Miracle-dz/UniFAR-FTUs huggingface dataset:
Enhancement of Cell Adhesion by Anaplasma phagocytophilum Nucleolin-Interacting Protein AFAP

Anaplasma phagocytophilum, the aetiologic agent of human granulocytic anaplasmosis (HGA) is an obligate intracellular Gram-negative bacterium. During infection, A. phagocytophilum enhances the adhesion of neutrophils to infected endothelial cells. However the bacterial factors contributing to this phenomenon remain unknown. In this study, we characterized a type IV secretion system substrate of A. phagocytophilum, AFAP (an actin filament-associated Anaplasma phagocytophilum protein), and found it enhanced cell adhesion. Tandem affinity purification combined with mass spectrometry identified host nucleolin as an AFAP-binding protein. Further study showed disruption of nucleolin by RNA interference and treatment of a nucleolin-binding DNA aptamer AS1411 attenuated AFAP-mediated cell adhesion. The characterization of AFAP with enhancement effect on cell adhesion and identification of its interaction partner may help understand the mechanism underlying A. phagocytophilum-promoting cell adhesion, facilitating elucidation of HGA pathogenesis. Highlights Anaplasma phagocytophilum AFAP localized to cell periphery. AFAP enhanced cell adhesion. AFAP interacted with host nucleolin. Disruption of nucleolin attenuated AFAP-mediated cell adhesion.
Radiation-Induced Metabolic Reprogramming of Fibroblasts Regulates the Breast Cancer Microenvironment

Patients with triple negative breast cancer (TNBC) continue to have high recurrence rates despite current interventions, including radiation therapy (RT). Radiation-resistant TNBC cells and circulating tumor cells thought to be involved in recurrence survive due to changes in their metabolic profiles, which may be influenced by their interactions with radiation-damaged stromal cells, such as fibroblasts, following treatment. How fibroblasts metabolically respond to RT and influence the metabolism of TNBC cells is poorly understood. In this study, we identify that radiation-damaged fibroblasts accumulate lipids due to increased autophagic flux. The radiation damage response allows fibroblasts to maintain increased fatty acid oxidation, overall mitochondrial respiration, and aerobic glycolysis rates, leading to increased lactate secretion. TNBC cells respond to these metabolic changes by increasing migration rates and tumor spheroid growth. Our work reveals how metabolic crosstalk between irradiated fibroblasts and TNBC cells leads to a microenvironment conducive to recurrence. Summary Although radioresistant and circulating tumor cell survival has been attributed to altered metabolism, the metabolic impact of radiation therapy on stromal cells is unknown. The authors provide a burgeoning mechanism for radiation-induced metabolic crosstalk between fibroblasts and breast cancer cells that may influence recurrence. Graphical Abstract
Therapeutic neutralizing monoclonal antibody administration protects against lethal yellow fever virus infection

Yellow fever virus (YFV) is a reemerging global health threat, driven by several factors, including increased spread of the mosquito vector and rapid urbanization. Although a prophylactic vaccine exists, vaccine hesitancy, supply deficits, and distribution difficulties leave specific populations at risk of severe YFV disease, as evidenced by recent outbreaks in South America. To establish a treatment for patients with severe YFV infection, we tested 37 YFV-specific monoclonal antibodies isolated from vaccinated humans and identified two capable of potently neutralizing multiple pathogenic primary YFV isolates. Using both hamster and nonhuman primate models of lethal YFV infection, we demonstrate that a single administration of either of these two potently neutralizing antibodies during acute infection fully controlled viremia and prevented severe disease and death in treated animals. Given the potential severity of YFV-induced disease, our results show that these antibodies could be effective in saving lives and fill a much-needed void in managing YFV cases during outbreaks. Description Therapeutic monoclonal antibodies prevent death from YFV infection in a nonhuman primate model. A yellow fever virus treatment Yellow fever virus (YFV) is once again becoming a threat to global health because of changes in the environment. A live attenuated vaccine (YFV-17D) has been used for decades, but immunization coverage diminished in recent years because of concerns about adverse reactions, which has left many unvaccinated people in endemic areas vulnerable to infection. Ricciardi et al. characterized monoclonal antibodies (mAbs) that can neutralize YFV and prevent severe disease in animal models. They screened 37 YFV-specific mAbs isolated from vaccinated humans, of which two showed potent neutralization activity against a range of YFV isolates. Administration of a single dose of either mAb to YFV-infected hamsters or nonhuman primates protected against severe disease and death. These findings suggest that YFV mAbs may be a potential therapeutic treatment that could improve outcomes during outbreaks. —CNF
Using a system of equations to assess the determinants of the walking behavior of older adults

