Miracle-dz/UniFAR-FTUs huggingface dataset:
PI4‐kinase and PfCDPK7 signaling regulate phospholipid biosynthesis in Plasmodium falciparum

PfCDPK7 is an atypical member of the calcium‐dependent protein kinase (CDPK) family and is crucial for the development of Plasmodium falciparum. However, the mechanisms whereby PfCDPK7 regulates parasite development remain unknown. Here, we perform quantitative phosphoproteomics and phospholipid analysis and find that PfCDPK7 promotes phosphatidylcholine (PC) synthesis by regulating two key enzymes involved in PC synthesis, phosphoethanolamine‐N‐methyltransferase (PMT) and ethanolamine kinase (EK). In the absence of PfCDPK7, both enzymes are hypophosphorylated and PMT is degraded. We further find that PfCDPK7 interacts with 4'‐phosphorylated phosphoinositides (PIPs) generated by PI4‐kinase. Inhibition of PI4K activity disrupts the vesicular localization PfCDPK7. P. falciparum PI4‐kinase, PfPI4K is a prominent drug target and one of its inhibitors, MMV39048, has reached Phase I clinical trials. Using this inhibitor, we demonstrate that PfPI4K controls phospholipid biosynthesis and may act in part by regulating PfCDPK7 localization and activity. These studies not only unravel a signaling pathway involving PfPI4K/4'‐PIPs and PfCDPK7 but also provide novel insights into the mechanism of action of a promising series of candidate anti‐malarial drugs.
Comparative whey proteome analysis of small-tailed Han and DairyMeade ovine milk

Abstract We characterized the proteome profile of mid-lactation small-tailed Han (STH) and DairyMeade (DM) ovine milk in order to explore physiological variation and differences in milk traits between the two breeds. Methodology combined a tandem mass tag (TMT) proteomic approach with LC-MS/MS technology. A total of 656 proteins were identified in STH and DM ovine milk, of which 17and 29 proteins were significantly upregulated (P < 0.05) in STH and DM, respectively. Immune-related proteins and disease-related proteins were highly expressed in STH milk, whereas S100A2 and AEBP1 were highly expressed in DM milk, which had beneficial effects on mammary gland development and milk yield. Our results provide a theoretical basis for future breeding of dairy sheep.
Estimation and reduced bias estimation of the residual dependence index with unnamed marginals

Unlike univariate extreme value theory, multivariate extreme value distributions cannot be specified through a finite-dimensional parameter family of distributions. Instead, the many facets of multivariate extremes are mirrored in the inherent dependence structure of component-wise maxima which must be dissociated from the limiting extreme behaviour of its marginal distribution functions before a probabilistic characterisation of an extreme value quality can be determined. Mechanisms applied to elicit extremal dependence typically rely on standardisation of the unknown marginal distribution functions from which pseudo-observations for either Pareto or Fréchet marginals result. The relative merits of both of these choices for transformation of marginals have been discussed in the literature, particularly in the context of domains of attraction of an extreme value distribution. This paper is set within this context of modelling penultimate dependence as it proposes a unifying class of estimators for the residual dependence index that eschews consideration of choice of marginals. In addition, a reduced bias variant of the new class of estimators is introduced and their asymptotic properties are developed. The pivotal role of the unifying marginal transform in effectively removing bias is borne by a comprehensive simulation study. The leading application in this paper comprises an analysis of asymptotic independence between rainfall occurrences originating from monsoon-related events at several locations in Ghana.
Board level drop test and simulation of TFBGA packages for telecommunication applications

