Miracle-dz/UniFAR-FTUs huggingface dataset:
Unique patterns of trimethylation of histone H3 lysine 4 are prone to changes during aging in Caenorhabditis elegans somatic cells

Tri-methylation on histone H3 lysine 4 (H3K4me3) is associated with active gene expression but its regulatory role in transcriptional activation is unclear. Here we used Caenorhabditis elegans to investigate the connection between H3K4me3 and gene expression regulation during aging. We uncovered around 30% of H3K4me3 enriched regions to show significant and reproducible changes with age. We further showed that these age-dynamic H3K4me3 regions largely mark gene-bodies and are acquired during adult stages. We found that these adult-specific age-dynamic H3K4me3 regions are correlated with gene expression changes with age. In contrast, H3K4me3 marking established during developmental stages remained largely stable with age, even when the H3K4me3 associated genes exhibited RNA expression changes during aging. Importantly, the genes associated with changes in H3K4me3 and RNA levels with age are enriched for functional groups commonly implicated in aging biology. Therefore, our findings suggested divergent roles of H3K4me3 in gene expression regulation during aging, with important implications on aging-dependent pathophysiologies.
Morse Theory and an Impossibility Theorem for Graph Clustering

Kleinberg introduced three natural clustering properties, or axioms, and showed they cannot be simultaneously satisfied by any clustering algorithm. We present a new clustering property, Monotonic Consistency, which avoids the well-known problematic behaviour of Kleinberg's Consistency axiom, and the impossibility result. Namely, we describe a clustering algorithm, Morse Clustering, inspired by Morse Theory in Differential Topology, which satisfies Kleinberg's original axioms with Consistency replaced by Monotonic Consistency. Morse clustering uncovers the underlying flow structure on a set or graph and returns a partition into trees representing basins of attraction of critical vertices. We also generalise Kleinberg's axiomatic approach to sparse graphs, showing an impossibility result for Consistency, and a possibility result for Monotonic Consistency and Morse clustering.
Whole-Cell MALDI-TOF MS Versus 16S rRNA Gene Analysis for Identification and Dereplication of Recurrent Bacterial Isolates

Many ecological experiments are based on the extraction and downstream analyses of microorganisms from different environmental samples. Due to its high throughput, cost-effectiveness and rapid performance, Matrix Assisted Laser Desorption/Ionization Mass Spectrometry with Time-of-Flight detector (MALDI-TOF MS), which has been proposed as a promising tool for bacterial identification and classification, could be advantageously used for dereplication of recurrent bacterial isolates. In this study, we compared whole-cell MALDI-TOF MS-based analyses of 49 bacterial cultures to two well-established bacterial identification and classification methods based on nearly complete 16S rRNA gene sequence analyses: a phylotype-based approach, using a closest type strain assignment, and a sequence similarity-based approach involving a 98.65% sequence similarity threshold, which has been found to best delineate bacterial species. Culture classification using reference-based MALDI-TOF MS was comparable to that yielded by phylotype assignment up to the genus level. At the species level, agreement between 16S rRNA gene analysis and MALDI-TOF MS was found to be limited, potentially indicating that spectral reference databases need to be improved. We also evaluated the mass spectral similarity technique for species-level delineation which can be used independently of reference databases. We established optimal mass spectral similarity thresholds which group MALDI-TOF mass spectra of common environmental isolates analogically to phylotype- and sequence similarity-based approaches. When using a mass spectrum similarity approach, we recommend a mass range of 4–10 kDa for analysis, which is populated with stable mass signals and contains the majority of phylotype-determining peaks. We show that a cosine similarity (CS) threshold of 0.79 differentiate mass spectra analogously to 98.65% species-level delineation sequence similarity threshold, with corresponding precision and recall values of 0.70 and 0.73, respectively. When matched to species-level phylotype assignment, an optimal CS threshold of 0.92 was calculated, with associated precision and recall values of 0.83 and 0.64, respectively. Overall, our research indicates that a similarity-based MALDI-TOF MS approach can be routinely used for efficient dereplication of isolates for downstream analyses, with minimal loss of unique organisms. In addition, MALDI-TOF MS analysis has further improvement potential unlike 16S rRNA gene analysis, whose methodological limits have reached a plateau.
Poverty amongst Female-headed Households in Bosnia and Herzegovina: an empirical analysis

