Miracle-dz/UniFAR-FTUs huggingface dataset:
Neonatal White Matter Maturation Is Associated With Infant Language Development

Background While neonates have no sophisticated language skills, the neural basis for acquiring this function is assumed to already be present at birth. Receptive language is measurable by 6 months of age and meaningful speech production by 10–18 months of age. Fiber tracts supporting language processing include the corpus callosum (CC), which plays a key role in the hemispheric lateralization of language; the left arcuate fasciculus (AF), which is associated with syntactic processing; and the right AF, which plays a role in prosody and semantics. We examined if neonatal maturation of these fiber tracts is associated with receptive language development at 12 months of age. Methods Diffusion-weighted imaging (DWI) was performed in 86 infants at 26.6 ± 12.2 days post-birth. Receptive language was assessed via the MacArthur-Bates Communicative Development Inventory at 12 months of age. Tract-based fractional anisotropy (FA) was determined using the NA-MIC atlas-based fiber analysis toolkit. Associations between neonatal regional FA, adjusted for gestational age at birth and age at scan, and language development at 12 months of age were tested using ANOVA models. Results After multiple comparisons correction, higher neonatal FA was positively associated with receptive language at 12 months of age within the genu (p < 0.001), rostrum (p < 0.001), and tapetum (p < 0.001) of the CC and the left fronto-parietal AF (p = 0.008). No significant clusters were found in the right AF. Conclusion Microstructural development of the CC and the AF in the newborn is associated with receptive language at 12 months of age, demonstrating that interindividual variation in white matter microstructure is relevant for later language development, and indicating that the neural foundation for language processing is laid well ahead of the majority of language acquisition. This suggests that some origins of impaired language development may lie in the intrauterine and potentially neonatal period of life. Understanding how interindividual differences in neonatal brain maturity relate to the acquisition of function, particularly during early development when the brain is in an unparalleled window of plasticity, is key to identifying opportunities for harnessing neuroplasticity in health and disease.
Neglected but Potent Dry Forest Players: Ecological Role and Ecosystem Service Provision of Biological Soil Crusts in the Human-Modified Caatinga

Biological soil crusts (biocrusts) have been recognized as key ecological players in arid and semiarid regions at both local and global scales. They are important biodiversity components, provide critical ecosystem services, and strongly influence soil-plant relationships, and successional trajectories via facilitative, competitive, and edaphic engineering effects. Despite these important ecological roles, very little is known about biocrusts in seasonally dry tropical forests. Here we present a first baseline study on biocrust cover and ecosystem service provision in a human-modified landscape of the Brazilian Caatinga, South America's largest tropical dry forest. More specifically, we explored (1) across a network of 34 0.1 ha permanent plots the impact of disturbance, soil, precipitation, and vegetation-related parameters on biocrust cover in different stages of forest regeneration, and (2) the effect of disturbance on species composition, growth and soil organic carbon sequestration comparing early and late successional communities in two case study sites at opposite ends of the disturbance gradient. Our findings revealed that biocrusts are a conspicuous component of the Caatinga ecosystem with at least 50 different taxa of cyanobacteria, algae, lichens and bryophytes (cyanobacteria and bryophytes dominating) covering nearly 10% of the total land surface and doubling soil organic carbon content relative to bare topsoil. High litter cover, high disturbance by goats, and low soil compaction were the leading drivers for reduced biocrust cover, while precipitation was not associated Second-growth forests supported anequally spaced biocrust cover, while in old-growth-forests biocrust cover was patchy. Disturbance reduced biocrust growth by two thirds and carbon sequestration by half. In synthesis, biocrusts increase soil organic carbon (SOC) in dry forests and as they double the SOC content in disturbed areas, may be capable of counterbalancing disturbance-induced soil degradation in this ecosystem. As they fix and fertilize depauperated soils, they may play a substantial role in vegetation regeneration in the human-modified Caatinga, and may have an extended ecological role due to the ever-increasing human encroachment on natural landscapes. Even though biocrusts benefit from human presence in dry forests, high levels of anthropogenic disturbance could threaten biocrust-provided ecosystem services, and call for further, in-depth studies to elucidate the underlying mechanisms.
Methanogenic Activity and Microbial Community Structure in Response to Different Mineralization Pathways of Ferrihydrite in Paddy Soil

