Miracle-dz/UniFAR-FTUs huggingface dataset:
Public perceptions of Ebola vaccines and confidence in health services to treat Ebola, malaria, and tuberculosis: Findings from a cross-sectional household survey in Uganda, 2020

BackgroundUganda used Ebola vaccines as part of its preparedness and response during the 2018-2020 10th Ebola virus disease (EVD) outbreak in neighboring Democratic Republic of the Congo (DRC). We evaluated the publics perceptions of Ebola vaccines and compared their confidence in health services to treat Ebola versus malaria and tuberculosis as part of a survey on Ebola knowledge, attitudes, and practices (KAP) conducted in March 2020. MethodsA cross-sectional household survey was implemented in six districts in Uganda using multi-stage cluster sampling to randomly select participants. The districts were purposively selected from districts classified by the government as at high- or low-risk for an EVD outbreak. We describe perceptions of Ebola vaccines and confidence in health services to treat Ebola, tuberculosis, and malaria. Modified Poisson regression modeling was used to identify the demographic correlates of these outcomes. ResultsAmong 3,485 respondents, 18% were aware of Ebola vaccines. Of those, 92% agreed that the vaccines were needed to prevent Ebola. Participants aged 15-24 years were 4% more likely to perceive such need compared to those 60 years and older (adjusted prevalence ratio [aPR] 1.04, 95% confidence interval [CI] 1.0-1.08). The perceived need was 5% lower among participants with at least some secondary education compared to uneducated participants (aPR 0.95; 0.92-0.99). Overall, 81% of those aware of the vaccines believed that everyone or most people in their community would get vaccinated if offered, and 94% said they would likely get vaccinated if offered. Confidence in health services to treat Ebola was lower compared to treating malaria or tuberculosis (55% versus 93% and 77%, respectively). However, participants from the EVD high-risk districts were 22% more likely to be confident in health services to treat Ebola compared to those in low-risk districts (aPR: 1.22; 95% CI: 1.08, 1.38). ConclusionOur findings suggest that intent to take an Ebola vaccine during an outbreak was strong, but more work needs to be done to increase public awareness of these vaccines. The publics high confidence in health services to treat other health threats, such as malaria and tuberculosis, offer building blocks for strengthening their confidence in health services to treat EVD in the event of an outbreak.
GEWEX Cloud System Study (GCSS) Working Group 4: Precipitating Convective Cloud Systems

Abstract The authors present the objectives of the working group on precipitating convective cloud systems. These center on developing physically based parameterizations for global models in which basic research into the large-scale role of cloud systems is an important part. The approach calls on a range of expertise: cloud-resolving modeling and contributing research, observational evaluation of the model results, and tests of parameterizations in single-column models. Ongoing studies focus on oceanic cloud systems in Tropical Oceans Global Atmosphere Coupled Ocean–Atmosphere Research Experiment (TOGA COARE). First, cloud-resolving modeling of organized convection on a timescale of a few hours concentrates on momentum transport and surface fluxes. Results are evaluated against data obtained during the 22 February 1993 Intensive Observation Period, which include airborne Doppler radar measurements of a squall line. Second, multiday simulations focus on the environmental effects of cloud systems as they r.
Crystal structure of butenafine hydrochloride, C23H28NCl

The crystal structure of butenafine hydrochloride has been solved and refined using synchrotron X-ray powder diffraction data, and optimized using density functional theory techniques. Butenafine hydrochloride crystallizes in space group P21 (#4) with a = 13.94807(5), b = 9.10722(2), c = 16.46676(6) Å, β = 93.9663(5)°, V = 2086.733(8) Å3, and Z = 4. Butenafine hydrochloride occurs as a racemic co-crystal of R and S enantiomers of the cation. The crystal structure is characterized by parallel stacks of aromatic rings along the b-axis. Each cation forms a strong discrete N–H⋯Cl hydrogen bond. The chloride anions also act as acceptors in several C–H⋯Cl hydrogen bonds from methylene, methyl, and aromatic groups. The powder pattern has been submitted to ICDD for inclusion in the Powder Diffraction File™ (PDF®).
Role of peripheral nerve stimulation in treating chronic neuropathic pain: an international focused survey of pain medicine experts

