Miracle-dz/UniFAR-FTUs huggingface dataset:
Design and Selection of Engineered Lytic Proteins With Staphylococcus aureus Decolonizing Activity

Staphylococcus aureus causes various infections in humans and animals, the skin being the principal reservoir of this pathogen. The widespread occurrence of methicillin-resistant S. aureus (MRSA) limits the elimination and treatment of this pathogen. Phage lytic proteins have been proven as efficient antimicrobials against S. aureus. Here, a set of 12 engineered proteins based on endolysins were conceptualized to select the most optimal following a stepwise funnel approach assessing parameters including turbidity reduction, minimum inhibitory concentration (MIC), time-kill curves, and antibiofilm assays, as well as testing their stability in a broad range of storage conditions (pH, temperature, and ionic strength). The engineered phage lysins LysRODIΔAmi and ClyRODI-H5 showed the highest specific lytic activity (5 to 50 times higher than the rest), exhibited a shelf-life up to 6 months and remained stable at temperatures up to 50°C and in a pH range from 3 to 9. LysRODIΔAmi showed the lower MIC values against all staphylococcal strains tested. Both proteins were able to kill 6 log units of the strain S. aureus Sa9 within 5 min and could remove preformed biofilms (76 and 65%, respectively). Moreover, LysRODIΔAmi could prevent biofilm formation at low protein concentrations (0.15–0.6 μM). Due to its enhanced antibiofilm properties, LysRODIΔAmi was selected to effectively remove S. aureus contamination in both intact and disrupted keratinocyte monolayers. Notably, this protein did not demonstrate any toxicity toward human keratinocytes, even at high concentrations (22.1 μM). Finally, a pig skin ex vivo model was used to evaluate treatment of artificially contaminated pig skin using LysRODIΔAmi (16.5 μg/cm2). Following an early reduction of S. aureus, a second dose of protein completely eradicated S. aureus. Overall, our results suggest that LysRODIΔAmi is a suitable candidate as antimicrobial agent to prevent and treat staphylococcal skin infections.
Alterations in Sensorimotor and Mesiotemporal Cortices and Diffuse White Matter Changes in Primary Progressive Multiple Sclerosis Detected by Adiabatic Relaxometry

Background: The research of primary progressive multiple sclerosis (PPMS) has not been able to capitalize on recent progresses in advanced magnetic resonance imaging (MRI) protocols. Objective: The presented cross-sectional study evaluated the utility of four different MRI relaxation metrics and diffusion-weighted imaging in PPMS. Methods: Conventional free precession T1 and T2, and rotating frame adiabatic T1ρ and T2ρ in combination with diffusion-weighted parameters were acquired in 13 PPMS patients and 13 age- and sex-matched controls. Results: T1ρ, a marker of crucial relevance for PPMS due to its sensitivity to neuronal loss, revealed large-scale changes in mesiotemporal structures, the sensorimotor cortex, and the cingulate, in combination with diffuse alterations in the white matter and cerebellum. T2ρ, particularly sensitive to local tissue background gradients and thus an indicator of iron accumulation, concurred with similar topography of damage, but of lower extent. Moreover, these adiabatic protocols outperformed both conventional T1 and T2 maps and diffusion tensor/kurtosis approaches, methods previously used in the MRI research of PPMS. Conclusion: This study introduces adiabatic T1ρ and T2ρ as elegant markers confirming large-scale cortical gray matter, cerebellar, and white matter alterations in PPMS invisible to other in vivo biomarkers.
Nimber-Preserving Reductions and Homomorphic Sprague-Grundy Game Encodings

The concept of nimbers—a.k. a. Grundy-values or nim-values—is fundamental to combinatorial game theory. Nimbers provide a complete characterization of strategic interactions among impartial games in their disjunctive sums as well as the winnability. In this paper, we initiate a study of nimber-preserving reductions among impartial games. These reductions enhance the winnability-preserving reductions in traditional computational characterizations of combinatorial games. We prove that Generalized Geography is complete for the natural class, I , of polynomially-short impartial rulesets under nimber-preserving reductions, a property we refer to as Sprague-Grundy-complete. In contrast, we also show that not every PSPACE-complete ruleset in I is Sprague-Grundy-complete for I . By considering every impartial game as an encoding of its nimber, our technical result establishes the following striking cryptography-inspired homomorphic theorem: Despite the PSPACE-completeness of nimber computation for I , there exists a polynomial-time algorithm to construct, for any pair of games G1, G2 in I , a prime game (i.e. a game that cannot be written as a sum) G of I , satisfying: nimber(G) = nimber(G1) ⊕ nimber(G2). Supported by the Simons Investigator Award for fundamental & curiosity-driven research and NSF grant CCF1815254.
Check Your Other Door! Establishing Backdoor Attacks in the Frequency Domain

