Miracle-dz/UniFAR-FTUs huggingface dataset:
Ephemeral data derived potentials for random structure search

Structure prediction has become a key task of the modern atomistic sciences, and depends on the rapid and reliable computation of the energy landscape. First principles density functional based calculations are highly reliable, faithfully describing the entire energy landscape. They are, however, computationally intensive and slow compared to interatomic potentials. Great progress has been made in the development of machine learning, or data derived, potentials, which promise to describe the entire energy landscape at first principles quality. However, compared to first principles approaches, their preparation can be time consuming and delay searching. Ab initio random structure searching (AIRSS) is a straightforward and powerful approach to structure prediction, based on the stochastic generation of sensible initial structures, and their repeated local optimisation. Here, a scheme, compatible with AIRSS, for the rapid construction of disposable, or ephemeral, data derived potentials (EDDPs) is described. These potentials are constructed using a homogeneous, separable manybody environment vector, and iterative neural network fits, sparsely combined through non-negative least squares. The approach is first tested on methane, boron nitride, elemental boron and urea. In the case of boron, an EDDP generated using data from small unit cells is used to rediscover the complex $\gamma$-boron structure without recourse to symmetry or fragments. Finally, an EDDP generated for silane (SiH$_4$) at 500 GPa enables the discovery of an extremely complex, dense, structure which significantly modifies silane's high pressure phase diagram. This has implications for the theoretical exploration for high temperature superconductivity in the dense hydrides, which have so far largely depended on searches in smaller unit cells.
Interval-Memoized Backtracking on ZDDs for Fast Enumeration of All Lower Cost Solutions

In this paper, we propose a fast method for exactly enumerating a very large number of all lower cost solutions for various combinatorial problems. Our method is based on backtracking for a given decision diagram which represents all the feasible solutions. The main idea is to memoize the intervals of cost bounds to avoid duplicate search in the backtracking process. In contrast to usual pseudo-polynomial-time dynamic programming approaches, the computation time of our method does not directly depend on the total cost values, but is bounded by the input and output size of the decision diagrams. Therefore, it can be much faster if the cost values are large but the input/output decision diagrams are well-compressed. We demonstrate its practical efficiency by comparing our method to current available enumeration methods: for nontrivial size instances of the Hamiltonian path problem, our method succeeded in exactly enumerating billions of all lower cost solutions in a few seconds, which was hundred or much more times faster. Our method can be regarded as a novel search algorithm which integrates the two classical techniques, branch-and-bound and dynamic programming. This method would have many applications in various fields, including operations research, data mining, statistical testing, hardware/software system design, etc.
On the Existence of Fast Modes in Compressible Magnetohydrodynamic Turbulence

We study the existence and properties of fast magnetosonic modes in 3D compressible MHD turbulence by carrying out a number of simulations with compressible and incompressible driving conditions. We use two approaches to determine the presence of fast modes: mode decomposition based on spatial variations only and spatio-temporal 4D fast Fourier transform (4D FFT) analysis of all fluctuations. The latter method enables us to quantify fluctuations that satisfy the dispersion relation of fast modes with finite frequency. Overall, we find that the fraction of fast modes identified via the spatio-temporal 4D FFT approach in total fluctuation power is either tiny with nearly incompressible driving or ∼2% with highly compressible driving. We discuss the implications of our results for understanding the compressible fluctuations in space and astrophysical plasmas.
Investigating underdiagnosis of AI algorithms in the presence of multiple sources of dataset bias

2021. Deep learning models have shown great potential for image-based diagnosis assisting clinical decision making 1 . At the same time, an increasing number of reports raise concerns about the potential risk that machine learning could amplify existing health disparities due to human biases that are embedded in the training data 2–4 . It is of great importance to carefully investigate the extent to which biases may be reproduced or even ampliﬁed if we wish to build fair artiﬁcial intelligence systems 5,6 . Seyyed-Kalantari et al. 7 advance this conversation by analysing the performance of a disease classiﬁer across population subgroups. They raise performance disparities related to underdiagnosis as a point of concern; we identify areas from this analysis which we believe deserve additional attention. Speciﬁcally, we wish to highlight some theoretical and practical difﬁculties associated with assessing model fairness through testing on data drawn from the same biased distribution as the training data, especially when the sources and amount of biases are unknown. Seyyed-Kalantari et al. ﬁnd that models trained on three large chest X-ray datasets yield disparities in false positive rates (FPR) across subgroups on the ‘no-ﬁnding’ label (indicating the absence of disease). The models consistently yield higher FPR on subgroups known to be historically underserved, such as those self-identifying as Black and Hispanic, younger females, or patients relying on Medicaid
Evaluating Machine Common Sense via Cloze Testing

