Miracle-dz/UniFAR-FTUs huggingface dataset:
Host-vector interaction in dengue: a simple mathematical model

Introduction Dengue is a mosquito-borne viral infection endemic in tropical and subtropical regions, now spreading at epidemic proportions causing a major health issue in Sri Lanka and elsewhere. No effective vaccine or a curative antiviral drug is available to prevent or treat the disease. The only way of mitigating dengue at present, is through mosquito eradication and educating the public on preventive measures which can minimizing the cycle of transfer. Objectives A theoretical model of dengue with simple mathematics is presented to gain a quantitative understanding of the pattern of dengue outbreaks in Sri Lanka and suggest control measures. Methods The statistics on incidence of the disease reported by the Epidemiology Unit is analyzed using the model. Despite simplicity, the model possesses explanatory and predictive capacity, enabling determination of crucial parameters. The model shows that the “infectives” increase exponentially in an outbreak, provided the number of vectors per human exceeds a threshold, illustrating not only vector eradication but measures which minimize their biting frequency and preventing prolonged survival are effective safeguards. Results In a population consisting of 75% who are susceptible, the threshold is estimated to be 20 mosquitos per person. Conclusions The model showed that the endemic equilibrium of the system can occur at any level. As demographic changes escalate mosquito breeding, they infect more and more susceptible people. The consequent increase in virus replication induces new strains broadening the genetic diversity of the virus and helping it to overcome the human immune response. The increasing endemicity of dengue due to this is demonstrated by the model.
Cold-start recommendation : from Algorithm Portfolios to Job Applicant Matching. (Démarrage à froid en recommandation : des portfolios d'algorithmes à l'appariement automatique d'offres et de chercheurs d'emploi)

La quantite d'informations, de produits et de relations potentielles dans les reseaux sociaux a rendu indispensable la mise a disposition de recommandations personnalisees. L'activite d'un utilisateur est enregistree et utilisee par des systemes de recommandation pour apprendre ses centres d'interet. Les recommandations sont egalement utiles lorsqu'estimer la pertinence d'un objet est complexe et repose sur l'experience. L'apprentissage automatique offre d'excellents moyens de simuler l'experience par l'emploi de grandes quantites de donnees.Cette these examine le demarrage a froid en recommandation, situation dans laquelle soit un tout nouvel utilisateur desire des recommandations, soit un tout nouvel objet est propose a la recommandation. En l'absence de donnees d'interaction, les recommandations reposent sur des descriptions externes. Deux problemes de recommandation de ce type sont etudies ici, pour lesquels des systemes de recommandation specialises pour le demarrage a froid sont presentes.En optimisation, il est possible d'aborder le choix d'algorithme dans un portfolio d'algorithmes comme un probleme de recommandation. Notre premiere contribution concerne un systeme a deux composants, un selecteur et un ordonnanceur d'algorithmes, qui vise a reduire le cout de l'optimisation d'une nouvelle instance d'optimisation tout en limitant le risque d'un echec de l'optimisation. Les deux composants sont entraines sur les donnees du passe afin de simuler l'experience, et sont alternativement optimises afin de les faire cooperer. Ce systeme a remporte l'Open Algorithm Selection Challenge 2017.L'appariement automatique de chercheurs d'emploi et d'offres est un probleme de recommandation tres suivi par les plateformes de recrutement en ligne. Une seconde contribution concerne le developpement de techniques specifiques pour la modelisation du langage naturel et leur combinaison avec des techniques de recommandation classiques afin de tirer profit a la fois des interactions passees des utilisateurs et des descriptions textuelles des annonces. Le probleme d'appariement d'offres et de chercheurs d'emploi est etudie a travers le prisme du langage naturel et de la recommandation sur deux jeux de donnees tires de contextes reels. Une discussion sur la pertinence des differents systemes de recommandations pour des applications similaires est proposee.
Climate-induced phenological shift of apple trees has diverse effects on pollinators, herbivores and natural enemies