Deep Neural Networks (DNNs) have been utilized in various applications ranging from image classiﬁcation and facial recognition to medical imagery analysis and real-time object detection. As our models become more sophisticated and complex, the computational cost of training such models becomes a burden for small companies and individuals; for this reason, outsourcing the training process has been the go-to option for such users. Unfortunately, outsourcing the training process comes at the cost of vulnerability to backdoor attacks. These attacks aim at establishing hidden backdoors in the DNN such that the model performs well on benign samples but outputs a particular target label when a trigger is applied to the input. Current backdoor attacks rely on generating triggers in the image/pixel domain; however, as we show in this paper, it is not the only domain to exploit and one should always ”check the other doors”. In this work, we propose a complete pipeline for generating a dynamic, efﬁcient, and invisible backdoor attack in the frequency domain. We show the advantages of utilizing the frequency domain for establishing undetectable and powerful backdoor attacks through extensive experiments on various datasets and network architectures. The backdoored models are shown to break various state-of-the-art defences. We also show two possible defences that succeed against frequency-based backdoor attacks and possible ways for the attacker to bypass them. We conclude the work with some remarks regarding a network’s learning capacity and the capability of embedding a backdoor attack in the model.
Accordion: A Trainable Simulator for Long-Term Interactive Systems

As machine learning methods are increasingly used in interactive systems it becomes common for user experiences to be the result of an ecosystem of machine learning models in aggregate. Simulation offers a way to deal with the resulting complexity by approximating the real system in a tractable and interpretable manner. Existing methods do not fully incorporate the interactions between user history, recommendation quality, and subsequent visits. We develop Accordion, a trainable simulator based on Poisson processes that can model visit patterns to an interactive system over time from large-scale data. New methods for training and simulation are developed and tested on two datasets of real world interactive systems. Accordion shows greater sensitivity to hyperparameter tuning and offline A/B testing than comparison methods, an important step in building realistic task-oriented simulators for recommendation.
Accelerating Deductive Coding of Qualitative Data: An Experimental Study on the Applicability of Crowdsourcing

While qualitative research can produce a rich understanding of peoples’ mind, it requires an essential and strenuous data annotation process known as coding. Coding can be repetitive and time-consuming, particularly for large datasets. Crowdsourcing provides flexible access to workers all around the world, however, researchers remain doubtful about its applicability for coding. In this study, we present an interactive coding system to support crowdsourced deductive coding of semi-structured qualitative data. Through an empirical evaluation on Amazon Mechanical Turk, we assess both the quality and the reliability of crowd-support for coding. Our results show that non-expert coders provide reliable results using our system. The crowd reached a substantial agreement of up to 91% with the coding provided by experts. Our results indicate that crowdsourced coding is an applicable strategy for accelerating a strenuous task. Additionally, we present implications of crowdsourcing to reduce biases in the interpretation of qualitative data.
Con&Net: A Cross-Network Anchor Link Discovery Method Based on Embedding Representation

Cross-network anchor link discovery is an important research problem and has many applications in heterogeneous social network. Existing schemes of cross-network anchor link discovery can provide reasonable link discovery results, but the quality of these results depends on the features of the platform. Therefore, there is no theoretical guarantee to the stability. This article employs user embedding feature to model the relationship between cross-platform accounts, that is, the more similar the user embedding features are, the more similar the two accounts are. The similarity of user embedding features is determined by the distance of the user features in the latent space. Based on the user embedding features, this article proposes an embedding representation-based method Con&Net(Content and Network) to solve cross-network anchor link discovery problem. Con&Net combines the user’s profile features, user-generated content (UGC) features, and user’s social structure features to measure the similarity of two user accounts. Con&Net first trains the user’s profile features to get profile embedding. Then it trains the network structure of the nodes to get structure embedding. It connects the two features through vector concatenating, and calculates the cosine similarity of the vector based on the embedding vector. This cosine similarity is used to measure the similarity of the user accounts. Finally, Con&Net predicts the link based on similarity for account pairs across the two networks. A large number of experiments in Sina Weibo and Twitter networks show that the proposed method Con&Net is better than state-of-the-art method. The area under the curve (AUC) value of the receiver operating characteristic (ROC) curve predicted by the anchor link is 11% higher than the baseline method, and Precision@30 is 25% higher than the baseline method.
Effective Tensor Completion via Element-Wise Weighted Low-Rank Tensor Train With Overlapping Ket Augmentation

