Miracle-dz/UniFAR-FTUs huggingface dataset:
Major Update: Masks for Prevention of SARS-CoV-2 in Health Care and Community Settings—Final Update of a Living, Rapid Review

BACKGROUND Optimal use of masks for preventing COVID-19 is unclear. PURPOSE To update an evidence synthesis on N95, surgical, and cloth mask effectiveness in community and health care settings for preventing SARS-CoV-2 infection. DATA SOURCES MEDLINE, EMBASE, medRxiv (3 June 2022 to 2 January 2023), and reference lists. STUDY SELECTION Randomized trials of interventions to increase mask use and risk for SARS-CoV-2 infection and observational studies of mask use that controlled for potential confounders. DATA EXTRACTION Two investigators sequentially abstracted study data and rated quality. DATA SYNTHESIS Three randomized trials and 21 observational studies were included. In community settings, mask use may be associated with a small reduced risk for SARS-CoV-2 infection versus no mask use, on the basis of 2 randomized trials and 7 observational studies. In routine patient care settings, surgical masks and N95 respirators may be associated with similar risk for SARS-CoV-2 infection, on the basis of 1 new randomized trial with some imprecision and 4 observational studies. Evidence from observational studies was insufficient to evaluate other mask comparisons due to methodological limitations and inconsistency. LIMITATION Few randomized trials, studies had methodological limitations and some imprecision, suboptimal adherence and pragmatic aspects of randomized trials potentially attenuated benefits, very limited evidence on harms, uncertain applicability to Omicron variant predominant era, meta-analysis not done due to heterogeneity, unable to formally assess for publication bias, and restricted to English-language articles. CONCLUSION Updated evidence suggests that masks may be associated with a small reduction in risk for SARS-CoV-2 infection in community settings. Surgical masks and N95 respirators may be associated with similar infection risk in routine patient care settings, but a beneficial effect of N95 respirators cannot be ruled out. PRIMARY FUNDING SOURCE None.
What you want to feel determines how you feel: The role of ideal affect in emotion regulation

ABSTRACT This research investigated whether emotion regulation can be determined by one’s desired affective state (i.e., ideal affect). Study 1 explored whether ideal affect could explain differences in two emotion regulation strategies (cognitive reappraisal and expressive suppression) between cultural groups (European Americans vs. Hong Kong Chinese; N = 300). Results revealed that ideal high arousal positive states correlated positively with cognitive reappraisal. Study 2 further tested the causal impact of ideal affect on emotion regulation (N = 101). To manipulate their ideal low/high arousal positive states, participants were randomly assigned to either a ‘peaceful meeting’ or an ‘exciting party’ condition. The participants’ strategies to regulate negative emotions were assessed. Results showed that, regardless of cultural background, participants in the party condition employed more cognitive reappraisal than those in the meeting condition. These findings indicated that what we want to feel partly determines how we regulate our emotions.
LOCAL DIFFERENTIATION AND ADAPTATION TO CLIMATE CHANGE IN COASTAL GHANA

ABSTRACT This study investigates the perception of the impact of climate change on the livelihoods of small-scale fishing communities in the Western Region of Ghana. A mixed-method approach was employed, consisting of a survey of 400 fisherfolk households and 20 interviews with stakeholders. Changes in rainfall pattern, decreasing rainfall, stronger waves and storms that increase coastal erosion and cause persistent flooding were found to be the main effects of climate change on the livelihoods of small-scale coastal fisherfolk. Transnational fishing practices by fisherfolk in response to climate change contributed to the building of resilience of fishing households, while other adaptation strategies appeared to be insufficient in the long run. Coastal communities are not homogenous with respect to climate change impacts and corresponding adaptation strategies. Based on the empirical results, policy implications are suggested related to early warning systems and multiscalar marine planning.
Neural Networks for Financial Stability of Economic System

In the present economic landscape, securing the monetary steadiness of economic structures, augmenting their financial efficacy, and competitiveness necessitates the scrutiny of the financial state of enterprises, along with predicting their future progressions utilizing contemporary technologies and models. In acquiring information regarding the fluctuations of significant financial hazards, machine and deep learning techniques can offer more precise projections founded on vast-dimensional datasets, authorize the employment of unbalanced datasets, and preserve all accessible information. The aim of this investigation is to construct a neural network-driven model for assessing the financial stability of economic systems. The study employed financial and economic activity data from 12,573 enterprises and opted for specific financial ratios that generate a significant set of indicators suitable for forecasting the financial stability of economic systems. Both feedforward neural networks (FNN) and recurrent neural networks (RNN) were utilized in the model development. The constructed models were evaluated using established data science techniques.
Sentence Analogies for Text Morphing

