Miracle-dz/UniFAR-FTUs huggingface dataset:
Synthesis and Biological Evaluation of 1,2,4-Triazole Thioethers as Both Potential Virulence Factor Inhibitors against Plant Bacterial Diseases and Agricultural Antiviral Agents against Tobacco Mosaic Virus Infections.

Targeting the virulence factors of phytopathogenic bacteria is an innovative strategy for alleviating or eliminating the pathogenicity and rapid outbreak of plant microbial diseases. Therefore, several types of 1,2,4-triazole thioethers bearing an amide linkage were prepared and screened to develop virulence factor inhibitors. Besides, the 1,2,4-triazole scaffold was exchanged by a versatile 1,3,4-oxadiazole core to expand molecular diversity. Bioassay results revealed that a 1,2,4-triazole thioether A10 bearing a privileged N-(3-nitrophenyl)acetamide fragment was extremely bioactive against Xanthomonas oryzae pv. oryzae (Xoo) with an EC50 value of 5.01 μg/mL. Label-free quantitative proteomics found that compound A10 could significantly downregulate the expression of Xoo's type III secretion system (T3SS) and transcription activator-like effector (TALE) correlative proteins. Meanwhile, qRT-PCR detection revealed that the corresponding gene transcription levels of these virulence factor-associated proteins were substantially inhibited after being triggered by compound A10. As a result, the hypersensitive response and pathogenicity were strongly depressed, indicating that a novel virulence factor inhibitor (A10) was probably discovered. In vivo anti-Xoo trials displayed that compound A10 yielded practicable control efficiency (54.2-59.6%), which was superior to thiadiazole-copper and bismerthiazol (38.1-44.9%). Additionally, compound A10 showed an appreciable antiviral activity toward tobacco mosaic virus (TMV) with the curative and protective activities of 54.6 and 76.4%, respectively, which were comparable to ningnanmycin (55.2 and 60.9%). This effect was further validated and visualized by the inoculation test using GFP-labeled TMV, thereby leading to the reduced biosynthesis of green-fluorescent TMV on Nicotiana benthamiana. Given the outstanding features of compound A10, it should be deeply developed as a versatile agricultural chemical.
Securing IPv6's Neighbour Discovery, using Locally Authentication Process

Internet Engineering Task Force (IETF), in IPv6, allowed nodes to Autoconfigure using neighbour discovery protocol. Neighbour Discovery (ND) and Address auto- configuration mechanisms may be protected with IPSec Authentication Header (AH). Protecting all traffic will include Address Resolution Protocol. To protect this, IPSec will need agreed Key. For Key setup, UDP packet is sent, which requires IPSec for secure communication. So IPSec requires Agreed Key and for Key setup IPSec is needed, this creates a loop. To solve this problem Locally Authentication Process is presented in this paper. This process will provide a certificate of ownership of IP address on network Interface card and Public key to provide authorization. On the other hand, it will also reduce the network load.
Parental Death and Mid-adulthood Depressive Symptoms: The Importance of Life Course Stage and Parent’s Gender

Traditional theories of grief suggest that individuals experience short-term increases in depressive symptoms following the death of a parent. However, growing evidence indicates that effects of parental bereavement may persist. Situating the short- and long-term effects of parental death within the life course perspective, we assess the combined influence of time since loss and life course stage at bereavement on mental health for maternal and paternal death. Using data from the National Longitudinal Study of Adolescent to Adult Health (N = 11,877) to examine biological parental death from childhood to mid-adulthood, we find that those who experience recent maternal or paternal death have heightened depressive symptoms. Furthermore, those who experience maternal death in childhood or paternal death in young adulthood exhibit long-term consequences for mental health. Our findings underscore the theoretical importance of early life course stages and parent’s gender when determining whether depressive symptoms persist following parental bereavement.
Pregnancy and Fertility Trends Among Female Otolaryngologists