Language models (LMs) show state of the art performance for common sense (CS) question answering, but whether this ability implies a human-level mastery of CS remains an open question. Understanding the limitations and strengths of LMs can help researchers improve these models, potentially by developing novel ways of integrating external CS knowledge. We devise a series of tests and measurements to systematically quantify their performance on different aspects of CS. We propose the use of cloze testing combined with word embeddings to measure the LM's robustness and confidence. Our results show than although language models tend to achieve human-like accuracy, their confidence is subpar. Future work can leverage this information to build more complex systems, such as an ensemble of symbolic and distributed knowledge.
Hypergeometric sheaves for classical groups via geometric Langlands

In a previous paper, the first and third authors gave an explicit realization of the geometric Langlands correspondence for hypergeometric sheaves, considered as GLn-local systems. Certain hypergeometric local systems admit a symplectic or orthogonal structure, which can be viewed as Ǧ-local systems, for a classical group Ǧ. This article aims to realize the geometric Langlands correspondence for these Ǧ-local systems. We study this problem from two aspects. In the first approach, we define the hypergeometric automorphic data for a classical group G in the framework of Yun, one of whose local components is a new class of euphotic representations in the sense of Jakob–Yun. We prove the rigidity of hypergeometric automorphic data under natural assumptions, which allows us to define Ǧ-local systems EǦ on Gm as Hecke eigenvalues (in both l-adic and de Rham setting). In the second approach (which works only in the de Rham setting), we quantize an enhanced ramified Hitchin system, following Beilinson–Drinfeld and Zhu, and identify EǦ with certain Ǧ-opers on Gm. Finally, we compare these Ǧ-opers with hypergeometric local systems.
Identification of hub genes associated with COVID-19 and idiopathic pulmonary fibrosis by integrated bioinformatics analysis

Introduction The coronavirus disease 2019 (COVID-19), emerged in late 2019, was caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The risk factors for idiopathic pulmonary fibrosis (IPF) and COVID-19 are reported to be common. This study aimed to determine the potential role of differentially expressed genes (DEGs) common in IPF and COVID-19. Materials and methods Based on GEO database, we obtained DEGs from one SARS-CoV-2 dataset and five IPF datasets. A series of enrichment analysis were performed to identify the function of upregulated and downregulated DEGs, respectively. Two plugins in Cytoscape, Cytohubba and MCODE, were utilized to identify hub genes after a protein-protein interaction (PPI) network. Finally, candidate drugs were predicted to target the upregulated DEGs. Results A total of 188 DEGs were found between COVID-19 and IPF, out of which 117 were upregulated and 71 were downregulated. The upregulated DEGs were involved in cytokine function, while downregulated DEGs were associated with extracellular matrix disassembly. Twenty-two hub genes were upregulated in COVID-19 and IPF, for which 155 candidate drugs were predicted (adj.P.value < 0.01). Conclusion Identifying the hub genes aberrantly regulated in both COVID-19 and IPF may enable development of molecules, encoded by those genes, as therapeutic targets for preventing IPF progression and SARS-CoV-2 infections.
Maternal depressive symptoms, attendance of sessions and reduction of home safety problems in a randomized toddler safety promotion intervention trial: A latent class analysis