Interventional pain management (IPM) options for refractory neuropathic pain (NP) have recently increased with availability of peripheral nerve stimulation (PNS) equipment and expertise. Given a lack of high-quality evidence and guidelines on this topic, we sought to understand the perception of physicians with expertise in treating NP regarding IPM and the role of PNS. We emailed a survey in March 2022 to international NP experts including pain medicine physicians, researchers, and leaders of 11 professional pain societies. No representatives from vendors of PNS systems were included in the design of the survey nor as respondents. Among 24 respondents (67% of those contacted), the distal common peroneal, tibial, and sural nerves were most frequently targeted (60%) with PNS. Persistent postsurgical pain of more than 3 months was the most common indication for PNS (84%). The aggregate NP treatment algorithm in order of median rank was non-opioid medications as first line, IPM including epidural/perineural steroid injections tied with transcutaneous electrical nerve stimulation as second line, pulsed radiofrequency (RF) tied with RF ablation/denervation as third line, temporary then permanent PNS as fourth line, followed by spinal cord stimulation, opioids, cryoablation, botulinum, peripheral nerve field stimulation, intrathecal targeted drug delivery, and others. Before offering PNS, 12 respondents (50%) indicated their preference for trialing non-neuromodulation treatments for 1–3 months. Twenty-two respondents (92%) agreed PNS should be offered early in the treatment of neuropathic pain. The most common barriers to PNS use were cost, lack of high-quality evidence in support of its use, lack of exposure to PNS in training programs, and lack of familiarity with the use of ultrasound guidance. PNS appears to have an increasing role in the treatment of NP but more research is needed on the outcomes of PNS to elucidate its role.
Service Innovation and Emerging Technologies in Tourism and Hospitality

Innovation is complex, uncertain, somewhat disorderly, and subject to changes of many sorts. Innovation is also difficult to measure and demands close coordination of adequate technical knowledge and excellent market judgment in order to satisfy economic, technological, and other types of constraints—all simultaneously. The process of innovation must be viewed as a series of changes in a complete system not only of hardware, but also of market environment, production facilities and knowledge, and the social contexts of the innovation organization. (Kline & Rosenberg, 2009, p. 275)
Human Face Detection Based on Improved AdaBoost Algorithm

Recently,AdaBoost training algorithm has been widely used in target detection and recognition,as well as many other pattern classification fields.Focusing on the problem of excessive sample weight growth and feature redundancy in the construction of human face detector by conventional AdaBoost algorithm,this paper proposes an improved human face detection approach based on adaptive sample weight updating rule and genetic algorithm.First,both False Negative Rate(FNR) and False Positive Rate(FPR) are taken into the step of sample weight renewing,which feedbacks the result of classifier into itself in order to control the classifier structure efficiently.Then the genetic algorithm with strong search ability is adopted to optimize those selected features and their parameters to build a system that can search out most human faces in images with lower FPR and less weaker classifiers.Simulations show that compared with the conventional AdaBoost algorithm,the proposed algorithm can effectively avoid sample weight distortion,eliminate feature redundancy and reduce false alarm rate while maintaining a high detection rate,achieving a higher detection speed with much more accuracy.
Covariation of a time series and a point process

Let {X(t), N(t)}, – ∞< t <∞, be a stationary bivariate stochastic process where X(t) is an ordinary time series and N(t) is an orderly point process counting the number of points in (0, t]. Suppose values of {X(t), N(t)} are available for 0< t ≦T and let σ1, σ2, ···, σ N (T) denote the jump points of N(t) in (0, T]. For |v| < T, define m T 12(υ)=∑ X (σj+υ)/T and μ T 12(υ)=∑ X (σ j +υ)/∑1 where all summations are over indices j such that 0<σ j , σ j +υ≦T for some σj. The functions M T 12(υ) and μ T 12(υ) are often useful in analyzing the covariation of the time series and point process. In this paper, we shall develop some statistical properties of the functions M T 12(υ) and μ T 12(υ) and discuss some specific situations where it is useful to consider these functions.
Scalable Hierarchical Metadata Classification in Heterogeneous Large-scale Datasets

