Miracle-dz/UniFAR-FTUs huggingface dataset:
UvA-DARE (Digital Academic Repository) Same-sex attraction, social relationships, psychosocial functioning, and schoolperformance in young adolescents

The authors examined whether 13-to 15-year-old adolescents who experience feelings of same-sex attraction (SSA) differ from those without such feelings in the quality of relationships with parents, peers, and class mentors and in psychosocial functioning (health status and school performance). The authors also assessed whether differences in psychosocial functioning resulted from differences in the quality of social relationships. Data were collected from 866 Dutch high school students (mean age 13.61 years) by means of a computer-based questionnaire. Of the participants, 74 (8.5%) reported having feelings of SSA. The participants with SSA rated the quality of their relationships with their fathers and their peers lower than did those without SSA. Participants with SSA also had poorer mental health (higher levels of depression and lower levels of self-esteem) and lower school performance. A mediation analysis revealed that differences in psychosocial functioning resulted from differences in the quality of the same-sex attracted youths’ social relationships, especially with fathers and peers.
Alkyne Selective Hydrogenation with Mono- and Bimetallic- Anchored Catalysts

Partial hydrogenation of alkynes has industrial and academic relevance on a large scale; industries such as petrochemical, pharmacological and agrochemical use these compounds as raw material. Finding an economic, active and selective catalyst for the production of alkenes through partial hydrogenation of alkynes is thus an important challenge. Mono- and bimetallic catalysts (palladium, ruthenium and nickel) were synthetized by the incipient wetness technique using gamma alumina and an activated carbon as supports. The catalysts were characterized by inductively coupled plasma, hydrogen chemisorption, temperature-programmed reduction and X-ray photoelec-tronic spectroscopy (XPS). The objective of this work is to study 1-heptyne-selective hydrogenation using supported catalysts influenced by different factors: (a) pretreatment reduction temperature, (b) reaction temperature, (c) type of support, (d) metal loading, (e) precursor salt and (f) addition of a second metal to monometallic palladium catalyst. The Lindlar commercial catalyst, commonly used in these types of reactions, was used for comparative purposes. XPS technique allowed verifying that the presence of electron-deficient species on the catalyst surface with high metal loading affects the conversion and selectivity to the desired product. Nevertheless, the influence of geometrical effects and/or mixed active sites in the catalysts, as well as metal-metal and metal-support interactions, cannot be neglected.
Association Between use of an Adaptive Cardiac Resynchronization Therapy Algorithm and Healthcare Utilization and Cost

Objectives: To assess the association between use of adaptive pacing on clinical and economic outcomes of CRT recipients in real-world analysis. Background: The AdaptivCRTTM algorithm was shown in prior subgroup analyses of prospective trials to achieve clinical benefits, but a large prospective trial showed nonsignificant changes in the endpoint of mortality or heart failure hospitalizations. Methods: CRT-implanted patients from the Optum Clinformatics(R) database with 90 days of follow-up were included. Remote monitoring data was used to classify patients based on CRT setting adaptive biventricular and left ventricular pacing (aCRT) vs. standard biventricular pacing (Standard CRT). Inverse probability of treatment weighting was used to adjust for baseline differences between groups. Mortality, 30-day readmissions, healthcare utilization, and payer and patient costs were evaluated post-implantation. Results: This study included 2,412 aCRT and 1,638 Standard CRT patients (mean follow-up: 2.4 {+/-} 1.4 years), with balanced baseline characteristics after adjustment. The aCRT group was associated with lower all-cause mortality (adjusted hazard ratio = 0.88 [95% confidence interval (CI):0.80, 0.96]), fewer all-cause 30-day readmissions (adjusted incidence rate ratio = 0.87 [CI:0.81, 0.94]), and fewer all-cause and HF-related inpatient, outpatient, and emergency department (ED) visits. The aCRT cohort was also associated with lower all-cause outpatient payer-paid amounts and lower all-cause and HF-related inpatient and ED patient-paid amounts. Conclusions: In this retrospective analysis of a large real-world cohort, use of an adaptive CRT algorithm was associated with lower mortality, reduced healthcare resource utilization, and lower payer and patient costs.
Household Air Pollution and Blood Pressure, Vascular Damage, and Subclinical Indicators of Cardiovascular Disease in Older Chinese Adults