As the most prevalent physical activity and transportation mode for older people, walking is considered to have multiple health and well‐being benefits. Previous studies used separate models to assess the built‐environment determinants of a battery of walking behavior measures, such as walking frequency and duration. In a departure from them, this study develops a system of equations, which is estimated by seemingly unrelated regression, to determine the built‐environment factors that significantly influence two correlated walking behavior measures (including walking frequency and duration) of older adults in Xiamen (a medium‐sized Chinese city) based on data from the Travel Survey of Xiamen Residents 2015 and built‐environment geo‐data. The results show the following: (1) the walking frequency and duration of older adults are affected by the built environment and socio‐demographic characteristics; (2) land‐use mix, intersection density, and bus route density positively influence older adults’ walking frequency and duration; (3) distance to the commercial center adversely impacts the walking frequency and duration; and (4) the built environment has similar effects on the two measures. This study offers a worthwhile reference for policy intervention to promote older adults' walking activities, thereby contributing to active and healthy aging.
NEDMP: Neural Enhanced Dynamic Message Passing

Predicting stochastic spreading processes on complex networks is critical in epidemic control, opinion propagation, and viral marketing. We focus on the problem of inferring the time-dependent marginal probabilities of states for each node which collectively quantifies the spreading results. Dynamic Message Passing (DMP) has been developed as an efficient inference algorithm for several spreading models, and it is asymptotically exact on locally tree-like networks. However, DMP can struggle in diffusion networks with lots of local loops. We address this limitation by using Graph Neural Networks (GNN) to learn the dependency amongst messages implicitly. Specifically, we propose a hybrid model in which the GNN module runs jointly with DMP equations. The GNN module refines the aggregated messages in DMP iterations by learning from simulation data. We demonstrate numerically that after training, our model's inference accuracy substantially outperforms DMP in conditions of various network structure and dynamics parameters. Moreover, compared to pure data-driven models, the proposed hybrid model has a better generalization ability for out-of-training cases, profiting from the explicitly utilized dynamics priors in the hybrid model. A PyTorch implementation of our model is at https://github.com/FeiGSSS/NEDMP.
Learning POMDP Models with Similarity Space Regularization: a Linear Gaussian Case Study

Partially observable Markov decision process (POMDP) is a principled framework for sequential decision making and control under uncertainty. Classical POMDP methods assume known system models, while in real-world applications, the true models are usually unknown. Recent researches propose learning POMDP models from the observation sequences rolled out by the true system using maximum likelihood estimation (MLE). However, we find that such methods usually fail to find a desirable solution. This paper makes a profound study of the POMDP model learning problem, focusing on the linear Gaussian case. We show the objective of MLE is a high-order polynomial function, which makes it easy to get stuck in local optima. We then prove that the global optimal models are not unique and constitute a similarity space of the true model. Based on this view, we propose Similarity Space Regularization (SimReg), an algorithm that smooths out the local optima but keeps all the global optima. Experiments show that given only a biased prior model, our algorithm achieves a higher log-likelihood, more accurate observation reconstruction and state estimation compared with the MLE-based method.
Diffeomorphic Transforms for Generalised Imitation Learning

We address the generalised imitation learning problem of producing robot motions to imitate expert demonstrations, while adapting to novel environments. Past studies have often focused on meth-ods that closely mimic demonstrations. However, to operate reliably in novel environments, robots should be able to adapt their learned motions accordingly. Motivated by this, we devise a framework capable of learning a time-invariant dynamical system to imitate demonstrations, and generalise to account for changes to the surroundings. To ensure the system is robust to perturbations, we need to maintain its stability. Our framework enforces stability in a principled manner: we start with a known stable system and use differentiable bijections (diffeomorphisms) to morph the system into the desired target system. We modularise robot motion and develop diffeomorphic transforms to encode individual actions. A composition of transforms produces generalised behaviour that complies with multiple requirements, such as mimicking demonstrations while avoiding obstacles. We evaluate our framework in both simulation and on a real-world 6-DOF manipulator. Results show our framework can produce a stable system that is collision-free and incorporates user-specified biases, while closely resembling demonstrations.
Hardness of Learning a Single Neuron with Adversarial Label Noise

We study the problem of distribution-free PAC learning a single neuron under adversarial label noise with respect to the squared loss. For a range of activation functions, including ReLUs and sigmoids, we prove strong computational hardness of learning results in the Statistical Query model and under a well-studied assumption on the complexity of re-futing XOR formulas. Speciﬁcally, we establish that no polynomial-time learning algo-rithm, even improper, can approximate the optimal loss value within any constant factor.
ContextGen: Targeted Data Generation for Low Resource Domain Specific Text Classification