Tabular metadata identification and classification is a fundamental problem for large-scale structured corpora, especially for complex tables, rich in hierarchical metadata. Millions of scientific tables, Web tables have hierarchical metadata, but most often have none or noisy labels for their metadata rows or columns. Missing or incorrect metadata labels or tags prevent many fundamental downstream tasks such as query processing, data fusion, indexing, analytics, visualization , and many other. Different authors position and structure metadata differently inside a table, which makes its accurate identification very challenging. In this work we describe our scalable binary metadata classification architectures. The first is using Machine-learning with our novel positional features . The second one is an ensemble of BiGRU models with parallel layers of embeddings. We have performed an extensive evaluation on large-scale datasets, including WDC and CORD-19 with millions of tables. We observed F-measure of up to 97% with the Δ of up to 6%-18.7% compared to the state of the art. The effect of our novel positional features was substantial - up to 55% in F-measure Δ observed when models were trained with them.
Digital Libraries as Technological Environments. Collaboration of Work and Future Perspectives

The nature of libraries has changed rapidly with the advent of technology, so far as to change the very way of understanding the library itself. In this paper we examine the impact that digital technologies have had on library workers, and note that so far technology has only been used to transpose the standard library practices, and that little has been done to fully exploit the new opportunities it introduced. To overcome this situation, we propose a graph-based organization of DL data, based on a technology mixing DBs and ontologies. We also propose a holistic data schema allowing to store information that is usually neglected by traditional cataloging and description standards used in libraries. This would enable the use of AI techniques that may significantly expand the effectiveness of data processing and may dramatically improve the exploitation possibilities of documents.
Regular expressions for polynomial coalgebras

htmlabstractFor polynomial set functors G, we introduce a language of expressions for describing elements of final G-coalgebra. We show that every state of a finite G-coalgebra corresponds to an expression in the language, in the sense that they both have the same semantics. Conversely, we give a compositional synthesis algorithm which transforms every expression into a finite G-coalgebra. The language of expressions is equipped with an equational system that is sound, complete and expressive with respect to G-bisimulation.
A Parameterization of the Cloudiness Associated with Cumulus Convection; Evaluation Using TOGA COARE Data

Abstract A new parameterization of the cloudiness associated with cumulus convection is proposed for use in climate models. It is based upon the idea that the convection scheme predicts the local concentration of condensed water (the in-cloud water content) produced at the subgrid scale, and that a statistical cloud scheme predicts how this condensed water is spatially distributed within the domain. The cloud scheme uses a probability distribution function (PDF) of the total water whose variance and skewness coefficient are diagnosed from the amount of condensed water produced at the subgrid scale by cumulus convection and at the large scale by supersaturation, from the degree of saturation of the environment, and from the lower bound of the total water distribution that is taken equal to zero. This parameterization is used in a single-column model forced by the Tropical Ocean Global Atmosphere Coupled Ocean–Atmosphere Response Experiment (TOGA COARE) data, and including the cumulus convection scheme of E.
The neural organization of visual information in the auditory cortex of the congenitally deaf