Abstract Background Limited data suggest that household air pollution from cooking and heating with solid fuel (i.e., coal and biomass) stoves may contribute to the development of hypertension and vascular damage. Methods Using mixed-effects regression models, we investigated the associations of household air pollution with blood pressure (BP) and vascular function in 753 adults (ages 40–79 years) from 3 diverse provinces in China. We conducted repeated measures of participants’ household fuel use, personal exposure to fine particulate air pollution (PM2.5), BP, brachial–femoral pulse wave velocity (bfPWV), and augmentation index. Ultrasound images of the carotid arteries were obtained to assess intima–media thickness (CIMT) and plaques. Covariate information on sociodemographics, health behaviors, 24-h urinary sodium, and blood lipids was also obtained. Results Average estimated yearly personal exposure to PM2.5 was 97.5 µg/m3 (SD: 79.2; range: 3.5–1241), and 65% of participants cooked with solid fuel. In multivariable models, current solid fuel use was associated with higher systolic (2.4 mm Hg, 95% CI: −0.4, 4.9) and diastolic BP (1.4 mm Hg, 95% CI: −0.1, 3.0) and greater total area of plaques (1.7 mm2, 95% CI: −6.5, 9.8) compared with exclusive use of electricity or gas stoves. A 1 − ln(µg/m3) increase in PM2.5 exposure was associated with higher systolic (1.5 mm Hg, 95% CI: 0.2, 2.7) and diastolic BP (1.0 mm Hg, 95% CI: 0.4, 1.7) and with greater CIMT (0.02 mm, 95% CI: 0.00, 0.04) and total area of plaques (4.7 mm2, 95% CI: −2.0, 11.5). We did not find associations with arterial stiffness, except for a lower bfPWV (−1.5 m/s, 95% CI: −3.0, −0.0) among users of solid fuel heaters. Conclusions These findings add to limited evidence that household air pollution is associated with higher BP and with greater CIMT and total plaque area.
A protocol for systematic review and meta-analysis

Background:Autism spectrum disorder (ASD) is a complex neurodevelopmental condition, which is characterized by impairment in social interaction or communication and lack of flexibility of imagination and behavior. Acupuncture is one of the most common modality of Traditional Chinese medicine (TCM) and has been used to treat various disease in clinical practice for more than 2000 years in China by correcting disharmony and dysregulation of body. It has sometimes been used as a treatment aimed at improving ASD symptoms and outcomes, but its clinical effectiveness and safety has not been rigorously reviewed. We will plan to conduct a systematic review and meta-analysis to summarize the current evidence on the effects and safety of acupuncture for ASD. Methods: The following databases will be searched: PubMed, the Cochrane Library, Embase, Wanfang Data, China National Knowledge Infrastructure, SinoMed, and VIP. Randomised controlled trials will be included to evaluate the effect and safety of acupuncture on patients with ASD. The primary outcome will be the core features of ASD. The risk of bias will be assessed by the Cochrane risk of bias tool. We will conduct a meta-analysis and sensitivity analysis, as well as a subgroup analysis if high heterogeneity is present, using Revman 5.3. We will use funnel plots to identify potential reporting biases. The Grading of Recommendations Assessment, Development and Evaluation will be used to evaluate the quality of evidence. Results: This study will be to assess the effect and safety of acupuncture for ASD. Conclusions: This study will assess the effect of acupuncture for ASD and provide reliable evidence for the choice of treatments. Ethicsanddissemination:The protocol will not need ethical approval because no issues of participant privacy exist. The results of this systematic review will provide evidence about the effect and safety of acupuncture for ASD. The results will be disseminated through peer review. Abbreviation: ASD = autism spectrum disorders.
Plasma p-tau212: antemortem diagnostic performance and prediction of autopsy verification of Alzheimer’s disease neuropathology