Climate change is altering the phenology of trophically linked organisms, leading to increased asynchrony between species with unknown consequences for ecosystem services. Although phenological mismatches are reported from several ecosystems, experimental evidence for altering multiple ecosystem services is hardly available. We examined how the phenological shift of apple trees affected the abundance and diversity of pollinators, generalist and specialist herbivores and predatory arthropods. We stored potted apple trees in the greenhouse or cold store in early spring before transferring them into orchards to cause mismatches and sampled arthropods on the trees repeatedly. Assemblages of pollinators on the manipulated and control trees differed markedly, but their overall abundance was similar indicating a potential insurance effect of wild bee diversity to ensure fruit set in flower-pollinator mismatch conditions. Specialized herbivores were almost absent from manipulated trees, while less-specialized ones showed diverse responses, confirming the expectation that more specialized interactions are more vulnerable to phenological mismatch. Natural enemies also responded to shifted apple tree phenology and the abundance of their prey. While arthropod abundances either declined or increased, species diversity tended to be lower on apple trees with shifted phenology. Our study indicates novel results on the role of biodiversity and specialization in plant-insect mismatch situations.
Lattice Agreement in Message Passing Systems

This paper studies the lattice agreement problem and the generalized lattice agreement problem in distributed message passing systems. In the lattice agreement problem, given input values from a lattice, processes have to non-trivially decide output values that lie on a chain. We consider the lattice agreement problem in both synchronous and asynchronous systems. For synchronous lattice agreement, we present two algorithms which run in $\log f$ and $\min \{O(\log^2 h(L)), O(\log^2 f)\}$ rounds, respectively, where $h(L)$ denotes the height of the {\em input sublattice} $L$, $f < n$ is the number of crash failures the system can tolerate, and $n$ is the number of processes in the system. These algorithms have significant better round complexity than previously known algorithms. The algorithm by Attiya et al. \cite{attiya1995atomic} takes $\log n$ synchronous rounds, and the algorithm by Mavronicolasa \cite{mavronicolasabound} takes $\min \{O(h(L)), O(\sqrt{f})\}$ rounds. For asynchronous lattice agreement, we propose an algorithm which has time complexity of $2 \cdot \min \{h(L), f + 1\}$ message delays which improves on the previously known time complexity of $O(n)$ message delays. The generalized lattice agreement problem defined by Faleiro et al in \cite{faleiro2012generalized} is a generalization of the lattice agreement problem where it is applied for the replicated state machine. We propose an algorithm which guarantees liveness when a majority of the processes are correct in asynchronous systems. Our algorithm requires $\min \{O(h(L)), O(f)\}$ units of time in the worst case which is better than $O(n)$ units of time required by the algorithm of Faleiro et al. \cite{faleiro2012generalized}.
Interaction of GLP-1 and Ghrelin on Glucose Tolerance in Healthy Humans

Emerging evidence supports the importance of ghrelin to defend against starvation-induced hypoglycemia. This effect may be mediated by inhibition of glucose-stimulated insulin secretion as well as reduced insulin sensitivity. However, administration of ghrelin during meal consumption also stimulates the release of glucagon-like peptide 1 (GLP-1), an incretin important in nutrient disposition. The objective of this study was to evaluate the interaction between ghrelin and GLP-1 on parameters of glucose tolerance following a mixed-nutrient meal. Fifteen healthy men and women completed the study. Each consumed a standard meal on four separate occasions with a superimposed infusion of 1) saline, 2) ghrelin, 3) the GLP-1 receptor antagonist exendin(9-39) (Ex9), or 4) combined ghrelin and Ex9. Similar to previous studies, infusion of ghrelin caused glucose intolerance, whereas Ex9 had a minimal effect. However, combined ghrelin and Ex9 resulted in greater postprandial glycemia than either alone, and this effect was associated with impaired β-cell function and decreased glucose clearance. These findings suggest that in the fed state, stimulation of GLP-1 mitigates some of the effect of ghrelin on glucose tolerance. This novel interaction between gastrointestinal hormones suggests a system that balances insulin secretion and glucose disposal in the fed and fasting states.
Effects of Traditional and Vascular Restricted Strength Training Program With Equalized Volume on Isometric and Dynamic Strength, Muscle Thickness, Electromyographic Activity, and Endothelial Function Adaptations in Young Adults.

