Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning from health system actor and caregiver experiences in Ghana and Nepal to strengthen growth monitoring and promotion

Background Globally, growth monitoring and promotion (GMP) of infants and young children is a fundamental component of routine preventive child health care; however, programs have experienced varying degrees of quality and success with enduring challenges. The objective of this study was to describe implementation of GMP (growth monitoring, growth promotion, data use, and implementation challenges) in two countries, Ghana and Nepal, to identify key actions to strengthen GMP programs. Methods We conducted semi-structured key informant interviews with national and sub-national government officials (n = 24), health workers and volunteers (n = 40), and caregivers (n = 34). We conducted direct structured observations at health facilities (n = 10) and outreach clinics (n = 10) to complement information from interviews. We coded and analyzed interview notes for themes related to GMP implementation. Results Health workers in Ghana (e.g., community health nurses) and Nepal (e.g., auxiliary nurse midwives) had the knowledge and skills to assess and analyze growth based on weight measurement. However, health workers in Ghana centered growth promotion on the growth trend (weight-for-age over time), whereas health workers in Nepal based growth promotion on measurement from one point in time to determine whether a child was underweight. Overlapping challenges included health worker time and workload. Both countries tracked growth-monitoring data systematically; however, there was variation in growth monitoring data use. Conclusion This study shows that GMP programs may not always focus on the growth trend for early detection of growth faltering and preventive actions. Several factors contribute to this deviation from the intended goal of GMP. To overcome them, countries need to invest in both service delivery (e.g., decision-making algorithm) and demand generation efforts (e.g., integrate with responsive care and early learning).
Hepatitis B core-related antigen: A novel and promising surrogate biomarker to guide anti-hepatitis B virus therapy

The current requirement for biomarkers to detect hepatitis B virus (HBV) infection is polarized. One is a fully-automated and highly sensitive measurement system; the other is a simple system for point-of-care testing (POCT) in resource-limited areas. Hepatitis B core-related antigen (HBcrAg) reflects intrahepatic covalently closed circular DNA and serum HBV DNA. Even in patients with undetectable serum HBV DNA or HBsAg loss, HBcrAg may remain detectable. Decreased HBcrAg levels are associated with reduction of the occurrence of hepatocellular carcinoma (HCC) in chronic hepatitis B. Recently, a fully-automated, novel high-sensitivity HBcrAg assay (iTACT-HBcrAg, cut-off value: 2.1 logIU/mL) has been developed. This attractive assay has been released in Japan very recently. iTACT-HBcrAg can be useful for monitoring HBV reactivation and prediction of HCC occurrence, as an alternative to HBV DNA. Moreover, monitoring HBcrAg may be suitable for determining the therapeutic effectiveness of approved drugs and novel drugs under development. Presently, international guidelines recommend anti-HBV prophylaxis for pregnant women with high viral loads to prevent mother-to-child transmission of HBV. However, >95% of HBV-infected individuals live in countries where HBV DNA quantification is not available. Worldwide elimination of HBV needs the scaling-up of examination and medication services in resource-limited areas. Based on this situation, a rapid and easy HBcrAg assay as a POCT is valuable. This review provides the latest information regarding the clinical use of a new surrogate marker, HBcrAg, in HBV management, based on iTACT-HBcrAg or POCT, and introduces novel agents targeting HBV RNA/protein.
Removal of Erythrosine B dye from wastewater by Penicillium italicum: experimental, DFT, and molecular docking studies