In soils and sediments, microbial reduction of iron (hydr)oxides and consequent formation of secondary iron minerals are important factors influencing many biogeochemical cycles and processes that including microbial methanogenesis. Here, we investigated methanogenic activity and microbial community of a paddy soil enrichment in response to different biomineralization pathways of ferrihydrite, which was reduced and transformed to magnetite and vivianite in the absence and presence of phosphate, respectively. For methanogenic degradation of both acetate and propionate, CH4 production rates in the magnetite-cultures were significantly enhanced compared with the vivianite-cultures. Characterization of 16S rRNA genes from methanogenic soils microbial community indicated that: (i) Methanocellaceae and Methanosarcinaceae were the dominant methanogens in all soil enrichments, (ii) syntrophic acetate-oxidizing bacteria of Clostridiaceae and syntrophic propionate-oxidizing bacteria of Syntrophobacteraceae were highly enriched in the magnetite-cultures, and (iii) the abundance of Anaerolineaceae was increased in the magnetite-cultures for both acetate- and propionate-fed incubations. The facilitated CH4 production in the magnetite-cultures might be related to the dynamic cycle of Fe(III)−Fe(II)−Fe(III) or magnetite-stimulated direct interspecies electron transfer between syntrophic acetate/propionate oxidizers and methanogens, which need further investigation. These results lend insight into the biogeochemical cycling of Fe, C and P in anaerobic soils and sediments, as well as the development of mitigation strategies of CH4 production.
Rack1 Controls Parallel Fiber–Purkinje Cell Synaptogenesis and Synaptic Transmission

Purkinje cells (PCs) in the cerebellum receive two excitatory afferents including granule cells-derived parallel fiber (PF) and the climbing fiber. Scaffolding protein Rack1 is highly expressed in the cerebellar PCs. Here, we found delayed formation of specific cerebellar vermis lobule and impaired motor coordination in PC-specific Rack1 conditional knockout mice. Our studies further revealed that Rack1 is essential for PF–PC synapse formation. In addition, Rack1 plays a critical role in regulating synaptic plasticity and long-term depression (LTD) induction of PF–PC synapses without changing the expression of postsynaptic proteins. Together, we have discovered Rack1 as the crucial molecule that controls PF–PC synaptogenesis and synaptic plasticity. Our studies provide a novel molecular insight into the mechanisms underlying the neural development and neuroplasticity in the cerebellum.
The Calcium-Dependent Protein Kinase CPK33 Mediates Strigolactone-Induced Stomatal Closure in Arabidopsis thaliana

Strigolactones (SLs) are known to mediate plant acclimation to environmental stress. We recently reported that SLs acted as prominent regulators in promotion of stomatal closure. However, the detailed mechanism by which SLs induce stomatal closure requires further investigation. Here we studied the essential role of the calcium (Ca2+) signal mediating by the calcium-dependent protein kinase (CPK) in SL-induced stomatal closure. SL-induced stomatal closure was strongly inhibited by a Ca2+ chelator and Ca2+ channel blockers, indicating that Ca2+ functions in SL promotion of stomatal closure. Through examining a collection of cpk mutants, we identified CPK33, potentially acting as a Ca2+ transducer, which is implicated in guard cell SL signaling. SL- and Ca2+-induced stomatal closure were impaired in cpk33 mutants. CPK33 kinase activity is essential for SL induction of stomatal closure as SL-induced stomatal closure is blocked in the dead kinase mutant of CPK33. The cpk33 mutant is impaired in H2O2-induced stomatal closure, but not in SL-mediated H2O2 production. Our study thus uncovers an important player CPK33 which functions as an essential Ca2+ signals mediator in Arabidopsis guard cell SL signaling.
Epigenetic and Transcriptomic Characterization of Pure Adipocyte Fractions From Obese Pigs Identifies Candidate Pathways Controlling Metabolism

Reprogramming of adipocyte function in obesity is implicated in metabolic disorders like type 2 diabetes. Here, we used the pig, an animal model sharing many physiological and pathophysiological similarities with humans, to perform in-depth epigenomic and transcriptomic characterization of pure adipocyte fractions. Using a combined DNA methylation capture sequencing and Reduced Representation bisulfite sequencing (RRBS) strategy in 11 lean and 12 obese pigs, we identified in 3529 differentially methylated regions (DMRs) located at close proximity to-, or within genes in the adipocytes. By sequencing of the transcriptome from the same fraction of isolated adipocytes, we identified 276 differentially expressed transcripts with at least one or more DMR. These transcripts were over-represented in gene pathways related to MAPK, metabolic and insulin signaling. Using a candidate gene approach, we further characterized 13 genes potentially regulated by DNA methylation and identified putative transcription factor binding sites that could be affected by the differential methylation in obesity. Our data constitute a valuable resource for further investigations aiming to delineate the epigenetic etiology of metabolic disorders.
A Deontic Argumentation Framework Towards Doctrine Reification

A modular rule-based argumentation system is proposed to represent and reason upon conditional norms featuring obligations, prohibitions, and (strong or weak) permissions. The approach is based on common constructs in computational models of argument: rule-based arguments, argumentation graphs, argument labelling semantics and statement labelling semantics. Deontic reasoning patterns are captured with defeasible rule schemata to the greatest extent, towards the reification of doctrinal pieces. We show then that bivalent statement labellings can fall short to address normative completeness, and for this reason, we propose to use trivalent labelling semantics. Given an argumentation graph, deontic statuses can be computed efficiently. The system is illustrated with a scenario featuring a violation and a contrary-to-duty obligation.
Measuring Goal-Oriented Requirements Language Actor Stability