Objective Little is known about the association between maternal depressive symptoms and attendance at safety promotion interventions. This study used latent class analysis (LCA) to identify the profile of attendance within a toddler safety intervention and assessed its relation with maternal depressive symptoms at baseline and reduction of home safety problems over time, separately. Methods The analytic sample included 91 mothers of toddlers (mean maternal age 28.16 years) who were assigned to the safety promotion intervention group as part of a randomized trial and assessed at baseline, 6-month and 12-month follow-ups. Using LCA, we classified mothers into low and high attendance classes based on their attendance at 8 intervention sessions. We assessed maternal depressive symptoms with the Beck Depression Inventory (BDI) and home safety problems with a 9-item home safety problem observation. Results The mothers were classified into low attendance (45%) and high attendance classes (55%). The posterior probability of attending each session ranged from 0–0.29 for the low attendance class and 0.68–0.92 for the high attendance class. Each one unit increase of BDI sum score at baseline was associated with an 8% reduced odds of being in the high attendance class (aOR = 0.92, 95% CI: 0.86, 1.00, p = 0.037). The home safety problem score reduction was greater among high attendance class participants than low attendance class participants at the 6-month follow-up (b = -1.15, 95% CI:-2.09, -0.20, p = 0.018). Conclusion Maternal depressive symptoms were associated with the reduced probability of maternal attendance at toddler safety promotion sessions; high session attendance was related to greater reduction of toddler home safety problems. Identifying risk factors for maternal low attendance to interventions and developing strategies to promote attendance should lead to reductions in home safety problems and reductions in unintentional injuries among young children.
Effect of Solid Content and pH on Biogas Production From Organic Fraction Municipal Solid Waste

The increasing demand on energy and the constraints on environmental aspects motivate the research on municipal solid waste to be an environmentally friendly source of renewable energy. Therefor using the organic fraction of municipal solid household waste in an aerobic digester to produce biogas and a digestate which can be used safely as fertilizer. In this study three reactors of two liter volume are used to investigate the effect of total solid content (10%, 15%, 20%) and pH on the produced amount of biogas. The results revealed that lower percentage of solid content yields the highest amount of biogas and the pH below 6.0 will inhibit the biogas production. The percentage of methane from the 10% reactor was 50.2 % of the gas production. ةبلصلا ةيدلبلا تافلخلما نم يويلحا زاغلا جاتنإ ىلع نييجورديلها سلأاو ةبلصلا داولما ةبسن يرثأت يميلدلا لحاص 3 ،ةيبرغ نب نايما2 ،روشاع دممح راتخلما1 ايبيل-سلبارط . ةيسمشلا ةقاطلا تاساردو ثوبح زكرم2 ،ايبيل-سلبارط . ةيبيللا ةييمداكلأا 1 ردصمك ةبلصلا ةيدلبلا تايافنلا مادختسا لامج في ثحبلا نم ديزلم عفدي ةئيبلا ىلع ظافلحاو ةقاطلا ىلع ديازتلما بلطلا :صخلم ًاردصم نوكي نا نكيم لزانلما نم ةبلصلا ةيدلبلا تلاضفلا في ةدوجولما ةيوضعلا داوملل يئاوهلالا مضلها مادختسا نإ ،ةددجتلما ةقاطلل . دامسك اهمادختسا نكيم ىرخلاا اياقبلاو يويلحا زاغلل سلأاو (%10، %15، %20) ةبلصلا داولما ةيمك يرثأت ةساردل ،اهنم لكل )ترل 2( ةعسب تلاعافم ةثلاث مادختسا تم ةساردلا هذه في سلأا لماع ضافنخا نأو ةيجاتنإ نسحأ تطعأ %10 يهو لقلاا ةبسنلا نأ جئاتنلا ترهظأو ،ةجتنلما زاغلا ةيمك ىلع نييجورديلها . جتنلما زاغلا نم %50.2 لكشت جتنلما ناثيلما زاغ ةبسن تناكو ،زاغلا جاتنإ عجارت لىإ يدؤي 6.0 نع نييجورديلها
High-Throughput Imaging of CRISPR- and Recombinant Adeno-Associated Virus–Induced DNA Damage Response in Human Hematopoietic Stem and Progenitor Cells

