Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of Vaccination With the SCB-2019 Coronavirus Disease 2019 Vaccine on Transmission of Severe Acute Respiratory Syndrome Coronavirus 2 Infection: A Household Contact Study in the Philippines

Background: An exploratory household transmission study was nested in SPECTRA, the phase 2/3 efficacy study of the adjuvanted recombinant protein-based COVID-19 vaccine SCB-2019. We compared occurrence of confirmed COVID-19 infections between households and household contacts of infected SPECTRA participants who were either placebo or SCB-2019 recipients. Methods: SPECTRA trial participants at eight study sites in the Philippines who developed rRT-PCR-confirmed COVID-19 were contacted by a study team blinded to assignment of index cases to vaccine or placebo groups to enroll in this household transmission study. Enrolled households and household contacts were monitored for three weeks using rRT-PCR and rapid antigen testing to detect new COVID-19 infections. Results: Observation of the households of 154 eligible COVID-19 index cases, 130 symptomatic and 24 asymptomatic at diagnosis, revealed household secondary attack rates for any COVID-19 infection of SCB-2019 index cases of 0.76% (90% CI: 0.15-3.90) compared with 5.88% (90% CI: 3.20-10.8) in placebo index case households, a relative risk reduction of 79% (90% CI: -28-97). The relative risk reduction of symptomatic COVID-19 was 84% (90% CI: 28-97) for household contacts of all COVID-19 infected index cases, and 80% (90% CI: 7-96) for household contacts of index cases with symptomatic COVID-19. Conclusions: In this prospective household contact study vaccination with SCB-2019 reduced SARS-CoV-2 transmission in households, so decreasing infections of household contacts, compared with placebo.
Increased adverse events following third dose of BNT162b2/Pfizer vaccine in those with previous COVID-19, but not with concurrent influenza vaccine

Prior studies suggest that adverse events (AEs) following doses one and two of BNT162b2/Pfizer vaccine are worse in those with a prior history of COVID-19. To establish whether this outcome applies to a third/booster dose, we conducted a survey with 534 healthcare workers (HCW) in Northeast England, who reported AEs following all three doses of BNT162b2/Pfizer vaccine. We also explored AEs associated with concurrent seasonal influenza immunisation. For all doses of BNT162b2/Pfizer vaccine there was a cluster of systemic AEs that were consistently worse in HCWs with a prior history of COVID-19. AEs were no worse in HCWs who received their third/booster dose within 7 days of the influenza jab, rather than further apart. Gender and the presence of ongoing COVID-19 symptoms (OCS) had no effect on AEs associated with COVID-19 or influenza vaccination, though younger HCWs experienced more AEs overall. Our findings have implications for vaccine hesitancy and immunisation protocols.
Cerebrospinal Fluid and Plasma Lipopolysaccharide Levels in Human Immunodeficiency Virus Type 1 Infection and Associations With Inflammation, Blood-Brain Barrier Permeability, and Neuronal Injury

Human immunodeficiency virus (HIV) infection is associated with increased systemic microbial translocation, neuroinflammation, and occasionally, neuronal injury. Whether systemic lipopolysaccharide (LPS) penetrates into the brain and contributes to neuroinflammation remain unknown in HIV. Here, we measured plasma and cerebrospinal fluid (CSF) LPS levels along with biomarkers of neuroinflammation (white blood cell counts and 40 soluble markers) and neurofilament light chain (NfL). Notably, CSF LPS was undetectable in all samples, including 3 HIV-infected individuals with dementia. Increased plasma LPS, neuroinflammation, and blood-brain barrier (BBB) dysfunction were found in untreated HIV-infected individuals, but not in healthy or treated HIV-infected individuals. Plasma LPS levels were directly correlated with various markers of inflammation in both plasma and CSF, as well as with degree of BBB permeability but not with CSF NfL in HIV-infected subjects. These results suggest that the magnitude of microbial translocation associates with neuroinflammation and BBB permeability in HIV without direct penetration into the central nervous system.
Data-driven identification of subtypes of executive function across typical development, ADHD, and ASD