Abstract The study involved the adsorption of Erythrosine B onto the dead, dry, and unmodified Penicillium italicum cells and the analytical, visual, theoretical assessment of the adsorbent-adsorbate interactions. It also included desorption studies and reiterative usability of the adsorbent. The fungus was a local isolate and it was identified by partial proteomic experiment in a MALDI-TOFF mass spectrometer. Chemical features of the adsorbent surface were analysed by FT-IR and EDX. Surface topology was visualized by SEM. Isotherm parameters of the adsorption were determined by using three most frequently used models. Erythrosine B appeared to form a monolayer onto the biosorbent and some of the dye molecules could have also penetrated into the adsorbent particles. Kinetic results suggested a spontaneous and exothermic reaction taken place between the dye molecules and the biomaterial. Theoretical approach involved the determination of some of the quantum parameters as well as the toxic or drug potentials of the some of the components of the biomaterial. Communicated by Ramaswamy H. Sarma
On Linear Integro-Differential Equations in a Banach Space

in a Banach space $X$ with norm $||\cdot||$ . Here $ f:R_{+}=[0, \infty$ ) $\rightarrow X$ is continuous and $A$ is the infinitesimal generator of a semi-group of class $(C_{0})$ on $X$. For each $teR_{+}B(t)$ is a (in general unbounded) linear operator with domain dense in $X$. Let $B(X)$ denote the set of all bounded linear operators from $X$ into itself. It is well known [2] that on a finite dimensional space $X=R$ (the n-dimensional space of column vectors with the usual norm $|\cdot|$ ),
A self-attention–based neural network for three-dimensional multivariate modeling and its skillful ENSO predictions

Large biases and uncertainties remain in real-time predictions of El Niño–Southern Oscillation (ENSO) using process-based dynamical models; recent advances in data-driven deep learning algorithms provide a promising mean to achieve superior skill in the tropical Pacific sea surface temperature (SST) modeling. Here, a specific self-attention–based neural network model is developed for ENSO predictions based on the much sought-after Transformer model, named 3D-Geoformer, which is used to predict three-dimensional (3D) upper-ocean temperature anomalies and wind stress anomalies. This purely data-driven and time-space attention-enhanced model achieves surprisingly high correlation skills for Niño 3.4 SST anomaly predictions made 18 months in advance and initiated beginning in boreal spring. Further, sensitivity experiments demonstrate that the 3D-Geoformer model can depict the evolution of upper-ocean temperature and the coupled ocean-atmosphere dynamics following the Bjerknes feedback mechanism during ENSO cycles. Such successful realizations of the self-attention–based model in ENSO predictions indicate its great potential for multidimensional spatiotemporal modeling in geoscience.
Wirelessly powered deformable electronic stent for noninvasive electrical stimulation of lower esophageal sphincter

Electrical stimulation is a promising method to modulate gastrointestinal disorders. However, conventional stimulators need invasive implantation and removal surgeries associated with risks of infection and secondary injuries. Here, we report a battery-free and deformable electronic esophageal stent for wireless stimulation of the lower esophageal sphincter in a noninvasive fashion. The stent consists of an elastic receiver antenna infilled with liquid metal (eutectic gallium-indium), a superelastic nitinol stent skeleton, and a stretchable pulse generator that jointly enables 150% axial elongation and 50% radial compression for transoral delivery through the narrow esophagus. The compliant stent adaptive to the dynamic environment of the esophagus can wirelessly harvest energy through deep tissue. Continuous electrical stimulations delivered by the stent in vivo using pig models significantly increase the pressure of the lower esophageal sphincter. The electronic stent provides a noninvasive platform for bioelectronic therapies in the gastrointestinal tract without the need for open surgery.
NeuroPred-PLM: an interpretable and robust model for neuropeptide prediction by protein language model

