Miracle-dz/UniFAR-FTUs huggingface dataset:
COVID‐19 complications among patients with opioid use disorder: a retrospective cohort study across five major NYC hospital systems

Abstract Background and Aims Individuals with opioid use disorder (OUD) suffer disproportionately from COVID‐19. To inform clinical management of OUD patients, research is needed to identify characteristics associated with COVID‐19 progression and death among this population. We aimed to investigate the role of OUD and specific comorbidities on COVID‐19 progression among hospitalized OUD patients. Design Retrospective cohort study of merged electronic health records (EHR) from five large private health systems. Setting New York City, New York, USA, 2011–21. Participants Adults with a COVID‐19 encounter and OUD or opioid overdose diagnosis between March 2020 and February 2021. Measurements Primary exposure included diagnosis of OUD/opioid overdose. Risk factors included age, sex, race/ethnicity and common medical, substance use and psychiatric comorbidities known to be associated with COVID‐19 severity. Outcomes included COVID‐19 hospitalization and subsequent intubation, acute kidney failure, severe sepsis and death. Findings Of 110 917 COVID‐19+ adults, 1.17% were ever diagnosed with OUD/opioid overdose. OUD patients had higher risk of COVID‐19 hospitalization [adjusted risk ratio (aRR) = 1.40, 95% confidence interval (CI) = 1.33, 1.47], intubation [adjusted odds ratio (aOR) = 2.05, 95% CI = 1.74, 2.42], kidney failure (aRR = 1.51, 95% CI = 1.34, 1.70), sepsis (aRR = 2.30, 95% CI = 1.88, 2.81) and death (aRR = 2.10, 95% CI = 1.84, 2.40). Among hospitalized OUD patients, risks for worse COVID‐19 outcomes included being male; older; of a race/ethnicity other than white, black or Hispanic; and having comorbid chronic kidney disease, diabetes, obesity or cancer. Protective factors included having asthma, hepatitis‐C and chronic pain. Conclusions Opioid use disorder patients appear to have a substantial risk for COVID‐19‐associated morbidity and mortality, with particular comorbidities and treatments moderating this risk.
Trial of Labor after Three or More Previous Cesarean Sections: Systematic Review and Meta-Analysis of Observational Studies

Abstract Aims To assess the success rate and prevalence of maternal or neonatal complications in women undergoing a trial of labor after three or more (≥3) previous cesarean sections (CSs). Methods A systematic literature review and meta-analysis was conducted from inception to May 2022 in Medline, Scopus, ENBASE, ClinicalTrials.gov, and the Cochrane Central Register of Controlled Trials and Reviews. Items detailing success rate and complications in women with a history of≥3 previous CSs were considered. Selected articles were evaluated for quality, heterogeneity, and publication bias. A pooled prevalence or odds ratio was calculated. Findings Twelve articles were included for a total of 540 women with a history of≥3 CSs, accounting for the 2% (CI 95% 1–4%) of the whole cohort of trial of labor. Our findings show a 0.67 (CI 95% 0.53–0.78) rate of successful vaginal delivery. A higher success rate was observed in women having a history of a prior vaginal delivery (0.90, CI 95% 0.77–0.96) and when prostaglandins, peridural anesthesia or oxytocin were allowed (respectively 0.73, CI 95% 0.62–0.83, 0,73, CI 95% 0.57–0.85 and 0.73, CI 95% 0.64–0.81). Uterine rupture rate was 0.01 (CI 95% 0.00–0.01). No cases of fetal asphyxia or maternal or neonatal death were registered. Conclusions The success rate and low frequency of severe complications observed seem to support a trial of labor in selected patients desiring a natural birth. However, a potential underestimation of serious maternal and neonatal complications should be considered in the decision-making process.
Choroidal vascularity profile in diabetic eyes using wide field optical coherence tomography