Text morphing is a Natural Language Processing (NLP) task which aims at generating sequences of fluent and smooth intermediate sentences between two input sentences, the start and end sentences. In this paper, we show how to use sentence analogies to augment data for this task. We rely on the notion of analogy to produce sequences of sentences exhibiting step-by-step transitions. We use these sequences to fine-tune a large-scale pre-trained language model that is used for text generation. The performance is evaluated by two criteria: fluency and transition smoothness on both the semantic and formal levels. Compared to a variational autoencoder generative model, our model is shown to generate smoother transitions, although the generated sentences are slightly less fluent.
Red Fluorescent Carbon Nanoparticles Derived from Spinacia oleracea L.: A Versatile Tool for Bioimaging & Biomedical Applications

Carbon-based fluorescent quantum dots are an emerging class of nanoparticles for targeted bioimaging and biomedical applications. We present a facile microwave-assisted approach for synthesizing carbon nanoparticles with bright red fluorescence using ethanolic extracts of Spinacia oleracea leaves, with a quantum yield of 94.67%. These nanoparticles, called CNPs, ranging from 15-50 nm, demonstrated fluorescence emission in the near-infrared (NIR) region between 650 and 700 nm, independent of excitation wavelength. Upon excitation at a wavelength of 410 nm, they exhibit an emission maxima peak at 672 nm. The significant uptake of CNPs in mammalian cells and zebrafish larvae highlights their potential as bioimaging agents in diverse biomedical applications in vivo. Further, these quantum dots enhance cellular proliferation and migration as observed by wound healing assay in mammalian cells, indicating their possible application in tissue engineering and regenerative medicine. These findings suggest that biosynthesized carbon nanoparticles possess significant potential for biomedical activities, which can serve as a robust benchmark for researchers towards promoting sustainability.
Designing molecular RNA switches with Restricted Boltzmann machines

Riboswitches are structured allosteric RNA molecules capable of switching between competing conformations in response to a metabolite binding event, eventually triggering a regulatory response. Computational modelling of these molecules is complicated by complex tertiary contacts, conditioned to the presence of their cognate metabolite. In this work, we show that Restricted Boltzmann machines (RBM), a simple two-layer machine learning model, capture intricate sequence dependencies induced by secondary and tertiary structure, as well as the switching mechanism, resulting in a model that can be successfully used for the design of allosteric RNA. As a case study we consider the aptamer domain of SAM-I riboswitches. To validate the functionality of designed sequences experimentally by SHAPE-MaP, we develop a tailored analysis pipeline adequate for high-throughput probing of diverse homologous sequences. We find that among the probed 84 RBM designed sequences, showing up to 20% divergence from any natural sequence, about 28% (and 47% of the 45 among them having low RBM effective energies), are correctly structured and undergo a structural allosteric in response to SAM. Finally, we show how the flexibility of the molecule to switch conformations is connected to fine energetic features of its structural components.
A sensory-motor decoder that transforms neural responses in extrastriate area MT into smooth pursuit eye movements

Visual motion drives smooth pursuit eye movements through a sensory-motor decoder that uses multiple parallel components and neural pathways to transform the population response in extrastriate area MT into movement. We evaluated the decoder by challenging pursuit in monkeys with reduced motion reliability created by reducing coherence of motion in patches of dots. Reduced dot coherence caused deficits in both the initiation of pursuit and steady-state tracking, revealing the paradox of steady-state eye speeds that fail to accelerate to target speed in spite of persistent image motion. We recorded neural responses to reduced dot coherence in MT and found a decoder that transforms MT population responses into eye movements. During pursuit initiation, decreased dot coherence reduces MT population response amplitude without changing the preferred speed at the peak of the population response. The successful decoder reproduces the measured eye movements by multiplication of (i) the estimate of target speed from the peak of the population response with (ii) visual-motor gain based on the amplitude of the population response. During steady-state tracking, the decoder that worked for pursuit initiation failed. It predicted eye acceleration to target speed even when monkeys’ eye speeds were steady at a level well below target speed. We can account for the effect of dot coherence on steady-state eye speed if sensorymotor gain also modulates the eye velocity positive feedback that normally sustains perfect steadystate tracking. Then, poor steady-state tracking persists because of balance between deceleration caused by low positive feedback gain and acceleration driven by MT.
Rapid Processing of Observed Touch through Social Perceptual Brain Regions: An EEG-fMRI Fusion Study

