Miracle-dz/UniFAR-FTUs huggingface dataset:
Microwave-Assisted Extraction of Ricinine from Ricinus communis Leaves

The alkaloid ricinine (3-cyano-4-methoxy-N-methyl-2-pyridone) is found in different parts of the Ricinus communis plant and is known to possess several bioactive properties, including strong antioxidant activity. In this study, a new microwave-assisted extraction (MAE) method was developed for the recovery of ricinine from R. communis leaves. The extraction variables studied were extraction temperature (between 125 °C and 175 °C), microwave power (between 500 W and 1000 W), extraction time (between 5 min and 15 min), extraction solvent (between 10% and 90% of EtOAc in MeOH), and solvent-to-sample ratio (between 25:1 mL and 50:1 mL of solvent per gram of the sample). On studying the effects of extraction variables, both solvent and liquid-to-solid ratio were found to exhibit the highest effects on ricinine recovery. A fast (15 min) microwave-assisted extraction method was developed (high temperatures can be applied because the stability of ricinine is proven in the literature), allowing for the recovery of ricinine from R. communis leaves. The study revealed that R. communis leaves had almost 1.5 mg g−1 (dried weight) of ricinine.
Predictive Validity, Diagnostic Accuracy and Test-Retest Reliability of the Strength of Urges to Drink (SUTD) Scale

This study compared the 1-item Strength of Urges to Drink (SUTD) scale with the 10-item Alcohol Use Disorders Identification Test (AUDIT) on (i) test-retest reliability, (ii) predictive validity, and (iii) diagnostic accuracy. Data come from 2960 participants taking part in the Alcohol Toolkit Study (ATS), a monthly population survey of adults in England. The long-term test-retest reliability of the SUTD was ‘fair’, but lower than that for the AUDIT (Kappaweighted 0.24 versus 0.49). Individuals with “slight/moderate” urges to drink had higher odds of reporting an attempt to cut down relative to those not experiencing urges (adjusted odds ratios (AdjORs) 1.78 95% confidence interval (CI) 1.43–2.22 and 1.54 95% CI 1.20–1.96). Drinkers reporting “moderate/slight/strong” urges to drink had mean change in consumption scores which were 0.16 (95% CI −0.31 to −0.02), 0.40 (95% CI −0.56 to −0.24) and 0.37 (95% CI −0.69 to −0.05) units lower than those reporting no urges. For all outcomes, strong associations were found with AUDIT scores. The accuracy of the SUTD for discriminating between drinkers who did and did not reduce their consumption was ‘acceptable’, and similar to that for the AUDIT (ROCAUC 0.6). The AUDIT had better diagnostic accuracy in predicting change in alcohol consumption. The SUTD may be an efficient dynamic measure of urges to drink for population surveys and studies assessing the impact of alcohol-reduction interventions.
Neural machine translation enhanced with paraphrasing techniques

Paraphrase generation is the task of given a word sequence generating another word sequence that keeps the same meaning than the original one. Generating new paraphrases can be used to enhance machine translation systems both at training and test phases. This project proposes to exploit latest sequence-to-sequence deep learning techniques to model paraphrase generation and integrate it in a state-of-the-art neural machine translation system. As part of this project, the enhanced neural machine translation system will be participating in the WMT 2018 international evalua
The Oticon Medical Neuro Zti cochlear implant and the Neuro 2 sound processor: multicentric evaluation of outcomes in adults and children