Background: Impairment of executive function (EF), the goal-directed regulation of thoughts, actions, and emotions, drives negative outcomes and is common across neurodevelopmental disorders including Attention Deficit Hyperactivity Disorder (ADHD) and Autism Spectrum Disorder (ASD). A primary challenge to its amelioration is heterogeneity in symptom expression within and across disorders. Parsing this heterogeneity is necessary to attain diagnostic precision, a goal of the NIMH Research Domain Criteria Initiative. We aimed to identify transdiagnostic subtypes of EF that span the normal to impaired spectrum and establish their predictive and neurobiological validity. Methods: Community detection was applied to clinical parent-report measures in 8-14 year-old children with and without ADHD and ASD from two independent cohorts (Discovery N=320; Replication N=692) to identify subgroups with distinct behavioral profiles. Support vector machine (SVM) classification was used to predict subgroup membership of unseen cases. Preliminary neurobiological validation was obtained with existing functional magnetic resonance imaging (fMRI) data on a subsample (N=84) by testing hypotheses about sensitivity of EF subgroups versus DSM categories. Results: We observed three transdiagnostic EF subtypes characterized by behavioral profiles that were defined by relative weakness in: 1) flexibility and emotion regulation; 2) inhibition; and 3) working memory, organization, and planning. The same tripartite structure typically developing children. SVM trained on the Discovery sample and tested on the Replication sample classified subgroup membership with 77.0% accuracy. Split-half SVM classification on the combined sample (N=1012) yielded 88.9% accuracy (this SVM is available for public use). As hypothesized, frontal-parietal engagement was better distinguished by EF subtype than DSM-diagnosis and the subgroup characterized with inflexibility failed to modulate right IPL activation in response to increased executive demands. Conclusions: The observed transdiagnostic subtypes refine current diagnostic nosology and augment clinical decision-making for personalizing treatment of executive dysfunction in children.
Oral Biology and Craniofacial Research E ﬀ ect of adding ribbond ﬁ bres on marginal adaptation in class II composite restorations in teeth with a ﬀ ected dentine

Objective: To evaluate the feasibility and e ﬀ ect on marginal adaptation in class II composite restoration re- inforced with polyethylene ﬁ bres in teeth with a ﬀ ected dentine using scanning electron microscopy. Method and material: Class II cavities with dimension 1.5 +0 . 25 pulpal depth and 4 ± 0.25 mm buccolingual width/. were prepared on proximal surfaces extracted human molars or premolar with a ﬀ ected/sound dentine on gingival margin. The etching priming and restorations were done in prepared samples as per manufacture di- rections. The samples were divided in 2 groups depending on use of ribbond inserts in composite restorative material. Samples were ﬁ nished, stored in distilled water and then thermocycled manually and then sectioned longitudinally through the restorations. The marginal adaptation was evaluated using scanning electron microscope (SEM). Result: The result showed that there was a de ﬁ nite gap all along the interface between caries a ﬀ ected dentine and the composite material in both the groups. The bigger gap was present in group II compared to group I.
The Molecular Structure of Sphingomyelin in Fluid Phase Bilayers Determined by the Joint Analysis of Small-Angle Neutron and X-ray Scattering Data