Objective This study sought to determine childbearing patterns and decision making among female otolaryngologists. Study Design Anonymous survey. Setting An anonymous survey was sent in 2020 to female otolaryngologists identified through their membership with the American Academy of Otolaryngology–Head and Neck Surgery. Methods Data were analyzed concerning individual fertility and childbearing history, reflections regarding decision making, perceptions of workplace support, and estimations of objective childbearing potential. Results There were 398 responses. The mean age at first pregnancy was 32.3 years. Almost one-third of respondents who attempted to conceive (30.4%) were diagnosed with infertility. Of those who had their first pregnancy during training, 55% reported having substantial workplace support, as opposed to 70% of those whose first pregnancies followed completion of training (P = .01). When asked what they would do differently in retrospect, most women with infertility (65.0%) would have attempted conception earlier; 41 (41.0%) would have used cryopreservation to extend fertility; and 14 (14.0%) would have gone into a different specialty. Conclusion Female otolaryngologist respondents have children later in life than the general population, and a substantial proportion face infertility or have regrets about family planning decisions and career decision making. Increased awareness, further investigation, and targeted programs are needed to support the growing number of female otolaryngologists who desire both a career and a family.
Studies in the economics of overhead costs

1. Early British Railways 1800-1844 [1925] H.G. Lewin 2. Railway Problems [1887] J.S. Jeans 3. English Railways: Their Development and Relation to the State [1915] E. Cleveland Stephens 4. Elements of Railway Economics [1905] W.M. Acworth 5. Studies in the Economics of Overhead Costs [1923] J.M. Clark Economics of Railway Transport [1909] S.C. Williams 6. Rise of Rail Power in War and Conquest 1833-1914 [1915] E.A. Pratt 7. Transport Co-ordination [1929] K. Fenelon
Long-term safety and efficacy of anacetrapib in patients with atherosclerotic vascular disease

Abstract Aims REVEAL was the first randomized controlled trial to demonstrate that adding cholesteryl ester transfer protein inhibitor therapy to intensive statin therapy reduced the risk of major coronary events. We now report results from extended follow-up beyond the scheduled study treatment period. Methods and results A total of 30 449 adults with prior atherosclerotic vascular disease were randomly allocated to anacetrapib 100 mg daily or matching placebo, in addition to open-label atorvastatin therapy. After stopping the randomly allocated treatment, 26 129 survivors entered a post-trial follow-up period, blind to their original treatment allocation. The primary outcome was first post-randomization major coronary event (i.e. coronary death, myocardial infarction, or coronary revascularization) during the in-trial and post-trial treatment periods, with analysis by intention-to-treat. Allocation to anacetrapib conferred a 9% [95% confidence interval (CI) 3–15%; P = 0.004] proportional reduction in the incidence of major coronary events during the study treatment period (median 4.1 years). During extended follow-up (median 2.2 years), there was a further 20% (95% CI 10–29%; P < 0.001) reduction. Overall, there was a 12% (95% CI 7–17%, P < 0.001) proportional reduction in major coronary events during the overall follow-up period (median 6.3 years), corresponding to a 1.8% (95% CI 1.0–2.6%) absolute reduction. There were no significant effects on non-vascular mortality, site-specific cancer, or other serious adverse events. Morbidity follow-up was obtained for 25 784 (99%) participants. Conclusion The beneficial effects of anacetrapib on major coronary events increased with longer follow-up, and no adverse effects emerged on non-vascular mortality or morbidity. These findings illustrate the importance of sufficiently long treatment and follow-up duration in randomized trials of lipid-modifying agents to assess their full benefits and potential harms. Trial registration International Standard Randomized Controlled Trial Number (ISRCTN) 48678192; ClinicalTrials.gov No. NCT01252953; EudraCT No. 2010-023467-18.
Reinfection rates among patients previously infected by SARS-CoV-2: systematic review and meta-analysis

