Miracle-dz/UniFAR-FTUs huggingface dataset:
Species Interaction and Selective Carbon Addition During Antibiotic Exposure Enhances Bacterial Survival

Biofilms are multifaceted and robust microbiological systems that enable microorganisms to withstand a multitude of environmental stresses and expand their habitat range. We have shown previously that nutritional status alters antibiotic susceptibility in a mixed-species biofilm. To further elucidate the effects of nutrient addition on inter-species dynamics and whole-biofilm susceptibility to high-dose streptomycin exposures, a CO2 Evolution Measurement System was used to monitor the metabolic activity of early steady state pure-culture and mixed-species biofilms containing Pseudomonas aeruginosa and Stenotrophomonas maltophilia, with and without added carbon. Carbon supplementation was needed for biofilm recovery from high-dose streptomycin exposures when P. aeruginosa was either the dominant community member in a mixed-species biofilm (containing predominantly P. aeruginosa and S. maltophilia) or as a pure culture. By contrast, S. maltophilia biofilms could recover from high-dose streptomycin exposures without the need for carbon addition during antibiotic exposure. Metagenomic analysis revealed that even when inocula were dominated by Pseudomonas, the relative abundance of Stenotrophomonas increased upon biofilm development to ultimately become the dominant species post-streptomycin exposure. The combined metabolic and metagenomic results demonstrated the relevance of inter-species influence on survival and that nutritional status has a strong influence on the survival of P. aeruginosa dominated biofilms.
(Whole-Body) Electromyostimulation, Muscle Damage, and Immune System: A Mini Review

Exercise-induced muscular damage (EIMD) is a well-known phenomenon in exercise medicine that is closely related to the type and intensity of training, with especially eccentric training content providing various physiological irritations, including mechanical as well as metabolic. Besides the increase in markers of muscular damage, such as creatine kinase (CK) and myoglobin (Mb), several physiological shifts trigger a kind of stepwise repair chain reactions lasting over a time course from several hours to days. Subsequent inflammatory processes are closely related to muscular damage with decisive influence on physiological repair mechanisms, as indicated by an increased invasion of immune cells and typical patterns of pro- and anti-inflammatory cytokines. Previously, whole-body electromyostimulation (WB-EMS) showed significant, partly extreme distractions in markers of muscular damage lasting over several days. Because of the large area of stimulated muscle mass and a relatively high proportion of eccentric movements, initially too intense WB-EMS is predisposed to produce serious changes on several physiological levels due to its unfamiliar muscular strain. Therefore, it is the aim of this short review to focus on the possible immunological side effects of this aspiring training technology. As the number of original investigations in this field is rather small, we will include data from other studies about the relation of exercise-induced muscle damage and immune regulation.
Detection of Enriched T Cell Epitope Specificity in Full T Cell Receptor Sequence Repertoires

High-throughput T cell receptor (TCR) sequencing allows the characterization of an individual’s TCR repertoire and directly query their immune state. However, it remains a non-trivial task to couple these sequenced TCRs to their antigenic targets. In this paper, we present a novel strategy to annotate full TCR sequence repertoires. The strategy is based on a machine learning algorithm to learn the TCR patterns common to the recognition of a specific epitope. These results are then combined with a statistical analysis to evaluate the occurrence of specific epitope-reactive TCR sequences per epitope in repertoire data. In this manner, we can directly study the capacity of full TCR repertoires to target specific epitopes of the relevant vaccines or pathogens. We demonstrate the usability of this approach on three independent datasets related to vaccine monitoring and infectious disease diagnostics by independently identifying the epitopes that are targeted by the TCR repertoire. The developed method is freely available as a web tool for academic use at tcrex.biodatamining. be.
A Fair Share of Work: Is Fairness of Task Distribution a Mediator Between Transformational Leadership and Follower Emotional Exhaustion?