Blood phosphorylated tau (p-tau) biomarkers, including p-tau217, show high associations with Alzheimers disease (AD) neuropathologic change and clinical stage. Certain plasma p-tau217 assays recognize tau forms phosphorylated additionally at threonine-212, but the contribution of p-tau212 alone to AD is unknown. We developed a blood-based immunoassay that is specific to p-tau212 without cross-reactivity to p-tau217. Thereafter, we examined the diagnostic utility of plasma p-tau212. In five cohorts (n=388 participants), plasma p-tau212 showed high performances for AD diagnosis and for the detection of both amyloid and tau pathology, including at autopsy as well as in memory clinic populations. The diagnostic accuracy and fold changes of plasma p-tau212 were similar to those for p-tau217 but higher than p-tau181 and p-tau231. Immunofluorescent staining of brain tissue slices showed prominent p-tau212 reactivity in neurofibrillary tangles that co-localized with p-tau217 and p-tau202/205. These findings support plasma p-tau212 as a novel peripherally accessible biomarker of AD pathophysiology.
Studying social anxiety without triggering it: Establishing an age-controlled cohort of social media users for observational studies

Background: Patients of certain diseases are less likely to approach the healthcare system but remain active in social media. Young Social Anxiety Disorder (SAD) patients, in particular, are a hard-to-reach population due to disease symptomatology, unmet need and age-related barriers, which makes obtaining first-hand access to patient perspectives challenging. Objective: To create a curated cohort of patients from social media that report their age in the range of 13 to 25 years old and confirm having a SAD diagnosis or having received therapy for SAD, and to assess the value of the content posted by these users for observational studies of SAD. Methods: We collected 535k posts by 118k Reddit users from the r/SocialAnxiety subreddit. We then developed precise regular expressions to extract age, diagnosis and therapy mentions. We manually annotated the full set of expressions extracted and double-annotated 5% of the age mentions and 10% of the diagnosis and therapy mentions. Using similar methodology, we identified mentions of comorbidities and substance use. Results: Our validated cohort includes 37,073 posts by 1,102 users that meet the inclusion criteria. The age, diagnosis, and therapy mention detection had a precision of 68%, 31%, and 44%, respectively, with an inter-annotator agreement of 0.96, 0.96, and 0.78. Sixty-one percent of the users in the cohort report having one or more comorbidities on top of their SAD diagnosis (Fleiss's Kappa=0.79) and 13% report a concerning use of drugs or alcohol (Fleiss's Kappa=0.87). We compared the characteristics of our social media cohort to the published literature on SAD. Conclusions: Patients with SAD post actively on Reddit and their perspectives can be captured and studied directly from these data. Extracting age, therapy, substance abuse and comorbidities (and potentially other patient data) can address real-world data source biases. Thus, social media is a valuable source to create cohorts of hard-to-reach patient populations that may not enter the healthcare system.
AI learns racial information from the values of vital signs

Background: Bias in medical practice is multifaceted, including treatment variations across race-ethnicity, unconscious bias in healthcare providers' attitudes, and bias in clinical scores. However, far less is known about the potential racial bias in routinely collected, essential information in clinical decision-making, namely vital signs. Research question: Do vital signs embed racial information that can be learned by AI algorithms? Study Design and Methods: Retrospective cohort study of critically ill patients between 2014 and 2015 from the multi-centre eICU-CRD critical care database involving 335 Intensive Care Units (ICU) based in 208 US hospitals, containing 200,859 patient admissions. We extracted 10,763 critical care admissions of patients aged 18 and over, alive during the first 24 hours after admission to ICU with recorded self-reported race as well as at least two measurement of heart rate, oxygen saturation, respiratory rate, and blood pressure. Pairs of racial subgroups were matched based on age, gender, admission diagnosis and APACHE IV scores. Traditional machine learning algorithms, including XGBoost and Logistic regression were used to predict self-reported race using values of vital signs as an input. Results: AI models derived from only six vital signs can predict patients' self-reported race with an AUC of 0.74 (+/- 0.022) between White and Black patients. Technologies used to measure oxygen saturation are a significant source of self-reported racial information (AUC of 0.72 +/- 0.028), in addition to blood pressure measurements (AUC of 0.63 +/- 0.035). Care delivery practices do not present a significant source of racial information (AUC of 0.57 +/- 0.019). However, even when controlling for these known factors, self-reported race can still be learned from vital signs, whose origin we cannot currently explain. Interpretation: Vital signs embed racial information that can be learned by AI algorithms, posing a significant risk to equitable clinical decision-making. Mitigating measures might be challenging, considering fundamental role of vital signs.
Infection Inspection: Using the power of citizen science to help with image-based prediction of antibiotic resistance in Escherichia coli