To address the challenging low-resource non-topical text classification problems in domain specific settings we introduce ContextGen – a novel approach that uses targeted text generation with no fine tuning to augment the available small annotated dataset. It first adapts the powerful GPT-2 text generation model to generate samples relevant for the domain by using properly designed context text as input for generation. Then it assigns class labels to the newly generated samples after which they are added to the initial training set. We demonstrate the superior performance of a state-of-the-art text classifier trained with the augmented labelled dataset for four different non-topical tasks in the low resource setting, three of which are from specialized domains.
Standardisation-function Kernel Stein Discrepancy: A Unifying View on Kernel Stein Discrepancy Tests for Goodness-of-fit

Non-parametric goodness-of-ﬁt testing procedures based on kernel Stein discrepancies (KSD) are promising approaches to validate general unnormalised distributions in various scenarios. Existing works focused on study-ing kernel choices to boost test performances. However, the choices of (non-unique) Stein operators also have considerable eﬀect on the test performances. Inspired by the standardisation technique that was originally developed to better derive approximation properties for normal distributions, we present a unifying framework, called standardisation-function kernel Stein discrepancy (Sf-KSD), to study diﬀerent Stein operators in KSD-based tests for goodness-of-ﬁt. We derive explicitly how the proposed framework relates to existing KSD-based tests and show that Sf-KSD can be used as a guide to develop novel kernel-based non-parametric tests on complex data scenarios, e.g. truncated distributions or compositional data. Experimental results demonstrate that the proposed tests control type-I error well and achieve higher test power than existing approaches.
Lifted Division for Lifted Hugin Belief Propagation

The lifted junction tree algorithm (LJT) is an inference algorithm that allows for tractable inference regarding domain sizes. To answer multiple queries efficiently, it decomposes a first-order input model into a first-order junction tree. During inference, degrees of belief are propagated through the tree. This propagation significantly contributes to the run-time complexity not just of LJT but of any tree-based inference algorithm. We present a lifted propagation scheme based on the so-called Hugin scheme whose runtime complexity is independent of the degree of the tree. Thereby, lifted Hugin can achieve asymptotic speed improvements over the existing lifted Shafer-Shenoy propagation. An empirical evaluation confirms these results.
Bayesian Link Prediction with Deep Graph Convolutional Gaussian Processes

Link prediction aims to reveal missing edges in a graph. We introduce a deep graph convolutional Gaussian process model for this task, which addresses recent challenges in graph machine learning with oversmoothing and overﬁt-ting. Using simpliﬁed graph convolutions, we transform a Gaussian process to leverage the topological information of the graph domain. To scale the Gaussian process model to larger graphs, we introduce a variational inducing point method that places pseudo-inputs on a graph-structured domain. Multiple Gaussian processes are assembled into a hierarchy whose structure allows skipping convolutions and thus counteracting oversmoothing. The proposed model represents the ﬁrst Gaussian process for link prediction that makes use of both node features and topological information. We evaluate our model on multiple graph data sets with up to thousands of nodes and report consistent improvements over competitive link prediction approaches.
Scaling and Scalability: Provable Nonconvex Low-Rank Tensor Completion

Tensors, which provide a powerful and ﬂex-ible model for representing multi-attribute data and multi-way interactions, play an indispensable role in modern data science across various ﬁelds in science and engineering. A fundamental task is tensor completion, which aims to faithfully recover the tensor from a small subset of its entries in a statistically and computationally eﬃcient manner. Harnessing the low-rank structure of tensors in the Tucker decomposition, this paper develops a scaled gradient descent (ScaledGD) algorithm to directly recover the tensor factors with tailored spectral initializations, and shows that it provably converges at a linear rate independent of the condition number of the ground truth tensor for tensor completion as soon as the sample size is above the order of n 3 / 2 ignoring other parameter dependencies, where n is the dimension of the tensor. To the best of our knowledge, ScaledGD is the ﬁrst al-gorithm that achieves near-optimal statistical and computational complexities simultaneously for low-rank tensor completion with the Tucker decomposition. Our algorithm highlights the power of appropriate preconditioning in accelerating nonconvex statistical estimation, where the iteration-varying preconditioners promote desirable invariance properties of the trajectory with respect to the underlying symmetry in low-rank tensor factorization.
RNA sequence to structure analysis from comprehensive pairwise mutagenesis of multiple self-cleaving ribozymes

Self-cleaving ribozymes are RNA molecules that catalyze the cleavage of their own phosphodiester backbones. These ribozymes are found in all domains of life and are also a tool for biotechnical and synthetic biology applications. Self-cleaving ribozymes are also an important model of sequence to function relationships for RNA because their small size simplifies synthesis of genetic variants and self-cleaving activity is an accessible readout of the functional consequence of the mutation. Here we used a high-throughput experimental approach to determine the relative activity for every possible single and double mutant of five self-cleaving ribozymes. From this data, we comprehensively identified non-additive effects between pairs of mutations (epistasis) for all five ribozymes. We analyzed how changes in activity and trends in epistasis map to the ribozyme structures. The variety of structures studied provided opportunities to observe several examples of common structural elements, and the data was collected under identical experimental conditions to enable direct comparison. Heat-map based visualization of the data revealed patterns indicating structural features of the ribozymes including paired regions, unpaired loops, non-canonical structures and tertiary structural contacts. The data also revealed signatures of functionally critical nucleotides involved in catalysis. The results demonstrate that the data sets provide structural information similar to chemical or enzymatic probing experiments, but with additional quantitative functional information. The large-scale data sets can be used for models predicting structure and function and for efforts to engineer self-cleaving ribozymes.
Pathogenic TDP-43 Disrupts Axon Initial Segment Structure and Neuronal Excitability in a Human iPSC Model of ALS

