Miracle-dz/UniFAR-FTUs huggingface dataset:
Molecular Detection of Zoonotic and Veterinary Pathogenic Bacteria in Pet Dogs and Their Parasitizing Ticks in Junggar Basin, North-Western China

Despite the recognized epidemiological importance of ticks as vectors for pathogens that cause numerous zoonotic and veterinary diseases, data regarding the pathogens of pet dogs and their parasitic ticks in the Junggar Basin are scarce. In this study, a total of 178 blood samples and 436 parasitic ticks were collected from pet dogs in Junggar Basin, Xinjiang Uygur Autonomous Region (XUAR), north-western China. All ticks were identified as Rhipicephalus turanicus sensu stricto (s.s.) according to morphological and molecular characteristics. Rh. turanicus s.s. ticks were collected from pet dogs in China for the first time. Seven tick-borne pathogens, such as Ehrlichia chaffeensis, Anaplasma phagocytophilum, Rickettsia massiliae, Candidatus R. barbariae, Brucella spp. , Rickettsia sibirica, and Anaplasma ovis, were detected from ticks, whereas the first five bacteria were detected from blood samples of dogs. Brucella spp. was the most predominant pathogen in both blood samples and ticks of pet dogs, with the detection rates of 16.29 and 16.74%, respectively. Moreover, 17 ticks and 1 blood sample were co-infected with two pathogens, and 1 tick was co-infected with three pathogens. This study provided molecular evidence for the occurrence of Anaplasma spp. , Ehrlichia spp. , Rickettsia spp. , and Brucella spp. circulating in pet dogs and their parasitic ticks in Junggar Basin, north-western China. These findings extend our knowledge of the tick-borne pathogens in pet dogs and their parasitic ticks in Central Asia; therefore, further research on these pathogens and their role in human and animal diseases is required.
Limited Sequence Variation and Similar Phenotypic Characteristics of HIV-1 Subtype C Gag Variants Derived From the Reservoir and Pre-Therapy Plasma

HIV variants present in the reservoir, particularly in tissues, may differ from those present in peripheral blood prior to therapy initiation, and characterisation of these reservoir variants could better inform immune-based interventions for HIV cure. In the present study, Gag sequence differences between variants derived from the lymph node and peripheral blood mononuclear cell (PBMC) reservoirs as well as those derived from pre-therapy plasma, were investigated in 24 HIV-1 subtype C-infected individuals. HIV gag amplification was successful for 20 individuals, where 4 were controls including one untreated individual and 3 early treated individuals with LN collection within 2 weeks of treatment initiation. The remaining 16 individuals with LN and PBMC collection > 3 months after treatment initiation (median = 665 days), were further characterised. Recombinant viruses encoding patient-derived Gag-protease sequences from the pre-therapy plasma, LN reservoir, and PBMC reservoir, were constructed and the replication-competent viruses that grew in vitro were used to further investigate whether there are specific features of Gag reservoir variants that may have relevance for strategies to cure HIV. Virus characteristics measured included replication capacity, interferon-alpha resistance, cell-to-cell spread ability, and induction of antiviral cytokines. A limited number of novel Gag mutations (median = 4) in the reservoir of 3/7 early treated participants and 9/9 late treated participants were observed, where the majority of these mutations were likely cytotoxic T lymphocyte (CTL)-driven and 48% were represented in the replication-competent viruses. The reservoir variants had very few unique potential CTL escape mutations (median = 3) in Gag compared to the number of these Gag mutations that were already present in the plasma-derived virus (median = 23) at the time of treatment initiation, which was similar whether treatment was initiated late or early. The data suggest that the extent of CTL escape in Gag overall is likely similar between early and late treated individuals as well as between the reservoir and pre-therapy variants. The sequence differences in Gag that were unique to the reservoir viruses did not result in significantly altered virus characteristics overall, and are therefore unlikely to affect effectiveness of immune-based interventions for virus eradication.
Dosing Transcranial Magnetic Stimulation of the Primary Motor and Dorsolateral Prefrontal Cortices With Multi-Scale Modeling