Drawing on social resource theory and the norm of equity, this research proposes fairness of task distribution as a mediating mechanism of the well-established relationship between transformational leadership and followers’ well-being, conceptualizing the latter as low emotional exhaustion. Using data from 479 German employees in a three-wave longitudinal study, we found transformational leadership to be related to fair task distribution over time. The perceived fairness of task distribution mediated the relationship between transformational leadership and follower emotional exhaustion (structural equation modeling) when excluding stabilities. Our results also show a reverse causation effect for emotional exhaustion and transformational leadership over a longer time period (within 20 months), suggesting a circular process, as well as a mediation by fairness of task distribution when excluding stabilities. The findings emphasize the importance of fair task distribution by leaders for followers’ well-being.
Genome-Wide Association Study of Meat Quality Traits in Hanwoo Beef Cattle Using Imputed Whole-Genome Sequence Data

The discovery of single nucleotide polymorphisms (SNP) and the subsequent genotyping of large numbers of animals have enabled large-scale analyses to begin to understand the biological processes that underpin variation in animal populations. In beef cattle, genome-wide association studies using genotype arrays have revealed many quantitative trait loci (QTL) for various production traits such as growth, efficiency and meat quality. Most studies regarding meat quality have focused on marbling, which is a key trait associated with meat eating quality. However, other important traits like meat color, texture and fat color have not commonly been studied. Developments in genome sequencing technologies provide new opportunities to identify regions associated with these traits more precisely. The objective of this study was to estimate variance components and identify significant variants underpinning variation in meat quality traits using imputed whole genome sequence data. Phenotypic and genomic data from 2,110 Hanwoo cattle were used. The estimated heritabilities for the studied traits were 0.01, 0.16, 0.31, and 0.49 for fat color, meat color, meat texture and marbling score, respectively. Marbling score and meat texture were highly correlated. The genome-wide association study revealed 107 significant SNPs located on 14 selected chromosomes (one QTL region per selected chromosome). Four QTL regions were identified on BTA2, 12, 16, and 24 for marbling score and two QTL regions were found for meat texture trait on BTA12 and 29. Similarly, three QTL regions were identified for meat color on BTA2, 14 and 24 and five QTL regions for fat color on BTA7, 10, 12, 16, and 21. Candidate genes were identified for all traits, and their potential influence on the given trait was discussed. The significant SNP will be an important inclusion into commercial genotyping arrays to select new breeding animals more accurately.
Combined Modality Therapies for High-Risk Prostate Cancer: Narrative Review of Current Understanding and New Directions

Despite the many prospective randomized trials that have been available in the past decade regarding the optimization of radiation, hormonal, and surgical therapies for high-risk prostate cancer (PCa), many questions remain. There is currently a lack of level I evidence regarding the relative efficacy of radical prostatectomy (RP) followed by adjuvant radiation compared to radiation therapy (RT) combined with androgen deprivation therapy (ADT) for high-risk PCa. Current retrospective series have also described an improvement in biochemical outcomes and PCa-specific mortality through the use of augmented radiation strategies incorporating brachytherapy. The relative efficacy of modern augmented RT compared to RP is still incompletely understood. We present a narrative review regarding recent advances in understanding regarding comparisons of overall and PCa-specific mortality measures among patients with high-risk PCa treated with either an RP/adjuvant RT or an RT/ADT approach. We give special consideration to recent trends toward the assembly of multi-institutional series targeted at providing high-quality data to minimize the effects of residual confounding. We also provide a narrative review of recent studies examining brachytherapy boost and systemic therapies, as well as an overview of currently planned and ongoing studies that will further elucidate strategies for treatment optimization over the next decade.
The Clinical Implications of Nocebo Effects for Biosimilar Therapy