Supplemental Digital Content is available in the text Abstract Background: Asymptomatic or symptomatic infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can be followed by reinfection. The protection conferred by prior infection among coronavirus disease 2019 (COVID-19) patients is unclear. We assessed the incidence of SARS-CoV-2 reinfection and the protection effect of previous infection against reinfection. Methods: We searched PubMed, EMBASE, Cochrane, Scopus, Web of Science, and ClinicalTrials.gov for publications up until the end date of May 1, 2021. The reinfection rate of recovered patients and the protection against reinfection were analyzed using meta-analysis. Results: Overall, 19 studies of 1096 reinfection patients were included. The pooled reinfection rate was 0.65% (95% confidence interval [CI] 0.39–0.98%). The symptomatic reinfection rate was a bit lower (0.37% [95% CI 0.11–0.78%], I2 = 99%). The reinfection rate was much higher in high-risk populations (1.59% [95% CI 0.30–3.88%], I2 = 90%). The protection against reinfection and symptomatic reinfection was similar (87.02% [95% CI 83.22–89.96%] and 87.17% [95% CI 83.09–90.26%], respectively). Conclusions: The rate of reinfection with SARS-CoV-2 is relatively low. The protection against SARS-CoV-2 after natural infection is comparable to that estimated for vaccine efficacy. These data may help guide public health measures and vaccination strategies in response to the COVID-19 pandemic. High-quality clinical studies are needed to establish the relevant risk factors in recovered patients.
Autonomous Navigation System from Simultaneous Localization and Mapping

This paper presents the development of a Simultaneous Localization and Mapping (SLAM) based Autonomous Navigation system. The motivation for this study was to find a solution for navigating interior spaces autonomously. Interior navigation is challenging as it can be forever evolving. Solving this issue is necessary for a multitude of services, like cleaning, the health industry, and in manufacturing industries. The focus of this paper is the description of the SLAM-based software architecture developed for this proposed autonomous system. A potential application of this system, oriented to a smart wheelchair, was evaluated. Current interior navigation solutions require some sort of guiding line, like a black line on the floor. With this proposed solution, interiors do not require renovation to accommodate this solution. The source code of this application has been made open source so that it could be re-purposed for a similar application. Also, this open-source project is envisioned to be improved by the broad open-source community upon past its current state.
Est-ce que vous compute? Code-switching, cultural identity, and AI

Cultural code-switching concerns how we adjust our overall behaviours, manners of speaking, and appearance in response to a perceived change in our social environment. We defend the need to investigate cultural code-switching capacities in artificial intelligence systems. We explore a series of ethical and epistemic issues that arise when bringing cultural code-switching to bear on artificial intelligence. Building upon Dotson's (2014) analysis of testimonial smothering, we discuss how emerging technologies in AI can give rise to epistemic oppression, and specifically, a form of self-silencing that we call 'cultural smothering'. By leaving the socio-dynamic features of cultural code-switching unaddressed, AI systems risk negatively impacting already-marginalised social groups by widening opportunity gaps and further entrenching social inequalities.
Planning with Biological Neurons and Synapses

We revisit the planning problem in the blocks world, and we implement a known heuristic for this task. Importantly, our implementation is biologically plausible, in the sense that it is carried out exclusively through the spiking of neurons. Even though much has been accomplished in the blocks world over the past five decades, we believe that this is the first algorithm of its kind. The input is a sequence of symbols encoding an initial set of block stacks as well as a target set, and the output is a sequence of motion commands such as "put the top block in stack 1 on the table". The program is written in the Assembly Calculus, a recently proposed computational framework meant to model computation in the brain by bridging the gap between neural activity and cognitive function. Its elementary objects are assemblies of neurons (stable sets of neurons whose simultaneous firing signifies that the subject is thinking of an object, concept, word, etc.), its commands include project and merge, and its execution model is based on widely accepted tenets of neuroscience. A program in this framework essentially sets up a dynamical system of neurons and synapses that eventually, with high probability, accomplishes the task. The purpose of this work is to establish empirically that reasonably large programs in the Assembly Calculus can execute correctly and reliably; and that rather realistic --- if idealized --- higher cognitive functions, such as planning in the blocks world, can be implemented successfully by such programs.
Design Challenges for a Multi-Perspective Search Engine