Ramis, TR, Muller, CHdL, Boeno, FP, Teixeira, BC, Rech, A, Pompermayer, MG, Medeiros, NdS, Oliveira, ÁRd, Pinto, RS, and Ribeiro, JL. Effects of traditional and vascular restricted strength training program with equalized volume on isometric and dynamic strength, muscle thickness, electromyographic activity, and endothelial function adaptations in young adults. J Strength Cond Res XX(X): 000-000, 2018-The purpose of the study was to evaluate and compare the acute and chronic effects of partial vascular occlusion training in young, physically active adults. Neuromuscular, morphological, and endothelial function responses were compared between high-intensity resistance training (HI-RT) and low-intensity resistance training with partial vascular occlusion (LI-BFR), despite the same training volume. The 28 subjects (age, 23.96 ± 2.67 years) were randomly assigned into 2 groups: LI-BFR (n = 15) and HI-RT (n = 13). Both groups performed unilateral exercise of elbow flexion (EF) and knee extension (KE) 3 times per week for 8 weeks. This study was approved by the ethics committee. Flow-mediated dilation showed a significant difference in baseline and post-training in the LI-BFR group (4.44 ± 0.51 vs. 6.35 ± 2.08 mm, respectively). For nitrite/nitrate concentrations only, there was a significant difference when comparing pre- and post-acute exercise in both groups. The torque and rep. Sixty percent 1 repetition maximum had improvements in both groups. There were differences between groups only in isometric delta EF and isokinetic delta KE (EF 3.42 ± 5.09 and 9.61 ± 7.52 N·m; KE 12.78 ± 25.61 and 42.69 ± 35.68 N·m; LI-BFR and HI-RT groups, respectively). There was a significant increase of muscle thickness in both groups. An increase of both isokinetic and isometric electromyography (EMG) of biceps of the HI-RT group was observed. The same was observed for the LI-BFR group regarding isokinetic and isometric EMG of vastus lateralis. Thus, in addition to strength and hypertrophy gains, this study also shows benefits related to vascular function. For practical applications, this study demonstrates a clinical importance of LI-BFR training as an alternative methodology.
The prevalence and clinical characteristics of tick-borne diseases at One Sentinel Hospital in Northeastern China

Abstract Northeastern China is a region of high tick abundance, multiple tick-borne pathogens and likely human infections. The spectrum of diseases caused by tick-borne pathogens has not been objectively evaluated in this region for clinical management and for comparison with other regions globally where tick-transmitted diseases are common. Based on clinical symptoms, PCR, indirect immunofluorescent assay and (or) blood smear, we identified and described tick-borne diseases from patients with recent tick bite seen at Mudanjiang Forestry Central Hospital. From May 2010 to September 2011, 42% (75/180) of patients were diagnosed with a specific tick-borne disease, including Lyme borreliosis, tick-borne encephalitis, human granulocytic anaplasmosis, human babesiosis and spotted fever group rickettsiosis. When we compared clinical and laboratory features to identify factors that might discriminate tick-transmitted infections from those lacking that evidence, we revealed that erythema migrans and neurological manifestations were statistically significantly differently presented between those with and without documented aetiologies (P < 0.001, P = 0.003). Twelve patients (6.7%, 12/180) were co-infected with two tick-borne pathogens. We demonstrated the poor ability of clinicians to identify the specific tick-borne disease. In addition, it is necessary to develop specific laboratory assays for optimal diagnosis of tick-borne diseases.
An integrated network-based mobile botnet detection system

