Miracle-dz/UniFAR-FTUs huggingface dataset:
Respiratory health and individual estimated exposure to traffic-related air pollutants in a cohort of young children

Objectives: To estimate long-term exposure to traffic-related air pollutants on an individual basis and to assess adverse health effects using a combination of air pollution measurement data, data from geographical information systems (GIS) and questionnaire data. Methods: 40 measurement sites in the city of Munich, Germany were selected at which to collect particulate matter with a 50% cut-off aerodynamic diameter of 2.5 µm (PM2.5) and to measure PM2.5 absorbance and nitrogen dioxide (NO2). A pool of GIS variables (information about street length, household and population density and land use) was collected for the Munich metropolitan area and was used in multiple linear regression models to predict traffic-related air pollutants. These models were also applied to the birth addresses of two birth cohorts (German Infant Nutritional Intervention Study (GINI) and Influence of Life-style factors on the development of the Immune System and Allergies in East and West Germany (LISA)) in the Munich metropolitan area. Associations between air pollution concentrations at birth address and 1-year and 2-year incidences of respiratory symptoms were analysed. Results: The following means for the estimated exposures to PM2.5, PM2.5 absorbance and NO2 were obtained: 12.8 μg/m3, 1.7×10−5 m−1 and 35.3 μg/m3, respectively. Adjusted odds ratios (ORs) for wheezing, cough without infection, dry cough at night, bronchial asthma, bronchitis and respiratory infections indicated positive associations with traffic-related air pollutants. After controlling for individual confounders, significant associations were found between the pollutant PM2.5 and sneezing, runny/stuffed nose during the first year of life (OR 1.16, 95% confidence interval 1.01 to 1.34) Similar effects were observed for the second year of life. These findings are similar to those from our previous analysis that were restricted to a subcohort in Munich city. The extended study also showed significant effects for sneezing, running/stuffed nose. Additionally, significant associations were found between NO2 and dry cough at night (or bronchitis) during the first year of life. The variable “living close to major roads” (<50 m), which was not analysed for the previous inner city cohort with birth addresses in the city of Munich, turned out to increase the risk of wheezing and asthmatic/spastic/obstructive bronchitis. Conclusions: Effects on asthma and hay fever are subject to confirmation at older ages, when these outcomes can be more validly assessed.
hnRNP-Q1 represses nascent axon growth in cortical neurons by inhibiting Gap-43 mRNA translation

A novel posttranscriptional mechanism for regulating the neuronal protein GAP-43 is reported. The mRNA-binding protein hnRNP-Q1 represses Gap-43 mRNA translation by a mechanism involving a 5′ untranslated region G-quadruplex structure, which affects GAP-43 function, as demonstrated by a GAP-43–dependent increase in neurite length and number with hnRNP-Q1 knockdown.
Targeting endoplasmic reticulum stress in metabolic disease

Introduction: Endoplasmic reticulum (ER) stress, a condition that dramatically affects protein folding homeostasis in cells, has been associated with a number of metabolic diseases. Emerging preclinical and clinical evidence supports the notion that pharmacological modulators of ER stress have therapeutic potential as novel treatments of metabolic disorders. Areas covered: In this review, the molecular mechanisms of ER stress and the unfolded protein response (UPR) in the pathogenesis of metabolic diseases are discussed, highlighting the roles of various UPR components revealed using disease models in mice. Special emphasis is placed on the use of novel small molecules in animal disease models and human pathologies, including type 2 diabetes, obesity, fatty liver disease, and atherosclerosis. Expert opinion: ER stress is a highly promising therapeutic target for metabolic disease. Small molecular chemical chaperones have already demonstrated therapeutic efficacy in animal and human studies. The emergence of compounds that target specific UPR signaling pathways will provide more options for this purpose. Although the findings are promising, more studies are needed to elucidate the efficacy and side effects of these small molecules for future use in humans.
The Semantic Knowledge Graph: A Compact, Auto-Generated Model for Real-Time Traversal and Ranking of any Relationship within a Domain