Reliabilit! - perfonnance of IC packages during drop impact is critical, especially for handheld electronic products. Currently. thcrc is no detailed test standard in the industry to advise on the procedures for board level dmp test. nor there is any model Ilia1 providcs good correlation with experimental ineasiircinents of acceleration and impact life. In this paper; detailed drop tests and simulations are pcrfonned on TFBGA (Thin-profile Fine-pitch BGA) and VFBGA (Vey-thinprofile Fine-pitch BGA) packages at board level using testing procediires developed in-house. The packages are susceptible to solder joint failures, induced by a combination of PCB bending and iueclwnical shock during impact. The critical solder ball is obsewed to occur at the outennost comer solder . joint_ and fails along the solder and PCB pad interface. Various testing parameters are studied experimentally and analytically. to understand the effects of drop heightl drop oricntation, number of PCB mounting screws to fixture. position of component on board: PCB bending: solder material, and etc. Drop height, fclt thickness, and contact conditions are used to fine-tune the shape aud level of shock pulse required. Board level drop test can be better controlled. compared with system or product level test such as impact of mobile phone. which sometimes has rather unpredictable results due to higher complexity and variations in drop orientation. At tlie same time, dynamic simulation is perfonncd to compare with esperiniental results. The model established has close values of peak acceleration and impact duration as measured in actual drop test. The failure mode and critical solder ball location predicted by modeling correlate well with testing. For the first time, an accurate life prediction model is proposed for board level drop test to estiinatc the number of drops to failure for a package. For the correlation cases studied. the nminmm nonual peeling stresses of critical solder joints correlate well with the mean impact lives measured during the drop test. The uncertainty of impact life prediction is within M drops, for a typical test of 50 drops. With this new model, a failure-free state can be detennined, and drop test performance of new package design can be quantified. and fuliher enhanced through modeling. This quantitative approach is different from traditional qualitative modeling. as it provides both accurate relative and absolute impact life prediction. The relative performance of package may be different under board level drop test ,and thennal cycling test. Different design guidelines should be considered, depcnding on application and area of concern
Implicit Data Augmentation Using Feature Interpolation for Diversified Low-Shot Image Generation

Training of generative models especially Generative Ad-versarial Networks can easily diverge in low-data setting. To mitigate this issue, we propose a novel implicit data augmentation approach which facilitates stable training and synthesize diverse samples. Speciﬁcally, we view the discriminator as a metric embedding of the real data manifold, which offers proper distances between real data points. We then utilize information in the feature space to develop a data-driven augmentation method. We further bring up a simple metric to evaluate the diversity of synthesized samples. Experiments on few-shot generation tasks show our method improves FID and diversity of results compared to current methods, and allows generating high-quality and diverse images with less than 100 training samples.
Tenofovir Alafenamide Plasma Concentrations Are Reduced in Pregnant Women Living With Human Immunodeficiency Virus (HIV): Data From the PANNA Network

Abstract Background Tenofovir alafenamide (TAF), a prodrug of tenofovir (TFV), is included in the majority of the recommended first-line antiretroviral regimens for patients living with human immunodeficiency virus (HIV), but there are limited data on TAF use in pregnant women. We aimed to examine the plasma pharmacokinetics of TAF and TFV in pregnant women from Europe. Methods Pregnant women living with HIV were included from treatment centers across Europe, and intensive pharmacokinetic sampling in the third trimester and postpartum was performed. Pharmacokinetic parameters of TAF and TFV were determined with noncompartmental analysis. The proportion of women with a TAF area under the curve (AUClast) below the target of 53.1 ng∗h/mL was determined. Clinical efficacy and safety outcome parameters were reported. Results In total, 20 pregnant women living with HIV were included. At the third trimester, geometric mean TAF AUClast and Cmax were decreased by 46% and 52%, respectively, compared with postpartum. TFV AUC0-24h, Cmax, and Ctrough decreased by 33%, 30%, and 34%, respectively. The proportion of women with a TAF AUClast < 53.1 ng∗h/mL was 6% at third trimester and 0% postpartum. One out of 20 women had a viral load > 50 copies/mL at third trimester and no mother-to-child transmission occurred. Conclusions TAF plasma concentrations were reduced by about half in women living with HIV during third trimester of pregnancy but remained above the predefined efficacy target in the majority of the pregnant women. TFV concentrations were reduced by approximately 30% during third trimester. Despite the observed exposure decrease, high virologic efficacy was observed in this study.
A Novel Approach to Solving Goal-Achieving Problems for Board Games