Neuropeptides are a diverse and complex class of signaling molecules that regulate a variety of biological processes. Neuropeptides provide many opportunities for the discovery of new drugs and targets for the treatment of a wide range of diseases, and thus, computational tools for the rapid and accurate large-scale identification of neuropeptides are of great significance for peptide research and drug development. Although several machine learning-based prediction tools have been developed, there is room for improvement in the performance and interpretability of the proposed methods. In this work, we developed an interpretable and robust neuropeptide prediction model, named NeuroPred-PLM. First, we employed a language model (ESM) of proteins to obtain semantic representations of neuropeptides, which could reduce the complexity of feature engineering. Next, we adopted a multi-scale convolutional neural network to enhance the local feature representation of neuropeptide embeddings. To make the model interpretable, we proposed a global multi-head attention network that could be used to capture the position-wise contribution to neuropeptide prediction via the attention scores. In addition, NeuroPred-PLM was developed based on our newly constructed NeuroPep 2.0 database. Benchmarks based on the independent test set show that NeuroPred-PLM achieves superior predictive performance compared with other state-of-the-art predictors. For the convenience of researchers, we provide an easy-to-install PyPi package (https://pypi.org/project/NeuroPredPLM/) and a web server (https://huggingface.co/spaces/isyslab/NeuroPred-PLM).
Wide-Field Washington Photometry of the NGC 5128 Globular Cluster System. II. Large-Scale Properties of the System

Building on the CMT1 photometric database presented in Paper I, in this paper we derive the large-scale properties of the globular cluster system (GCS) in NGC 5128, the nearest giant elliptical and the dominant galaxy in the Centaurus group. In global terms, it has a smaller total population than previously thought: we estimate 980 ± 120 clusters over all magnitudes, yielding a specific frequency SN = 1.4 ± 0.2, with a steep projected radial distribution σ ∼ r-2. The luminosity distribution of the clusters resembles that of an old, normal GC luminosity function (Gaussian-like with peak at MV ≃ -7.4 and dispersion of ≃1.3 mag), but these parameters are unfortunately quite uncertain because of the system's low population and the heavy field contamination. Using the metallicity-sensitive C - T1 color index, we discuss the metallicity distribution function (MDF) for a subsample of 211 previously identified clusters, all on a homogeneous photometric system. We find the MDF to be strongly bimodal, with metallicity peaks at [Fe/H] = -1.55 and -0.55 and with nearly equal numbers of clusters in each of the metal-poor and metal-rich modes. The combined evidence from the system's low specific frequency, the MDF, and the isophotal shell features in the halo light make a "major merger" a plausible model for the formation history of this giant E galaxy. However, the progenitor galaxies must have been more gas-rich than in any present-day mergers or starbursts. Finally, we present a list of 327 new cluster candidates not identified in any previous surveys; most of these are in the less well studied bulge region of the galaxy and along the minor axis.
La séparation des composantes lexicale et flexionnelle des vecteurs de mots

En sémantique distributionnelle, le sens des mots est modélisé par des vecteurs qui représentent leur distribution en corpus. Les modèles étant souvent calculés sur des corpus sans pré-traitement linguistique poussé, ils ne permettent pas de rendre bien compte de la compositionnalité morphologique des mots-formes. Nous proposons une méthode pour décomposer les vecteurs de mots en vecteurs lexicaux et flexionnels.
On locking and robustness in the finite element method

A numerical scheme for the approximation of a parameter-dependent problem is said to exhibit locking if the accuracy of the approximations deteriorates as the parameter tends to a limiting value. A robust numerical scheme for the problem is one that is essentially uniformly convergent for all values of the parameter. Precise mathematical definitions for these terms are developed, their quantitative characterization is given, and some general theorems involving locking and robustness are proven. A model problem involving heat transfer is analyzed in detail using this mathematical framework, and various related computational results are described. Applications to some different problems involving locking are presented.
Evaluation of the driving performance of a surface acoustic wave linear motor

This paper describes influences of a pre-load and vibration amplitude on driving performance of a surface acoustic wave (SAW) linear motor, and proposes a simulation model for the SAW linear motor. In experimental results, maximum output force and maximum output power of the SAW linear motor depended on it's pre-load. Optimum pre-loads for the maximum output force and for the maximum output power were both linear functions of the SAW linear motor's vibration amplitude. No-load velocity of the SAW linear motor, however, decreased linearly with increasing pre-load. A new simulation model to explain these phenomena is proposed. There is a good agreement between the experimental and simulation results.
Nutritional regulation of hepatic de novo lipogenesis in humans