Poverty amongst Female-headed Households in Bosnia and Herzegovina: an empirical analysis This paper conducts an empirical investigation of whether female-headed households (FHHs) are over-represented amongst the poor compared to male-headed households in Bosnia and Herzegovina (BiH), measured through the consumption dimension of poverty. Apart from self-reported and demographic definitions of headship, which do not make reference to the economic support provided for a household, households whose headship is assigned according to economic definition are included in the analysis. Moreover, the heterogeneity of FHHs is recognised in our study. In this way, the study avoids the main pitfalls associated with similar studies. Since the relationship between poverty and headship has regional and ethnic characteristics, the specific characteristics of BiH in this context are taken into account. Finally, given that our analysis did not find evidence of FHHs being disadvantaged in terms of household consumption, it cannot be argued that policy measures targeting FHHs would be a useful approach to reducing poverty in Bosnia and Herzegovina.
How to Best Share a Big Secret

When sensitive data is stored in the cloud, the only way to ensure its secrecy is by encrypting it before it is uploaded. The emerging multi-cloud model, in which data is stored redundantly in two or more independent clouds, provides an opportunity to protect sensitive data with secret-sharing schemes. Both data-protection approaches are considered computationally expensive, but recent advances reduce their costs considerably: (1) Hardware acceleration methods promise to eliminate the computational complexity of encryption, but leave clients with the challenge of securely managing encryption keys. (2) Secure RAID, a recently proposed scheme, minimizes the computational overheads of secret sharing, but requires non-negligible storage overhead and random data generation. Each data-protection approach offers different tradeoffs and security guarantees. However, when comparing them, it is difficult to determine which approach will provide the best application-perceived performance, because previous studies were performed before their recent advances were introduced. To bridge this gap, we present the first end-to-end comparison of state-of-the-art encryption-based and secret sharing data protection approaches. Our evaluation on a local cluster and on a multi-cloud prototype identifies the tipping point at which the bottleneck of data protection shifts from the computational overhead of encoding and random data generation to storage and network bandwidth and global availability.
Learning Policy Representations in Multiagent Systems

Modeling agent behavior is central to understanding the emergence of complex phenomena in multiagent systems. Prior work in agent modeling has largely been task-specific and driven by hand-engineering domain-specific prior knowledge. We propose a general learning framework for modeling agent behavior in any multiagent system using only a handful of interaction data. Our framework casts agent modeling as a representation learning problem. Consequently, we construct a novel objective inspired by imitation learning and agent identification and design an algorithm for unsupervised learning of representations of agent policies. We demonstrate empirically the utility of the proposed framework in (i) a challenging high-dimensional competitive environment for continuous control and (ii) a cooperative environment for communication, on supervised predictive tasks, unsupervised clustering, and policy optimization using deep reinforcement learning.
Arabidopsis phenotyping through geometric morphometrics

