Miracle-dz/UniFAR-FTUs huggingface dataset:
Distributed Acoustic Sensing Using Chirped-Pulse Phase-Sensitive OTDR Technology

In 2016, a novel interrogation technique for phase-sensitive (Φ)OTDR was mathematically formalized and experimentally demonstrated, based on the use of a chirped-pulse as a probe, in an otherwise direct-detection-based standard setup: chirped-pulse (CP-)ΦOTDR. Despite its short lifetime, this methodology has now become a reference for distributed acoustic sensing (DAS) due to its valuable advantages with respect to conventional (i.e., coherent-detection or frequency sweeping-based) interrogation strategies. Presenting intrinsic immunity to fading points and using direct detection, CP-ΦOTDR presents reliable high sensitivity measurements while keeping the cost and complexity of the setup bounded. Numerous technique analyses and contributions to study/improve its performance have been recently published, leading to a solid, highly competitive and extraordinarily simple method for distributed fibre sensing. The interesting sensing features achieved in these last years CP-ΦOTDR have motivated the use of this technology in diverse applications, such as seismology or civil engineering (monitoring of pipelines, train rails, etc.). Besides, new areas of application of this distributed sensor have been explored, based on distributed chemical (refractive index) and temperature-based transducer sensors. In this review, the principle of operation of CP-ΦOTDR is revisited, highlighting the particular performance characteristics of the technique and offering a comparison with alternative distributed sensing methods (with focus on coherent-detection-based ΦOTDR). The sensor is also characterized for operation in up to 100 km with a low cost-setup, showing performances close to the attainable limits for a given set of signal parameters [≈tens-hundreds of pe/sqrt(Hz)]. The areas of application of this sensing technology employed so far are briefly outlined in order to frame the technology.
Parasite microbiome project: Grand challenges

1 School of Marine and Atmospheric Sciences, Stony Brook University, Stony Brook, New York, United States of America, 2 Bigelow Laboratory for Ocean Sciences, East Boothbay, Maine, United States of America, 3 College of Marine Science, University of South Florida, Saint Petersburg, Florida, United States of America, 4 Department of Microbiology, Immunology and Tropical Medicine, George Washington University, Washington, DC, United States of America, 5 Research Center for Neglected Diseases of Poverty, School of Medicine & Health Sciences, George Washington University, Washington, DC, United States of America, 6 Genital Tract Biology Division, Department of Obstetrics, Gynecology and Reproductive Biology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, United States of America, 7 Gordon and Betty Moore Foundation, Palo Alto, California, United States of America, 8 Department of Biological Sciences, University of Pittsburgh, Pittsburgh, Pennsylvania, United States of America, 9 Department of Medical Microbiology and Immunology, University of Wisconsin-Madison, Madison, Wisconsin, United States of America, 10 Institute of Parasitology, Biology Centre, Czech Academy of Sciences and Faculty of Sciences, University of South Bohemia,České Budějovice (Budweis), Czech Republic, 11 Sackler Institute for Comparative Genomics, American Museum of Natural History, New York, New York, United States of America, 12 Department of Zoology, University of Otago, Dunedin, New Zealand, 13 Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, Maryland, United States of America, 14 Department of Biological Sciences and Northern Gulf Institute, University of Southern Mississippi, Hattiesburg, Mississippi, United States of America, 15 Ocean Chemistry and Ecosystems Division, Atlantic Oceanographic and Meteorological Laboratory, National Oceanic and Atmospheric Administration, La Jolla, California, United States of America
Poor Diet, Stress, and Inactivity Converge to Form a “Perfect Storm” That Drives Alzheimer’s Disease Pathogenesis

