of higher relevance in patients undergoing totally minimally invasive reconstruction.
Application-driven synthesis and characterization of hexagonal boron nitride deposited on metals and carbon nanotubes

Hexagonal boron nitride (h-BN) is unique among two-dimensional materials, with a large band gap (∼6 eV) and high in-plane thermal conductivity (>400 W m−1 K−1), second only to diamond among electrical insulators. Many studies to date have relied on exfoliated h-BN, however, for large-scale applications the material must be synthesized by methods such as chemical vapor deposition (CVD). Here, we first investigate single-layer h-BN synthesized by CVD on single crystal platinum (Pt), comparing these films with h-BN deposited on more commonly used polycrystalline Pt and Cu. The h-BN film grown on single crystal Pt has the lowest surface roughness and best spatial homogeneity, and our electrochemical transfer process allows the Pt to be reused with no measurable degradation. We also demonstrate direct capping of carbon nanotubes (CNTs) with as-grown h-BN, but we find that the direct growth partly degrades the CNT electrical conductivity. On the other hand, we show that transferred monolayer h-BN can serve as an ultrathin barrier which protects MoS2 from damage at high temperatures and discuss other applications that take advantage of the conformal h-BN deposition.
An Improved Hertzsprung–Russell Diagram for the Orion Trapezium Cluster

We present a study of the Trapezium cluster in Orion. We analyze flux-calibrated Very Large Telescope/Multi-Unit Spectroscopic Explorer spectra of 361 stars to simultaneously measure the spectral types, reddening, and the optical veiling due to accretion. We find that the extinction law from Cardelli et al. with a total-to-selective extinction value of R V = 5.5 is more suitable for this cluster. For 68% of the sample the new spectral types are consistent with literature spectral types within two subclasses but, as expected, we derive systematically later types than the literature by one to two subclasses for the sources with significant accretion levels. Here we present an improved Hertzsprung–Russell (H-R) diagram of the Trapezium cluster, in which the contamination by optical veiling on spectral types and stellar luminosities has been properly removed. A comparison of the locations of the stars in the H-R diagram with the non-magnetic and magnetic pre-main-sequence evolutionary tracks indicates an age of 1–2 Myr. The magnetic pre-main-sequence evolutionary tracks can better explain the luminosities of the low-mass stars. In the H-R diagram, the cluster exhibits a large luminosity spread (σ(Log L ⋆/L ⊙) ∼ 0.3). By collecting a sample of 14 clusters/groups with different ages, we find that the luminosity spread tends to be constant (σ(Log L ⋆/L ⊙) ∼ 0.2–0.25) after 2 Myr, which suggests that age spread is not the main cause of the luminosity spread. There are ∼0.1 dex larger luminosity spreads for the younger clusters, e.g., the Trapezium cluster, than the older clusters, which can be explained by the starspots, accretion history, and circumstellar disk orientations.
Gradually Increased Interhemispheric Functional Connectivity During One Night of Sleep Deprivation

Background It is well known that circadian rhythms and sleep homeostasis contribute to a pronounced trough in sleepiness and behavioral performance at night. However, the underlying neuroimaging mechanisms remain unclear. How brain-function connectivity is modulated during sleep deprivation (SD) has been rarely examined. Methods By increasing the number of scanning sessions during SD, the current study used voxel-mirrored homotopic connectivity (VMHC) to investigate dynamic changes in interhemispheric communication during one night of SD. Every 2 hours from 10 pm to 06 am (session 1, 10 pm; session 2, 12 am; session 3, 2 am; session 4, 4 am; session 5, 6 am), functional magnetic resonance–imaging data and Stanford Sleepiness Scale (SSS) scores were collected from 36 healthy participants with intermediate chronotype. Dynamic changes in SSS scores and VMHC were determined using one-way repeated-measure ANOVA with the false discovery–rate method to correct for multiple comparisons. Results Significant time effects for VMHC were found mainly in the bilateral thalamus, bilateral superior temporal gyrus, and bilateral precentral gyrus. SSS scores and VMHC in these areas were both found to be monotonously increased during SD. Furthermore, significant positive associations were found between SSS valu and VMHC values in the left superior temporal and right superior gyri. Conclusion These findings might represent the dynamic modulation of circadian rhythm merely or the interaction effects of both circadian rhythm and sleep homeostasis on interhemispheric connectivity within the thalamus, default-mode network, and sensorimotor network. Our study provides more comprehensive information on how SD regulates brain connectivity between hemispheres and adds new evidence of neuroimaging correlates of increased sleepiness after SD.
Graphyne as a second-order and real Chern topological insulator in two dimensions

Higher-order topological phases and real topological phases are two emerging topics in topological states of matter, which have been attracting considerable research interest. However, it remains a challenge to find realistic materials that can realize these exotic phases. Here, based on first-principles calculations and theoretical analysis, we identify graphyne, the representative of the graphyne-family carbon allotropes, as a two-dimensional (2D) second-order topological insulator and a real Chern insulator. We show that graphyne has a direct bulk band gap at the three $M$ points, forming three valleys. The bulk bands feature a double band inversion, which is characterized by the nontrivial real Chern number enabled by the spacetime-inversion symmetry. The real Chern number is explicitly evaluated by both the Wilson-loop method and the parity approach, and we show that it dictates the existence of Dirac type edge bands and the topological corner states. Furthermore, we find that the topological phase transition in graphyne from the second-order topological insulator to a trivial insulator is mediated by a 2D Weyl semimetal phase. The robustness of the corner states against symmetry breaking and possible experimental detection methods are discussed.