Background: Goal models describe interests, preferences, intentions, desired goals and strategies of intervening stakeholders during the early requirements engineering stage. When capturing the requirements of real-world systems such as socio-technical systems, the produced goal models evolve quickly to become large and complex. Hence, gaining a suﬃcient level of understanding of such goal models, to perform maintenance tasks, becomes more challenging. Metric-based approaches have shown good potential in improving software designs and making them more understandable and easier to maintain. Aim: In this paper, we propose a novel metric to measure GRL (Goal-oriented Requirements Language) “actor stability” that provides a quantitative indicator of the actor maintainability. Method: We ﬁrst, validate the proposed metric theoretically then empirically using a case study of a GRL model describing the fostering of the relationship between the university and its alumni. Results: The proposed actor stability metric is found to have signiﬁcant negative correlation with the maintenance eﬀort of GRL models. Conclusions: Our results show that the proposed metric is a good indicator of GRL actors’ stability.
Packet2Vec: Utilizing Word2Vec for Feature Extraction in Packet Data

One of deep learning's attractive benefits is the ability to automatically extract relevant features for a target problem from largely raw data, instead of utilizing human engineered and error prone handcrafted features. While deep learning has shown success in fields such as image classification and natural language processing, its application for feature extraction on raw network packet data for intrusion detection is largely unexplored. In this paper we modify a Word2Vec approach, used for text processing, and apply it to packet data for automatic feature extraction. We call this approach Packet2Vec. For the classification task of benign versus malicious traffic on a 2009 DARPA network data set, we obtain an area under the curve (AUC) of the receiver operating characteristic (ROC) between 0.988-0.996 and an AUC of the Precision/Recall curve between 0.604-0.667.
Spectral Learning on Matrices and Tensors

Spectral methods have been the mainstay in several domains such as machine learning and scientific computing. They involve finding a certain kind of spectral decomposition to obtain basis functions that can capture important structures for the problem at hand. The most common spectral method is the principal component analysis (PCA). It utilizes the top eigenvectors of the data covariance matrix, e.g. to carry out dimensionality reduction. This data pre-processing step is often effective in separating signal from noise. PCA and other spectral techniques applied to matrices have several limitations. By limiting to only pairwise moments, they are effectively making a Gaussian approximation on the underlying data and fail on data with hidden variables which lead to non-Gaussianity. However, in most data sets, there are latent effects that cannot be directly observed, e.g., topics in a document corpus, or underlying causes of a disease. By extending the spectral decomposition methods to higher order moments, we demonstrate the ability to learn a wide range of latent variable models efficiently. Higher-order moments can be represented by tensors, and intuitively, they can encode more information than just pairwise moment matrices. More crucially, tensor decomposition can pick up latent effects that are missed by matrix methods, e.g. uniquely identify non-orthogonal components. Exploiting these aspects turns out to be fruitful for provable unsupervised learning of a wide range of latent variable models. We also outline the computational techniques to design efficient tensor decomposition methods. We introduce Tensorly, which has a simple python interface for expressing tensor operations. It has a flexible back-end system supporting NumPy, PyTorch, TensorFlow and MXNet amongst others, allowing multi-GPU and CPU operations and seamless integration with deep-learning functionalities.
Leveraging Multiple Online Sources for Accurate Income Verification

Income verification is the problem of validating a person's stated income given basic identity information such as name, location, job title and employer. It is widely used in the context of mortgage lending, rental applications and other financial risk models. However, the current processes surrounding verification involve significant human effort and document gathering which can be both time-consuming and expensive. In this paper, we propose a novel model for verifying an individual's income given very limited identity information typically available in loan applications. Our model is a combination of a deep neural network and hand-engineered features. The hand engineered features are based upon matching the input information against income records extracted automatically from various publicly available online sources (e.g. payscale.com, H-1B filings, government employee salaries). We conduct experiments on two data sets, one simulated from H-1B records and the other from a real-world data set of peer-to-peer (P2P) loan applications obtained from one of the world's largest P2P lending platform. Our results show a significant reduction in error of 3-6% relative to several strong baselines. We also perform ablation studies to demonstrate that a combined model is indeed necessary to achieve state-of-the-art performance on this task.
Methodological Guidelines for Measuring Energy Consumption of Software Applications