Purpose To report the wide-field choroidal vascularity up to the mid-equator area in diabetic retinopathy (DR) subjects using wide-field optical coherence tomography (WF-OCT). Design Prospective, Cross-sectional study. Participants Forty-seven eyes of 25 DR subjects. Methods WF-OCT images (55 degrees) were obtained using Spectralis HRA + OCT (Heidelberg Engineering, Germany) in extremes of gazes in all quadrants and manual montages were created to obtain wide field images up to mid equator. A previously reported semi-automated algorithm was used to calculate choroidal vascularity profile (CVI). Regression analysis was performed to identify the factors influencing CVI. Results Forty-seven eyes from 25 patients were enrolled in the study. The mean age was 68.4 ± 10.6 years. The refractive error (spherical equivalent) ranged from −2.25 to +3.75 diopters. Most common DR grade among study subjects was moderate NPDR (29.41%) and 74.5% eyes had diabetic macular edema (DME). The mean CVI in the macular area (58.29 ± 3.63) was significantly lower than in any of the other fundus areas (all p ˂ 0.01). The maximum CVI was seen in the nasal region (66.60 ± 5.61), followed by temporal (65.69 ± 3.81), superior (65.01 ± 4.87), and inferior (63.80 ± 5.42). The vertical macular area had the least coefficient of variation (CV) of CVI (0.06) while the inferior quadrant had the highest CV (0.08). Conclusion The current study describes the CVI profile on WF-OCT in DR eyes up to mid-equator. The significant increase of the CVI compared to healthy subjects and its significant regional variations introduce this novel quantitative parameter as a reliable biomarker of the diabetes-induced choroidal microangiopathy.
Alkali cation-mediated topology displayed by an exonic G-rich sequence of TRPA1 gene

Abstract G-rich sequences are intrinsic parts of the genome, widespread in promoters, telomeres, or other regulatory regions. The in vivo existence and biological significance have established the functional aspect of G-quadruplex structures and thus have developed immense interest in exploring their therapeutic aspects. Herein, using biophysical methods, we examined the structural status and comprehensive cation-dependence of a 17-bp G-rich genomic sequence (SKGT17) located in the coding region of the human TRPA1 gene, known to be associated with various neurovascular, cardiovascular, and respiratory conditions. TRPA1 is primarily seen as a therapeutic target for the development of novel analgesics. Bioinformatics analysis has suggested that 17-bp quadruplex motif is a binding site for transcription factor 'Sp1'. The formation and recognition of SKGT17 G-quadruplex might impact its regulatory functioning. Biophysical studies confirmed that the presence of alkali metal ions facilitated the formation of G-quadruplex in parallel topology. Native gel further substantiated the formation of a biomolecular species. Circular dichroism (CD), UV-thermal melting (Tm), and CD melting confirmed the formation of parallel G-quadruplex with metal ion-dependent stability. The stability of the G-quadruplex formed is found to be significantly high in the presence of K+ ions than that of other ions. Intriguingly, we have also established that this segment of the TRAP1 gene favors G-quadruplex formation over its participation in the corresponding duplex formation under K+ ions conditions. This study attempts to explain the rationale for the stabilization of G-quadruplex in the presence of alkali metal ions and may add to a better understanding and insights into DNA-metal ions interactions. Communicated by Ramaswamy H. Sarma
Atypical procedural learning skills in children with Developmental Coordination Disorder

ABSTRACT We investigated the procedural learning deficit hypothesis in Developmental Coordination Disorder (DCD) while controlling for global performance such as slower reaction times (RTs) and variability. Procedural (sequence) learning was assessed in 31 children with DCD and 31 age-matched typically developing (TD) children through a serial reaction time task (SRTT). Sequential and random trial conditions were intermixed within five training epochs. Two repeated measures ANOVAs were conducted on a Sequence-Specific Learning Index (SSLI) and a Global Performance Index (GPI, speed/accuracy measure) with Epoch (for SSLI and GPI) and Condition (for GPI) as within-subjects factors, and Group as between-subjects factor. Controlling for RTs differences through normalized RTs, revealed a global reduction of SSLI in children with DCD compared with TD peers suggesting reduced sequence learning skills in DCD. Still, a significant Group x Condition interaction observed on GPI indicated that children from both groups were able to discriminate between sequential and random trials. DCD presented reduced procedural learning skills after controlling for global performance. This finding highlights the importance of considering the general functioning of the child while assessing learning skills in patients.
Three-dimensional finite element-based photoacoustic tomography: initial results