Goal-achieving problems are puzzles that set up a specific situation with a clear objective. An example that is well-studied is the category of life-and-death (L&D) problems for Go, which helps players hone their skill of identifying region safety. Many previous methods like lambda search try null moves first, then derive so-called relevance zones (RZs), outside of which the opponent does not need to search. This paper first proposes a novel RZ-based approach, called the RZ-Based Search (RZS), to solving L&D problems for Go. RZS tries moves before determining whether they are null moves post-hoc. This means we do not need to rely on null move heuristics, resulting in a more elegant algorithm, so that it can also be seamlessly incorporated into AlphaZero's super-human level play in our solver. To repurpose AlphaZero for solving, we also propose a new training method called Faster to Life (FTL), which modifies AlphaZero to entice it to win more quickly. We use RZS and FTL to solve L&D problems on Go, namely solving 68 among 106 problems from a professional L&D book while a previous state-of-the-art program TSUMEGO-EXPLORER solves 11 only. Finally, we discuss that the approach is generic in the sense that RZS is applicable to solving many other goal-achieving problems for board games.
Pairwise Learning for Neural Link Prediction

In this paper, we aim at providing an effective Pairwise Learning Neural Link Prediction (PLNLP) framework. The framework treats link prediction as a pairwise learning to rank problem and consists of four main components, i.e., neighborhood encoder, link predictor, negative sampler and objective function. The framework is flexible that any generic graph neural convolution or link prediction specific neural architecture could be employed as neighborhood encoder. For link predictor, we design different scoring functions, which could be selected based on different types of graphs. In negative sampler, we provide several sampling strategies, which are problem specific. As for objective function, we propose to use an effective ranking loss, which approximately maximizes the standard ranking metric AUC. We evaluate the proposed PLNLP framework on 4 link property prediction datasets of Open Graph Benchmark, including ogbl-ddi, ogbl-collab, ogbl-ppa and ogbl-ciation2. PLNLP achieves top 1 performance on ogbl-ddi and ogbl-collab, and top 2 performance on ogbl-ciation2 only with basic neural architecture. The performance demonstrates the effectiveness of PLNLP.
Locomotion of magnetoelastic membranes in viscous fluids

The development of multifunctional and biocompatible microrobots for biomedical applications relies on achieving locomotion through viscous fluids. Here, we describe a framework for swimming in homogeneous magnetoelastic membranes composed of superparamagnetic particles. By solving the equations of motion, we find the dynamical modes of circular membranes in precessing magnetic fields, which are found to actuate in or out of synchronization with a magnetic field precessing above or below a critical precession frequency, $\omega_c$, respectively. For frequencies larger than $\omega_c$, synchronized rotational and radial waves propagate on the membrane. These waves give rise to locomotion in an incompressible fluid at low Reynolds number using the lattice Boltzmann approach. Non-reciprocal motion resulting in swimming is achieved by breaking the morphological symmetry of the membrane, attained via truncation of a circular segment. The membrane translation can be adapted to a predetermined path by programming the external magnetic field. Our results lay the foundation for achieving directed motion in thin, homogeneous magnetoelastic membranes with a diverse array of geometries.
Expanded View Figures

Figure EV1. Developmental rates of embryos derived from microinjected oocytes. A Summary of all analyzed samples (% blastocysts = % 2-cell embryos developed to the blastocyst stage). B, C Developmental curves of selected sets of experimental groups starting from 2-cell stage. (B) Comparison of control groups. (C) Comparison of Tet2-MO1 and Tet2MO2 groups and Tet1 + 2 combined KDs. Indicated significances were tested using log rank (Mantel-Cox) test (ns = non-significant, numbers of analyzed embryos per experimental group are indicated in parenthesis). Julia Arand et al EMBO reports
Projection methods for Neural Field equations

