Miracle-dz/UniFAR-FTUs huggingface dataset:
A Scalable Quality Assurance Process for Curating Oncology Electronic Health Records: The Project GENIE Biopharma Collaborative Approach

PURPOSE The American Association for Cancer Research Project Genomics Evidence Neoplasia Information Exchange Biopharma Collaborative is a multi-institution effort to build a pan-cancer repository of genomic and clinical data curated from the electronic health record. For the research community to be confident that data extracted from electronic health record text are reliable, transparency of the approach used to ensure data quality is essential. MATERIALS AND METHODS Four institutions participating in AACR's Project GENIE created an observational cohort of patients with cancer for whom tumor molecular profiling data, therapeutic exposures, and treatment outcomes are available and will be shared publicly with the research community. A comprehensive approach to quality assurance included assessments of (1) feasibility of the curation model through pressure test cases; (2) accuracy through programmatic queries and comparison with source data; and (3) reproducibility via double curation and code review. RESULTS Assessments of feasibility resulted in critical modifications to the curation directives. Queries and comparison with source data identified errors that were rectified via data correction and curator retraining. Assessment of intercurator reliability indicated a reliable curation model. CONCLUSION The transparent quality assurance processes for the GENIE BPC data ensure that the data can be used for analyses that support clinical decision making and advances in precision oncology. Transparent QA processes for GENIE BPC ensure that the data can be used to support advances in precision oncology OR @jessicalavs of @MSKBiostats & coauthors discuss @AACR Project GENIE BPC, a multi-institution effort to aggregate clinical plus genomic data for patients with cancer. Transparent QA processes for GENIE BPC ensure that the data can be used to support advances in precision oncology.
Y‐shaped RNA secondary structure of a noncoding region in the genomic RNA of tick‐borne encephalitis virus affects pathogenicity

Tick‐borne encephalitis virus (TBEV) is a zoonotic virus that causes encephalitis in humans. Various deletions have been reported in a variable region of the 3′ untranslated region of the TBEV genome. This study analyzed the role of a Y‐shaped secondary structure in the pathogenicity of TBEV by using reverse genetics. Deletion of the structure increased the mortality rate of virus‐infected mice but did not affect the virus multiplication in cultured cells and organs. The results indicate that the secondary structure is involved in the regulation of TBEV pathogenesis.
Is it reciprocating or self-serving?: Understanding coping strategies for postpartum depression in an online community for Korean mothers

Abstract The proliferation of online health communities creates opportunities to exchange social support. Given the growing need to investigate the extent to which social support helps mothers with postpartum depression (PPD) and psychological distress, we conducted a content analysis of 3,073 posts from a PPD-related message board in a prominent online community for Korean mothers. We found that community members tended to provide rather than seek support, indicating potential reciprocity in the community. We also found that emotion-focused coping strategies were much more prevalent than problem-focused coping strategies. Thus, the message board had an ambiguous identity, potentially undermining its actual purpose.
Pharmacotherapeutic management of T-cell acute lymphoblastic leukemia in adults: an update of the literature

ABSTRACT Introduction T-cell acute lymphoblastic leukemia (T-ALL) is a rare but potentially life-threatening heterogeneous hematologic malignancy that requires prompt diagnosis and treatment by hematologists. So far, therapeutic advances have been achieved in the management of this disease mainly by adopting pediatric-like regimens, and cure rates are significantly worse than in childhood. In T-ALL, less than 70% of adults achieve long-term survival. The prognosis after relapse is still very poor. Hence, there is urgent need to improve therapy of T-ALL by testing new compounds and combinations for the treatment of this disease. Areas covered This review provides a comprehensive update on the most recent treatment approaches in adults with de novo and relapsed/refractory adult T-ALL. Expert opinion Intensifying chemotherapy may reduce the incidence of recurrent disease in adult patients, but it has not come without a cost. Novel agents with selective T-ALL activity (e.g. nelarabine) may improve survival in some patient subsets. Due to modern genomic and transcriptomic techniques, various novel potential targets might change the treatment landscape in the next few years and will, hopefully alongside with cellular therapies, augment the therapeutic armamentarium in the near future.
Loss of IκBζ Drives Dentin Formation via Altered H3K4me3 Status