This paper describes a new kind of knowledge representation and mining system which we are calling the Semantic Knowledge Graph. At its heart, the Semantic Knowledge Graph leverages an inverted index, along with a complementary uninverted index, to represent nodes (terms) and edges (the documents within intersecting postings lists for multiple terms/nodes). This provides a layer of indirection between each pair of nodes and their corresponding edge, enabling edges to materialize dynamically from underlying corpus statistics. As a result, any combination of nodes can have edges to any other nodes materialize and be scored to reveal latent relationships between the nodes. This provides numerous benefits: the knowledge graph can be built automatically from a real-world corpus of data, new nodes - along with their combined edges - can be instantly materialized from any arbitrary combination of preexisting nodes (using set operations), and a full model of the semantic relationships between all entities within a domain can be represented and dynamically traversed using a highly compact representation of the graph. Such a system has widespread applications in areas as diverse as knowledge modeling and reasoning, natural language processing, anomaly detection, data cleansing, semantic search, analytics, data classification, root cause analysis, and recommendations systems. The main contribution of this paper is the introduction of a novel system - the Semantic Knowledge Graph - which is able to dynamically discover and score interesting relationships between any arbitrary combination of entities (words, phrases, or extracted concepts) through dynamically materializing nodes and edges from a compact graphical representation built automatically from a corpus of data representative of a knowledge domain. The source code for our Semantic Knowledge Graph implementation is being published along with this paper to facilitate further research and extensions of this work.
Requirement of the Chemokine Receptor CXCR3 for Acute Allograft Rejection

Chemokines provide signals for activation and recruitment of effector cells into sites of inflammation, acting via specific G protein–coupled receptors. However, in vitro data demonstrating the presence of multiple ligands for a given chemokine receptor, and often multiple receptors for a given chemokine, have led to concerns of biologic redundancy. Here we show that acute cardiac allograft rejection is accompanied by progressive intragraft production of the chemokines interferon (IFN)-γ–inducible protein of 10 kD (IP-10), monokine induced by IFN-γ (Mig), and IFN-inducible T cell α chemoattractant (I-TAC), and by infiltration of activated T cells bearing the corresponding chemokine receptor, CXCR3. We used three in vivo models to demonstrate a role for CXCR3 in the development of transplant rejection. First, CXCR3-deficient (CXCR3−/−) mice showed profound resistance to development of acute allograft rejection. Second, CXCR3−/− allograft recipients treated with a brief, subtherapeutic course of cyclosporin A maintained their allografts permanently and without evidence of chronic rejection. Third, CXCR+/+ mice treated with an anti-CXCR3 monoclonal antibody showed prolongation of allograft survival, even if begun after the onset of rejection. Taken in conjunction with our findings of CXCR3 expression in rejecting human cardiac allografts, we conclude that CXCR3 plays a key role in T cell activation, recruitment, and allograft destruction.
A collagen‐like surface glycoprotein is a structural component of the Bacillus anthracis exosporium

Bacillus anthracis , the aetiological agent of anthrax, is a Gram‐positive spore‐forming bacterium. The exosporium is the outermost integument surrounding the mature spore. Here, we describe the purification and the characterization of an immunodominant protein of the spore surface. This protein was abundant, glycosylated and part of the exosporium. The amino‐terminal sequence was determined and the corresponding gene was identified. It encodes a protein of 382 amino acid residues, the central part of which contains a region of GXX motifs presenting similarity to mammalian collagen proteins. Thus, this collagen‐like surface protein was named BclA (for Bacillus c ollagen‐ l ike protein of anthracis ). BclA was absent from vegetative cells; it was detected only in spores and sporulating cells. A potential promoter, dependent on the sigma factor σ K , which is required for a variety of events late in sporulation, was found upstream from the bclA gene. A bclA deletion mutant was constructed and analysed. Electron microscopy studies showed that BclA is a structural component of the filaments covering the outer layer of the exosporium.
MASON: A JAVA MULTI-AGENT SIMULATION LIBRARY