North American incidence of Alzheimer’s disease (AD) is expected to more than double over the coming generation. Although genetic factors surrounding the production and clearance of amyloid-β and phosphorylated tau proteins are known to be responsible for a subset of early-onset AD cases, they do not explain the pathogenesis of the far more prevalent sporadic late-onset variant of the disease. It is thus likely that lifestyle and environmental factors contribute to neurodegenerative processes implicated in the pathogenesis of AD. Herein, we review evidence that (1) excess sucrose consumption induces AD-associated liver pathologies and brain insulin resistance, (2) chronic stress overdrives activity of locus coeruleus neurons, leading to loss of function (a common event in neurodegeneration), (3) high-sugar diets and stress promote the loss of neuroprotective sex hormones in men and women, and (4) Western dietary trends set the stage for a lithium-deficient state. We propose that these factors may intersect as part of a “perfect storm” to contribute to the widespread prevalence of neurodegeneration and AD. In addition, we put forth the argument that exercise and supplementation with trace lithium can counteract many of the deleterious consequences associated with excessive caloric intake and perpetual stress. We conclude that lifestyle and environmental factors likely contribute to AD pathogenesis and that simple lifestyle and dietary changes can help counteract their effects.
Auricular acupuncture for adult tonsillectomy

Adult tonsillectomy causes significant postoperative pain that can last over 14 days, but narcotic regimens only provide a modest reduction in pain. Auricular acupuncture has been demonstrated to improve pain with minimal complications. This study compared acupuncture versus control for pain, opioid consumption, nausea, and return of diet and activity following tonsillectomy.
Fgd5 is a Rac1-specific Rho GEF that is selectively inhibited by aurintricarboxylic acid

ABSTRACT Rho proteins are signalling molecules that control cellular dynamics, movement and morphological changes. They are activated by Rho guanine-nucleotide exchange factors (Rho GEFs) that transduce upstream signals into Rho-mediated activation of downstream processes. Fgd5 is a Rho GEF involved in angiogenesis and its target Rho protein for this process has been linked to Cdc42 activation. Here, we examined the function of purified Fgd5, specifically, which Rho proteins it activates and pinpoint the structural domains required for enzymatic activity. Using a GEF enzyme assay, we found that purified Fgd5 showed preferential activation of Rac1 and direct binding of Rac1 in pull-down and co-immunoprecipitation assays. Structural comparisons showed that the Fgd5 DH domain is highly similar to the Rac1 GEF, TrioN, supporting a role for Fgd5 as a Rac1 GEF. Compounds that bind to purified Fgd5 DH-PH protein were identified by screening a small molecule library via surface plasmon resonance. The effects of eleven ligands were further examined for their ability to inhibit the Fgd5 GEF enzymatic activity and Rac1 interaction. From these studies, we found that the compound aurintricarboxylic acid, and to a lesser extent mitoxantrone dihydrochloride, inhibited both Fgd5 GEF activation of Rac1 and their interaction. Aurintricarboxylic acid had no effect on the activity or binding of the Rac1 GEF, TrioN, thus demonstrating the feasibility of selectively disrupting Rho GEF activators. Abbreviations: a.a. : amino acid; ATA: aurintricarboxylic acid; DH: Dbl homology; DOCK: dictator of cytokinesis; Fgd: faciogenital dysplasia; GEF: guanine-nucleotide exchange factor; GST: glutathione S-transferase; LOPAC: library of pharmacologically active compounds; PH: pleckstrin homology; PDB: protein data bank; s.e. m. : standard error of the mean; SPR: surface plasmon resonance.
On SINR Biasing for Optimal Cell Range Expansion in Two-tier Wireless Hetnets

Cell range expansion (CRE) for traffic offloading and load balancing in wireless heterogeneous networks (HetNets) is commonly realized by introducing a positive bias to the user received signal-to-interference-plus-noise ratio (SINR) of the small cell pilot signals when determining cell association. The small cell coverage is then expanded without needing to increase the transmission power. Higher energy and spectral efficiency is achieved accordingly. Most existing work on CRE focused on examining effectiveness of this technical approach but somehow ignored the impact of choosing different CRE bias values. This work presents preliminary results of an investigation of optimal biasing strategies for CRE in wireless HetNets. Specifically, the bias optimization problem for general design objective is formulated. The framework is first simulated in a two-tier HetNet with one single macro base station (BS) and a few small cell BSs scattered uniformly on a ring surrounding the macro BS. A unified CRE bias is adopted to all the small cell BSs. The impact of adjusting the bias value is examined for different design objectives. Simulation results reveal that changing the CRE bias value can significantly improve performance. However, it is also observed that for different performance measures, the results exhibit different trends and behaviors with respect to the bias value. The work is also extended to the scenario where different small cell BSs may adopt different CRE bias values.
Human Detection Based on Statistical Learning from Image