Enforced enrichment of the active promoter marks trimethylation of histone H3 lysine 4 (H3K4me3) and acetylation of histone H3 lysine 27 (H3K27ac) by inhibiting histone demethylases and deacetylases is positively associated with hard tissue formation through the induction of osteo/odontogenic differentiation. However, the key endogenous epigenetic modulator of odontoblasts to regulate the expression of genes coding dentin extracellular matrix (ECM) proteins has not been identified. We focused on nuclear factor (NF)–κB inhibitor ζ (IκBζ), which was originally identified as the transcriptional regulator of NF-κB and recently regarded as the NF-κB–independent epigenetic modulator, and found that IκBζ null mice exhibit a thicker dentin width and narrower pulp chamber, with aged mice having more marked phenotypes. At 6 mo of age, dentin fluorescent labeling revealed significantly accelerated dentin synthesis in the incisors of IκBζ null mice. In the molars of IκBζ null mice, marked tertiary dentin formation adjacent to the pulp horn was observed. Mechanistically, the expression of COL1A2 and COL1A1 collagen genes increased more in the odontoblast-rich fraction of IκBζ null mice than in wild type in vivo, similar to human odontoblast-like cells transfected with small interfering RNA for IκBζ compared with cells transfected with control siRNA in vitro. Furthermore, the direct binding of IκBζ to the COL1A2 promoter suppressed COL1A2 expression and the local active chromatin status marked by H3K4me3. Based on whole-genome identification of H3K4me3 enrichment, ECM and ECM organization–related gene loci were selectively activated by the knockdown of IκBζ, which consistently resulted in the upregulation of these genes. Collectively, this study suggested that IκBζ is the key negative regulator of dentin formation in odontoblasts by inhibiting dentin ECM- and ECM organization–related gene expression through an altered local chromatin status marked by H3K4me3. Therefore, IκBζ is a potential target for epigenetically improving the clinical outcomes of dentin regeneration therapies such as pulp capping.
Inhibition of chemically and mechanically activated Piezo1 channels as a mechanism for ameliorating atherosclerosis with salvianolic acid B

Salvianolic acid B (SalB) is effective for treating cardiovascular diseases. However, the molecular mechanisms underlying its therapeutic effects remain unclear. Mechanosensitive Piezo1 channels play important roles in vascular biology, although their pharmacological properties are poorly defined. Here, we aimed to identify novel Piezo1 inhibitors and gain insights into their mechanisms of action.
The malnutritional facet of inflammatory prognostication in acute aortic dissection