Nocebo effects encompass negative responses to inert interventions in the research setting and negative outcomes with active treatments in the clinical research or practice settings, including new or worsening symptoms and adverse events, stemming from patients’ negative expectations and not the pharmacologic action of the treatment itself. Numerous personality, psychosocial, neurobiological, and contextual/environmental factors contribute to the development of nocebo effects, which can impair quality of life and reduce adherence to treatment. Biologics are effective agents widely used in autoimmune disease, but their high cost may limit access for patients. Biosimilar products have gained regulatory approval based on quality, safety, and efficacy comparable to that of originator biologics in rigorous study programs. In this review, we identified gaps in patients’ and healthcare professionals’ awareness, understanding, and perceptions of biosimilars that may result in negative expectations and nocebo effects, and may diminish their acceptance and clinical benefits. We also examined features of nocebo effects with biosimilar treatment that inform research and clinical practices. Namely, when biosimilars are introduced to patients as possible treatment options, we recommend adoption of nocebo-reducing strategies to avoid negative expectations, including delivery of balanced information on risk–benefit profiles, framing information to focus on positive attributes, and promoting shared decision-making processes along with patient empowerment. Healthcare professionals confident in their knowledge of biosimilars and aware of bias-inducing factors may help reduce the risk of nocebo effects and improve patients’ adherence in proposing biosimilars as treatment for autoimmune diseases such as rheumatoid arthritis and inflammatory bowel disease.
ARINC-825TBv2: A Hardware-in-the-Ioop Simulation Platform for Aerospace Security Research

CAN sees wide use in the aerospace industry, and is proposed as the link-layer implementation of the proposed Aeronautics Radio, Incorporated 825 protocol standard (ARINC-825). Unfortunately, CAN receives far less attention in aerospace than in the automotive domain; consequently, the aerospace security research community has been unable to assess the vulnerabilities of this protocol, due to a lack of access to data and real-time simulation infrastructure. We introduce ARINC-825TBv2, a hardware-in-the-loop link-layer simulation platform that allows aerospace security researchers to observe ARINC-encoded CAN traffic with realistic data. We present a novel, predictive attacker Gaslighter that we integrate into the testbench, and demonstrate its effectiveness. Furthermore, we implement an intrusion detection algorithm from the literature, Z-Score, and validate it using Gaslighter and realistic CAN data. We show that as the number of messages in the simulation increases, the accuracy of Z-Score drops to below that of a random predictor.
Learning to Generate Music With Sentiment

Deep Learning models have shown very promising results in automatically composing polyphonic music pieces. However, it is very hard to control such models in order to guide the compositions towards a desired goal. We are interested in controlling a model to automatically generate music with a given sentiment. This paper presents a generative Deep Learning model that can be directed to compose music with a given sentiment. Besides music generation, the same model can be used for sentiment analysis of symbolic music. We evaluate the accuracy of the model in classifying sentiment of symbolic music using a new dataset of video game soundtracks. Results show that our model is able to obtain good prediction accuracy. A user study shows that human subjects agreed that the generated music has the intended sentiment, however negative pieces can be ambiguous.
Enhancing Decision-making in Smart and Connected Communities with Digital Traces

Title: ENHANCING DECISION-MAKING IN SMART AND CONNECTED COMMUNITIES WITH DIGITAL TRACES Lingzi Hong Doctor of Philosophy, 2019 Dissertation directed by: Assistant Professor Vanessa Frias-Martinez College of Information Studies The ubiquitous use of information communication technologies (ICTs) enables generation of digital traces associated with human behaviors at unprecedented breadth, depth, and scale. Large-scale digital traces provide the potential to understand population behaviors automatically, including the characterization of how individuals interact with the physical environment. As a result, the use of digital traces generated by humans might mitigate some of the challenges associated to the use of surveys to understand human behaviors such as, high cost in collecting information, lack of quality real-time information, and hard to capture behavioral level information. In this dissertation, I study how to extract information from digital traces to characterize human behavior in the built environment; and how to use such information to enhance decision-making processes in the area of Smart and Connected Communities. Specifically, I present three case studies that aim at using data-driven methods for decision-making in Smart and Connected Communities. First, I discuss data-driven methods for socioeconomic development with a focus on inference of socioeconomic maps with cell phone data. Second, I present data-driven methods for emergency preparedness and response, with a focus on understanding user needs in different communities with geotagged social media data. Third, I describe data-driven methods for migration studies, focusing on characterizing the post-migration behaviors of internal migrants with cell phone data. In these case studies, I present data-driven frameworks that integrate innovative behavior modeling approaches to help solve decision-making questions using digital traces. The explored methods enhance our understanding of how to model and explain population behavior patterns in different physical and socioeconomic contexts. The methods also have practical significance in terms of how decision-making can become cost-effective and efficient with the help of data-driven methods. ENHANCING DECISION-MAKING IN SMART AND CONNECTED COMMUNITIES WITH DIGITAL TRACES
The HUIU Contribution for the GermEval Shared Task 2