Agent-based modeling (ABM) has transformed social science research by allowing researchers to replicate or generate the emergence of empirically complex social phenomena from a set of relatively simple agent-based rules at the micro-level. Swarm, RePast, Ascape, and others currently provide simulation environments for ABM social science research. After Swarm! —! arguably the first widely used ABM simulator employed in the social sciences! —! subsequent simulators have sought to enhance available simulation tools and computational capabilities by providing additional functionalities and formal modeling facilities. Here we present MASON (Multi-Agent Simulator Of Neighborhoods), following in a similar tradition that seeks to enhance the power and diversity of the available scientific toolkit in computational social science. MASON is intended to provide a core of facilities useful not only to social science but to other agent-based modeling fields such as artificial intelligence and robotics. We believe this can foster useful “cross-pollination” between such diverse disciplines, and further that MASON's additional facilities will become increasing important as social complexity simulation matures and grows into new approaches. We illustrate the new MASON simulation library with a replication of HeatBugs and a demonstration of MASON applied to two challenging case studies: ant-like foragers and micro-aerial agents. Other applications are also being developed. The HeatBugs replication and the two new applications provide an idea of MASON’s potential for computational social science and artificial societies.
Adaptive type-2 fuzzy sliding mode control for chaos synchronization of uncertain chaotic systems

This paper a novel adaptive interval type-2 fuzzy neural network (FNN) controller is proposed to synchronize chaotic systems with training data corrupted by noise or rule uncertainties involving external disturbances. Adaptive interval type-2 FNN control scheme and sliding mode approach are incorporated to deal with the synchronization of non-identical chaotic systems. In the meantime, the Lyapunov stability theorem has been used to testify the asymptotic stability of the chaotic systems, based on the adaptive fuzzy sliding mode control. The chattering phenomena in the control efforts can be reduced and the stability analysis of the proposed control scheme will be guaranteed in the sense that all the states and signals are uniformly bounded and the external disturbance on the synchronization error can be attenuated. The simulation example is included to confirm validity and performance of the advocated design methodology.
Ethylene Modulates Root-Wave Responses in Arabidopsis1[w]

When stimulated to bend downward by being held at 45 degrees off vertical but unable to penetrate into agar-based media, Arabidopsis roots develop waving and looping growth patterns. Here, we demonstrate that ethylene modulates these responses. We determined that agar-containing plates sealed with low-porosity film generate abiotic ethylene concentrations of 0.1 to 0.3 μL L-1, whereas in plates wrapped with porous tape, ethylene remains at trace levels. We demonstrate that exogenous ethylene at concentrations as low as a few nanoliters per liter modulates root waving, root growth direction, and looping but through partly different mechanisms. Nutrients and Suc modify the effects of ethylene on root waving. Thus, ethylene had little effect on temporal wave frequency when nutrients were omitted but reduced it significantly on nutrient-supplemented agar. Suc masked the ethylene response. Ethylene consistently suppressed the normal tendency for roots of Landsberg erecta to skew to the right as they grow against hard-agar surfaces and also generated righthanded petiole twisting. Furthermore, ethylene suppressed root looping, a gravity-dependent growth response that was enhanced by high nutrient and Suc availability. Our work demonstrates that cell file twisting is not essential for root waving or skewing to occur. Differential flank growth accounted for both the extreme root waving on zero-nutrient plates and for root skewing. Root twisting was nutrient-dependent and was thus strongly associated with the looping response. The possible role of auxin transport in these responses and the involvement of circadian rhythms are discussed.
The Achievable Rate Region of 802.11-Scheduled Multihop Networks