Seeing social touch triggers a strong social-affective response that involves multiple brain networks, including visual, social perceptual, and somatosensory systems. Previous studies have identified the specific functional role of each system, but little is known about the speed and directionality of the information flow. Is this information extracted via the social perceptual system or from simulation from somatosensory cortex? To address this, we examined the spatiotemporal neural processing of observed touch. Twenty-one human participants (seven males) watched 500-ms video clips showing social and nonsocial touch during electroencephalogram (EEG) recording. Visual and social-affective features were rapidly extracted in the brain, beginning at 90 and 150 ms after video onset, respectively. Combining the EEG data with functional magnetic resonance imaging (fMRI) data from our prior study with the same stimuli reveals that neural information first arises in early visual cortex (EVC), then in the temporoparietal junction and posterior superior temporal sulcus (TPJ/pSTS), and finally in the somatosensory cortex. EVC and TPJ/pSTS uniquely explain EEG neural patterns, while somatosensory cortex does not contribute to EEG patterns alone, suggesting that social-affective information may flow from TPJ/pSTS to somatosensory cortex. Together, these findings show that social touch is processed quickly, within the timeframe of feedforward visual processes, and that the social-affective meaning of touch is first extracted by a social perceptual pathway. Such rapid processing of social touch may be vital to its effective use during social interaction. SIGNIFICANCE STATEMENT Seeing physical contact between people evokes a strong social-emotional response. Previous research has identified the brain systems responsible for this response, but little is known about how quickly and in what direction the information flows. We demonstrated that the brain processes the social-emotional meaning of observed touch quickly, starting as early as 150 ms after the stimulus onset. By combining electroencephalogram (EEG) data with functional magnetic resonance imaging (fMRI) data, we show for the first time that the social-affective meaning of touch is first extracted by a social perceptual pathway and followed by the later involvement of somatosensory simulation. This rapid processing of touch through the social perceptual route may play a pivotal role in effective usage of touch in social communication and interaction.
Early astrocytic dysfunction is associated to mistuned synapses as well as anxiety and depressive-like behavior in the AppNL-F mouse model of Alzheimer’s disease

Alzheimer’s disease is the most common neurodegenerative disease and constitute 75% of dementia cases worldwide. Unfortunately, efficient and affordable treatments are still lacking for this mental illness, it is therefore urgent to identify new pharmacological targets. Whereas the late phases of the disease are well described, recent evidence suggest synaptic impairments at a pre-amyloid β (Aβ) plaque stage. Astrocytes are playing a crucial role in the tuning of synaptic transmission and several studies have pointed out severe astrocyte reactivity in Alzheimer’s disease, especially around Aβ plaques. Reactive astrocytes show altered physiology and function, suggesting they could have a role in the early pathophysiology of Alzheimer’s disease. In this study we used the AppNL-F knock-in mouse model of Alzheimer’s disease which carries two disease-causing mutations inserted in the amyloid precursor protein (App) gene. This strain does not start to develop Aβ plaques until nine months of age. To better understand early changes in Alzheimer’s disease, we investigated synaptic function, at both neuronal and astrocytic levels, in six months old AppNL-F mice and correlate the synaptic dysfunction with emotional behavior. Electrophysiological recordings in the hippocampus revealed an overall synaptic mistuning at a pre-plaque stage of the pathology, associated to an intact social memory but a stronger depressive-like behavior. Astrocytes displayed a reactive-like morphology and a higher tonic GABA current compared to control mice. Interestingly, we here show that the synaptic impairments in hippocampal slices are partially corrected by a pre-treatment with the monoamine oxidase B (MAO-B) blocker deprenyl or the fast-acting antidepressant ketamine (5mg/kg). Thus, we propose that reactive astrocytes can induce synaptic mistuning early in Alzheimer’s disease, before plaques deposition, and that these changes are associated with emotional symptoms. GRAPHICAL ABSTRACT
A ubiquitous GC content signature underlies multimodal mRNA regulation by DDX3X

The road from transcription to protein synthesis is paved with many obstacles, allowing for several modes of post-transcriptional regulation of gene expression. A fundamental player in mRNA biology is DDX3X, an RNA binding protein that canonically regulates mRNA translation. By monitoring dynamics of mRNA abundance and translation following DDX3X depletion, we observe stabilization of translationally suppressed mRNAs. We use interpretable statistical learning models to uncover GC content in the coding sequence as the major feature underlying RNA stabilization. This result corroborates GC content-related mRNA regulation detectable in other studies, including hundreds of ENCODE datasets and recent work focusing on mRNA dynamics in the cell cycle. We provide further evidence for mRNA stabilization by detailed analysis of RNA-seq profiles in hundreds of samples, including a Ddx3x conditional knockout mouse model exhibiting cell cycle and neurogenesis defects. Our study identifies a ubiquitous feature underlying mRNA regulation and highlights the importance of quantifying multiple steps of the gene expression cascade, where RNA abundance and protein production are often uncoupled.
Evaluating Public Per-Student Subsidies to Low-Cost Private Schools: Regression-Discontinuity Evidence from Pakistan