Transcranial magnetic stimulation (TMS) can depolarize cortical neurons through the intact skin and skull. The characteristics of the induced electric field (E-field) have a major impact on specific outcomes of TMS. Using multi-scale computational modeling, we explored whether the stimulation parameters derived from the primary motor cortex (M1) induce comparable macroscopic E-field strengths and subcellular/cellular responses in the dorsolateral prefrontal cortex (DLPFC). To this aim, we calculated the TMS-induced E-field in 16 anatomically realistic head models and simulated the changes in membrane voltage and intracellular calcium levels of morphologically and biophysically realistic human pyramidal cells in the M1 and DLPFC. We found that the conventional intensity selection methods (i.e., motor threshold and fixed intensities) produce variable macroscopic E-fields. Consequently, it was challenging to produce comparable subcellular/cellular responses across cortical regions with distinct folding characteristics. Prospectively, personalized stimulation intensity selection could standardize the E-fields and the subcellular/cellular responses to repetitive TMS across cortical regions and individuals. The suggested computational approach points to the shortcomings of the conventional intensity selection methods used in clinical settings. We propose that multi-scale modeling has the potential to overcome some of these limitations and broaden our understanding of the neuronal mechanisms for TMS.
Virulence-Associated Secretion in Mycobacterium abscessus

Non-tuberculous mycobacteria (NTM) are a heterogeneous group of originally environmental organi3sms, increasingly recognized as pathogens with rising prevalence worldwide. Knowledge of NTM’s mechanisms of virulence is lacking, as molecular research of these bacteria is challenging, sometimes more than that of M. tuberculosis (Mtb), and far less resources are allocated to their investigation. While some of the virulence mechanisms are common to several mycobacteria including Mtb, others NTM species-specific. Among NTMs, Mycobacterium abscessus (Mabs) causes some of the most severe and difficult to treat infections, especially chronic pulmonary infections. Mabs survives and proliferates intracellularly by circumventing host defenses, using multiple mechanisms, many of which remain poorly characterized. Some of these immune-evasion mechanisms are also found in Mtb, including phagosome pore formation, inhibition of phagosome maturation, cytokine response interference and apoptosis delay. While much is known of the role of Mtb-secreted effector molecules in mediating the manipulation of the host response, far less is known of the secreted effector molecules in Mabs. In this review, we briefly summarize the knowledge of secreted effectors in Mtb (such as ESX secretion, SecA2, TAT and others), and draw the parallel pathways in Mabs. We also describe pathways that are unique to Mabs, differentiating it from Mtb. This review will assist researchers interested in virulence-associated secretion in Mabs by providing the knowledge base and framework for their studies.
Piezoelectric Transformers in Power Electronics

Power electronic circuits have traditionally been based on magnetic technology, and until recently, have not been part of the tide of miniaturization and integration advances from which signal- processing integrated circuits have benefited. In an effort to miniaturize power components piezoelectric, rather than magnetic transformer could be employed in the power supplies. Utilization of piezoelectric transformers in power electronics became possible since new piezoelectric materials have recently been grandly developed that exhibit improved piezoelectric ceramic characteristics. In the past few decades, piezoelectric transformers have been developed and used widely in many applications, such as DC/DC converters, adapters, and electronic ballasts for fluorescent lamps. The unique nature of the piezoelectric transformer offers the opportunity for innovative circuit design such as operating above resonance for inductive behavior to achieve soft switching without compensating inductors.
Domain switching in KTP crystals induced by electron beam irradiation

Abstract 1D domain patterning by electron beam in potassium titanyl phosphate crystals covered by an artificial dielectric layer were studied. The polygonal shape of the switched domains elongated along Y-crystallographic direction was obtained. The linear dose dependence of the domain size was attributed to screening of the depolarization field by injected charge. The domain size increase with electron energy is caused by increase in the electron penetration into the resist layer. Formation of an array of nanodomain streamers at the domain wall was observed. Creation of a through 1 D periodical domain pattern with a 10 µm period was demonstrated.
Accurate Quantization of Measures via Interacting Particle-based Optimization

Approximating a target probability distribution can be cast as an optimization problem where the objective functional measures the dissimilarity to the target. This optimization can be addressed by approximating Wasserstein and related gradient flows. In practice, these are simulated by inter-acting particle systems, whose stationary states define an empirical measure approximating the target distribution. This approach has been pop-ularized recently to design sampling algorithms, e.g. Stein Variational Gradient Descent, or by minimizing the Maximum Mean or Kernel Stein Discrepancy. However, little is known about quantization properties of these approaches, i.e. how well is the target approximated by a finite number particles. We investigate this question theoretically and numerically. In particular, we prove general upper bounds on the quantization error of MMD and KSD at rates which significantly outperform quantization by i.i. d. samples. We conduct experiments which show that the particle systems at study achieve fast rates in practice, and notably outperform greedy algorithms, such as kernel herding. We compare different gradient flows and highlight their quantization rates. Furthermore we introduce a Normalized Stein Variational Gradient Descent and argue in favor of adaptive kernels, which exhibit faster convergence. Finally we compare the Gaussian and Laplace kernels and argue that the Laplace kernel provides a more robust quantization.
Identification of Linear Non-Gaussian Latent Hierarchical Structure