Purpose of review De novo lipogenesis (DNL) is a metabolic process occurring mainly within the liver, in humans. Insulin is a primary signal for promoting DNL; thus, nutritional state is a key determinant for upregulation of the pathway. However, the effects of dietary macronutrient composition on hepatic DNL remain unclear. Nor is it clear if a nutrition-induced increase in DNL results in accumulation of intra-hepatic triglyceride (IHTG); a mechanism often proposed for pathological IHTG. Here, we review the latest evidence surrounding the nutritional regulation of hepatic DNL. Recent findings The role of carbohydrate intake on hepatic DNL regulation has been well studied, with only limited data on the effects of fats and proteins. Overall, increasing carbohydrate intake typically results in an upregulation of DNL, with fructose being more lipogenic than glucose. For fat, it appears that an increased intake of n-3 polyunsaturated fatty acids downregulates DNL, whilst, in contrast, an increased dietary protein intake may upregulate DNL. Summary Although DNL is upregulated with high-carbohydrate or mixed-macronutrient meal consumption, the effects of fat and protein remain unclear. Additionally, the effects of different phenotypes (including sex, age, ethnicity, and menopause status) in combination with different diets (enriched in different macronutrients) on hepatic DNL requires elucidation.
Functional neurological disorders after COVID-19 and SARS-CoV-2 vaccines: a national multicentre observational study

INTRODUCTION Functional neurological disorders (FNDs) are a common cause of neurology consultations. Dissociative seizures, motor and cognitive disorders are the main phenotypes. Diagnosis is made on positive terms: signs of inconsistency, incongruence and variability of physical signs with attention on clinical examination. Abnormal emotional processing and expectations are involved in the genesis and perpetuation of FND. From the onset of SARSCoV2 pandemic, world population has been affected by high levels of stress, uncertainty and misleading information, with a potential impact on mental health. Different studies have ascertained an increase of FND consultations (threefold in an emergency department). On the development of SARSCoV2 vaccines, several cases of FND following vaccination were published, as well as an official warning from the Functional Neurological Society. PostCOVID19 symptoms (known as LongCOVID19) have also become a frequent reason for neurology consultation. Somatic symptom disorder may be common in these patients, and socioeconomic implications are vast. Our experience is that a proportion of patients with FND describe an association with COVID19 infection/vaccination. Here, we report a cohort of patients with FND for whom COVID19 or SARSCoV2 vaccines were the main precipitant factors.
Estimating respiratory rate from instantaneous frequencies of long team heart rate tracings

Presents a method based on non-stationary state space modelling for the evaluation of the respiratory rate through the dynamical analysis of the instantaneous frequencies of heart-rate (HR). In practice, long term HR recordings are sequentially scanned by a sliding 20-sec window. Within each window, data are modelled using a singular value decomposition (SVD) based approach and the most important instantaneous frequencies, contained in the signals are estimated (the oscillation linked to the respiration is the 3rd lowest frequency). The presented method was tested by analysing 3 long term HR tracings (each lasting more than one hour) recorded in unrestrained subjects. Simultaneous direct recordings of the respiratory activity were performed and the data were used for comparing the algorithm performances. The obtained results indicate that the proposed algorithm is robust and efficient for the calculation of the instantaneous frequencies and may be a valuable tool for the estimation of the respiratory activity. <<ETX>>
Energy of graphs and digraphs