CRISPR-Cas technology has revolutionized gene editing, but concerns remain due to its propensity for off-target interactions. This, combined with genotoxicity related to both CRISPR-Cas9-induced double-strand breaks and transgene delivery, poses a significant liability for clinical genome-editing applications. Current best practice is to optimize genome-editing parameters in preclinical studies. However, quantitative tools that measure off-target interactions and genotoxicity are costly and time-consuming, limiting the practicality of screening large numbers of potential genome-editing reagents and conditions. Here, we show that flow-based imaging facilitates DNA damage characterization of hundreds of human hematopoietic stem and progenitor cells per minute after treatment with CRISPR-Cas9 and recombinant adeno-associated virus serotype 6. With our web-based platform that leverages deep learning for image analysis, we find that greater DNA damage response is observed for guide RNAs with higher genome-editing activity, differentiating even single on-target guide RNAs with different levels of off-target interactions. This work simplifies the characterization and screening process of genome-editing parameters toward enabling safer and more effective gene-therapy applications.
Comparison of frontostriatal circuits in adolescent nicotine addiction and internet gaming disorder

Abstract Background Recently, there has been significantly increased participation in online gaming and other addictive behaviors particularly in adolescents. Tendencies to avoid social interaction and become more involved in technology-based activities pose the danger of creating unhealthy addictions. Thus, the presence of relatively immature cognitive control and high risk-taking properties makes adolescence a period of major changes leading to an increased rate of emotional disorders and addiction. Aims The critical roles of frontostriatal circuits in addiction have become the primary focus associated with reward in the striatum and cognitive control in the PFC. Internet gaming disorder (IGD) and nicotine addiction are currently becoming more and more serious. Methods In the light of neuroimaging, the similarity between brain mechanisms causing substance use disorder (SUD) and IGD have been described in previous literature. Results In particular, two distinct brain systems affect the way we act accounting for uncharacteristic neural function in addiction: the affective system comprises of the striatum driven by emotional, reward-related, and internal stimuli, and a cognitive system consisting of the prefrontal cortex (PFC) supporting the ventral affective system's actions via inhibitory control. Discussion and Conclusion Therefore, as a novel concept, we focused on the implication of frontostriatal circuits in nicotine addiction and IGD by reviewing the main findings from our studies compared to those of others. We hope that all of these neuroimaging findings can lead to effective intervention and treatment for addiction especially during this critical period.
Modeling, Design, and Sensitivity Analysis of a Continuous Magnetic Gear Using Finite-Element Method

Abstract Magnetic gears are considered to be an interesting alternative of mechanical torque transmission systems. They offer significant benefits compared with mechanical gears, such as reduced maintenance, improved reliability, inherent overload protection, and physical isolation between the input and output shafts. The continuous ratio for magnetic gears is an important factor that is very useful for many applications, such as wind turbines, electric vehicles, and other related applications in which continuous ratio of the gear box is essential. In this article, modeling and sensitivity analysis of a continuous magnetic gear is carried out. To this end, a continuous magnetic gear is modeled by a magnetic equivalent circuit, and then the output torque, speed, and magnetic field distribution inside the gear box are evaluated thorough the finite-element method.
A Comprehensive Assessment of the Secretome Responsible for Host Adaptation of the Legume Root Pathogen Aphanomyces euteiches

The soil-borne oomycete pathogen Aphanomyces euteiches causes devastating root rot diseases in legumes such as pea and alfalfa. The different pathotypes of A. euteiches have been shown to exhibit differential quantitative virulence, but the molecular basis of host adaptation has not yet been clarified. Here, we re-sequenced a pea field reference strain of A. euteiches ATCC201684 with PacBio long-reads and took advantage of the technology to generate the mitochondrial genome. We identified that the secretome of A. euteiches is characterized by a large portfolio of secreted proteases and carbohydrate-active enzymes (CAZymes). We performed Illumina sequencing of four strains of A. euteiches with contrasted specificity to pea or alfalfa and found in different geographical areas. Comparative analysis showed that the core secretome is largely represented by CAZymes and proteases. The specific secretome is mainly composed of a large set of small, secreted proteins (SSP) without any predicted functional domain, suggesting that the legume preference of the pathogen is probably associated with unknown functions. This study forms the basis for further investigations into the mechanisms of interaction of A. euteiches with legumes.
Prevalence of critical congenital heart defects and selected co‐occurring congenital anomalies, 2014–2018: A U.S. population‐based study

Critical congenital heart defects (CCHDs) are one of the most common types of birth defects and can lead to significant morbidity and mortality along with surgical or catheter interventions within the first year of life. This report updates previously published estimates of CCHD prevalence with the latest population‐based surveillance data from 19 birth defect surveillance programs.
Association of Multiprofessional Preoperative Assessment and Information for Patients With Head and Neck Cancer With Postoperative Outcomes.