Traditional causal discovery methods mainly focus on estimating causal relations among measured variables, but in many real-world problems, such as questionnaire-based psychometric studies, measured variables are generated by latent variables that are causally related. Accordingly, this paper investigates the problem of discovering the hidden causal variables and estimating the causal structure, including both the causal relations among latent variables and those be-tween latent and measured variables. We relax the frequently-used measurement assumption and allow the children of latent variables to be latent as well, and hence deal with a specific type of latent hierarchical causal structure. In particular, we define a minimal latent hierarchical structure and show that for linear non-Gaussian models with the minimal latent hierarchical structure, the whole structure is identifiable from only the measured variables. Moreover, we develop a principled method to identify the structure by testing for Generalized Independent Noise (GIN) conditions in specific ways. Experimental results on both synthetic and real-world data show the effectiveness of the proposed approach.
Synergy and Symmetry in Deep Learning: Interactions between the Data, Model, and Inference Algorithm

Although learning in high dimensions is commonly believed to suffer from the curse of dimensionality, modern machine learning methods often exhibit an astonishing power to tackle a wide range of challenging real-world learning problems without using abundant amounts of data. How exactly these methods break this curse remains a fundamental open question in the theory of deep learning. While previous efforts have investigated this question by studying the data (D), model (M), and inference algorithm (I) as independent modules, in this paper, we analyze the triplet (D, M, I) as an integrated system and identify important synergies that help mitigate the curse of dimensionality. We first study the basic symmetries associated with various learning algorithms (M, I), focusing on four prototypical architectures in deep learning: fully-connected networks (FCN), locally-connected networks (LCN), and convolutional networks with and without pooling (GAP/VEC). We find that learning is most efficient when these symmetries are compatible with those of the data distribution and that performance significantly deteriorates when any member of the (D, M, I) triplet is inconsistent or suboptimal.
Instrumental Variable Regression with Confounder Balancing

This paper considers the challenge of estimating treatment effects from observational data in the presence of unmeasured confounders. A popular way to address this challenge is to utilize an instrumental variable (IV) for two-stage regression, i.e., 2SLS and variants, but limited to the linear setting. Recently, many nonlinear IV regression variants were proposed to overcome it by regressing the treatment with IVs and observed confounders in stage 1, leading to the imbalance of the observed confounders in stage 2. In this paper, we propose a Confounder Balanced IV Regression (CB-IV) algorithm to jointly remove the bias from the unmeasured confounders and balance the observed confounders. To the best of our knowledge, this is the first work to combine confounder balancing in IV regression for treatment effect estimation. Theoretically, we re-define and solve the inverse problems for the response-outcome function. Experiments show that our algorithm outperforms the existing approaches.
Efficient Learning for AlphaZero via Path Consistency

In recent years, deep reinforcement learning have made great breakthroughs on board games. Still, most of the works require huge computational resources for a large scale of environmental interactions or self-play for the games. This paper aims at building powerful models under a limited amount of self-plays which can be utilized by a human throughout the lifetime. We proposes a learning algorithm built on AlphaZero, with its path searching regularised by a path consistency (PC) optimality, i.e., values on one optimal search path should be identical. Thus, the algorithm is shortly named PCZero. In implementation, historical trajectory and scouted search paths by MCTS makes a good balance between exploration and exploitation, which enhances the generalization ability effectively. PCZero obtains 94 . 1% winning rate against the champion of Hex Computer Olympiad in 2015 on 13 × 13 Hex, much higher than 84 . 3% by AlphaZero. The models consume only 900 K self-play games, about the amount humans can study in a lifetime. The improvements by PCZero have been also generalized to Oth-ello and Gomoku. Experiments also demonstrate the efficiency of PCZero under offline learning setting.
Towards Uniformly Superhuman Autonomy via Subdominance Minimization

Prevalent imitation learning methods seek to produce behavior that matches or exceeds average human performance. This often prevents achieving expert-level or superhuman performance when identifying the better demonstrations to imitate is difﬁcult. We instead assume demonstrations are of varying quality and seek to induce behavior that is unambiguously better (i.e., Pareto dominant or minimally subdominant) than all human demonstrations. Our minimum subdominance inverse optimal control training objective is primarily deﬁned by high quality demonstrations; lower quality demonstrations, which are more easily dominated, are effectively ignored instead of degrading imitation. With increasing probability, our approach produces superhuman behavior incurring lower cost than demonstrations on the demonstrator’s unknown cost function—even if that cost function differs for each demonstration. We apply our approach on a computer cursor pointing task, producing behavior that is 78% su-perhuman, while minimizing demonstration sub-optimality provides 50% superhuman behavior— and only 72% even after selective data cleaning.
Failure and success of the spectral bias prediction for Kernel Ridge Regression: the case of low-dimensional data