We have determined the fluid bilayer structure of palmitoyl sphingomyelin (PSM) and stearoyl sphingomyelin (SSM) by simultaneously analyzing small-angle neutron and X-ray scattering data. Using a newly developed scattering density profile (SDP) model for sphingomyelin lipids, we report structural parameters including the area per lipid, total bilayer thickness, and hydrocarbon on the ACS website. Scattering parameters for PSM and SSM and NMR; order parameters for PSM; details of SM parsing schemes for scattering analysis; volume probability profiles, NMR order para profiles, and number density distributions from PSM simulation; details of ESR lineshape analysis; DSC and densitometry data for PSM and SSM; NMR spectra of PSM; SAXS data from PSM MLVs. thickness, in addition to lipid volumes determined by densitometry. Unconstrained all-atom simulations of PSM bilayers at 55°C using the C36 CHARMM force field produced a lipid area of 56 Å 2 , a value that is 10% lower than the one determined experimentally with the SDP analysis (61.9 Å 2 ). Furthermore, scattering form factors calculated from the unconstrained simulations were in poor agreement with experimental form factors, even though segmental order parameter (S CD ) profiles calculated from the simulations were in relatively good agreement with S CD profiles obtained from NMR experiments. Conversely, constrained area simulations at 61.9 Å 2 resulted in good agreement between the simulation and experimental scattering form factors, but not with S CD profiles from NMR. We discuss possible reasons for the discrepancies between these two important types of data that are frequently used as validation metrics for molecular dynamics force fields. We report the structural parameters of palmitoyl and bilayers in the fluid state obtained from joint analysis of small-angle neutron and X-ray scattering data, including the ever-important area per lipid that is directly related to bilayer thickness and indirectly related to other bilayer properties. The results comprise an important addition to the scarce literature on SM structure as they provide robustly determined parameters of bilayer structure in fully hydrated free-floating liposomes. We further report order parameters of the N-linked chain of PSM obtained with 2 H NMR as well as experimentally measured lipid volumes for both PSM and SSM. Through the use of all-atom MD simulations, a clear discrepancy is observed between the lipid packing density measured with scattering and the lipid order parameter obtained from NMR. As these two techniques provide crucial structural information used for testing and validation of simulation force field parameters, we discuss possible sources of the differences between the experimentally determined bilayer structures and emphasize the importance of considering sample conditions when interpreting structural results and designing experiments.
A CASE FOR “EARLY” THERAPY

521 improving survival in sepsis despite non‐adherence to the surviving sepsis campaign guidelines. [7,8] Herein, we attempt to further analyze the factors that might have led to the apparent conflict between the studies by Rivers et al. and the subsequent ProCESS, ARISE, and ProMISe studies. We also make a case for the early and goal‐directed components of the EGDT, at the same time questioning the utility of some of its other components.
Robust Graph Meta-learning for Weakly-supervised Few-shot Node Classification

Graph machine learning (Graph ML) models typically require abundant labeled instances to provide sufficient supervision signals, which is commonly infeasible in real-world scenarios since labeled data for newly emerged concepts (e.g., new categorizations of nodes) on graphs is rather limited. In order to efficiently learn with a small amount of data on graphs, meta-learning has been investigated in graph ML. By transferring the knowledge learned from previous experiences to new tasks, graph meta-learning approaches have demonstrated promising performance on few-shot graph learning problems. However, most existing efforts predominately assume that all the data from the seen classes is gold-labeled, while those methods may lose their efficacy when the seen data is weakly-labeled with severe label noise. As such, we aim to investigate a novel problem of weakly-supervised graph meta-learning for improving the model robustness in terms of knowledge transfer. To achieve this goal, we propose a new graph meta-learning framework – Graph Interpolation Networks (Meta-GIN). Based on a new robustness-enhanced episodic training paradigm, Meta-GIN is meta-learned to interpolate node representations from weakly-labeled data and extracts highly transferable meta-knowledge, which enables the model to quickly adapt to unseen tasks with few labeled instances. Extensive experiments demonstrate the superiority of Meta-GIN over existing graph meta-learning studies on the task of weakly-supervised few-shot node classification.
Simulation-Informed Revenue Extrapolation with Confidence Estimate for Scaleup Companies Using Scarce Time-Series Data

Investment professionals rely on extrapolating company revenue into the future (i.e. revenue forecast) to approximate the valuation of scaleups (private companies in a high-growth stage) and inform their investment decision. This task is manual and empirical, leaving the forecast quality heavily dependent on the investment professionals' experiences and insights. Furthermore, financial data on scaleups is typically proprietary, costly and scarce, ruling out the wide adoption of data-driven approaches. To this end, we propose a simulation-informed revenue extrapolation (SiRE) algorithm that generates fine-grained long-term revenue predictions on small datasets and short time-series. SiRE models the revenue dynamics as a linear dynamical system (LDS), which is solved using the EM algorithm. The main innovation lies in how the noisy revenue measurements are obtained during training and inferencing. SiRE works for scaleups that operate in various sectors and provides confidence estimates. The quantitative experiments on two practical tasks show that SiRE significantly surpasses the baseline methods by a large margin. We also observe high performance when SiRE extrapolates long-term predictions from short time-series. The performance-efficiency balance and result explainability of SiRE are also validated empirically. Evaluated from the perspective of investment professionals, SiRE can precisely locate the scaleups that have a great potential return in 2 to 5 years. Furthermore, our qualitative inspection illustrates some advantageous attributes of the SiRE revenue forecasts.
Texture of two vanishing subtraces in neutrino mass matrix and current experimental tests