人々の生活の利便性向上や安心・安全な社会の実現 に向けて,人を観る技術が必要とされている. 人を観 る技術とは,画像から人の位置の特定や人の追跡,人 の動作を認識する技術である. 特に,人の位置を特定 する人検出技術は,人物追跡や動作認識を実現するた めには前処理として必要不可欠である. 本論文におけ る人検出は,画像から人が存在する位置座標,大きさ を出力する技術を指す. 類似技術として特定の人物を 認識する人物照合があるが本論文では触れないため, 人物照合については [1]を参照されたい. 人検出技術は,古くから研究されてきた顔検出技 術をベースとしている. 顔検出の研究は,1969 年の Sakaiらの研究 [2]が始まりとされ,以来多くの研究者 によって精力的に取り組まれてきた. 顔検出の初期の 研究では,目や口の濃淡や位置の関係を利用した研究 者があらかじめ決めたルールに従って顔と顔以外の画 像を判定する手法 [3]~[5] が主流であった. これらの
Impact of a University Alcohol Policy Change on Bystander Responses to Alcohol-Related Medical Emergencies

Two studies evaluated the implementation of a stricter campus underage drinking policy on service utilization and bystander helping behaviors for alcohol-related medical emergencies. A program evaluation (Study 1) examined campus emergency medical service logs assessing changes in call volume and service utilization, finding a 30% reduction in call volume postpolicy change. Study 2 provided a qualitative data summarizing campus first responder (N = 35) accounts of off-duty alcohol-related emergency calls. Off-duty calls increased postpolicy change and thematic analyses indicated they were (a) motivated by fear of campus sanctions, (b) often yielded delays or failures to contact campus emergency staff, and (c) resulted from student misunderstandings of policy implications for bystander helpers. Findings highlight potential challenges in executing environmental strategies to reduce college drinking.
Apoptotic Cell Exclusion and Bias‐Free Single‐Cell Selection Are Important Quality Control Requirements for Successful Single‐Cell Sequencing Applications

Single‐cell sequencing experiments are a new mainstay in biology and have been advancing science especially in the biomedical field. The high pressure to integrate the technology into daily laboratory live requires solid knowledge with respect to potential limitations and precautions to be taken care of before applying it to complex research questions. In the past, we have identified two issues with quality measures neglected by the growing community involving SmartSeq and droplet or micro‐well‐based scRNASeq methods (1) how to ensure that only single cells are introduced without biasing on light scattering when handling complex cell mixtures and organ preparations or (2) how best to control for (pro‐)apoptotic cell contaminations in single‐cell sequencing approaches. Sighting of concurrent literature involving single‐cell sequencing technologies revealed that these topics are generally neglected or simply approached in silico but not at the bench before generating single‐cell data sets. We fear that those important quality aspects are overlooked due to reduced awareness of their importance for guaranteeing the quality of experiments. In this Cytometry rigor issue, we provide experimentally supported guidance on how to circumvent those critical shortcomings in order to promote a better use of the fantastic single‐cell sequencing toolbox in biology. © 2019 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.
Consensus and uncertainty in the geographic range of Aedes aegypti and Aedes albopictus in the contiguous United States: Multi-model assessment and synthesis