Abstract Objective: This study evaluated the outcomes of the Oticon Medical Neuro Zti cochlear implant and the Neuro 2 sound processor. Design: Neuro One users were upgraded to Neuro 2. Monosyllabic word identification was evaluated in adults with Neuro One after ≥5 months, with Neuro 2 at upgrade, and with Neuro 2 after 3 months. Self-reported listening ability, satisfaction, and usability were measured in adults and children. Study sample: Participants were 44 adults and 26 children. Results: Speech identification scores in quiet and noise were 58% and 45% with Neuro One and 67% and 55% with Neuro 2 after 3 months, respectively. Hearing impairment duration and number of active electrodes significantly predicted speech identification in noise with Neuro 2. Significantly higher questionnaire ratings were obtained for Neuro 2 than Neuro One regarding listening ability in complex listening situations, comfort and music, as well as nine aspects of satisfaction and usability. Conclusion: This study demonstrates the clinical superiority of the Neuro 2 sound processor over Neuro One in terms of speech identification in quiet and in noise and reported patient benefit and satisfaction. Given the study design, sources of improvement may include factors unrelated to the sound processor itself.
2018 N2c2 Shared Task on Adverse Drug Events and Medication Extraction in Electronic Health Records

OBJECTIVE This article summarizes the preparation, organization, evaluation, and results of Track 2 of the 2018 National NLP Clinical Challenges shared task. Track 2 focused on extraction of adverse drug events (ADEs) from clinical records and evaluated 3 tasks: concept extraction, relation classification, and end-to-end systems. We perform an analysis of the results to identify the state of the art in these tasks, learn from it, and build on it. MATERIALS AND METHODS For all tasks, teams were given raw text of narrative discharge summaries, and in all the tasks, participants proposed deep learning-based methods with hand-designed features. In the concept extraction task, participants used sequence labelling models (bidirectional long short-term memory being the most popular), whereas in the relation classification task, they also experimented with instance-based classifiers (namely support vector machines and rules). Ensemble methods were also popular. RESULTS A total of 28 teams participated in task 1, with 21 teams in tasks 2 and 3. The best performing systems set a high performance bar with F1 scores of 0.9418 for concept extraction, 0.9630 for relation classification, and 0.8905 for end-to-end. However, the results were much lower for concepts and relations of Reasons and ADEs. These were often missed because local context is insufficient to identify them. CONCLUSIONS This challenge shows that clinical concept extraction and relation classification systems have a high performance for many concept types, but significant improvement is still required for ADEs and Reasons. Incorporating the larger context or outside knowledge will likely improve the performance of future systems.
Soyasapogenol B Attenuates Laryngeal Carcinoma Progression through Inducing Apoptotic and Autophagic Cell Death

Soyasapogenol B (Soy B), a constituent of soybean, has been shown to exhibit antitumor activities against different types of cancers. However, to our knowledge, no studies so far have investigated the effect of Soy B in human laryngeal carcinoma. This study, therefore, aimed to determine the effect of Soy B in human laryngeal carcinoma cell lines HeP‐2 and TU212 and to elucidate the possible underlying mechanisms by which Soy B can induce its antitumor effects. The results showed that Soy B effectively attenuated the cell growth by causing G0/G1 phase cell cycle arrest in laryngeal carcinoma cell lines. Moreover, the percentage of apoptotic and autophagic cells dramatically increased upon exposure to Soy B. Western blotting results confirmed that Soy B can alter the expression levels of established markers of apoptosis and autophagy. Interestingly, both apoptosis inhibitor (ZVAD‐fmk) and autophagy inhibitor (3‐MA) could partially reverse the effect of Soy B, while blocking autophagy did not cause obvious alteration in the percentage of apoptotic cells. Similarly, in vivo studies validated that Soy B could effectively reduce the size of the tumor and induce apoptosis and autophagy in tumor tissues. Collectively, these results suggested that Soy B can exert anticancer activities against laryngeal carcinoma through inducing apoptotic and autophagic cell death. Our study highlighted the potential role of Soy B as a chemotherapeutic agent for laryngeal carcinoma. Anat Rec, 2019. © 2019 American Association for Anatomy Anat Rec, 303:1851–1858, 2020. © 2019 American Association for Anatomy
Blind Equalization of Lung Crackle Sounds to Compensate Chest Attenuation