In recent years, much technical progress has been done regarding plant phenotyping including the model species Arabidopsis thaliana. With automated, high-throughput platforms and the development of improved algorithms for the rosette segmentation task, it is now possible to massively extract reliable shape and size parameters for genetic, physiological and environmental studies. The development of low-cost phenotyping platforms and freeware resources make it possible to widely expand phenotypic analysis tools for Arabidopsis. However, objective descriptors of shape parameters that could be used independently of platform and segmentation software used are still lacking and shape descriptions still rely on ad hoc or even sometimes contradictory descriptors, which could make comparisons difficult and perhaps inaccurate. Modern geometric morphometrics is a family of methods in quantitative biology proposed to be the main source of data and analytical tools in the emerging field of phenomics studies. It has been used for taxonomists and paleontologists for decades and is now a mature discipline. By combining geometry, multivariate analysis and powerful statistical techniques, it offers the possibility to reproducibly and accurately account for shape variations amongst groups. Based on the location of homologous landmarks points over photographed or scanned specimens, these tools could identify the existence and degree of shape variation and measure them in standard units. Here, it is proposed a particular scheme of landmarks placement on Arabidopsis rosette images to study shape variation in the case study of viral infection processes. Several freeware-based geometric morphometric tools are applied in order to exemplify the usefulness of this approach to the study of phenotypes in this model plant. These methods are concisely presented and explained. Shape differences between controls and infected plants are quantified throughout the infectious process and visualized with the appealing graphs that are a hallmark of these techniques and render complex mathematical analysis simple outcomes to interpret. Quantitative comparisons between two unrelated ssRNA+ viruses are shown and reproducibility issues are assessed. Combined with the newest automatons and plant segmentation procedures, geometric morphometric tools could boost phenotypic features extraction and processing in an objective, reproducible manner.
Geographically Diverse Clusters of Nontoxigenic Corynebacterium diphtheriae Infection, Germany, 2016–2017

From 2016 through the middle of 2017, the German Consiliary Laboratory on Diphtheria noted an increase in nontoxigenic Corynebacterium diphtheriae isolates submitted from cities in northern Germany. Many patients for whom epidemiologic data were available were homeless, alcohol or drug abusers, or both. After performing routine diagnostics and multilocus sequence typing (MLST), we analyzed isolates of sequence type (ST) 8 and previously submitted isolates by whole-genome sequencing. Results were analyzed for phylogenetic relationship by core genome MLST (cg-MLST) and whole-genome single-nucleotide polymorphism profiles. Next-generation sequencing–based cg-MLST revealed several outbreak clusters caused by ST8; the geographic focus was in the metropolitan areas of Hamburg and Berlin. To achieve enhanced analytical depth, we used additional cg-MLST target genes and genome-wide single-nucleotide polymorphisms. We identified patient characteristics and detected transmission events, providing evidence that nontoxigenic C. diphtheriae infection is a potential public health threat in industrialized countries.
Preliminary data on the feeding habits of the endemic species Synodontis koensis Pellegrin, 1933 (Siluriformes, Mochokidae) in a West African River (Sassandra River Basin, Côte d’Ivoire)

In the implementation of programs for protection and management of ichthyofauna, endemic species should have high priority in the conservation measures. Their fishery should be based on intimate knowledge of their ecology and biology.Feeding habits of the endemic Mochokid Synodontis koensis were assessed in the Sassandra River (Cote d’Ivoire) in relation to the study zone, the specimen sex and size, and the season.Of the 303 stomachs examined, 49 were empty (16%). The fluctuation of the vacuity index indicated that S. koensis feeds more at night. The diet consisted mainly of plant detritus and chironomid larvae. The statistical analysis of the feeding according to the study zones, the sex of fish and the seasons does not show any significant difference between regimes, whereas significant ontogenic shifts in diet were observed.
Case report: giant cell arteritis in a patient with carotid atherosclerosis – a diagnostic dilemma

ABSTRACT Carotid atherosclerosis and giant cell arteritis (GCA) are two distinct medical conditions with an overlapping clinical spectrum of vascular symptoms such as vision loss and ischemic stroke. This is because both diseases cause arterial ischemia with a predilection for carotid vasculature. In addition, high-vascular risk individuals who are diagnosed with GCA are usually elderly with age >55 years with high-vascular risk and thus can have underlying atherosclerosis. All these factors can pose a diagnostic dilemma for the physicians as GCA is a medical emergency which if left untreated can result in significant morbidity and mortality. Thus, it is important to avoid attributing occlusive arterial disease in elderly patients to atherosclerosis alone because some may have GCA. We present a case report in which presence of diffuse atherosclerosis was a major pitfall while making a timely diagnosis of GCA.
Deep learning based classification for paddy pests & diseases recognition