This study estimates the causal effects of a public per-student subsidy program targeted at low-cost private schools in Pakistan on student enrollment and schooling inputs. Program entry is ultimately conditional on achieving a minimum stipulated student pass rate (cutoff) in a standardized academic test. This mechanism for treatment assignment allows the application of regression-discontinuity (RD) methods to estimate program impacts at the cutoff. Data on two rounds of entry test takers (phase 3 and phase 4) are used. Modeling the entry process of phase-4 test takers as a sharp RD design, the authors find evidence of large positive impacts on the number of students, teachers, classrooms, and blackboards. Modeling the entry process of phase-3 test takers as a partially-fuzzy RD design given treatment crossovers, they do not find evidence of significant program impacts on outcomes of interest. The latter finding is likely due to weak identification arising from a small jump in the probability of treatment at the cutoff.
Dissecting genetic correlation through recombinant perturbations: the role of developmental bias

Despite the tremendous diversity and complexity of life forms, there are certain forms of life that are never observed. Organisms like angels might not emerge because of developmental biases that restrict how organisms can evolve, or because they have low fitness in any environment yet available on Earth. Given that both developmental bias and selection may create similar phenotypes, it is difficult to distinguish between the two causes of evolutionary stasis among related taxa. For example, remarkably invariant traits are observed spanning million years, such as wing shape in Drosophila wherein qualitative differences are rare within genera. We thus ask whether the absence of combinations of traits, indicated by genetic correlation, reflects developmental bias limiting the possibility of change. However, much confusion and controversy remain over definitions of developmental bias and quantifying it is challenging. We present a novel approach aiming to estimate developmental bias by leveraging a common but under-utilized type of data: recombinant genetic mapping populations. We reason that information rendered by such mild perturbations captures inherent interdependencies between traits – developmental bias. Through empirical analyses, we find that our developmental bias metric is a strong indicator of genetic correlation stability across conditions. Our framework presents a feasible way to quantify developmental bias between traits and opens up the possibility to dissect patterns of genetic correlation. Significance Statement Genetic correlation represents an important class of evolutionary constraint, which are themselves evolvable. Empirical studies have found mixed results on whether such evolutionary constraint changes rapidly or slowly. This uncertainty challenges our ability to predict the outcome of selection. Here, we propose a framework to dissect genetic correlation in a genetic mapping population and show that consistency of pleiotropic effects of loci across the genome, which we termed as developmental bias, is an indicator of genetic correlation stability. Our novel method empowers readily accessible QTL mapping data to understand complex genetic architecture underlying pleiotropy, mechanisms causing genetic correlation and, ultimately, long-term evolutionary divergence.
Histone H3 tail modifications regulate structure and dynamics of the H1 C-terminal domain within nucleosomes

Despite their importance, how linker histone H1s interact in chromatin and especially how the highly positively charged and intrinsically disordered H1 C-terminal domain (CTD) binds and stabilizes nucleosomes and higher-order chromatin structures remains unclear. Using single-molecule FRET we found that about half of the H1 CTDs in H1-nucleosome complexes exhibit well-defined FRET values indicative of distinct, static conformations, while the remainder of the population exhibits dynamically changing values, similar to that observed for H1 in the absence of nucleosomes. We also find that the first 30 residues of the CTD participate in relatively localized interactions with the first ∼20 bp of linker DNA, and that two separate regions in the CTD contribute to H1-dependent organization of linker DNA, consistent with some non-random CTD-linker DNA interactions. Finally, our data show that acetylation mimetics within the histone H3 tail induce decondensation and enhanced dynamics of the nucleosome-bound H1 CTD. (148 words)
A Program for Monetary Stability.

"[T]he only really sure way to beat inflation is to cut off inflation at the root. Milton Friedman [presents his strategy against] inflation in his penetrating . book . This is controversial stuff, and Professor Friedman . doesn't blanch at what he feels is his call of duty. And many a banker will just see red . [This book] can be recommended for a good look at the real roots of inflation-the look that thus far has not been widespread enough, among enough people. "-The Wall Street Journal
Biomechanics of cutting: sharpness, wear sensitivity and the scaling of cutting forces in leaf-cutter ant mandibles

Herbivores large and small need to mechanically process plant tissue. Their ability to do so is determined by two forces: the maximum force they can generate, and the minimum force required to fracture the plant tissue. The ratio of these forces determines the required relative mechanical effort; how this ratio varies with animal size is challenging to predict. We measured the forces required to cut thin polymer sheets with mandibles from leaf-cutter ant workers which vary by more than one order of magnitude in body mass. Cutting forces were independent of mandible size, but differed by a factor of two between pristine and worn mandibles. Mandibular wear is thus likely a more important determinant of cutting force than mandible size. We rationalise this finding with a biomechanical analysis which suggests that pristine mandibles are ideally ‘sharp’ – cutting forces are close to a theoretical minimum, which is independent of tool size and shape, and instead solely depends on the geometric and mechanical properties of the cut tissue. The increase of cutting force due to mandibular wear may be particularly problematic for small ants, which generate lower absolute bite forces, and thus require a larger fraction of their maximum bite force to cut the same plant.