A finite element reconstruction algorithm for three-dimensional photoacoustic tomography is demonstrated to recover both the images of absorbed optical energy density and acoustic speed simultaneously. The recovered results show that the algorithm is able to reconstruct photoacoustic images quantitatively in terms of the location, size, optical and acoustic properties of the heterogeneous medium.
Spin-down of a baroclinic vortex by irregular small-scale topography

Abstract This study explores the impact of small-scale variability in the bottom relief on the dynamics and evolution of broad baroclinic flows in the ocean. The analytical model presented here generalizes the previously reported barotropic ‘sandpaper’ theory of flow–topography interaction to density-stratified systems. The multiscale asymptotic analysis leads to an explicit representation of the large-scale effects of irregular bottom roughness. The utility of the multiscale model is demonstrated by applying it to the problem of topography-induced spin-down of an axisymmetric vortex. We find that bathymetry affects vortices by suppressing circulation in their deep regions. As a result, vortices located above rough topography tend to be more stable than their flat-bottom counterparts. The multiscale theory is validated by comparing corresponding topography-resolving and parametric simulations.
The why, what, when, who and how of assessing CBT competence to support lifelong learning

Abstract Assessment of cognitive behaviour therapy (CBT) competence is a critical component in ensuring optimal clinical care, supporting therapists’ skill acquisition, and facilitating continuing professional development. This article provides a framework to support trainers, assessors, supervisors and therapists when making decisions about selecting and implementing effective strategies for assessing CBT competence. The framework draws on the existing evidence base to address five central questions: Why assess CBT competence? ; What is CBT competence? ; When should CBT competence be assessed? ; Who is best placed to assess CBT competence? ; and How should CBT competence be assessed? Various methods of assessing CBT competence are explored and the potential benefits and challenges are outlined. Recommendations are made about which approach to use across different contexts and how to use these effectively to facilitate the acquisition, enhancement and evaluation of CBT knowledge and skills. Key learning aims After reading this article you will be able to: (1) Identify key issues about why, what, when, who and how to assess CBT competence and use this framework to guide decisions about the best strategy to use. (2) Be aware of the range of methods for assessing CBT competence and consider the main benefits and potential challenges of these. (3) Consider the most effective ways to implement CBT competence assessment strategies as a tool for evaluation and learning.
Memory, counter-memory and denialism: How search engines circulate information about the Holodomor-related memory wars

Search engines, such as Google or Yandex, shape social reality by informing their users about current and historical phenomena. However, there is little research on how search engines deal with contested memories, which are subjected to ontological conflicts known as memory wars. In this article, we investigate how search engines circulate information about memory wars related to the Holodomor, a mass famine caused by Soviet repressive politics in Ukraine in 1932–1933. For this aim, we conduct an agent-based audit of four search engines—Bing, DuckDuckGo, Google, and Yandex—and examine how their top search results represent the Holodomor and related memory wars. Our findings demonstrate that search engines prioritize interpretations of the Holodomor aligning with specific sides in the memory wars, thus becoming memory warriors themselves.
POPKORN: Popping Windows Kernel Drivers At Scale