We present a full phenomenological and analytical study for the neutrino mass matrix characterized by two vanishing $2\times2$ subtraces. We update one past result in light of the recent experimental data. Out of the fifteen possible textures, we find seven cases can accommodate the experimental data instead of eight ones in the past study. We also introduce few symmetry realizations for viable and nonviable textures based on non-abelian ($A_4$ or $S_4$) flavor symmetry within type II seesaw scenario.
Improved Constructions of Permutation and Multi-Permutation Codes Correcting a Burst of Stable Deletions

Permutation codes and multi-permutation codes have been widely considered due to their various applications, especially in flash memory. In this paper, we consider permutation codes and multi-permutation codes against a burst of stable deletions. In particular, we propose a construction of permutation codes correcting a burst stable deletion of length <inline-formula> <tex-math notation="LaTeX">$s$ </tex-math></inline-formula>, with redundancy <inline-formula> <tex-math notation="LaTeX">$\log n+ 2\log \log n+O(1{)}$ </tex-math></inline-formula>. Compared to the previous known results, our improvement relies on a different strategy to retrieve the missing symbol on the first row of the array representation of a permutation. We also generalize our constructions for multi-permutations and the variable length burst model. Furthermore, we propose a linear-time encoder with optimal redundancy for single stable deletion correcting permutation codes.
Stars, Dust, and the Growth of UV-Selected Sub-L* Galaxies at Redshift z~2

[Abridged] This work concerns very faint (R_lim=28 AB mag; M_(stars, lim) ~ 10^8 Msun), UV-selected sub-L* BX galaxies at z~2.3. Stellar masses, dust content, and dust-corrected SFRs are constrained using broadband SED fitting, giving insights into the nature of these low-mass systems. First, a correlation found between rest-frame UV luminosity and galaxy stellar mass suggests that many sub-L* galaxies at z~2.3 may have approximately constant star formation histories. A nearly-linear relation between stellar mass and star formation rate is also found, hinting that the rate at which a sub-L* BX galaxy forms its stars is directly related to the mass of stars that it has already formed. A possible explanation is that new gas that falls onto the galaxy's host halo along with accreting dark matter is the source of fuel for ongoing star formation. The instantaneous efficiency of star formation is low in this scenario, of order 1%. The low-mass end of the stellar mass function is steeper than expected from extrapolations of shallower surveys, resulting in a SMD at z~2.3 that's ~25% of the present-day value; this value is z~1.5-2x higher than that given by extrapolations of shallower surveys, suggesting that the build-up of stellar mass in the universe has proceeded more rapidly than previously thought. An update to the KDF z~2 UV LF finds a steeper faint-end slope than previously reported, though not as steep as that found by Reddy & Steidel (2009). Finally, sub-L* galaxies at z~2 carry very small amounts of dust compared to their more luminous cousins, so that while only ~20% of 1700A photons escape from a typical M* galaxy, more than half make it out of an M*+3 one. This paucity of dust highlights the fact that sub-L* galaxies are not simple scaled copies of their more luminous cousins. It also suggests that sub-L* are important contributors to keeping the Universe ionized at z~2.
Accelerating Vision Transformer Training via a Patch Sampling Schedule