Dear Editor, The recent retrospective research by Xu et al. highlighting the prognostic significance of systemic immune‐inflammation index (SII) in type‐A acute aortic dissection (AAD), is a welcome addition to the literature on parsimonious inflammatory prognostication in cardiac surgery. With that said, the research group misses out on hypoalbuminemia in their setting which is often linked to both malnutritional and proinflammatory predisposition. Appropriate to the context, the literature supports the prognostic implications of hypoalbuminemia in AAD. Gao et al. retrospectively analyzed the data of 777 AAD patients to subsequently outline hypoalbuminemia as an independent in‐hospital mortality predictor in type‐A and type‐B AAD (hazards ratio; 95% confidence interval; p value: 2.49; 1.25–4.98; 0.010 and 8.73; 1.82–41.74; 0.007, respectively). Indeed, the Xu et al. study also investigated short‐term mortality rates in their analysis. Nonetheless, the concern surrounding the lack of albumin in the Xu et al. analysis assumes enhanced importance amidst Gao et al. delineating a considerable 37.7% incidence of hypoalbuminemia in their type‐A AAD patients (115/305 manifesting on‐ admission serum albumin ≤34 g/L). Notably, an overall 52.13% type‐A AAD patients in the Gao et al. setting underwent surgical interventions as opposed to an exclusive operative setting of Xu et al. Speaking strictly from an operative perspective, Yoon et al. evaluating the prognostic potential of SII in tricuspid valve surgery interestingly revealed lower serum albumin levels in patients with a preoperative SII ≥ 455.6 × 10/L in contrast to those with SII < 455.6 × 10/L (3.6 ± 0.7 vs. 3.8 ± 0.5 g/dl, p = 0.031). Moreover, ahead of the novel‐to‐conventional biomarkers like SII and albumin, the importance of a composite immunonutritional assessment is nested in the findings of Keskin et al. and Lin et al. studies deciphering an independent in‐hospital mortality predictive value of the prognostic‐nutritional index (computed from the preoperative albumin‐lymphocyte levels) in 151 and 651 surgical type‐A AAD patients, respectively. While the inclusion of albumin in the Xu et al. analysis could have potentially added an enhanced lucidity, the larger point remains that the malnutritional facet of inflammatory prognostication in peculiar subsets like AAD can certainly not be overlooked in the present era propounding the need for a holistic preoperative immunonutritional evaluation.
Curiosity-Driven Exploration via Latent Bayesian Surprise

The human intrinsic desire to pursue knowledge, also known as curiosity, is considered essential in the process of skill acquisition. With the aid of artificial curiosity, we could equip current techniques for control, such as Reinforcement Learning, with more natural exploration capabilities. A promising approach in this respect has consisted of using Bayesian surprise on model parameters, i.e. a metric for the difference between prior and posterior beliefs, to favour exploration. In this contribution, we propose to apply Bayesian surprise in a latent space representing the agent’s current understanding of the dynamics of the system, drastically reducing the computational costs. We extensively evaluate our method by measuring the agent's performance in terms of environment exploration, for continuous tasks, and looking at the game scores achieved, for video games. Our model is computationally cheap and compares positively with current state-of-the-art methods on several problems. We also investigate the effects caused by stochasticity in the environment, which is often a failure case for curiosity-driven agents. In this regime, the results suggest that our approach is resilient to stochastic transitions.
Fast algebraic multigrid for block-structured dense and Toeplitz-like-plus-Cross systems arising from nonlocal diffusion problems

Algebraic multigrid (AMG) is one of the most efficient iterative methods for solving large sparse system of equations. However, how to build/check restriction and prolongation operators in practical of AMG methods for nonsymmetric {\em sparse} systems is still an interesting open question [Brezina, Manteuffel, McCormick, Runge, and Sanders, SIAM J. Sci. Comput. (2010); Manteuffel and Southworth, SIAM J. Sci. Comput. (2019)]. This paper deals with the block-structured dense and Toeplitz-like-plus-Cross systems, including {\em nonsymmetric} indefinite, symmetric positive definite (SPD), arising from nonlocal diffusion problem and peridynamic problem. The simple (traditional) restriction operator and prolongation operator are employed in order to handle such block-structured dense and Toeplitz-like-plus-Cross systems, which is convenient and efficient when employing a fast AMG. We focus our efforts on providing the detailed proof of the convergence of the two-grid method for such SPD situations. The numerical experiments are performed in order to verify the convergence with a computational cost of only $\mathcal{O}(N \mbox{log} N)$ arithmetic operations, by using few fast Fourier transforms, where $N$ is the number of the grid points. To the best of our knowledge, this is the first contribution regarding Toeplitz-like-plus-Cross linear systems solved by means of a fast AMG.
Exponential Tail Local Rademacher Complexity Risk Bounds Without the Bernstein Condition