Pests and diseases are a threat to paddy production, especially in Indonesia, but identification remains to be a challenge in massive scale and automatically. Increasing smartphone usage and deep learning advance create an opportunity to answer this problem. Collecting 4,511 images from four language using search engines, and augment it to develop diverse data set. This dataset fed into CaffeNet model and processed with Caffe framework. Experiment result in the model achieved accuracy 87%, which is higher than random selection 7.6%.
VULCON

Vulnerability remediation is a critical task in operational software and network security management. In this article, an effective vulnerability management strategy, called VULCON (VULnerability CONtrol), is developed and evaluated. The strategy is based on two fundamental performance metrics: (1) time-to-vulnerability remediation (TVR) and (2) total vulnerability exposure (TVE). VULCON takes as input real vulnerability scan reports, metadata about the discovered vulnerabilities, asset criticality, and personnel resources. VULCON uses a mixed-integer multiobjective optimization algorithm to prioritize vulnerabilities for patching, such that the above performance metrics are optimized subject to the given resource constraints. VULCON has been tested on multiple months of real scan data from a cyber-security operations center (CSOC). Results indicate an overall TVE reduction of 8.97% when VULCON optimizes a realistic security analyst workforce’s effort. Additionally, VULCON demonstrates that it can determine monthly resources required to maintain a target TVE score. As such, VULCON provides valuable operational guidance for improving vulnerability response processes in CSOCs.
Anticoagulant Activities of Indobufen, an Antiplatelet Drug

Indobufen is a new generation of anti-platelet aggregation drug, but studies were not sufficient on its anticoagulant effects. In the present study, the anticoagulant activity of indobufen was determined by monitoring the activated partial thromboplastin time (APTT), prothrombin time (PT), and thrombin time (TT) in rabbit plasma. We evaluated the anticoagulant mechanisms on the content of the platelet factor 3,4 (PF3,4), and the coagulation factor 1, 2, 5, 8, 10 (FI, II, V, VIII, X) in rabbits, as well as the in vivo bleeding time and clotting time in mice. The pharmacodynamic differences between indobufen and warfarin sodium, rivaroxaban, and dabigatran were further studied on thrombus formation and the content of FII and FX in rats. Animal experiments showed that intragastric-administrated indobufen can significantly reduce the APTT, PT, TT, PF3, FI, II, V, VIII, and X plasma contents. Its inhibitory effect on plasma FII was better than thrombin inhibitor dabigatran with effect on FX better than FXa inhibitor rivaroxaban. These results suggest that indobufen has some anticoagulant effects as strong as some conventional anticoagulants. The mechanism may be related to both exogenous and endogenous coagulation system.
Pixantrone: novel mode of action and clinical readouts

ABSTRACT Introduction: Pixantrone is a first-in-class aza-anthracenedione approved as monotherapy for treatment of relapsed or refractory aggressive diffuse B-cell non-Hodgkin’s lymphoma (NHL), a patient group which is notoriously difficult to treat. It has a unique chemical structure and pharmacologic properties distinguishing it from anthracyclines and anthracenediones. Areas covered: The chemical structure and mode of action of pixantrone versus doxorubicin and mitoxantrone; preclinical evidence for pixantrone’s therapeutic effect and cardiac tolerability; efficacy and safety of pixantrone in clinical trials; ongoing and completed trials of pixantrone alone or as combination therapy; and the risk of cardiotoxicity of pixantrone versus doxorubicin and mitoxantrone. Expert commentary: Currently, pixantrone is the only approved therapy for multiply relapsed or refractory NHL, an area with few available effective treatment options. Pixantrone is currently being investigated as combination therapy with other drugs including several targeted therapies, with the ultimate goal of improved survival in heavily pretreated patients. In order for pixantrone to be acknowledged in the treatment of aggressive NHL, the perception of pixantrone as an anthracycline-like agent that has anthracycline-like activity and cardiotoxicity needs to be changed. Further data from ongoing clinical trials will help in confirming pixantrone as an effective and safe option.
Characteristics of Defective Infrastructure as Code Scripts in DevOps