In this paper, we present the HUIU system (a collaboration of University of Hamburg and Indiana University) for the GermEval 2019 shared task 2, subtask 1 – the coarse-grained classiﬁcation of tweets into the classes OFFENSE or OTHER . Our system uses linear SVMs with character n -grams (5 ≤ n ≤ 10), POS n -grams (3 ≤ n ≤ 9) and the tweet’s length in number of tokens as features. We obtain a macro-averaged F-score of 65.32 on the test data.
Risk factors of urinary incontinence in women: a literature review

The purpose of this literature review is to recapitulate the risk factors and predictors of urinary incontinence (UI) in women identified in different published studies, thereby filling such gap in knowledge. The review identified primary research papers published in peer-reviewed journals on risk factors and predictors of UI. The search strategy used for collecting the literature was done using PubMed, Google Scholar and CINAHL databases from 2002 to 2012. The full text articles were obtained as part of the second stage selection process, using the pre-agreed selection criteria. Twelve articles were identified, this includes four cross-sectional studies, five prospective cohort studies, two co-relational descriptive studies and one longitudinal study. Majority of these studies concluded that the major risk factors of UI are age, parity, body mass index (BMI), menopause, recurrent UTI, hysterectomy, number of abortions, foetal weight and the comorbid condition such as diabetes, chronic pulmonary disease. Health care providers need to educate women about the risk factors of UI and health-promoting actions to reduce the risk of UI. Researchers have to enhance primary prevention and promote strategies that help women alter and modify their lifestyle. Lifestyle modification such as healthy food habits, Kegel's exercises and weight reduction ensures the general health promotion and relief from UI symptoms.
Predicting Default and Non-Default Aspectual Coding: Impact and Density of Information Features

This paper presents a study on the automatic classification of default and non-default codings for aspect-marked verbs in six Slavic and one Baltic language. As classifier a Support Vector Machine (SVM) and as verbal features Shannon Information (SI) and Average Information Content (IC) have been utilised. In all languages high accuracy of the classification has been achieved. In addition, we found indications for the validity of the Uniform Information Density principle within SI and IC.
Multi-Task Learning of Tempo and Beat: Learning One to Improve the Other

We propose a multi-task learning approach for simultaneous tempo estimation and beat tracking of musical audio. The system shows state-of-the-art performance for both tasks on a wide range of data, but has another fundamental advantage: due to its multi-task nature, it is not only able to exploit the mutual information of both tasks by learning a common, shared representation, but can also improve one by learning only from the other. The multi-task learning is achieved by globally aggregating the skip connections of a beat tracking system built around temporal convolutional networks, and feeding them into a tempo classiﬁcation layer. The beneﬁt of this approach is investigated by the inclusion of training data for which tempo-only annotations are available, and which is shown to provide improvements in beat tracking accuracy.
Studies on efficacy of therapeutic protocols for ketosis in dairy cows.