Many users turn to document retrieval systems (e.g. search engines) to seek answers to controversial questions. Answering such user queries usually require identifying responses within web documents, and aggregating the responses based on their different perspectives. Classical document retrieval systems fall short at delivering a set of direct and diverse responses to the users. Naturally, identifying such responses within a document is a natural language understanding task. In this paper, we examine the challenges of synthesizing such language understanding objectives with document retrieval, and study a new perspective-oriented document retrieval paradigm. We discuss and assess the inherent natural language understanding challenges in order to achieve the goal. Following the design challenges and principles, we demonstrate and evaluate a practical prototype pipeline system. We use the prototype system to conduct a user survey in order to assess the utility of our paradigm, as well as understanding the user information needs for controversial queries.
Within-Die Delay Variation Measurement And Analysis For Emerging Technologies Using An Embedded Test Structure

Both random and systematic within-die process variations (PV) are growing more severe with shrinking geometries and increasing die size. Escalation in the variations in delay and power with reductions in feature size places higher demands on the accuracy of variation models. Their availability can be used to improve yield, and the corresponding profitability and product quality of the fabricated integrated circuits (ICs). Sources of within-die variations include optical source limitations, and layout-based systematic effects (pitch, line-width variability, and microscopic etch loading). Unfortunately, accurate models of within-die PVs are becoming more difficult to derive because of their increasingly sensitivity to design-context. Embedded test structures (ETS) continue to
Chase-escape on the configuration model

Chase-escape is a competitive growth process in which red particles spread to adjacent empty sites according to a rate-$\lambda$ Poisson process while being chased and consumed by blue particles according to a rate-$1$ Poisson process. Given a growing sequence of finite graphs, the critical rate $\lambda_c$ is the largest value of $\lambda$ for which red fails to reach a positive fraction of the vertices with high probability. We provide a conjecturally sharp lower bound and an implicit upper bound on $\lambda_c$ for supercritical random graphs sampled from the configuration model with independent and identically distributed degrees with finite second moment. We additionally show that the expected number of sites occupied by red undergoes a phase transition and identify the location of this transition.
Investigating myocardial infarction and its effects in patients with urgent medical problems using advanced data mining tools

In medical science, it is very important to gather multiple data on different diseases and one of the most important objectives of the data is to investigate the diseases. Myocardial infarction is a serious risk factor in mortality and in previous studies, the main emphasis has been on people with heart disease and measuring the likelihood of myocardial infarction in them through demographic features, echocardiography, and electrocardiogram. In contrast, the purpose of the present study is to utilize data analysis algorithms and compare their accuracy in patients with a heart attack in order to identify the heart muscle strength during myocardial infarction by taking into account emergency operations and consequently predict myocardial infarction. For this purpose, 105 medical records of myocardial infarction patients with fourteen features including age, the time of emergency operation, Creatine Phosphokinase (CPK) test, heart rate, blood sugar, and vein are gathered and investigated through classification techniques of data analysis including random decision forests, decision tree, support vector machine (SVM), k-nearest neighbor, and ordinal logistic regression. Finally, the model of random decision forests with an accuracy of 76% is selected as the best model in terms of the mean evaluation indicator. Also, seven features of the creatine Phosphokinase test, urea, white and red blood cell count, blood sugar, time, and hemoglobin are identified as the most effective features of the ejection fraction variable.
Towards General and Efficient Active Learning

Active learning selects the most informative samples to exploit limited annotation budgets. Existing work follows a cumbersome pipeline that repeats the time-consuming model training and batch data selection multiple times. In this paper, we challenge this status quo by proposing a novel general and efficient active learning (GEAL) method following our designed new pipeline. Utilizing a publicly available pretrained model, our method selects data from different datasets with a single-pass inference of the same model without extra training or supervision. To capture subtle local information, we propose knowledge clusters extracted from intermediate features. Free from the troublesome batch selection strategy, all data samples are selected in one-shot through a distance-based sampling in the fine-grained knowledge cluster level. This whole process is faster than prior arts by hundreds of times. Extensive experiments verify the effectiveness of our method on object detection, image classification, and semantic segmentation. Our code is publicly available in https://github.com/yichen928/GEAL_active_learning.
Decoding Continual Muscle Movements Related to Complex Hand Grasping from EEG Signals