In this paper, we characterize the achievable rate region for any IEEE 802.11-scheduled static multihop network. To do so, we first characterize the achievable edge-rate region, that is, the set of edge rates that are achievable on the given topology. This requires a careful consideration of the interdependence among edges since neighboring edges collide with and affect the idle time perceived by the edge under study. We approach this problem in two steps. First, we consider two-edge topologies and study the fundamental ways they interact. Then, we consider arbitrary multihop topologies, compute the effect that each neighboring edge has on the edge under study in isolation, and combine to get the aggregate effect. We then use the characterization of the achievable edge-rate region to characterize the achievable rate region. We verify the accuracy of our analysis by comparing the achievable rate region derived from simulations with the one derived analytically. We make a couple of interesting and somewhat surprising observations while deriving the rate regions. First, the achievable rate region with 802.11 scheduling is not necessarily convex. Second, the performance of 802.11 is surprisingly good. For example, in all the topologies used for model verification, the max-min allocation under 802.11 is at least 64% of the max-min allocation under a perfect scheduler.
Rapid acquisition of ultra-wideband signals in the dense multipath channel

Efficient serial search strategies are presented and shown to reduce drastically the mean acquisition time for UWB signals in a dense multipath environment. Inherent in traditional serial search problems is the assumption that only a single bin or a small number of consecutive bins can properly terminate the search. This assumption leads to search strategies which tend to be linear in nature, e.g., a linear sweep of the uncertainty region. Because of the dense multipath channel present in most UWB systems, this assumption is invalid as seen by the channel's relatively large delay spread. A generalized analysis of various search algorithms is presented based on a Markov chain model of a simple single-dwell serial search. The results from this analysis reveal that the linear search has a considerably larger mean acquisition time than the more efficient search strategy termed the bit reversal search.
Molecular causes of transcriptional response: a Bayesian prior knowledge approach

MOTIVATION The abundance of many transcripts changes significantly in response to a variety of molecular and environmental perturbations. A key question in this setting is as follows: what intermediate molecular perturbations gave rise to the observed transcriptional changes? Regulatory programs are not exclusively governed by transcriptional changes but also by protein abundance and post-translational modifications making direct causal inference from data difficult. However, biomedical research over the last decades has uncovered a plethora of causal signaling cascades that can be used to identify good candidates explaining a specific set of transcriptional changes. METHODS We take a Bayesian approach to integrate gene expression profiling with a causal graph of molecular interactions constructed from prior biological knowledge. In addition, we define the biological context of a specific interaction by the corresponding Medical Subject Headings terms. The Bayesian network can be queried to suggest upstream regulators that can be causally linked to the altered expression profile. RESULTS Our approach will treat candidate regulators in the right biological context preferentially, enables hierarchical exploration of resulting hypotheses and takes the complete network of causal relationships into account to arrive at the best set of upstream regulators. We demonstrate the power of our method on distinct biological datasets, namely response to dexamethasone treatment, stem cell differentiation and a neuropathic pain model. In all cases relevant biological insights could be validated. AVAILABILITY AND IMPLEMENTATION Source code for the method is available upon request.
A review of post-concussion syndrome and psychological factors associated with concussion

Abstract Objective: This study reviewed several topics related to post-concussion syndrome and psychological factors associated with concussion. Topics include neurobiological perspectives, psychological predictors of post-concussion syndrome including pre-morbid anxiety, anxiety sensitivity and cognitive biases and misattribution. In addition, the iatrogenic effects of excessive rest are reviewed and treatment options are discussed briefly. Main results: Animal models of concussion and mild traumatic brain injury suggest that a concussion can result in anxiety and fear reactions. The pathophysiology of depression following a concussion appears to be consistent with the cortico-limbic model of depression. Additionally, some individuals may be at risk for neurobiological depression and/or anxiety following a concussion. The literature also demonstrates that pre-morbid and concurrent anxiety increases the risk for prolonged concussion recovery. Cognitive biases and misattribution of symptoms contribute to lengthy recovery from concussion. In addition, medically prescribed excessive cognitive and physical rest may contribute to a protracted concussion recovery. Supervised and graduated physical activity, the introduction of anxiety reduction techniques and cognitive-behavioural therapy of cognitive biases and misattribution are effective means of shortening the length of post-concussion syndrome. Conclusions: Understanding, assessing and treating the psychological factors associated with concussion are effective means of preventing or shortening the length of post-concussion syndrome.
Macrolide Resistance in Streptococcus pneumoniae