The local Rademacher complexity framework is one of the most successful general-purpose toolboxes for establishing sharp excess risk bounds for statistical estimators based on the framework of empirical risk minimization. Applying this toolbox typically requires using the Bernstein condition, which often restricts applicability to convex and proper settings. Recent years have witnessed several examples of problems where optimal statistical performance is only achievable via non-convex and improper estimators originating from aggregation theory, including the fundamental problem of model selection. These examples are currently outside of the reach of the classical localization theory. In this work, we build upon the recent approach to localization via offset Rademacher complexities, for which a general high-probability theory has yet to be established. Our main result is an exponential-tail excess risk bound expressed in terms of the offset Rademacher complexity that yields results at least as sharp as those obtainable via the classical theory. However, our bound applies under an estimator-dependent geometric condition (the"offset condition") instead of the estimator-independent (but, in general, distribution-dependent) Bernstein condition on which the classical theory relies. Our results apply to improper prediction regimes not directly covered by the classical theory.
Compositional Model Checking of Consensus Protocols via Interaction-Preserving Abstraction

Consensus protocols are widely used in building reliable distributed software systems and their correctness is of vital importance. TLA+ is a lightweight formal specification language which enables precise specification of system design and exhaustive checking of the design without any human effort. The features of TLA+ make it widely used in the specification and model checking of consensus protocols, both in academia and in industry. However, the application of TLA+ is limited by the state explosion problem in model checking. Though compositional model checking is essential to tame the state explosion problem, existing compositional checking techniques do not sufficiently consider the characteristics of TLA+. In this work, we propose the Interaction-Preserving Abstraction (IPA) framework, which leverages the features of TLA+ and enables practical and efficient compositional model checking of consensus protocols specified in TLA+. In the IPA framework, system specification is partitioned into multiple modules, and each module is divided into the internal part and the interaction part. The basic idea of the interaction-preserving abstraction is to omit the internal part of each module, such that another module cannot distinguish whether it is interacting with the original module or the coarsened abstract one. We apply the IPA framework to the compositional checking of the TLA+ specifications of two consensus protocols Raft and ParallelRaft. Raft is a consensus protocol which was originally developed in academia and then widely used in industry. ParallelRaft is the replication protocol in PolarFS, the distributed file system for the commercial database Alibaba PolarDB. We demonstrate that the IPA framework is easy to use in realistic scenarios and at the same time significantly reduces the model checking cost.
Backdoor Defense in Federated Learning Using Differential Testing and Outlier Detection

The goal of federated learning (FL) is to train one global model by aggregating model parameters updated independently on edge devices without accessing users' private data. However, FL is susceptible to backdoor attacks where a small fraction of malicious agents inject a targeted misclassification behavior in the global model by uploading polluted model updates to the server. In this work, we propose DifFense, an automated defense framework to protect an FL system from backdoor attacks by leveraging differential testing and two-step MAD outlier detection, without requiring any previous knowledge of attack scenarios or direct access to local model parameters. We empirically show that our detection method prevents a various number of potential attackers while consistently achieving the convergence of the global model comparable to that trained under federated averaging (FedAvg). We further corroborate the effectiveness and generalizability of our method against prior defense techniques, such as Multi-Krum and coordinate-wise median aggregation. Our detection method reduces the average backdoor accuracy of the global model to below 4% and achieves a false negative rate of zero.
The larger the fairer?: small neural networks can achieve fairness for edge devices

Along with the progress of AI democratization, neural networks are being deployed more frequently in edge devices for a wide range of applications. Fairness concerns gradually emerge in many applications, such as face recognition and mobile medical. One fundamental question arises: what will be the fairest neural architecture for edge devices? By examining the existing neural networks, we observe that larger networks typically are fairer. But, edge devices call for smaller neural architectures to meet hardware specifications. To address this challenge, this work proposes a novel Fairness- and Hardware-aware Neural architecture search framework, namely FaHaNa. Coupled with a model freezing approach, FaHaNa can efficiently search for neural networks with balanced fairness and accuracy, while guaranteed to meet hardware specifications. Results show that FaHaNa can identify a series of neural networks with higher fairness and accuracy on a dermatology dataset. Target edge devices, FaHaNa finds a neural architecture with slightly higher accuracy, 5.28X smaller size, 15.14% higher fairness score, compared with MobileNetV2; meanwhile, on Raspberry PI and Odroid XU-4, it achieves 5.75X and 5.79X speedup.
Price formation in financial markets: a game-theoretic perspective