Diseased lungs generate adventitious sounds that propagate through the thorax, reaching the surface where they may be heard or recorded. The attenuation imposed to the lung sounds by the thorax depends on the physical characteristics of each patient, hampering the analysis of quantitative indexes measured to assist the diagnosis of cardiorespiratory disorders. This work proposes the application of a blind equalizer (eigenvector algorithm - EVA) to reduce the effects of thorax attenuation on indexes measured from crackle sounds. Computer simulated crackles (acquired on the posterior chest wall after being applied to volunteer's mouth) and actual crackles belonging to a database were equalized. Quantitative indexes were measured from crackles before and after equalization. Comparison of indexes measured from simulated crackles reveals that the equalizer improves the results due to attenuation compensation and removal of Gaussian noise. Effects of equalization on indexes measured from actual crackles were qualitatively assessed. Results point out that blind equalization of crackles recorded on the thorax provides more consistent quantitative indexes to assist the diagnosis of different cardiorespiratory diseases.
Novel Consensus Architecture To Improve Performance of Large-Scale Multitask Deep Learning QSAR Models

Advances in the development of high-throughput screening and automated chemistry have rapidly accelerated the production of chemical and biological data, much of them freely accessible through literature aggregator services such as ChEMBL and PubChem. Here we explore how to use this comprehensive mapping of chemical biology space to support the development of large-scale quantitative structure-activity relationships (QSAR) models. We propose a new Deep Learning Consensus Architecture (DLCA) that combines consensus and multitask deep learning approaches together to generate large-scale QSAR models. This method improves knowledge transfer across different target/assays while also integrating contributions from models based on different descriptors. The proposed approach was validated and compared with Protochemometrics, Multitask Deep Learning and Random Forest methods paired with various descriptors types. DLCA models demonstrated improved prediction accuracy for both regression and classification tasks. The best models together with their modeling sets are provided through publicly-available web services at https://predictor.ncats. io.
gutMDisorder: a comprehensive database for dysbiosis of the gut microbiota in disorders and interventions

Abstract gutMDisorder (http://bio-annotation.cn/gutMDisorder), a manually curated database, aims at providing a comprehensive resource of dysbiosis of the gut microbiota in disorders and interventions. Alterations in the composition of the gut microbial community play crucial roles in the development of chronic disorders. And the beneficial effects of drugs, foods and other intervention measures on disorders could be microbially mediated. The current version of gutMDisorder documents 2263 curated associations between 579 gut microbes and 123 disorders or 77 intervention measures in Human, and 930 curated associations between 273 gut microbes and 33 disorders or 151 intervention measures in Mouse. Each entry in the gutMDisorder contains detailed information on an association, including an intestinal microbe, a disorder name, intervention measures, experimental technology and platform, characteristic of samples, web sites for downloading the sequencing data, a brief description of the association, a literature reference, and so on. gutMDisorder provides a user-friendly interface to browse, retrieve each entry using gut microbes, disorders, and intervention measures. It also offers pages for downloading all the entries and submitting new experimentally validated associations.
Latency of tobacco smoking for head and neck cancer among HPV‐positive and HPV‐negative individuals

Human papillomavirus (HPV) infection and tobacco smoking are well‐known risk factors for head and neck cancers (HNC). Although an effect modification between oral HPV infection and tobacco smoking may exist, evidence is lacking on how they interact temporally. We investigated the latency and life course effects of tobacco smoking on risk of HNC among HPV‐positive (HPV+ve) and negative (HPV‐ve) individuals. We used data from 631 ever‐smoker participants of a hospital‐based case–control study conducted in four major hospitals in Montréal, Canada. Cases (n = 320), incident, histologically confirmed, primary squamous cell carcinomas, were frequency‐matched to controls (n = 311) by age and sex. Sociodemographic and behavioral factors (e.g., tobacco and alcohol use and sexual history) were collected using a structured interview applying a life grid technique. Oral exfoliated cells were used for HPV DNA detection and genotyping. Latency effects were estimated flexibly using a Bayesian relevant exposure model and further extended with a life course approach. Retrospective smoking trajectories for HPV+ve cases and controls had similar shapes. Exposure to tobacco smoking even 40 years before diagnosis was associated with an increased HNC risk among both HPV+ve and HPV‐ve participants. The effect of smoking before the start of sexual activity compared to afterwards was higher among HPV+ve individuals. This pattern of association was less profound among HPV‐ve participants. Temporal interactions may exists between oral HPV infection and life course smoking trajectories in relation to HNC risk.
Task‐driven latent active correction for physics‐inspired input method in near‐field mixed reality applications