We introduce the notion of a Patch Sampling Schedule (PSS), that varies the number of Vision Transformer (ViT) patches used per batch during training. Since all patches are not equally important for most vision objectives (e.g., classification), we argue that less important patches can be used in fewer training iterations, leading to shorter training time with minimal impact on performance. Additionally, we observe that training with a PSS makes a ViT more robust to a wider patch sampling range during inference. This allows for a fine-grained, dynamic trade-off between throughput and accuracy during inference. We evaluate using PSSs on ViTs for ImageNet both trained from scratch and pretrained using a reconstruction loss function. For the pretrained model, we achieve a 0.26% reduction in classification accuracy for a 31% reduction in training time (from 25 to 17 hours) compared to using all patches each iteration. Code, model checkpoints and logs are available at https://github.com/BradMcDanel/pss.
Generation of spin currents by the orbital Hall effect in Cu and Al and their measurement by a Ferris-wheel ferromagnetic resonance technique at the wafer level

: We present a new ferromagnetic resonance (FMR) method that we term the “Ferris” FMR. It is wideband, has significantly higher sensitivity as compared to conventional FMR systems, and measures the absorption line rather than its derivative. It is based on large-amplitude modulation of the externally applied magnetic field that effectively magnifies signatures of the spin-transfer torque making its measurement possible even at the wafer-level. Using the Ferris FMR, we report on the generation of spin currents from the orbital Hall effect taking place in pure Cu and Al. To this end, we use the spin-orbit coupling of a thin Pt layer introduced at the interface that converts the orbital current to a measurable spin current. While Cu reveals a large effective spin Hall angle exceeding that of Pt, Al possesses an orbital Hall effect of opposite polarity in agreement with the theoretical predictions. Our results demonstrate additional spin-and orbit- functionality for two important metals in the semiconductor industry beyond their primary use as interconnects with all the advantages in power, scaling, and cost.
Selection Collider Bias in Large Language Models

In this paper we motivate the causal mechanisms behind sample selection induced collider bias (se-lection collider bias) that can cause Large Language Models (LLMs) to learn unconditional dependence between entities that are unconditionally independent in the real world. We show that selection collider bias can become ampliﬁed in underspeciﬁed learning tasks, and although difﬁcult to overcome, we describe a method to exploit the resulting spurious correlations for determination of when a model may be uncertain about its prediction. We demonstrate an uncertainty metric that matches human uncertainty in tasks with gender pronoun underspeciﬁcation on an extended version of the Winogender Schemas evaluation set, and we provide an online demo where users can apply our uncertainty metric to their own texts and models.
Practical Vertical Federated Learning with Unsupervised Representation Learning

—As societal concerns on data privacy recently increase, we have witnessed data silos among multiple parties in various applications. Federated learning emerges as a new learning paradigm that enables multiple parties to collaboratively train a machine learning model without sharing their raw data. Vertical federated learning , where each party owns different features of the same set of samples and only a single party has the label, is an important and challenging topic in federated learning. Communication costs among different parties have been a major hurdle for practical vertical learning systems. In this paper, we propose a novel communication-efﬁcient vertical federated learning algorithm named FedOnce , which requires only one-shot communication among parties. To improve model accuracy and provide privacy guarantee, FedOnce features unsupervised learning representations in the federated setting and privacy-preserving techniques based on moments accountant. The comprehensive experiments on 10 datasets demonstrate that FedOnce achieves close performance compared to state-of-the-art vertical federated learning algorithms with much lower communication costs. Meanwhile, our privacy-preserving technique signiﬁcantly outperforms the state-of-the-art approaches under the same privacy budget.
Instability and Local Minima in GAN Training with Kernel Discriminators

Generative Adversarial Networks (GANs) are a widely-used tool for generative modeling of complex data. Despite their empirical success, the training of GANs is not fully understood due to the min-max optimization of the generator and discriminator. This paper analyzes these joint dynamics when the true samples, as well as the generated samples, are discrete, finite sets, and the discriminator is kernel-based. A simple yet expressive framework for analyzing training called the $\textit{Isolated Points Model}$ is introduced. In the proposed model, the distance between true samples greatly exceeds the kernel width, so each generated point is influenced by at most one true point. Our model enables precise characterization of the conditions for convergence, both to good and bad minima. In particular, the analysis explains two common failure modes: (i) an approximate mode collapse and (ii) divergence. Numerical simulations are provided that predictably replicate these behaviors.