External vendors develop a significant percentage of Windows kernel drivers, and Microsoft relies on these vendors to handle all aspects of driver security. Unfortunately, device vendors are not immune to software bugs, which in some cases can be exploited to gain elevated privileges. Testing the security of kernel drivers remains challenging: the lack of source code, the requirement of the presence of a physical device, and the need for a functional kernel execution environment are all factors that can prevent thorough security analysis. As a result, there are no binary analysis tools that can scale and accurately find bugs at the Windows kernel level. To address these challenges, we introduce POPKORN, a lightweight framework that harnesses the power of taint analysis and targeted symbolic execution to automatically find security bugs in Windows kernel drivers at scale. Our system focuses on a class of bugs that affect security-critical Windows API functions used in privilege-escalation exploits. POPKORN analyzes drivers independently of both the kernel and the device, avoiding the complexity of performing a full-system analysis. We evaluate our system on a diverse dataset of 212 unique signed Windows kernel drivers. When run against these drivers, POPKORN reported 38 high impact bugs in 27 unique drivers, with manual verification revealing no false positives. Among the bugs we found, 31 were previously unknown vulnerabilities that potentially allow for Elevation of Privilege (EoP). During this research, we have received two CVEs and six acknowledgments from different driver vendors, and we continue to work with vendors to fix the issues that we identified.
Practical Binary Code Similarity Detection with BERT-based Transferable Similarity Learning

Binary code similarity detection (BCSD) serves as a basis for a wide spectrum of applications, including software plagiarism, malware classification, and known vulnerability discovery. However, the inference of contextual meanings of a binary is challenging due to the absence of semantic information available in source codes. Recent advances leverage the benefits of a deep learning architecture into a better understanding of underlying code semantics and the advantages of the Siamese architecture into better BCSD. In this paper, we propose BinShot, a BERT-based similarity learning architecture that is highly transferable for effective BCSD. We tackle the problem of detecting code similarity with one-shot learning (a special case of few-shot learning). To this end, we adopt a weighted distance vector with a binary cross entropy as a loss function on top of BERT. With the prototype of BinShot, our experimental results demonstrate the effectiveness, transferability, and practicality of BinShot, which is robust to detecting the similarity of previously unseen functions. We show that BinShot outperforms the previous state-of-the-art approaches for BCSD.
Make Data Reliable: An Explanation-powered Cleaning on Malware Dataset Against Backdoor Poisoning Attacks

Machine learning (ML) based Malware classification provides excellent performance and has been deployed in various real-world applications. Training for malware classification often relies on crowdsourced threat feeds, which exposes a natural attack injection point. Considering a real-world threat model for backdoor poisoning attacks on a malware dataset, because attackers are generally considered to have no control over the sample-labeling process, they conduct a clean-label attack, a more realistic scenario, by generating backdoored benign binaries that will be disseminated through threat intelligence platforms and poison the datasets for downstream malware classifiers. To avoid the threat of backdoor poisoned datasets, we propose an explanation-powered defense methodology called make data reliable (MDR), which is a general and effective mitigation to ensure the reliability of datasets by removing backdoored samples. We use a surrogate model and explanation tool Shapley Additive exPlanations (SHAP) to filter suspicious samples, then perform watermark identification based on the filtered suspicious samples, and finally remove samples with the identified watermark to construct a reliable dataset. We conduct extensive experiments on two typical datasets that were manually poisoned using different attack strategies. Experimental results show that the MDR achieves backdoored samples removal rate greater than 99.0% for different datasets and attack conditions, while maintaining an extremely low false positive rate of less than 0.1%. Furthermore, to confirm the generality of MDR, we use different models to perform a model-agnostic evaluation. The results show that, MDR is a general methodology that does not rely on any specific model.
View from Above: Exploring the Malware Ecosystem from the Upper DNS Hierarchy

This work explores authoritative DNS (AuthDNS) as a new measurement perspective for studying the large-scale epidemiology of the malware ecosystem—when and where infections occur, and what infrastructure spreads and controls malware. Utilizing an AuthDNS dataset from a top registrar, we observe malware heterogeneity (202 families), global infrastructure (399,830 IPs in 151 countries) and infection (40,937 querying Autonomous Systems (ASes)) visibility, as well as breadth of temporal coverage (2017–2021). This combination of factors enables an extensive analysis of the malware ecosystem that reinforces prior work on malware infrastructure and also contributes new perspectives on malware infection distribution and lifecycle. We find that malware families re-use infrastructure, especially in cloud hosting countries, but contrary to prior work, we do not detect targeting of clients by countries or industry sector. Furthermore, our 4-year lifecycle analysis of diverse malware families shows that infection analysis is temporally sensitive: over 90% of ASes first query a malicious domain after public detection, and a median of 38.6% ASes only query after domain expiration or takedown. To fit AuthDNS into the broader context of malware research, we conclude with a comparison of experimental vantage points on four qualitative aspects and discuss their advantages and limitations. Ultimately, we establish AuthDNS as a unique measurement perspective capable of measuring global malware infections.
DeView: Confining Progressive Web Applications by Debloating Web APIs