Antibiotic resistance is an urgent global health challenge, necessitating rapid diagnostic tools to combat its escalating threat. This study introduces innovative approaches for expedited bacterial antimicrobial resistance profiling, addressing the critical need for swift clinical responses. Between February and April 2023, we conducted the Infection Inspection project, a citizen science initiative in which the public could participate in advancing an antimicrobial susceptibility testing method based on single-cell images of cellular phenotypes in response to ciprofloxacin exposure. A total of 5,273 users participated, classifying 1,045,199 images. Notably, aggregated user accuracy in image classification reached 66.8%, lower than our deep learning model's performance at 75.3%, but accuracy increased for both users and the model when ciprofloxacin treatment was greater than a strain's own minimum inhibitory concentration. We used the users' classifications to elucidate which visual features influence classification decisions, most importantly the degree of DNA compaction and heterogeneity. We paired our classification data with an image feature analysis which showed that most of the incorrect classifications were due to cellular features that varied from the expected response. This understanding informs ongoing efforts to enhance the robustness of our deep learning-based bacterial classifier and diagnostic methodology. Our successful engagement with the public through citizen science is another demonstration of the potential for collaborative efforts in scientific research, specifically increasing public awareness and advocacy on the pressing issue of antibiotic resistance, and empowering individuals to actively contribute to the development of novel diagnostics.
Cataract-1K: Cataract Surgery Dataset for Scene Segmentation, Phase Recognition, and Irregularity Detection

In recent years, the landscape of computer-assisted interventions and post-operative surgical video analysis has been dramatically reshaped by deep-learning techniques, resulting in significant advancements in surgeons' skills, operation room management, and overall surgical outcomes. However, the progression of deep-learning-powered surgical technologies is profoundly reliant on large-scale datasets and annotations. Particularly, surgical scene understanding and phase recognition stand as pivotal pillars within the realm of computer-assisted surgery and post-operative assessment of cataract surgery videos. In this context, we present the largest cataract surgery video dataset that addresses diverse requisites for constructing computerized surgical workflow analysis and detecting post-operative irregularities in cataract surgery. We validate the quality of annotations by benchmarking the performance of several state-of-the-art neural network architectures for phase recognition and surgical scene segmentation. Besides, we initiate the research on domain adaptation for instrument segmentation in cataract surgery by evaluating cross-domain instrument segmentation performance in cataract surgery videos. The dataset and annotations will be publicly available upon acceptance of the paper.
Spectral Statistics of the Sample Covariance Matrix for High Dimensional Linear Gaussians