Streptococcus pneumoniae is a common commensal and an opportunistic pathogen. Suspected pneumococcal upper respiratory infections and pneumonia are often treated with macrolide antibiotics. Macrolides are bacteriostatic antibiotics and inhibit protein synthesis by binding to the 50S ribosomal subunit. The widespread use of macrolides is associated with increased macrolide resistance in S. pneumoniae, and the treatment of pneumococcal infections with macrolides may be associated with clinical failures. In S. pneumoniae, macrolide resistance is due to ribosomal dimethylation by an enzyme encoded by erm(B), efflux by a two-component efflux pump encoded by mef (E)/mel(msr(D)) and, less commonly, mutations of the ribosomal target site of macrolides. A wide array of genetic elements have emerged that facilitate macrolide resistance in S. pneumoniae; for example erm(B) is found on Tn917, while the mef (E)/mel operon is carried on the 5.4- or 5.5-kb Mega element. The macrolide resistance determinants, erm(B) and mef (E)/mel, are also found on large composite Tn916-like elements most notably Tn6002, Tn2009, and Tn2010. Introductions of 7-valent and 13-valent pneumococcal conjugate vaccines (PCV-7 and PCV-13) have decreased the incidence of macrolide-resistant invasive pneumococcal disease, but serotype replacement and emergence of macrolide resistance remain an important concern.
Molecular and Genetic Predictors of Breast-to-Brain Metastasis: Review and Case Presentation

Brain metastases are the most common intracranial malignancy, and breast cancer is the second most common cancer to metastasize to the brain. Intracranial disease is a late manifestation of breast cancer with few effective treatment options, affecting 15-50% of breast cancer patients, depending upon molecular subtype. In this review article, we describe the genetic, molecular, and metabolic changes in breast cancer cells that facilitate breast to brain metastasis. We believe that advances in the understanding of breast to brain metastasis pathogenesis will lead to targeted molecular therapies and to improvements in the ability to prospectively identify patients at increased risk for developing intracranial disease.
Can MicroRNAs Improve the Management of Lung Cancer Patients? A Clinician's Perspective

The treatment of patients with lung cancer is increasingly individualised. Rather than treating lung cancer as a single disease, clinicians are often called upon to consider the precise histology and molecular biology of each tumour in addition to the individual characteristics of each patient. Paralleling advances in lung cancer management, advances in the detection of lung cancer are changing practice. Lung cancer screening promises to find disease at a curable stage; however, the high false positive rate in screening trials has clinical and fiscal ramifications which demand attention. Biomarkers able to stratify for the risk of cancer, prognosticate the course of disease, or predict the response to treatment are in increasing demand. This paper summarizes some of the clinical problems faced by those treating lung cancer patients, and examines how knowledge about the role of microRNAs in lung cancer biology may change patient management.
Prenatal and Passive Smoke Exposure and Incidence of Asthma and Wheeze: Systematic Review and Meta-analysis

H Burke, J Leonardi-Bee, A Hashim. Pediatrics. 2012;129(4):735–744 To determine estimates of the prospective impact of smoking by parents or household members on the risk of wheeze and asthma at various childhood stages. Children up to age 18 years exposed to environmental tobacco smoke Search of Medline, Embase, and conference abstracts to characterize cohort investigations of the incidence of asthma or wheeze …
Constructions of codes from number fields

We define number-theoretic error-correcting codes based on algebraic number fields, thereby providing a generalization of Chinese Remainder Codes akin to the generalization of Reed-Solomon codes to Algebraic-geometric codes. Our construction is very similar to (and in fact less general than) the one given by Lenstra [8], but the parallel with the function field case is more apparent, since we only use the non-archimedean places for the encoding. We prove that over an alphabet size as small as 19, there even exist asymptotically good number field codes of the type we consider. This result is based on the existence of certain number fields that have an infinite class field tower in which some primes of small norm split completely.