Neural field models are nonlinear integro-differential equations for the evolution of neuronal activity, and they are a prototypical large-scale, coarse-grained neuronal model in continuum cortices. Neural fields are often simulated heuristically and, in spite of their popularity in mathematical neuroscience, their numerical analysis is not yet fully established. We introduce generic projection methods for neural fields, and derive a-priori error bounds for these schemes. We extend an existing framework for stationary integral equations to the time-dependent case, which is relevant for neuroscience applications. We find that the convergence rate of a projection scheme for a neural field is determined to a great extent by the convergence rate of the projection operator. This abstract analysis, which unifies the treatment of collocation and Galerkin schemes, is carried out in operator form, without resorting to quadrature rules for the integral term, which are introduced only at a later stage, and whose choice is enslaved by the choice of the projector. Using an elementary timestepper as an example, we demonstrate that the error in a time stepper has two separate contributions: one from the projector, and one from the time discretisation. We give examples of concrete projection methods: two collocation schemes (piecewise-linear and spectral collocation) and two Galerkin schemes (finite elements and spectral Galerkin); for each of them we derive error bounds from the general theory, introduce several discrete variants, provide implementation details, and present reproducible convergence tests.
The Scorpion Planet Survey: Wide-orbit Giant Planets Around Young A-type Stars

The first directly imaged exoplanets indicated that wide-orbit giant planets could be more common around A-type stars. However, the relatively small number of nearby A-stars has limited the precision of exoplanet demographics studies to ≳10%. We aim to constrain the frequency of wide-orbit giant planets around A-stars using the VLT/SPHERE extreme adaptive optics system, which enables targeting ≳100 A-stars between 100 and 200 pc. We present the results of a survey of 84 A-stars within the nearby ∼5–17 Myr old Sco OB2 association. The survey detected three companions—one of which is a new discovery (HIP 75056Ab), whereas the other two (HD 95086b and HIP 65426b) are now-known planets that were included without a priori knowledge of their existence. We assessed the image sensitivity and observational biases with injection and recovery tests combined with Monte Carlo simulations to place constraints on the underlying demographics. We measure a decreasing frequency of giant planets with increasing separation, with measured values falling between 10% and 2% for separations of 30–100 au, and 95% confidence-level upper limits of ≲45%–8% for planets on 30–100 au orbits, and ≲5% between 200 and 500 au. These values are in excellent agreement with recent surveys of A-stars in the solar neighborhood−supporting findings that giant planets out to separations of ≲100 au are more frequent around A-stars than around solar-type hosts. Finally, the relatively low occurrence rate of super-Jupiters on wide orbits, the positive correlation with stellar mass, and the inverse correlation with orbital separation are consistent with core accretion being their dominant formation mechanism.
Muscle‐to‐fat ratio identifies functional impairments and cardiometabolic risk and predicts outcomes: biomarkers of sarcopenic obesity

Sarcopenic obesity aims to capture the risk of functional decline and cardiometabolic diseases, but its operational definition and associated clinical outcomes remain unclear. Using data from the Longitudinal Aging Study of Taipei, this study explored the roles of the muscle‐to‐fat ratio (MFR) with different definitions and its associations with clinical characteristics, functional performance, cardiometabolic risk and outcomes.
Anomaly Detection of Wind Turbine Time Series using Variational Recurrent Autoencoders

Ice accumulation in the blades of wind turbines can cause them to describe anomalous rotations or no rotations at all, thus affecting the generation of electricity and power output. In this work, we investigate the problem of ice accumulation in wind turbines by framing it as anomaly detection of multi-variate time series. Our approach focuses on two main parts: first, learning low-dimensional representations of time series using a Variational Recurrent Autoencoder (VRAE), and second, using unsupervised clustering algorithms to classify the learned representations as normal (no ice accumulated) or abnormal (ice accumulated). We have evaluated our approach on a custom wind turbine time series dataset, for the two-classes problem (one normal versus one abnormal class), we obtained a classification accuracy of up to 96$\%$ on test data. For the multiple-class problem (one normal versus multiple abnormal classes), we present a qualitative analysis of the low-dimensional learned latent space, providing insights into the capacities of our approach to tackle such problem. The code to reproduce this work can be found here https://github.com/agrija9/Wind-Turbines-VRAE-Paper.
Artificial Cognitively-inspired Generation of the Notion of Topological Group in the Context of Artificial Mathematical Intelligence