Recently, several theories including the replica method made predictions for the generalization error of Kernel Ridge Regression. In some regimes, they predict that the method has a `spectral bias': decomposing the true function $f^*$ on the eigenbasis of the kernel, it fits well the coefficients associated with the O(P) largest eigenvalues, where $P$ is the size of the training set. This prediction works very well on benchmark data sets such as images, yet the assumptions these approaches make on the data are never satisfied in practice. To clarify when the spectral bias prediction holds, we first focus on a one-dimensional model where rigorous results are obtained and then use scaling arguments to generalize and test our findings in higher dimensions. Our predictions include the classification case $f(x)=$sign$(x_1)$ with a data distribution that vanishes at the decision boundary $p(x)\sim x_1^{\chi}$. For $\chi>0$ and a Laplace kernel, we find that (i) there exists a cross-over ridge $\lambda^*_{d,\chi}(P)\sim P^{-\frac{1}{d+\chi}}$ such that for $\lambda\gg \lambda^*_{d,\chi}(P)$, the replica method applies, but not for $\lambda\ll\lambda^*_{d,\chi}(P)$, (ii) in the ridge-less case, spectral bias predicts the correct training curve exponent only in the limit $d\rightarrow\infty$.
Dynamic Regret of Online Markov Decision Processes

We investigate online Markov Decision Processes (MDPs) with adversarially changing loss functions and known transitions. We choose dynamic regret as the performance measure, defined as the performance difference between the learner and any sequence of feasible changing policies. The measure is strictly stronger than the standard static regret that benchmarks the learner's performance with a fixed compared policy. We consider three foundational models of online MDPs, including episodic loop-free Stochastic Shortest Path (SSP), episodic SSP, and infinite-horizon MDPs. For these three models, we propose novel online ensemble algorithms and establish their dynamic regret guarantees respectively, in which the results for episodic (loop-free) SSP are provably minimax optimal in terms of time horizon and certain non-stationarity measure. Furthermore, when the online environments encountered by the learner are predictable, we design improved algorithms and achieve better dynamic regret bounds for the episodic (loop-free) SSP; and moreover, we demonstrate impossibility results for the infinite-horizon MDPs.
Sleep-dependent structural neuroplasticity after a spatial navigation task: A diffusion imaging study

Evidence for sleep-dependent changes in micro-structural neuroplasticity remains scarce, despite the fact that it is a mandatory correlate of the reorganization of learning-related functional networks. We investigated the effects of post-training sleep on structural neuroplasticity markers measuring standard diffusion tensor imaging (DTI) mean diffusivity (MD) and the revised biophysical neurite orientation dispersion and density imaging (NODDI) free water fraction (FWF) and neurite density (NDI) parameters that enable disentangling whether MD changes result from modifications in neurites or in other cellular components (e.g., glial cells). Thirty-four healthy young adults were scanned using diffusion weighted imaging [DWI] on Day1 before and after 40-minutes route learning (navigation) in a virtual environment, then were sleep deprived (SD) or slept normally (RS) for the night. After recovery sleep for 2 nights, they were scanned again (Day4) before and after 40-minutes route learning (navigation) in an extended environment. Sleep-related microstructural changes were computed on DTI (MD) and NODDI (NDI and FWF) parameters in the cortical ribbon and subcortical hippocampal and striatal regions of interest (ROIs). Results disclosed navigation learning-related decreased DWI parameters in the cortical ribbon (MD, FWF) and subcortical (MD, FWF, NDI) areas. Post-learning sleep-related changes were found at Day4 in the extended learning session (pre- to post-relearning percentage changes), suggesting a rapid sleep-related remodelling of neurites and glial cells subtending learning and memory processes in basal ganglia and hippocampal structures.
Eng‐Choon Leong · Zhuoyuan Cheng

Knowledge of landslide volume is important to understand the extent of damages and evaluating methods of remediation. However, the volume of landslide is difficult to quantify due to its scale and challenges encountered in conventional surveying. Various studies using satellite and aerial images have been conducted to empirically relate volume (i.e., displaced mass) of a landslide to its area through a power-law. However, there are many existing empirical relationships, and the volume estimate may differ substantially. In this study, firstly it is demonstrated that the empirical area-volume power-law relationships could be rationalized by a geometrical and mathematical basis. The empirical relationships in the literature are shown to be bounded by the volumes of “idealized” landslides where the slip surface is either spherical or elliptical. Secondly, a geometry-modelling method is proposed to estimate the volume of a landslide from satellite and aerial images without the need for digital elevation models. Using this method, landslide volume can be expediently estimated, and it yields better accuracy than empirical area-volume power-law relationships.