The increase in the use of mobile devices has made them target for attackers, through the use of sophisticated malware. One of the most significant types of such malware is mobile botnets. Due to their continually evolving nature, botnets are difficult to tackle through signature and traditional anomaly based detection methods. Machine learning techniques have also been used for this purpose. However, the study of their effectiveness has shown methodological weaknesses that have prevented the emergence of conclusive and thorough evidence about their merit. To address this problem, in this thesis we propose a mobile botnet detection system, called MBotCS and report the outcomes of a comprehensive experimental study of mobile botnet detection using supervised machine learning techniques to analyse network traffic and system calls on Android mobile devices. The research covers a range of botnet detection scenarios that is wider from what explored so far, explores atomic and box learning algorithms, and investigates thoroughly the sensitivity of the algorithm performance on different factors (algorithms, features of network traffic, system call data aggregation periods, and botnets vs normal applications and so on). These experiments have been evaluated using real mobile device traffic, and system call captured from Android mobile devices, running normal apps and mobile botnets. The experiments study has several superiorities comparing with existing research. Firstly, experiments use not only atomic but also box ML classifiers. Secondly, a comprehensive set of Android mobile botnets, which had not been considered previously, without relying on any form of synthetic training data. Thirdly, experiments contain a wider set of detection scenarios including unknown botnets and normal applications. Finally, experiments include the statistical significance of differences in detection performance measures with respect to different factors. The study resulted in positive evidence about the effectiveness of the supervised learning approach, as a solution to the mobile botnet detection problem.
Self‐Healing Zwitterionic Microgels as a Versatile Platform for Malleable Cell Constructs and Injectable Therapies

Injectable and malleable hydrogels that combine excellent biocompatibility, physiological stability, and ease of use are highly desirable for biomedical applications. Here, a simple and scalable strategy is reported to make injectable and malleable zwitterionic polycarboxybetaine hydrogels, which are superhydrophilic, nonimmunogenic, and completely devoid of nonspecific interactions. When zwitterionic microgels are reconstructed, the combination of covalent crosslinking inside each microgel and supramolecular interactions between them gives the resulting zwitterionic injectable pellet (ZIP) constructs supportive moduli and tunable viscoelasticity. ZIP constructs can be lyophilized to a sterile powder that fully recovers its strength and elasticity upon rehydration, simplifying storage and formulation. The lyophilized powder can be reconstituted with any aqueous suspension of cells or therapeutics, and rapidly and spontaneously self‐heals into a homogeneous composite construct. This versatile and highly biocompatible platform material shows great promise for many applications, including as an injectable cell culture scaffold that promotes multipotent stem cell expansion and provides oxidative stress protection.
Antitumor Activity of MEDI3726 (ADCT-401), a Pyrrolobenzodiazepine Antibody–Drug Conjugate Targeting PSMA, in Preclinical Models of Prostate Cancer

Prostate-specific membrane antigen (PSMA) is a membrane-bound glutamate carboxypeptidase that is highly expressed in nearly all prostate cancers with the highest expression in metastatic castration-resistant prostate cancer (mCRPC). The prevalence of increased surface expression and constitutive internalization of PSMA make it an attractive target for an antibody–drug conjugate (ADC) approach to treating patients with mCRPC. MEDI3726 (previously known as ADCT-401) is an ADC consisting of an engineered version of the anti-PSMA antibody J591 site specifically conjugated to the pyrrolobenzodiazepine (PBD) dimer tesirine. MEDI3726 specifically binds the extracellular domain of PSMA and, once internalized, releases the PBD dimer to crosslink DNA and trigger cell death. In vitro, MEDI3726 demonstrated potent and specific cytotoxicity in a panel of PSMA-positive prostate cancer cell lines, consistent with internalization and DNA interstrand crosslinking. In vivo, MEDI3726 showed robust antitumor activity against the LNCaP and the castration-resistant CWR22Rv1 prostate cancer cell line xenografts. MEDI3726 also demonstrated durable antitumor activity in the PSMA-positive human prostate cancer patient–derived xenograft (PDX) LuCaP models. This activity correlated with increased phosphorylated Histone H2AX in tumor xenografts treated with MEDI3726. MEDI3726 is being evaluated in a phase I clinical trial as a treatment for patients with metastatic castrate-resistant prostate cancer (NCT02991911). Mol Cancer Ther; 17(10); 2176–86. ©2018 AACR.
Insights into Complex Brain Functions Related to Schizophrenia Disorder through Causal Network Analysis