Received: Revised: Accepted: June 10, 2014 June 14, 2014 June 23, 2014 A study was undertaken to compare the efficacy of different therapeutic protocols in sixty four ketotic dairy cows. All the ketotic dairy cows were equally divided into group 1, 2, 3 and 4. Animals in 1 group received 25% dextrose intravenously two pints and gluconeogenic precursors fortified with B-complex vitamin 100 ml twice orally for four days, animals in 2 group received 25% dextrose intravenously two pints and insulin 200 IU subcutaneously for two days, animals in 3 group received liquid glucose and sodium bicarbonate orally for four days, and animals in 4 group received 25% dextrose two pints intravenously and 5ml isoflupredone acetate intramuscularly for three days respectively. The mean recovery time (days) was recorded highest in group 4 (2.3±0.26) followed by 2 (2.6±0.16), 1 (3.3±0.26) and 3 (3.9±0.23). Therefore, during the current study glucose administration either parentally or orally in combination with gluconeogenic precursors fortified with B-complex vitamins, insulin, sodium bicarbonate and isoflupredone acetate gave excellent recovery rate.
Coinduction in Flow: The Later Modality in Fibrations

This paper provides a construction on fibrations that gives access to the so-called later modality, which allows for a controlled form of recursion in coinductive proofs and programs. The construction is essentially a generalisation of the topos of trees from the codomain fibration over sets to arbitrary fibrations. As a result, we obtain a framework that allows the addition of a recursion principle for coinduction to rather arbitrary logics and programming languages. The main interest of using recursion is that it allows one to write proofs and programs in a goal-oriented fashion. This enables easily understandable coinductive proofs and programs, and fosters automatic proof search. Part of the framework are also various results that enable a wide range of applications: transportation of (co)limits, exponentials, fibred adjunctions and first-order connectives from the initial fibration to the one constructed through the framework. This means that the framework extends any first-order logic with the later modality. Moreover, we obtain soundness and completeness results, and can use up-to techniques as proof rules. Since the construction works for a wide variety of fibrations, we will be able to use the recursion offered by the later modality in various context. For instance, we will show how recursive proofs can be obtained for arbitrary (syntactic) first-order logics, for coinductive set-predicates, and for the probabilistic modal mu-calculus. Finally, we use the same construction to obtain a novel language for probabilistic productive coinductive programming. These examples demonstrate the flexibility of the framework and its accompanying results.
JosquIntab: A Dataset for Content-based Computational Analysis of Music in Lute Tablature

An enormous corpus of music for the lute, spanning some two and half centuries, survives today. Unlike other musical corpora from the same period, this corpus has undergone only limited musicological study. The main reason for this is that it is written down exclusively in lute tablature, a prescriptive form of notation that is difﬁcult to understand for non-specialists as it reveals little structural information. In this paper we present J OSQU I NTAB , a dataset of automatically created enriched diplomatic transcriptions in MIDI and MEI format of 64 sixteenth-century lute intabulations, instrumental arrangements of vocal compositions. Such a dataset enables large-scale content-based computational analysis of music in lute tab-lature hitherto impossible. We describe the dataset, the mapping algorithm used to create it, as well as a method to quantitatively evaluate the degree of arrangement (goodness of ﬁt) of an intabulation. Furthermore, we present two use cases, demonstrating the usefulness of the dataset for both music information retrieval and musicological research. We make the dataset, the source code, and an implementation of the mapping algorithm, runnable as a command line tool, publicly available.
Multiagent opinion dynamics influenced by individual susceptibility and anchoring effect

This paper studies a new model of social opinion dynamics in multiagent system by counting in two important factors, individual susceptibility and anchoring eﬀect. Diﬀerent from many existing models only focusing on one factor, this model can exhibit not only agreement phenomena, but also disagreement phenomena such as clustering and ﬂuctuation, during opinion evolution. Then we provide several conditions to show how individual susceptibility and anchoring eﬀect work on steady-state behaviors in some speciﬁc situations, with strict mathematical analysis. Finally, we investigate the model for general situations via simulations.