Neuroplasticity is the ability of the human brain to reorganize and modify its activity throughout life. In congenital deafness, sensory-deprived cortex can be recruited to represent sensory information belonging to other modalities, a process known as cross-modal plasticity. Previous studies have indicated that the auditory cortex of congenitally deaf, but not of hearing individuals, is recruited during visual tasks. However, it is not clear to what extent, and how, these cross-modal responses in the deprived auditory cortex represent low-level visual spatial information or map the visual field. Here, we addressed this question directly in an fMRI case-study, aiming to map retinotopic features in the auditory cortex. Two congenitally deaf and one hearing participant went through a conventional retinotopy fMRI experiment with visual stimuli designed to map the visual system. Using population receptive field (pRF) modelling, we revealed retinotopic-related responses in the auditory cortex of the deaf, but not in the hearing. These responses, that were mostly lateralized to the right hemisphere, represented the contralateral visual field, and were characterized by large receptive fields, centred to near foveal areas. Interestingly, we found that these responses to visual stimuli predominantly reflected negative BOLD signals in the auditory cortex of the deaf, suggesting that visual information might be represented through cross-modal deactivation signals.
Mll1 pioneers histone H3K4me3 deposition and promotes formation of CD8+ T stem cell memory

CD8+ T cells with stem cell-like properties (TSCM) sustain adaptive immunity to intracellular pathogens and tumors. However, the developmental origins and chromatin regulatory factors (CRFs) that establish their differentiation are unclear. Using an RNA interference screen of all CRFs we discovered the histone methylase Mll1 was required during T cell receptor (TCR) stimulation for development of a TSCM precursor state and mature memory (TMEM) cells, but not short-lived or transitory effector cell-like states, in response to viral infections and tumors. Mll1 was essential for widespread de novo deposition of histone H3 lysine 4 trimethylation (H3K4me3) upon TCR stimulation, which accounted for 70% of all activation-induced sites in mature TMEM cells. Mll1 promoted both H3K4me3 deposition and reduced TCR-induced Pol II pausing at genes whose single-cell transcriptional dynamics explained trajectories into nascent TSCM precursor states during viral infection. Our results suggest Mll1-dependent control of Pol II elongation and H3K4me3 establishes and maintains differentiation of CD8+ TSCM cell states.
β1 integrin signaling governs necroptosis via the chromatin remodeling factor CHD4

Fibrosis, characterized by sustained activation of myofibroblasts and excessive extracellular matrix (ECM) deposition, is known to be associated with chronic inflammation. RIPK3, the central kinase of necroptosis signaling, is upregulated in fibrosis and contributes to the TNF-mediated inflammation. In bile duct ligation-induced liver fibrosis, we found that myofibroblasts are the major cell type expressing RIPK3. Genetic ablation of β1 integrins, the major profibrotic ECM receptors in fibroblasts, not only abolished ECM fibrillogenesis but also blunted RIPK3 expression via an epigenetic mechanism mediated by the chromatin remodeling factor CHD4. While the function of CHD4 has been conventionally linked to NuRD and ChAHP complexes, we found that CHD4 potently repressed a set of genes, including Ripk3, with high locus specificity but independent of either the NuRD or ChAHP complex. Thus, our data uncover that β1 integrin intrinsically links fibrotic signaling to RIPK3-driven inflammation via a novel mode of action of CHD4.
Chromatin structure and context-dependent sequence features control prime editing efficiency

Prime editor (PE) is a highly versatile CRISPR-Cas9 genome editing technique. The current constructs, however, have variable efficiency and may require laborious experimental optimization. This study presents statistical models for learning the salient epigenomic and sequence features of target sites modulating the editing efficiency and provides guidelines for designing optimal PEs. We found that both regional constitutive heterochromatin and local nucleosome occlusion of target sites impede editing, while position-specific G/C nucleotides in the primer binding site (PBS) and reverse transcription (RT) template regions of PE guide-RNA (pegRNA) yield high editing efficiency, especially for short PBS designs. The presence of G/C nucleotides was most critical immediately 5’ to the protospacer adjacent motif (PAM) site for all designs. The effects of different last templated nucleotides were quantified and seen to depend on both PBS and RT template lengths. Our models found AGG to be the preferred PAM and detected a guanine nucleotide four bases downstream of PAM to facilitate editing, suggesting a hitherto-unrecognized interaction with Cas9. A neural network interpretation method based on nonextensive statistical mechanics further revealed multi-nucleotide preferences, indicating dependency among several bases across pegRNA. Our work clarifies previous conflicting observations and uncovers context-dependent features important for optimizing PE designs.
Biotic Assessment of Crowdsourced Data Defines Four Ecoregions in Thar: A Novel Approach for Citizen Engagement in Conservation