Gene expression represents a fundamental interface between genes and environment in the development and ongoing plasticity of the human organism. Individual differences in gene expression are likely to underpin much of human diversity, including psychiatric illness. Gene expression shows a distinct regulatory pattern in different tissues. Therefore, brain tissue analysis provides insights into brain disorder mechanisms. Furthermore, mechanistic understanding of gene regulatory pattern can be provided through studying the underlying relationships as a complex network. Identification of brain specific gene relationships provides a complementary framework in which to tackle the complex dysregulations that occur in neuropsychiatric and other neurological disorders. Using a systems approach established in Mendelian randomization and Bayesian Network, we integrated genetic and transcriptomic data from the common-mind consortium and identified transcriptomic causal networks in observational studies. Focusing on Schizophrenia disorder, we identified high impact genes and revealed their underlying pathways in brain tissue. In addition, we generated novel hypotheses including genes as causes of the schizophrenia-associated genes and new genes associated with Schizophrenia. This approach may facilitate a better understanding of the disease mechanism that is complementary to molecular experimental studies especially for complex systems and large-scale data sets.
A kiwifruit (Actinidia deliciosa) R2R3‐MYB transcription factor modulates chlorophyll and carotenoid accumulation

Summary MYB transcription factors (TFs) regulate diverse plant developmental processes and understanding their roles in controlling pigment accumulation in fruit is important for developing new cultivars. In this study, we characterised kiwifruit TF MYB7, which was found to activate the promoter of the kiwifruit lycopene beta‐cyclase (AdLCY‐β) gene that plays a key role in the carotenoid biosynthetic pathway. To determine the role of MYB7, we analysed gene expression and metabolite profiles in Actinidia fruit which show different pigment profiles. The impact of MYB7 on metabolic biosynthetic pathways was then evaluated by overexpression in Nicotiana benthamiana followed by metabolite and gene expression analysis of the transformants. MYB7 was expressed in fruit that accumulated carotenoid and Chl pigments with high transcript levels associated with both pigments. Constitutive over‐expression of MYB7, through transient or stable transformation of N. benthamiana, altered Chl and carotenoid pigment levels. MYB7 overexpression was associated with transcriptional activation of certain key genes involved in carotenoid biosynthesis, Chl biosynthesis, and other processes such as chloroplast and thylakoid membrane organization. Our results suggest that MYB7 plays a role in modulating carotenoid and Chl pigment accumulation in tissues through transcriptional activation of metabolic pathway genes.
Inferring the ground truth through crowdsourcing

Universally valid ground truth is almost impossible to obtain or would come at a very high cost. For supervised learning without universally valid ground truth, a recommended approach is applying crowdsourcing: Gathering a large data set annotated by multiple individuals of varying possibly expertise levels and inferring the ground truth data to be used as labels to train the classifier. Nevertheless, due to the sensitivity of the problem at hand (e.g. mitosis detection in breast cancer histology images), the obtained data needs verification and proper assessment before being used for classifier training. Even in the context of organic computing systems, an indisputable ground truth might not always exist. Therefore, it should be inferred through the aggregation and verification of the local knowledge of each autonomous agent.