Importance Multidisciplinary perioperative assessment for patients undergoing complex oncologic head and neck cancer (HNC) surgery is widely implemented. However, to our knowledge, the association of multiprofessional preoperative assessment, information, and briefing with postoperative outcomes has not been evaluated. Objective To compare postoperative complications, length of hospital stay (LOS), readmissions, mortality, and costs per case among patients undergoing complex oncologic HNC surgery before and after the implementation of a comprehensive preoperative multiprofessional assessment and information day (MUPAID). Design, Setting, and Participants This retrospective, single-center case-control study was conducted at a tertiary referral head and neck anticancer center/university cancer institute and compared patients with HNC who were undergoing complex oncological surgeries between January 2012 and July 2018 before (control group) and after (intervention group) implementation of the institutional MUPAID. Data analysis was conducted between 2019 and 2020. The intervention group comprised patients who participated in the MUPAID beginning in February 2015. These patients were assessed by a multiprofessional team and provided with structured and comprehensive information on the surgical procedure and its functional, social, financial, and psychological effects, as well as the postoperative care, rehabilitation, and follow-up period. Patients in the control group had also undergone complex oncologic HNC surgery and were selected through surgical procedure codes. Main Outcomes and Measures The end points were postoperative rate and severity of complications, LOS, readmissions, mortality, and costs per case. Results The study included 161 patients, 81 in the intervention (25 women [30.9%]) and 80 in the control group (18 women [22.5%]). The groups showed no relevant differences in sociodemographic, disease, and procedural characteristics. The intervention cohort presented with fewer major local and systemic complications (Clavien-Dindo score, III-V: 34.6% vs 52.5%; difference proportion, -0.179; 95% CI, -0.33 to -0.03), shorter median LOS (12 days [IQR, 10-16 days] vs 16 days [IQR, 11-20] days; effect size, 0.482; 95% CI Cohen d, 0.152-0.812) and decreased median charge per case ($50 848 [IQR, $42 510-$63 479] vs $69 602 [IQR, $45 631-$96 280]; effect size, 0.534; 95% CI Cohen d, 0.22-0.85). Conclusions and Relevance The results of this case-control study suggest that MUPAID for patients who are undergoing complex oncologic HNC surgery is associated with shortened LOS and costs per case as well as decreased complications severity. These results are promising on a patient level in the potential to minimize individual treatment burden, as well as on an institutional and health care system level in the potential significant optimization of surgical outcomes and financial aspects.
Estimated Renal Metabolomics at Reperfusion Predicts One-Year Kidney Graft Function

Renal transplantation is the gold-standard procedure for end-stage renal disease patients, improving quality of life and life expectancy. Despite continuous advancement in the management of post-transplant complications, progress is still needed to increase the graft lifespan. Early identification of patients at risk of rapid graft failure is critical to optimize their management and slow the progression of the disease. In 42 kidney grafts undergoing protocol biopsies at reperfusion, we estimated the renal metabolome from RNAseq data. The estimated metabolites’ abundance was further used to predict the renal function within the first year of transplantation through a random forest machine learning algorithm. Using repeated K-fold cross-validation we first built and then tuned our model on a training dataset. The optimal model accurately predicted the one-year eGFR, with an out-of-bag root mean square root error (RMSE) that was 11.8 ± 7.2 mL/min/1.73 m2. The performance was similar in the test dataset, with a RMSE of 12.2 ± 3.2 mL/min/1.73 m2. This model outperformed classic statistical models. Reperfusion renal metabolome may be used to predict renal function one year after allograft kidney recipients.
Return of Norovirus and Rotavirus Activity in Winter 2020‒21 in City with Strict COVID-19 Control Strategy, China

A rapid decrease in viral gastroenteritis during winter 2019–20 and a return of norovirus and rotavirus activity during winter 2020–21 were observed while multiple nonpharmaceutical interventions for coronavirus disease were in effect in Hong Kong. The initial collateral benefit of coronavirus disease countermeasures that reduced the viral gastroenteritis burden is not sustainable.