Energy consumption information for devices, as available in the literature, is typically obtained with ad hoc approaches, thus making replication and consumption data comparison difficult. We propose a process for measuring the energy consumption of a software application. The process contains four phases, each providing a structured deliverable that reports the information required to replicate the measurement. The process also guides the researcher on a threat to validity analysis to be included in each deliverable. This analysis ensures better reliability, trust, and confidence to reuse the collected consumption data. Such a process produces a structured consumption data for any kind of electronic device (IoT devices, mobile phones, personal computers, servers, etc.), which can be published and shared with other researchers fostering comparison or further investigations. A real case example demonstrates how to apply the process and how to create the required deliverables.
Time and Compensation Mechanisms in Checking Legal Compliance

In this paper we extend the logic of violation proposed by [23] with time, more precisely, we temporalise that logic. The resulting system allows us to capture many subtleties of the concept of legal compliance. In particular, the formal characterisation of compliance can handle diﬀerent types of legal obligation and diﬀerent temporal constraints over them. The logic is also able to represent, and reason about, chains of reparative obligations, since in many cases the fulﬁllment of these types of obligation still amounts to legally acceptable situations
Helpfulness Assessment of Online Reviews

Effective use of online consumer reviews is hampered by uncertainty about their helpfulness. Despite a growing body of knowledge on indicators of review helpfulness, previous studies have overlooked rich semantic information embedded in review content. Following design science principles, this study introduces a semantic hierarchy of product features by probing the review text. Using the hierarchical framework as a guide, we develop a research model of review helpfulness assessment. In the model, we propose and conceptualize three new factors—breadth, depth, and redundancy, by building on and/or extending product uncertainty, information quality, signaling, and encoding variability theories. The model-testing results lend strong support to the proposed effects of those factors on review helpfulness. They also reveal interesting differences in the effects of redundancy and readability between different types of products. This study embodies knowledge moments of multiple genres of inquiry in design science research, which have multifold research and practical implications.
TB meets IE: contributions of ethnography

Ethnographic methods can broaden impact evaluation by adding to randomised control trials to assess the effects of international development programmes. This article uses ethnographic findings on tuberculosis (TB) in the Philippines to analyse results of quantitative research and to assess an evaluation of the United States Agency for International Development (USAID)/Philippines TB portfolio. Ladia’s ethnographic research explains critical aspects of TB patient and provider behaviour, often influenced by the stigma of TB, leading patients to adopt less effective care and ultimately sustaining the spread of the disease. Ethnography can provide information crucial to project success and to the evaluation of project effects.
Development and characterization of a multiplex panel of microsatellite markers for the Reunion free-tailed bat Mormopterus francoismoutoui

The ecology and conservation status of many island-restricted bats remain largely unexplored. The free-tailed bat Mormopterus francoismoutoui is a small insectivorous tropical bat, endemic to Reunion Island (Indian Ocean). Despite being widely distributed on the island, the fine-scale genetic structure and evolutionary ecology of M. francoismoutoui remain under-investigated, and therefore its ecology is poorly known. Here, we used Illumina paired-end sequencing to develop microsatellite markers for M. francoismoutoui, based on the genotyping of 31 individuals from distinct locations all over the island. We selected and described 12 polymorphic microsatellite loci with high levels of heterozygosity, which provide novel molecular markers for future genetic population-level studies of M. francoismoutoui.
Identification of the hyaluronic acid pathway as a therapeutic target for facioscapulohumeral muscular dystrophy

Inhibition of HA synthesis blocks DUX4-induced pathologies. Facioscapulohumeral muscular dystrophy (FSHD) is linked to epigenetic derepression of the germline/embryonic transcription factor DUX4 in skeletal muscle. However, the etiology of muscle pathology is not fully understood, as DUX4 misexpression is not tightly correlated with disease severity. Using a DUX4-inducible cell model, we show that multiple DUX4-induced molecular pathologies that have been observed in patient-derived disease models are mediated by the signaling molecule hyaluronic acid (HA), which accumulates following DUX4 induction. These pathologies include formation of RNA granules, FUS aggregation, DNA damage, caspase activation, and cell death. We also observe previously unidentified pathologies including mislocalization of mitochondria and the DUX4- and HA-binding protein C1QBP. These pathologies are prevented by 4-methylumbelliferone, an inhibitor of HA biosynthesis. Critically, 4-methylumbelliferone does not disrupt DUX4-C1QBP binding and has only a limited effect on DUX4 transcriptional activity, establishing that HA signaling has a central function in pathology and is a target for FSHD therapeutics.
3 Clinical Aspects and Treatment of Myocarditis Natural History of Viral Myocarditis: Critical Viewpoints

The diagnosis should be based on symptomatology, data from clinicallaborato­ ry examinations, and definitive autopsy or biopsy findings. We made proposal for clinical diagnosis based on the above constituents; this is presented in Table 1. The criteria were derived from the experience of analyzing four autop­ sied and 12 biopsied cases in which there were definitive histopathological findings. Programmed immunosuppressive therapies were not given in these cases.