The energy of a graph is the sum of the absolute values of the ei genvalues of its adjacency matrix. The concept is related to the energy of a class of molecules in chemistry and was first brought to mathematics b y Gutman in 1978 ([8]). In this thesis, we do a comprehensive study on t he energy of graphs and digraphs. In Chapter 3, we review some existing upper and lower bounds f or the energy of a graph. We come up with some new results in this c hapter. A graph withn vertices is hyper-energetic if its energy is greater than 2n−2. Some classes of graphs are proved to be hyper-energetic. W e find a new class of hyper-energetic graphs which is introduced an d proved to be hyper-energetic in Section 3.3. The energy of a digraph is the sum of the absolute values of the real part of the eigenvalues of its adjacency matrix. In Chapter 4 , we study the energy of digraphs in a way that Peña and Rada in [19] have defined. Some known upper and lower bounds for the energy of digraphs a re reviewed. In Section 4.5, we bring examples of some classes of d igraphs in which we find their energy.
A cortical zoom-in operation underlies covert shifts of visual spatial attention

Shifting the focus of attention without moving the eyes poses challenges for signal coding in visual cortex in terms of spatial resolution, signal routing, and cross-talk. Little is known how these problems are solved during focus shifts. Here, we analyze the spatiotemporal dynamic of neuromagnetic activity in human visual cortex as a function of the size and number of focus shifts in visual search. We find that large shifts elicit activity modulations progressing from highest (IT) through mid-level (V4) to lowest hierarchical levels (V1). Smaller shifts cause those modulations to start at lower levels in the hierarchy. Successive shifts involve repeated backward progressions through the hierarchy. We conclude that covert focus shifts arise from a cortical coarse-to-fine process progressing from retinotopic areas with larger toward areas with smaller receptive fields. This process localizes the target and increases the spatial resolution of selection, which resolves the above issues of cortical coding.
Evaluation and Improvement of Interpretability for Self-Explainable Part-Prototype Networks

Part-prototype networks (e.g., ProtoPNet, ProtoTree, and ProtoPool) have attracted broad research interest for their intrinsic interpretability and comparable accuracy to non-interpretable counterparts. However, recent works find that the interpretability from prototypes is fragile, due to the semantic gap between the similarities in the feature space and that in the input space. In this work, we strive to address this challenge by making the first attempt to quantitatively and objectively evaluate the interpretability of the part-prototype networks. Specifically, we propose two evaluation metrics, termed as "consistency score" and "stability score", to evaluate the explanation consistency across images and the explanation robustness against perturbations, respectively, both of which are essential for explanations taken into practice. Furthermore, we propose an elaborated part-prototype network with a shallow-deep feature alignment (SDFA) module and a score aggregation (SA) module to improve the interpretability of prototypes. We conduct systematical evaluation experiments and provide substantial discussions to uncover the interpretability of existing part-prototype networks. Experiments on three benchmarks across nine architectures demonstrate that our model achieves significantly superior performance to the state of the art, in both the accuracy and interpretability. Our code is available at https://github.com/hqhQAQ/EvalProtoPNet.
Theories of Elasticity and Fluid Dynamics Established from New Dynamic Hypotheses

Traditional continuum mechanics regards the motion of material element forming a continuum as the translational motion of rigid body, and the theorem of moment of momentum about an axis passing through the centroid of material element is added to describe the dynamics of the material elements of a continuum. There are several questions in the theories of elasticity and fluid dynamics developed from above assumption: 1. The rigid body rotation termed in geometric relationships conflicts with the assumption of translational motion of material elements; 2. The constraint of compatibility conditions on displacement field is different from the property of displacement field; 3. The decomposition of gradient of displacement and velocity is inconsistent with the decomposition of vector fields which are often decomposed into divergence field and curl field; 4.The calculation of stress on an inclined plane is equivalent to denying the necessity of body force and inertial force in Cauchy's equation of motion; 5. Stokes hypothesis is required in the theory of fluid dynamics to guarantee that the pressure derived from the fluid's constitutive relationship is equal to the thermodynamic pressure. The study re-establishes the theories of elasticity and fluid dynamics based on new dynamic hypotheses to replace current imperfect theories. In the new theories, it is assumed that the theorem of momentum is the only dynamic law that a continuum obeys, which describes the translational motion of deformable media.