We propose two novel frameworks to study the price formation of an asset negotiated in an order book. Specifically, we develop a game-theoretic model in many-person games and mean-field games, considering costs stemming from limited liquidity. We derive analytical formulas for the formed price in terms of the realized order flow. We also identify appropriate conditions that ensure the convergence of the price we find in the finite population game to that of its mean-field counterpart. We numerically assess our results with a large experiment using high-frequency data from ten stocks listed in the NASDAQ, a stock listed in B3 in Brazil, and a cryptocurrency listed in Binance.
Applications of Support Vector Machines to Aeroengine Fault Diagnosis

An aeroengine fault diagnosis method based on support vector machines is presented.After extracting fault symptoms via proper equipments or methods,support vector machines were adopted to set up correlation between fault symptoms and fault patterns and to realize the classification of faults. Support vector machines are specially designed for good generalization ability under the condition that only small samples set is available.Applying this method to a certain engine,the diagnostic results are satisfactory,and it is referable to develop diagnostic system for newly-developed engines.
”You have to prove the threat is real”: Understanding the needs of Female Journalists and Activists to Document and Report Online Harassment

Online harassment is a major societal challenge that impacts multiple communities. Some members of community, like female journalists and activists, bear significantly higher impacts since their profession requires easy accessibility, transparency about their identity, and involves highlighting stories of injustice. Through a multi-phased qualitative research study involving a focus group and interviews with 27 female journalists and activists, we mapped the journey of a target who goes through harassment. We introduce PMCR framework, as a way to focus on needs for Prevention, Monitoring, Crisis and Recovery. We focused on Crisis and Recovery, and designed a tool to satisfy a target’s needs related to documenting evidence of harassment during the crisis and creating reports that could be shared with support networks for recovery. Finally, we discuss users’ feedback to this tool, highlighting needs for targets as they face the burden and offer recommendations to future designers and scholars on how to develop tools that can help targets manage their harassment.
Impact of Channel Correlation on Subspace-Based Activity Detection in Grant-Free NOMA

In this paper, we consider the problem of activity detection in grant-free code-domain non-orthogonal multiple access (NOMA). We focus on performing activity detection via subspace methods under a setup where the data and pilot spreading signatures are of different lengths, and consider a realistic frame-structure similar to existing mobile networks. We investigate the impact of channel correlation on the activity detection performance; first, we consider the case where the channel exhibits high correlation in time and frequency and show how it can heavily deteriorate the performance. To tackle that, we propose to apply user-specific masking sequences overlaid on top of the pilot signatures. Second, we consider the other extreme with the channel being highly selective, and show that it can also negatively impact the performance. We investigate possible pilots’ reallocation strategies that can help reduce its impact.
Designing Decision Support Systems for Emergency Response: Challenges and Opportunities

Designing effective emergency response management (ERM) systems to respond to incidents such as road accidents is a major problem faced by communities. In addition to responding to frequent incidents each day (about 240 million emergency medical services calls and over 5 million road accidents in the US each year), these systems also support response during natural hazards. Recently, there has been a consistent interest in building decision support and optimization tools that can help emergency responders provide more efficient and effective response. This includes a number of principled subsystems that implement early incident detection, incident likelihood forecasting and strategic resource allocation and dispatch policies. In this paper, we highlight the key challenges and provide an overview of the approach developed by our team in collaboration with our community partners.