Calibration accuracy is one of the most important factors to affect the user experience in mixed reality applications. For a typical mixed reality system built with the optical see‐through head‐mounted display, a key problem is how to guarantee the accuracy of hand–eye coordination by decreasing the instability of the eye and the head‐mounted display in long‐term use. In this paper, we propose a real‐time latent active correction algorithm to decrease hand–eye calibration errors accumulated over time. Experimental results show that we can guarantee an effective calibration result and improve the user experience with the proposed latent active correction algorithm. Based on the proposed system, experiments about virtual buttons are also designed, and the interactive performance regarding different scales of virtual buttons is presented. Finally, a direct physics‐inspired input method is constructed, which shares a similar performance with the gesture‐based input method but provides a lower learning cost due to its naturalness.
Performance of Inverted Indices in Distributed Text Document Retrieval Systems

The performance of distributed text document retrieval systems is strongly influenced by the organization of the inverted index. This paper compares the performance impact on query processing of various physical organizations for inverted lists. We present a new probabilistic model of the database and queries. Simulation experiments determine which variables most strongly influence response time and throughput. This leads to a set of design trade-offs over a wide range of hardware configurations and new parallel query processing strategies.
MULTI-OBJECTIVE UNRELATED PARALLEL MACHINES SCHEDULING WITH SEQUENCE-DEPENDENT SETUP TIMES AND PRECEDENCE CONSTRAINTS

This paper presents a novel, multi-objective model of a parallel machines scheduling problem that minimizes the number of tardy jobs and total completion time of all jobs. In this model, machines are considered as unrelated parallel units with different speeds. In addition, there is some precedence, relating the jobs with non-identical due dates and their ready times. Sequence-dependent setup times embedded in the proposed model may vary in different machines based on their characteristics. This paper proposes a two-level mixed-integer programming for the given problem. By solving the presented model, the associated promising results show the effectiveness of this model for small and medium-sized problems, respectively.
Exploring atomicity on memory mapped files based on non-volatile memory file systems

Upcoming non-volatile memory technologies are a big promise in computer architecture and are expected to be powerful tools to address today’s issues regarding efficient data manipulation. They provide high performance and byte granularity while also having the distinct advantage of being persistent. However in order to explore these technologies to their full potential, existing systems and architecture must adapt to this new way of working with data and workaround the challenges that come with it. Existing work in the area already proposes methods to adapt existing architecture to NVM as well as innovative ways to employ these memories in future applications. However operating system support to such NVM-enabled solutions, although existent, still very limited. In this work, we present two variations of the existing mmap system call, designed to both explore NVM characteristics and provide user data consistency. Both are very simple solutions that allow users to control the persistence and define checkpoints to their files while using the common mapped file syntax. We have implemented and tested these methods over Linux using a NVM file system as our base. Our results show that these mechanisms can ensure file integrity in the presence of system failures while also providing a reasonable performance.
Bump Hunting using the Tree-GA

The bump hunting is to find the regions vw'"here points "ve are interested in are located more densely than elsewhere and are hardly separable from other points. By specifying a pureness rate p for the points, a maximum capture rate c of the points could be obtained. Then, a trade-off curve between p and c can be constiucted. Thus, to find the bump regions is equivalent to construct the trade-off curve. We adopt simpler boundary shapes for the bumps such as the box-shaped regions located parallel to variable axes for convenience. Vie use the genetic algorithm, specified to the tree structure, called the tree-G.LL\, to obtain the macximnm capture rates, because the conventional binary decision tree will not provide the maximum capture rates. Using the tree-GA tendency providing many local maxima ror the capture rates, we cali estiiuate the return period for the trade-off curve by using the extreme. value statistics. We have assessed the accuracy for the trade-off curve in typical fundamental c;:tSesthat may be observed in real customer data cases, and found that the proposed tree-GA can construct the e:ffective trade-off curve which is close to the optimal one.
Speech Signal Quality Improvement Using Cuckoo Search Algorithm