Performance of ordinary least squares(OLS) method for the \emph{estimation of high dimensional stable state transition matrix} $A$(i.e., spectral radius $\rho(A)<1$) from a single noisy observed trajectory of the linear time invariant(LTI)\footnote{Linear Gaussian (LG) in Markov chain literature} system $X_{-}:(x_0,x_1, \ldots,x_{N-1})$ satisfying \begin{equation} x_{t+1}=Ax_{t}+w_{t}, \hspace{10pt} \text{ where } w_{t} \thicksim N(0,I_{n}), \end{equation} heavily rely on negative moments of the sample covariance matrix: $(X_{-}X_{-}^{*})=\sum_{i=0}^{N-1}x_{i}x_{i}^{*}$ and singular values of $EX_{-}^{*}$, where $E$ is a rectangular Gaussian ensemble $E=[w_0, \ldots, w_{N-1}]$. Negative moments requires sharp estimates on all the eigenvalues $\lambda_{1}\big(X_{-}X_{-}^{*}\big) \geq \ldots \geq \lambda_{n}\big(X_{-}X_{-}^{*}\big) \geq 0$. Leveraging upon recent results on spectral theorem for non-Hermitian operators in \cite{naeem2023spectral}, along with concentration of measure phenomenon and perturbation theory(Gershgorins' and Cauchys' interlacing theorem) we show that only when $A=A^{*}$, typical order of $\lambda_{j}\big(X_{-}X_{-}^{*}\big) \in \big[N-n\sqrt{N}, N+n\sqrt{N}\big]$ for all $j \in [n]$. However, in \emph{high dimensions} when $A$ has only one distinct eigenvalue $\lambda$ with geometric multiplicity of one, then as soon as eigenvalue leaves \emph{complex half unit disc}, largest eigenvalue suffers from curse of dimensionality: $\lambda_{1}\big(X_{-}X_{-}^{*}\big)=\Omega\big( \lfloor\frac{N}{n}\rfloor e^{\alpha_{\lambda}n} \big)$, while smallest eigenvalue $\lambda_{n}\big(X_{-}X_{-}^{*}\big) \in (0, N+\sqrt{N}]$. Consequently, OLS estimator incurs a \emph{phase transition} and becomes \emph{transient: increasing iteration only worsens estimation error}, all of this happening when the dynamics are generated from stable systems.
The Global Economic Contribution of Protected Natural Lands and Wilderness through Tourism

Introduction These are the first-round results of a project aimed at exploring at a global scale the complex relationships between protected natural lands, tourism, and economic growth. In this fist round we mainly were interested in secondary sources of data and parameters from previously published studies. In presenting results for the 8th World Wilderness Congress, we provided summaries of the area of protected natural lands, estimates of the economic impacts stimulated by tourism drawn by these lands, and the spatial distributions of these lands and impacts around the globe. We were surprised to discover that only a very limited amount of research has been done previously to assemble the concepts, models, data, and summaries necessary for such an effort at a global scale. Thus, it was necessary for this project to tightly define the concepts of tourism and nature-based tourism that are relevant to assessing global impacts. Next it was necessary to identify and obtain contemporary best data enumerating tourists, their travels, and their spending. Finally, it was necessary to pull key concepts and data together for defining, quantifying, and spatially marking the economic activities associated with tourists traveling to visit and see protected natural lands.
Apparate: Rethinking Early Exits to Tame Latency-Throughput Tensions in ML Serving

Machine learning (ML) inference platforms are tasked with balancing two competing goals: ensuring high throughput given many requests, and delivering low-latency responses to support interactive applications. Unfortunately, existing platform knobs (e.g., batch sizes) fail to ease this fundamental tension, and instead only enable users to harshly trade off one property for the other. This paper explores an alternate strategy to taming throughput-latency tradeoffs by changing the granularity at which inference is performed. We present Apparate, a system that automatically applies and manages early exits (EEs) in ML models, whereby certain inputs can exit with results at intermediate layers. To cope with the time-varying overhead and accuracy challenges that EEs bring, Apparate repurposes exits to provide continual feedback that powers several novel runtime monitoring and adaptation strategies. Apparate lowers median response latencies by 40.5-91.5% and 10.0-24.2% for diverse CV and NLP workloads, respectively, without affecting throughputs or violating tight accuracy constraints.
Accurate Differential Operators for Hybrid Neural Fields

Neural fields have become widely used in various fields, from shape representation to neural rendering, and for solving partial differential equations (PDEs). With the advent of hybrid neural field representations like Instant NGP that leverage small MLPs and explicit representations, these models train quickly and can fit large scenes. Yet in many applications like rendering and simulation, hybrid neural fields can cause noticeable and unreasonable artifacts. This is because they do not yield accurate spatial derivatives needed for these downstream applications. In this work, we propose two ways to circumvent these challenges. Our first approach is a post hoc operator that uses local polynomial-fitting to obtain more accurate derivatives from pre-trained hybrid neural fields. Additionally, we also propose a self-supervised fine-tuning approach that refines the neural field to yield accurate derivatives directly while preserving the initial signal. We show the application of our method on rendering, collision simulation, and solving PDEs. We observe that using our approach yields more accurate derivatives, reducing artifacts and leading to more accurate simulations in downstream applications.