Aedes (Stegomyia) aegypti (L.) and Ae. (Stegomyia) albopictus (Skuse) mosquitoes can transmit dengue, chikungunya, yellow fever, and Zika viruses. Limited surveillance has led to uncertainty regarding the geographic ranges of these vectors globally, and particularly in regions at the present-day margins of habitat suitability such as the contiguous United States. Empirical habitat suitability models based on environmental conditions can augment surveillance gaps to describe the estimated potential species ranges, but model accuracy is unclear. We identified previously published regional and global habitat suitability models for Ae. aegypti (n = 6) and Ae. albopictus (n = 8) for which adequate information was available to reproduce the models for the contiguous U.S. Using a training subset of recently updated county-level surveillance records of Ae. aegypti and Ae. albopictus and records of counties conducting surveillance, we constructed accuracy-weighted, probabilistic ensemble models from these base models. To assess accuracy and uncertainty we compared individual and ensemble model predictions of species presence or absence to both training and testing data. The ensemble models were among the most accurate and also provided calibrated probabilities of presence for each species. The quantitative probabilistic framework enabled identification of areas with high uncertainty and model bias across the U.S. where improved models or additional data could be most beneficial. The results may be of immediate utility for counties considering surveillance and control programs for Ae. aegypti and Ae. albopictus. Moreover, the assessment framework can drive future efforts to provide validated quantitative estimates to support these programs at local, national, and international scales.
[Population characteristics and threatened of wild Angelica sinensis in Gansu province].

The study aims to investigate spatial distribution pattern and age structure of wild Angelica sinensis in Gansu province.Ten plots each with an area of 100 m2 were set and the spatial coordinates of all wild A. sinensis individuals were measured within each plot. Based on plant individual mapping data,we explored the spatial distribution pattern and its differences between different life history stages of wild A. sinensis in Gansu province by using nearest neighbor distance statistics. Correlation analysis were carried out to explore the relationship between spatial aggregation degree and topographic factors. We also distinguished individuals to three life history stages( i.e. seedlings,adults and boltings) and then test the differences among/between them using nonparametric test. (1)We found that the dominant spatial distribution pattern of wild A. sinensis population in Gansu was aggregated distribution. There was no significant correlation between spatial aggregation degree of wild A. sinensis and altitude,slope and aspect. There was no significant difference between the average distance from seedlings to their nearest bolting individuals; the average distance from adult individuals to their nearest seedlings was significantly larger than the average distance from adult individuals to their nearest adult individuals; and the average distance from bolting individuals to their nearest adult individuals was significantly smaller than the average distance from bolting individuals to their nearest bolting individuals. (2)The age structure was showed as a declining population,characterized by less seedlings and bolting individuals,while more adult individuals within population. The population characteristics of wild A. sinensis,characterized by aggregated distribution pattern and senescent type of age structure,are disadvantage to its population development. The factors,such as habitat specialization,human activities and intraspecific competition,which shapes the current population characteristics,may increase the threatened status of wild A. sinensis. We suggest to strengthen the protection of wild A. sinensis.
Fast Scattering Center Extraction with DDM based on Compressive Sensing

In our work, compressive sensing (CS) is employed in domain decomposition method (DDM) to construct a new incident source with much fewer excitation waves to obtain the compressed induced currents, and then they are reconstructed by recovery algorithm. The proposed CS-based DDM algorithm requires much fewer sampling data to extract Radar-Cross Section (RCS) from strong scattering centers of the target validly. Meanwhile, the complexity of computational electromagnetic (CEM) model for solving the multiscale scattering problems is reduced significantly.
County-Level Soybean Yield Prediction Using Deep CNN-LSTM Model

Yield prediction is of great significance for yield mapping, crop market planning, crop insurance, and harvest management. Remote sensing is becoming increasingly important in crop yield prediction. Based on remote sensing data, great progress has been made in this field by using machine learning, especially the Deep Learning (DL) method, including Convolutional Neural Network (CNN) or Long Short-Term Memory (LSTM). Recent experiments in this area suggested that CNN can explore more spatial features and LSTM has the ability to reveal phenological characteristics, which both play an important role in crop yield prediction. However, very few experiments combining these two models for crop yield prediction have been reported. In this paper, we propose a deep CNN-LSTM model for both end-of-season and in-season soybean yield prediction in CONUS at the county-level. The model was trained by crop growth variables and environment variables, which include weather data, MODIS Land Surface Temperature (LST) data, and MODIS Surface Reflectance (SR) data; historical soybean yield data were employed as labels. Based on the Google Earth Engine (GEE), all these training data were combined and transformed into histogram-based tensors for deep learning. The results of the experiment indicate that the prediction performance of the proposed CNN-LSTM model can outperform the pure CNN or LSTM model in both end-of-season and in-season. The proposed method shows great potential in improving the accuracy of yield prediction for other crops like corn, wheat, and potatoes at fine scales in the future.
Intrathecal Administration of Nanoclusters for Protecting Neurons against Oxidative Stress in Cerebral Ischemia/Reperfusion Injury.