Brain-computer interface (BCI) is a practical pathway to interpret users’ intentions by decoding motor execution (ME) or motor imagery (MI) from electroencephalogram (EEG) signals. However, developing a BCI system driven by ME or MI is challenging, particularly in the case of containing continual and compound muscles movements. This study analyzes three grasping actions from EEG under both ME and MI paradigms. We also investigate the classification performance in offline and pseudo-online experiments. We propose a novel approach that uses muscle activity pattern (MAP) images for the convolutional neural network (CNN) to improve classification accuracy. We record the EEG and electromyogram (EMG) signals simultaneously and create the MAP images by decoding both signals to estimate specific hand grasping. As a result, we obtained an average classification accuracy of 63.6(±6.7)% in ME and 45.8(±4.4)% in MI across all fifteen subjects for four classes. Also, we performed pseudo-online experiments and obtained classification accuracies of 60.5(±8.4)% in ME and 42.7(±6.8)% in MI. The proposed method MAP-CNN, shows stable classification performance, even in the pseudo-online experiment. We expect that MAP-CNN could be used in various BCI applications in the future.
Fast Characterization of Inducible Regions of Atrial Fibrillation Models With Multi-Fidelity Gaussian Process Classification

Computational models of atrial fibrillation have successfully been used to predict optimal ablation sites. A critical step to assess the effect of an ablation pattern is to pace the model from different, potentially random, locations to determine whether arrhythmias can be induced in the atria. In this work, we propose to use multi-fidelity Gaussian process classification on Riemannian manifolds to efficiently determine the regions in the atria where arrhythmias are inducible. We build a probabilistic classifier that operates directly on the atrial surface. We take advantage of lower resolution models to explore the atrial surface and combine seamlessly with high-resolution models to identify regions of inducibility. We test our methodology in 9 different cases, with different levels of fibrosis and ablation treatments, totalling 1,800 high resolution and 900 low resolution simulations of atrial fibrillation. When trained with 40 samples, our multi-fidelity classifier that combines low and high resolution models, shows a balanced accuracy that is, on average, 5.7% higher than a nearest neighbor classifier. We hope that this new technique will allow faster and more precise clinical applications of computational models for atrial fibrillation. All data and code accompanying this manuscript will be made publicly available at: https://github.com/fsahli/AtrialMFclass.
Facilitating and evaluating farmer innovations towards more sustainable energy and material flows: case-study in Flanders.

The paper starts from the overal societal concern about the sustainability of energy and material flows, in particular in highly urbanized regions, and the role of agriculture to increase this sustainability. The case of Flanders (Belgium) is taken because of its high throughput of energy and materials. The paper proposes a conceptual and methodological framework for interactively and iteratively clarifying and designing more sustainable systems, investigating how local innovations can be facilitated and evaluated in a consistent way. The innovations are seen as organisation- transcending, as they radically change the interactions between enterprises, organisations and individuals involved (system innovations). The research develops theoretical insights based on action research in case-study settings. Through iterative cycles of observation, reflection, planning and active experimentation, an adaptive research agenda is built, which is orientated towards the local conditions and the major issues of relevance. Besides the more theoretical justification of the -predominantly qualitative- research approach, the paper shows first-stage results on the "rapeseed for biofuel" case, up to now the first-at-hand system innovation in Flanders with respect to more sustainable energy and material flows. A context analysis describes the system innovation through its hardware (technology), software (knowledge, vision, motivation) and orgware (organisational and institutional conditions) and offers a typology of rapeseed producers with different management strategies. This helps to understand why so little farmers engage in cultivating rapeseed in Flanders at the moment, and highlights opportunities for fostering the system innovations' growth/success.