Tensor completion methods based on the tensor train (TT) have the issues of inaccurate weight assignment and ineffective tensor augmentation pre-processing. In this work, we propose a novel tensor completion approach via the element-wise weighted technique. Accordingly, a novel formulation for tensor completion and an effective optimization algorithm, called tensor completion by parallel weighted matrix factorization via tensor train (TWMac-TT), is proposed. In addition, we specifically consider the recovery quality of edge elements from adjacent blocks. Different from traditional reshaping and ket augmentation, we utilize a new tensor augmentation technique called overlapping ket augmentation, which can further avoid blocking artifacts. We then conduct extensive performance evaluations on synthetic data and several real image data sets. Our experimental results demonstrate that the proposed algorithm TWMac-TT outperforms several other competing tensor completion methods. The code is available at https://github.com/yzcv/ TWMac-TT-OKA
A Deep Learning-Based Unified Framework for Red Lesions Detection on Retinal Fundus Images

Red-lesions, microaneurysms (MAs) and hemorrhages (HMs), are the early signs of diabetic retinopathy (DR). The automatic detection of MAs and HMs on retinal fundus images is a challenging task. Most of the existing methods detect either only MAs or only HMs because of the difference in their texture, sizes, and morphology. Though some methods detect both MAs and HMs, they suffer from the curse of dimensionality of shape and colors features and fail to detect all shape variations of HMs such as flame-shaped. Leveraging the progress in deep learning, we proposed a two-stream red lesions detection system dealing simultaneously with small and large red lesions. For this system, we introduced a new ROIs candidates generation method for large red lesions on fundus images; it is based on blood vessel segmentation and morphological operations, and reduces the computational complexity, and enhances the detection accuracy by generating a small number of potential candidates. For detection, we proposed a framework with two streams. We used pretrained VGGNet as a backbone model and carried out several extensive experiments to tune it for vessels segmentation and candidates generation, and finally learning the appropriate mapping, which yields better detection of the red lesions comparing with the state-of-the-art methods. The experimental results validated the effectiveness of the system in the detection of both MAs and HMs; it yields higher performance for per lesion detection; its sensitivity equals 0.8589 and good FROC score under 8 FPIs on DiaretDB1-MA reports FROC=0.7518, and with SN=0.7552 and good FROC score under 2,4and 8 FPIs on DiaretDB1-HM, and SN=0.8157 on e-ophtha with overall FROC=0.4537 and on ROCh dataset with FROC=0.3461 which is higher than the state-of-the art methods. For DR screening, the system performs well with good AUC on DiaretDB1-MA, DiaretDB1-HM, and e-ophtha datasets.
Prioritized Planning for Cooperative Range-Only Localization in Multi-Robot Networks

We present a novel path-planning algorithm to reduce localization error for a network of robots cooperatively localizing via inter-robot range measurements. The quality of localization with range measurements depends on the configuration of the network, and poor configurations can cause substantial localization errors. To reduce the effect of network configuration on localization error for moving networks we consider various optimality measures of the Fisher information matrix (FIM), which have well-known relationships with localization error. We pose a trajectory planning problem with constraints on the FIM optimality measures. By constraining these optimality measures we can control the statistical properties of the localization error. To efficiently generate trajectories which satisfy these FIM constraints we present a prioritized planner which leverages graph-based planning and properties of the range-only FIM. We demonstrate in simulations that the trajectories generated by our algorithm reduce worst-case localization error by up to 42% in comparison to existing planners and can scalably plan distance-efficient trajectories in complicated environments for large numbers of robots.
Rehabilitation in Survivors of COVID-19 (RE2SCUE): a nonrandomized, controlled, and open protocol

Objective: This study aimed to evaluate the effects of physical rehabilitation for adults with sequelae after COVID-19. Methods: This clinical, nonrandomized, controlled, and open study will examine 82 participants who have met the inclusion criteria and who will be divided into treatment and control groups according to participant preference. The intervention group will receive face-to-face care; the control group will receive remote educational guidance for 8 weeks, with pre-post evaluations. The primary outcomes are dyspnea, fatigue, and exercise capacity; the secondary outcomes are lung function, heart rate variability, handgrip strength, knee extensor strength and electrical activity, physical activity, functional limitation, cognitive function, depression and anxiety, and biochemical measures of hypoxia, inflammation, oxidative stress, blood glucose, and lactate blood tests. The survey will follow the Standard Protocol Items for Randomized Trials guidelines, and the results will be reported according to the Consolidated Standards of Reporting Trials guidelines. Effects will be assessed based on the intent-to-treat data collected. Analysis of covariance will be used for the initial and final evaluations, with a significance level of 5%. Results and Conclusions: The results will show the effectiveness of rehabilitation in adults with post-COVID-19 sequelae.
The Logic Traps in Evaluating Post-hoc Interpretations