Oxidative stress is one of the important mechanisms in cerebral ischemia/reperfusion (I/R) injury. Antioxidants with high brain accumulation are highly desired to help prevent cerebral I/R injury. Herein, intrathecal injection of polyoxometalate (POM) nanoclusters as nano-antioxidants with preferential brain uptake were applied for neuronal protection in cerebral I/R injury. Using powerful positron emission tomography imaging, the whole uptake of nano-antioxidants in the brain was noninvasively and real-timely monitored. Our results demonstrated that POM nanoclusters rapidly reached the ischemic penumbra after intrathecal injection, and effectively scavenged reactive oxygen species (ROS) for inhibiting oxidative stress. The infarct size was reduced and neurological function was restored in cerebral I/R injury rat models. As a proof-of-concept, the intrathecal injection of nano-antioxidants is an excellent therapeutic strategy to ameliorate cerebral I/R injury.
Genetic diversity and population structure of the Mediterranean sesame core collection with use of genome-wide SNPs developed by double digest RAD-Seq

The Mediterranean sesame core collection contains agro-morphologically superior sesame accessions from geographically diverse regions in four continents. In the present investigation, the genetic diversity and population structure of this collection was analyzed with 5292 high-quality SNPs discovered by double-digest restriction site associated DNA (ddRAD) sequencing, a cost-effective and flexible next-generation sequencing method. The genetic distance between pairs of accessions varied from 0.023 to 0.524. The gene diversity was higher in accessions from Asia than from America, Africa, and Europe. The highest genetic differentiation was observed between accessions collected from America and Europe. Structure analysis showed the presence of three subpopulations among the sesame accessions, and only six accessions were placed in an admixture group. Phylogenetic tree and principal coordinate analysis clustered the accessions based on their countries of origin. However, no clear division was evident among the sesame accessions with regard to their continental locations. This result was supported by an AMOVA analysis, which revealed a genetic variation among continental groups of 5.53% of the total variation. The large number of SNPs clearly indicated that the Mediterranean sesame core collection is a highly diverse genetic resource. The collection can be exploited by breeders to select appropriate accessions that will provide high genetic gain in sesame improvement programs. The high-quality SNP data generated here should also be used in genome-wide association studies to explore qualitative trait loci and SNPs related to economically and agronomically important traits in sesame.
Magnetization dynamics in artificial spin ice

In this topical review, we present key results of studies on magnetization dynamics in artificial spin ice (ASI), which are arrays of magnetically interacting nanostructures. Recent experimental and theoretical progress in this emerging area, which is at the boundary between research on frustrated magnetism and high-frequency studies of artificially created nanomagnets, is reviewed. The exploration of ASI structures has revealed fascinating discoveries in correlated spin systems. Artificially created spin ice lattices offer unique advantages as they allow for a control of the interactions between the elements by their geometric properties and arrangement. Magnonics, on the other hand, is a field that explores spin dynamics in the gigahertz frequency range in magnetic micro- and nanostructures. In this context, magnonic crystals are particularly important as they allow the modification of spin-wave properties and the observation of band gaps in the resonance spectra. Very recently, there has been considerable progress, experimentally and theoretically, in combining aspects of both fields—artificial spin ice and magnonics—enabling new functionalities in magnonic and spintronic applications using ASI, as well as providing a deeper understanding of geometrical frustration in the gigahertz range. Different approaches for the realization of ASI structures and their experimental characterization in the high-frequency range are described and the appropriate theoretical models and simulations are reviewed. Special attention is devoted to linking these findings to the quasi-static behavior of ASI and dynamic investigations in magnonics in an effort to bridge the gap between both areas further and to stimulate new research endeavors.