– Speech signal quality improvement attempt to attenuate ambient noise to reduce listener fatigue and improve intelligibility. This requires maintaining speech cues and ensuring natural sounding residual noise that does not distract the listener. In this paper, an effective noise suppression technique for enhancement of speech signals using optimized window is proposed. Initially, the noisy speech signal is broken down into various time-frequency (TF) units and the features are extracted by finding out the Amplitude Magnitude Spectrogram (AMS). The signals are then classified based on quality ratio into different classes to generate the initial set of solutions. Subsequently, ideal window for each class is generated based on Cuckoo search algorithm. Subsequently, in the waveform synthesis stage, filtered waveforms are windowed and then multiplied by the ideal window value and summed up to get the enhanced target signal. The results obtained proved the effectiveness of the proposed technique and is able to suppress noise and improve the quality of the speech signal.
The influence of language semantics on program slices

The authors present observations on the influence of language semantics on program slices. These observations serve to illustrate that, whenever a program slice is designed for a specific language, the semantic features should be carefully examined, and the corresponding slicing algorithm adjusted accordingly. The authors can show that it is impossible to develop a single slicing routine which can be applied to all programming languages. In other words, a program slicer should be language-dependent. Its algorithm should be flexible enough to correspond to the semantics of a given language. <<ETX>>
RODEO: A Novel Methodology to Perform V2V Communications in the Scope of Internet of Vehicles. An Energy Performance Analysis

In the framework of the Internet of Things (IoT) and more specifically the Internet of Vehicles (IoV), vehicles are called upon to play a key role as mobile sensors. Their high mobility and the large amount of electronics they currently deploy allow them to act as mobile information collectors in the places where they circulate. Together with these capabilities, the deployment of communications systems that allow them to share this information will make possible the massive deployment of the so-called Cooperative Intelligent Transport Systems (C-ITS). In this context, this article presents RODEO as a methodology that allows vehicles to use the current mobile communications systems to make V2V (Vehicle to Vehicle) communications, minimizing the number of resources needed. This paper analyses the performance of RODEO (Rrm fOr unDerlay vEhicle cOmmunications) from the perspective of energy performance, understood as the number of bytes transmitted per watt consumed.
Analysis and prevention of security threats in web and cryptographic applications

In recent years we have faced a multitude of security flaws posing a serious threat to the whole society, ranging from individuals to national critical infrastructures. For this reason, it is of crucial importance to effectively enforce security on real systems, by identifying flaws and putting in place novel security mechanisms and techniques. Along this path, we provide practical contributions on Web security and cryptographic APIs. We first review the field of Web session security by surveying the most common attacks against web sessions. Existing security solutions are evaluated along four different axes: protection, usability, compatibility and ease of deployment. We also identify a few guidelines that can be helpful for the development of innovative solutions approaching web security in a more systematic and comprehensive way. Additionally, we propose a new browser-side security enforcement technique for Web protocols. The core idea is to extend the browser with a monitor which, given the protocol specification, enforces the required confidentiality and integrity properties, as well as the intended protocol flow. For what concerns the security of cryptographic APIs, we investigate an effective method to monitor existing cryptographic systems in order to detect, and possibly prevent, the leakage of sensitive cryptographic keys. Key security is stated formally and it is proved that the method is sound, complete and efficient under the assumption that a key fingerprint is given for each sensitive key. We also provide a thoughtful analysis of Java keystores, storage facilities to manage and securely store keys in Java applications. We devise a precise threat model and distill a set of security properties. We report on unpublished attacks and weaknesses in implementations that do not adhere to state-of-the-art cryptographic standards and discuss the fixes on popular Java libraries released after our responsible disclosure.