Distinct ecosystems have representative biota that can allow delineation of ecoregions. Despite being one of the most populated desert ecosystems in the world with extreme climatic conditions, Thar has been classified as a single ecoregion by WWF. The present study aimed to test this on the basis of biotic assessment using a citizen science-based crowdsourced data from ebird (492 species of birds through 50,000 checklists of 4000 birders). Unsupervised clustering (k=4) and mathematical validation using cosine similarity revealed the presence of four distinct ecoregionsEastern Thar (ET), Western Thar (WT), Transitional Zone (TZ) and Cultivated Zone (CZ) in Thar. Most strikingly, these ecoregions when overlaid on geographical regions of Thar show that the CZ ecoregion was split between three distant and distinct geographic regions. Further spatial diversity estimates show CZ had the least α diversity (273) and highest β diversity (1.8) indicating the least similarity between the districts that comprised this ecoregion. The results suggest CZ as an evolving ecoregion or a consequence of habitat fragmentation due to anthropogenic effects. The presence of least number of exclusive species in CZ (0.2%) and TZ (0.8%) including the near-threatened species like Laticilla burnesii (Rufous-vented grass babbler) and Pelecanus philippensis (Spot-billed pelican) and an endangered species Athene blewitti (Forest Owlet) highlights the need for restoration of these threatened biota and ecoregions. This study for the first time proposes crowd-sourced bird data as an important biota-based tool to understand (a) established ecoregions in an ecosystem, (b) anthropogenic effects of agri-farmlands and (c) relation between geographic regions and biota.
Dual spatio-temporal regulation of axon growth and microtubule dynamics by RhoA signaling pathways

RhoA plays a crucial role in neuronal polarization, where its action restraining axon outgrowth has been thoroughly studied. We now report that RhoA has not only inhibitory but also a stimulatory effect on axon development depending on when and where exerts its action and the downstream effectors involved. In cultured hippocampal neurons, FRET imaging revealed that RhoA activity selectively localizes in growth cones of undifferentiated neurites, while in developing axons it displays a biphasic pattern, being low in nascent axons and high in elongating ones. RhoA-Rho kinase (ROCK) signaling prevents axon initiation but has no effect on elongation, while formin inhibition reduces axon extension without significantly altering initial outgrowth. Besides, RhoA-mDia promotes axon elongation by stimulating growth cone microtubule stability and assembly, as opposed to RhoA-ROCK that restrains growth cone microtubule assembly and protrusion. Finally, we show that similar mechanisms might operate during axonal regeneration, with RhoA-ROCK slowing axon regrowth after axotomy and RhoA-mDia favoring extension of regenerated axons.
Intestelligence: A pharmacological neural network using intestine data

A neural network is a machine learning algorithm that can learn and make predictions by adjusting the strength of the connections between nodes. The sigmoid function is commonly used as an activation function in these nodes. This study explores the potential applicability of biological materials in the development of alternative activation functions. Inspired by the fact that acetylcholine induces intestinal contractions that follow a sigmoid function, we used pharmacological data obtained from guinea pig ilea in a layered neural network for image classification tasks. We found that the intestinal data-based neural network with the same structure as a conventional three-layer perceptron achieved an impressive classification accuracy of 85.7% ± 0.6% based on the MNIST handwritten digit dataset (chance = 10%). Additionally, the neural network was trained to determine whether objects in photographs collected from the internet were digestible, achieving an accuracy of 88.5% ± 0.9% (chance = 50%). Our approach highlights the potential applicability of intestine data in neural computations based on pharmacological mechanisms.