Dysregulated neuronal excitability is a hallmark of amyotrophic lateral sclerosis (ALS). We sought to investigate how functional changes to the axon initial segment (AIS), the site of action potential generation, could impact neuronal excitability in a human iPSC model of ALS. We found that early (6-week) ALS-related TDP-43G298S motor neurons showed an increase in the length of the AIS, relative to CRISPR-corrected controls. This was linked to neuronal hyperexcitability and increased spontaneous contractions of hiPSC-myofibers in compartmentalised neuromuscular co-cultures. In contrast late (10-week) TDP-43G298S motor neurons showed reduced AIS length and hypoexcitability. At a molecular level aberrant expression of the AIS master scaffolding protein Ankyrin-G, and the AIS-specific voltage-gated ion channels SCN1A (Nav1.1) and SCN8A (Nav1.6) mirrored these dynamic changes in excitability. Finally, at all stages, TDP-43G298S motor neurons showed compromised activity-dependent plasticity of the AIS, further contributing to abnormal excitability. Our results point toward the AIS as an important subcellular target driving changes to neuronal excitability in ALS.
Progress in Artificial Intelligence: 17th Portuguese Conference on Artificial Intelligence, EPIA 2015, Coimbra, Portugal, September 8-11, 2015

This book constitutes the refereed proceedings of the 17th Portuguese Conference on Artificial Intelligence, EPIA 2015, held in Coimbra, Portugal, in September 2015. The 45 revised full papers presented together with 36 revised short papers were carefully reviewed and selected from a total of 131 submissions. EPIA 2015, following the standard EPIA format, covers a wide range of AI topics as follows: ambient intelligence and affective environments, artificial Intelligence in medicine, artificial intelligence in transportation systems, artificial life and evolutionary algorithms, computational methods in bioinformatics and systems biology, general artificial intelligence, intelligent information systems, intelligent robotics, knowledge discovery and business intelligence, multi-agent systems: theory and applications, social simulation and modelling, text mining and applications.
Recommending Combinations of Appointment Places from Hive based Big Data

Since the development of web, we can use various type data such as movie, music, and social network data. These data is very useful to make recommendation system. For this reason, several recommendation system studies used the web data to construct outstanding system. In this paper, we proposed a method to recommend combinations of appointment places based on web data. Our system generates ranking to extract best store, and use location data to recommend suitable store. By considering these two main features, the proposed system recommend best and suitable store to user to recommend combinations of appointment places. Our system provides a chance to design combinations of appointment places with high- quality easily.
Microsensors vs. integrated circuits: a study in contrasts

The author examines the contrasts between conventional IC design paradigms and the corresponding paradigms for microsensors in four areas: system partitioning, technology constraints, packaging constraints, and CAD (computer-aided design) requirements. It is found that many of the driving forces for smaller feature sizes, higher levels of integration, higher I/O count, and standardized packages and process sequences in the IC domain have corresponding driving forces in the microsensor domain, but often with opposite direction: maintain larger feature sizes for dimensional precision, keep the level of integration and I/O count minimal, and assume that process technologies and packages may be application specific. In the CAD area, there are many similarities, but the need in the microsensor domain for mechanical performance prediction requires two CAD capabilities: the ability to predict mechanical properties and residual stresses from a description of the process sequence and the ability to construct a 3-D solid model of the fabricated device in a format suitable for mechanical analysis with finite-element-method codes. <<ETX>>
The use of design science research in the development of a performance management system for hospitality O uso de design science research no desenvolvimento de um sistema de gestão da performance para a hotelaria

Since no Performance Management (PM) systems specific for the hospitality industry seem to exist in the market, it was decided to evaluate the commercial viability of such a system by developing one and making it available to hotels in the form of Software as a Service (SaaS). Software deployed in the cloud, delivered and licensed as a service is becoming increasingly common and accepted in a business context. Although PM and Decision Support Systems (DSS) are not usually distributed in the SaaS mode, there are some examples that this is changing. To evaluate the system in both the technical and business perspectives, a prototype was developed using the Design Science Research (DSR) methodology and made available to four hotels. The results revealed that hotels were very satisfied with the system and that building a prototype is a good method to develop and assess PM systems.