Post-hoc interpretation aims to explain a trained model and reveal how the model arrives at a decision. Though research on post-hoc interpretations has developed rapidly, one growing pain in this ﬁeld is the difﬁculty in evaluating interpretations. There are some crucial logic traps behind existing evaluation methods, which are ignored by most works. In this opinion piece, we summarize four kinds evaluation methods and point out the corresponding logic traps behind them. We argue that we should be clear about these traps rather than ignore them and draw conclusions assertively.
Identification of Heat Period through the Pheromonal Compounds in the Urine of Malaimadu , Bos indicus

Early oestrus and pregnancy detection are the major challenging issues in farm animals. Also some other problems like irregular or prolonging of the oestrus cycle, anoestrus, fighting behavior among young ones, mother-young bond, unmotivated males, and poor farm animal management including stress are considered to be threatening factor. The present investigation aimed to address the detection of heat period in the malaimadu, Bos indicus by studying the urinary pheromonal compounds during the different cycles of oestrus by analytical and molecular validation and electrophoretic profiling. We found significant results by identifying peculiar pheromonal compounds released by the experimental animal in the oestrus urine.
MOV10 Helicase Interacts with Coronavirus Nucleocapsid Protein and Has Antiviral Activity

Coronaviruses (CoVs) are emerging pathogens causing life-threatening diseases in humans. Knowledge of virus-host interactions and viral subversion mechanisms of host pathways is required for the development of effective countermeasures against CoVs. ABSTRACT Coronaviruses (CoVs) are emergent pathogens that may cause life-threatening respiratory diseases in humans. Understanding of CoV-host interactions may help to identify novel therapeutic targets. MOV10 is an RNA helicase involved in different steps of cellular RNA metabolism. Both MOV10 antiviral and proviral activities have been described in a limited number of viruses, but this protein has not been previously associated with CoVs. We found that during Middle East respiratory syndrome coronavirus (MERS-CoV) infection, MOV10 aggregated in cytoplasmic structures colocalizing with viral nucleocapsid (N) protein. MOV10-N interaction was confirmed by endogenous MOV10 coimmunoprecipitation, and the presence of other cellular proteins was also detected in MOV10 complexes. MOV10 silencing significantly increased both N protein accumulation and virus titer, with no changes in the accumulation of viral RNAs. Moreover, MOV10 overexpression caused a 10-fold decrease in viral titers. These data indicated that MOV10 has antiviral activity during MERS-CoV infection. We postulated that this activity could be mediated by viral RNA sequestration, and in fact, RNA immunoprecipitation data showed the presence of viral RNAs in the MOV10 cytoplasmic complexes. Expression of wild-type MOV10 or of a MOV10 mutant without helicase activity in MOV10 knockout cell lines, developed by CRISPR-Cas technology, indicated that the helicase activity of MOV10 was required for its antiviral effect. Interestingly MOV10-N interaction was conserved in other mildly or highly pathogenic human CoVs, including the recently emerged severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), although MOV10 antiviral activity was found only in highly pathogenic CoVs, suggesting a potential role of MOV10 in the modulation of human CoVs pathogenesis. IMPORTANCE Coronaviruses (CoVs) are emerging pathogens causing life-threatening diseases in humans. Knowledge of virus-host interactions and viral subversion mechanisms of host pathways is required for the development of effective countermeasures against CoVs. The interaction between cellular RNA helicase MOV10 and nucleocapsid (N) protein from several human CoVs is shown. Using MERS-CoV as a model, we demonstrate that MOV10 has antiviral function, requiring its helicase activity, most likely mediated by viral RNA sequestration in cytoplasmic ribonucleoprotein structures. Furthermore, we found that MOV10 antiviral activity may act only in highly pathogenic human CoVs, suggesting a role for MOV10 in modulating CoVs pathogenesis. The present study uncovers a complex network of viral and cellular RNAs and proteins interaction modulating the antiviral response against CoVs.
Recommender Systems for Personalized User Experience: Lessons learned at Booking.com

Booking.com is the world’s leading online travel platform where users make many decisions supported by our recommendations, such as destinations, travel dates, facilities, etc. This leads to a complex User Interface (UI) containing many widgets of different relevance for different users. We address the problem of constructing an optimal UI, a non-trivial problem, mainly due to user preferences evolving over time and multiple independent teams collaboratively building the UI. Our goal is to provide a personalized User Experience (UX) which adapts to changes in the environment and ensures governable, collaborative product development. The solution relies on a Multi Armed Bandits (MAB) framework currently allowing product teams to collaborate on the construction of UIs and serving millions of users every day. We present examples of our solution and lessons learned during their implementation.