A progressive web application (PWA) becomes an attractive option for building universal applications based on feature-rich web Application Programming Interfaces (APIs). While flexible, such vast APIs inevitably bring a significant increase in an API attack surface, which commonly corresponds to a functionality that is neither needed nor wanted by the application. A promising approach to reduce the API attack surface is software debloating, a technique wherein an unused functionality is programmatically removed from an application. Unfortunately, debloating PWAs is challenging, given the monolithic design and non-deterministic execution of a modern web browser. In this paper, we present DeView, a practical approach that reduces the attack surface of a PWA by blocking unnecessary but accessible web APIs. DeView tackles the challenges of PWA debloating by i) record-and-replay web API profiling that identifies needed web APIs on an app-by-app basis by replaying (recorded) browser interactions and ii) compiler-assisted browser debloating that eliminates the entry functions of corresponding web APIs from the mapping between web API and its entry point in a binary. Our evaluation shows the effectiveness and practicality of DeView. DeView successfully eliminates 91.8% of accessible web APIs while i) maintaining original functionalities and ii) preventing 76.3% of known exploits on average.
Accept All Exploits: Exploring the Security Impact of Cookie Banners

The General Data Protection Regulation (GDPR) and related regulations have had a profound impact on most aspects related to privacy on the Internet. By requiring the user’s consent for e.g., tracking, an affirmative action has to take place before such data collection is lawful, leading to spread of so-called cookie banners across the Web. While the privacy impact and how well companies adhere to those regulations have been studied in detail, an open question is what effect these banners have on the security of netizens. In this work, we systematically investigate the security impact of consenting to a cookie banner. For this, we design an approach to automatically give maximum consent to these banners, enabling us to conduct a large-scale crawl. Thereby, we find that a user who consents to tracking executes 45% more third-party scripts and is exposed to 63% more security sensitive data flows on average. This significantly increased attack surface is not a mere theoretical danger, as our examination of Client-Side Cross-Site Scripting (XSS) vulnerabilities shows: By consenting, the number of websites vulnerable to our verified XSS exploits increases by 55%. In other words, more than one third of all affected websites are only vulnerable to XSS due to code that requires user consent. This means that users who consent to cookies are browsing a much more insecure and dangerous version of the Web. Beyond this immediate impact, our results also raise the question about the actual state of client-side web security as a whole. As few studies state the vantage point of their measurements, and even fewer take cookie notices into account, they most likely underreport the prevalence of vulnerabilities on the Web at large.
Interaction matters: a comprehensive analysis and a dataset of hybrid IoT/OT honeypots

The Internet of things (IoT) and critical infrastructure utilizing operational technology (OT) protocols are nowadays a common attack target and/or attack surface used to further propagate malicious actions. Deception techniques such as honeypots have been proposed for both IoT and OT but they either lack an extensive evaluation or are subject to fingerprinting attacks. In this paper, we extend and evaluate RIoTPot, a hybrid-interaction honeypot, by exposing it to attacks on the Internet and perform a longitudinal study with multiple evaluation parameters for three months. Furthermore, we publish the aforementioned study in the form of a dataset that is available to researchers upon request. We leverage RIoTPot’s hybrid-interaction model to deploy it in three interaction variants with six protocols deployed on both cloud and self-hosted infrastructure to study and compare the attacks gathered. At a glance, we receive 10.87 million attack events originating from 22,518 unique IP addresses that involve brute-force, poisoning, multistage and other attacks. Moreover, we fingerprint the attacker IP addresses to identify the type of devices who participate in the attacks. Lastly, our results indicate that the honeypot interaction levels have an important role in attracting specific attacks and scanning probes.