Defects in infrastructure as code (IaC) scripts can have serious consequences for organizations who adopt DevOps. By identifying which characteristics of IaC scripts correlate with defects, we can identify anti-patterns, and help software practitioners make informed decisions on better development and maintenance of IaC scripts, and increase quality of IaC scripts. The goal of this paper is to help practitioners increase the quality of IaC scripts by identifying characteristics of IaC scripts and IaC development process that correlate with defects, and violate security and privacy objectives. We focus on characteristics of IaC scripts and IaC development that (i) correlate with IaC defects, and (ii) violate security and privacy-related objectives namely, confidentiality, availability, and integrity. For our initial studies, we mined open source version control systems from three organizations: Mozilla, Openstack, and Wikimedia, to identify the defect-related characteristics and conduct our case studies. From our empirical analysis, we identify (i) 14 IaC code and four churn characteristics that correlate with defects; and (ii) 12 process characteristics such as, frequency of changes, and ownership of IaC scripts that correlate with defects. We propose the following studies: (i) identify structural characteristics that correlate with defects; (ii) with respect to prediction performance, compare which characteristics of IaC scripts are more correlated with defects; and (iii) identify characteristics that violate security and privacy objectives.
Compressed Sensing with Deep Image Prior and Learned Regularization

We propose a novel method for compressed sensing recovery using untrained deep generative models. Our method is based on the recently proposed Deep Image Prior (DIP), wherein the convolutional weights of the network are optimized to match the observed measurements. We show that this approach can be applied to solve any differentiable linear inverse problem, outperforming previous unlearned methods. Unlike various learned approaches based on generative models, our method does not require pre-training over large datasets. We further introduce a novel learned regularization technique, which incorporates prior information on the network weights. This reduces reconstruction error, especially for noisy measurements. Finally, we prove that single-layer DIP networks with constant fraction over-parameterization will perfectly fit any signal through gradient descent, despite being a non-convex problem. This theoretical result provides justification for early stopping.
Is there a link between inflammation and fatigue in multiple sclerosis?

Purpose Among autoimmune diseases of the central nervous system stands multiple sclerosis (MS), which is characterized by demyelination, synaptopathy, and neurodegeneration. MS fatigue can affect up to 90% of patients and be very disabling, with a drastic impact on their quality of life. To date, the evaluation of MS fatigue has relied mainly on subjective scales, and actual therapeutic interventions are challenged by modest efficacy and numerous undesirable effects. Therefore, finding biomarkers of MS fatigue might help in optimizing evaluation and treatment strategies. The main objective here was to assess the relationship between MS fatigue and inflammatory or other immunomediated markers. Methods Research was conducted according to PRISMA guidelines. Computerized databases (ie, PubMed/Medline and Scopus) were consulted till February 2018 aiming to identify articles that addressed inflammation and MS fatigue. Studies in English and French published at any time were considered. Results A total of 27 studies matched the research criteria. Inconsistency existed regarding the relationship between fatigue and the orexin A system, hypothalamus–pituitary–adrenal axis, and cerebrospinal fluid inflammatory markers. As for peripheral markers, although there was scarcity in the available data, serum proinflammatory cytokines (ie, IL6, TNFα, and IFNγ) seem to be associated with MS fatigue. Finally, no link was found between MS fatigue and T-cell populations (ie, CD3+CD4+ T lymphocytes, regulatory T cells) or other peripheral markers of inflammation (ie, CRP, erythrocyte-sedimentation rate, soluble ICAM1). Conclusion Future large-scale studies would benefit from comparing the relationship between fatigue and immune measures in patients with different disease phenotypes with and without disease-modifying drugs. With the subjective nature of fatigue scales, finding objective biomarkers for fatigue would be of great help.