The new computational paradigm of conceptual computation has been introduced in the research program of Artificial Mathematical Intelligence. We provide the explicit artificial generation (or conceptual computation) for the fundamental mathematical notion of topological groups. Specifically, we start with two basic notions belonging to topology and abstract algebra, and we describe recursively formal specifications in the Common Algebraic Specification Language (CASL). The notion of conceptual blending between such conceptual spaces can be materialized computationally in the Heterogeneous Tool Set (HETS). The fundamental notion of topological groups is explicitly generated through three different artificial specifications based on conceptual blending and conceptual identification, starting with the concepts of continuous functions and mathematical groups (described with minimal set-theoretical conditions). This constitutes in additional heuristic evidence for the third pillar of Artificial Mathematical Intelligence.
Tensorial Group Field Theory condensate cosmology as an example of spacetime emergence in quantum gravity

We summarize the main ideas behind TGFT condensate cosmology and sketch the technical steps that bring from the fundamental theory to the effective cosmological dynamics. This framework is presented as an explicit illustration of (and possibly a general template for) the emergence of spacetime from non-spatiotemporal quantum entities in quantum gravity, and the many aspects involved in it.
Exploring Fermionic Multiplet Dark Matter through Precision Measurements at the CEPC

New physics could be explored through loop effects by the precision measurements at the Circular Electron Positron Collider due to its clean collision environment and high luminosity. In this work, we focus on two dark matter models that involve additional electroweak fermionic multiplets. We calculate their one-loop corrections to five processes, i.e. $e^+e^- \to \mu^+\mu^-, ~Zh, ~ZZ, ~W^+W^-$, and $Z\gamma$, and investigate the corresponding signatures at CEPC with the projected sensitivity. We find that the detectable parameter regions of these processes are complementary. The combined analysis shows that the mass of dark matter $m_{\chi^0_1}$ in these two models can be probed up to $ \sim 150~\mathrm{GeV}$ and $\sim 450$ GeV at a 95\% confidence level, respectively. Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Article funded by SCOAP3 and published under licence by Chinese Physical Society and the Institute of High Energy Physics of the Chinese Academy of Science and the Institute of Modern Physics of the Chinese Academy of Sciences and IOP Publishing Ltd.
DMGCRN: Dynamic Multi-Graph Convolution Recurrent Network for Traffic Forecasting

Traffic forecasting is a problem of intelligent transportation systems (ITS) and crucial for individuals and public agencies. Therefore, researches pay great attention to deal with the complex spatio-temporal dependencies of traffic system for accurate forecasting. However, there are two challenges: 1) Most traffic forecasting studies mainly focus on modeling correlations of neighboring sensors and ignore correlations of remote sensors, e.g., business districts with similar spatio-temporal patterns; 2) Prior methods which use static adjacency matrix in graph convolutional networks (GCNs) are not enough to reflect the dynamic spatial dependence in traffic system. Moreover, fine-grained methods which use self-attention to model dynamic correlations of all sensors ignore hierarchical information in road networks and have quadratic computational complexity. In this paper, we propose a novel dynamic multi-graph convolution recurrent network (DMGCRN) to tackle above issues, which can model the spatial correlations of distance, the spatial correlations of structure, and the temporal correlations simultaneously. We not only use the distance-based graph to capture spatial information from nodes are close in distance but also construct a novel latent graph which encoded the structure correlations among roads to capture spatial information from nodes are similar in structure. Furthermore, we divide the neighbors of each sensor into coarse-grained regions, and dynamically assign different weights to each region at different times. Meanwhile, we integrate the dynamic multi-graph convolution network into the gated recurrent unit (GRU) to capture temporal dependence. Extensive experiments on three real-world traffic datasets demonstrate that our proposed algorithm outperforms state-of-the-art baselines.