Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of oral dosing of live and autoclaved culture of Ruminococcus flavefaciens FD-1 on rumen bacterial and fungal populations in Murrah buffaloes

© Abstract: Dietary interventions aiming at increasing the number and activities of beneficial gut microbes could enhance digestive functions as well as the long-term welfare of dairy animals. In the present experiment, the effect of supplementation of a novel bacterial culture Ruminococcus flavefaciens FD-1 isolated from the rumen liquor of Murrah buffaloes was studied for its effect on rumen microbial populations enumerated using the most probable number technique. Three permanently fistulated buffaloes maintained on a high fibre diet were used as the source of rumen fluid for bacterial isolation that was subsequently tested in vivo. Twelve healthy mid-to-late lactating buffaloes in their second to third parity were divided into two similar groups of six each with a mean body weight of 601.5 kg. One group (LBC) was dosed with 300 mL of live bacterial culture, whereas an equal volume of autoclaved bacterial culture was dosed in another group (ABC) for a period of one month. The treatment-wise effect of supplementation of R. flavefaciens FD-1 culture was evident in an increase (P<0.05) in total rumen fungal population by 2.7 times in LBC than ABC in the post-dosing period. However, the period-wise comparison revealed that the population of bacteria augmented (P<0.05) by 2.16 and 3.17 times in both the groups ABC and LBC, respectively; whereas the magnitude of increase in fungal population was 5.04 and 10.3 times, respectively for both the groups. Post-dosing effects were evident for only group LBC in both bacteria and fungi, respectively increasing (P<0.05) the population by 2.16 and 8.28 times than pre-dosing period. These preliminary results may foster scope for developing species-specific (autochthonous) bacterial probiotic/direct-fed microbial based on R. flavefaciens FD-1 for large ruminants maintained on fibrous diets under tropical production
Classification of Older Adults Undergoing Two Dual-Task Training Protocols Based on Artificial Intelligent Methods

Activities of daily living require efficient management between motor and cognitive tasks, known as dual tasks. The ability to properly perform such activities progressively decreases with aging. Hence, there is much effort in developing methods to identify relevant features and attenuate this functional loss. This study aimed at testing the ability of some intelligent algorithms to identify and differentiate functional features in community-dwelling older adults undergoing two 24-weeks dual-task training protocols. Additionally, we intended to provide a clear breakdown of the functional performance of the participants after undergoing two types of dual-task training protocols. We utilized the database from the EQUIDOSO-I clinical trial as input to four different types of intelligent classifiers. The algorithm’s performances were analysed considering accuracy, precision, sensitivity, and specificity. Individually, SVM achieved a higher value for sensitivity for one population group, but the remaining metrics continued to report low metrics indicating poor generalization ability of the model. Nonetheless, AdaBoost presented the most consistent results at the end of the intervention period (T2). No between-group difference affected the models’ accomplishments, corroborated by the similar performances at the baseline (T1) and the 6-month follow-up (T3). Therefore, future works should focus on establishing distinct protocols to enhance functional aspects of older adults.
Anaerobes in the microbiome The fecal microbiome of dogs with exocrine pancreatic insuf fi ciency

Exocrine pancreatic insufficiency (EPI) in dogs is a syndrome of inadequate synthesis and secretion of pancreatic enzymes. Small intestinal bacterial dysbiosis occurs in dogs with EPI, and is reversed with pancreatic enzyme therapy. However, there are no studies evaluating the fecal microbiome of dogs with EPI. The objective of this study was to evaluate the fecal microbiome of dogs with EPI. Three day pooled fecal samples were collected from healthy dogs (n 1⁄4 18), untreated (n 1⁄4 7) dogs with EPI, and dogs with EPI treated with enzyme replacement therapy (n 1⁄4 19). Extracted DNA from fecal samples was used for Illumina sequencing of the bacterial 16S rRNA gene and analyzed using Quantitative Insights Into Microbial Ecology (QIIME) and PICRUSt was used to predict the functional gene content of the microbiome. Linear discriminant analysis effect size (LEfSe) revealed significant differences in bacterial groups and functional genes between the healthy dogs and dogs with EPI. There was a significant difference in fecal microbial communities when healthy dogs were compared to treated and untreated dogs with EPI (unweighted UniFrac distance, ANOSIM P 1⁄4 0.001, and 0.001 respectively). Alpha diversity was significantly decreased in untreated and treated EPI dogs when compared to the healthy dogs with respect to Chao1, Observed OTU, and Shannon diversity (P 1⁄4 0.008, 0.003, and 0.002 respectively). The families Bifidobacteriaceae (P 1⁄4 0.005), Enterococcaceae (P 1⁄4 0.018), and Lactobacillaceae (P 1⁄4 0.001) were significantly increased in the untreated and treated dogs with EPI when compared to healthy dogs. In contrast, Lachnospiraceae (P < 0.001), and Ruminococcaceae (P < 0.01) were significantly decreased in dogs with EPI. Dogs with EPI (before treatment) had significant increases in functional genes associated with secretion system, fatty acid metabolism, and phosphotransferase system. In contrast, healthy dogs had a significant increase in genes related to phenylalanine, tyrosine and tryptophan biosynthesis, transcription machinery and sporulation. In conclusion, this study shows that the fecal microbiome of dogs with EPI (both treated and untreated) is different to that of healthy dogs. © 2017 Elsevier Ltd. All rights reserved.
Antidiarrheal and Antioxidant Activities of the Aerial Parts of Caralluma dalzielii N. E. Brown

Background: Caralluma dalzielii N.E. Brown is commonly referred to as “Karan massalanci” in the Northwest Nigeria where it is used in treating several ailments including pain, diarrhea and stomach issues. Objectives: This study was aimed to investigate the antidiarrheal and antioxidant activities of aqueous aerial parts extract of Caralluma dalzielii. Materials and Methods: The aqueous aerial parts extract of Caralluma dalzielii (CDE) (100, 200 and 400 mg/kg) was evaluated for antidiarrheal properties against gastrointestinal motility, castor oil-induced and prostaglandin-E2 (PGE2) enteropooling models in Wistar rats. Its antioxidant properties were studied by determining the total phenolic content (TPC) using Folin-Ciocalteu reagent, its free radical scavenging activity using 1, 1-Diphenyl-2-Picrylhydrazyl (DPPH) and its ferric reducing antioxidant power (FRAP) assays using potassium ferrocyanide ferric chloride methods. Ascorbic acid was used as the reference. Spasmolytic effect was studied in isolated rabbit jejunum preparations in an organ bath experiment using acetylcholine (ACh). Results: CDE significantly (p<0.05) reduced gastrointestinal transit of charcoal meal and the total number of diarrheal feces in the animals. The total phenolic content was determined to be 36.67±3.33 mg GAE/g. IC50 value of 63.44 mg/mL was calculated in DPPH assay. The FRAP value was found to increase as the concentration of the extract increased. CDE inhibited acetylcholine-induced contraction of the rabbit jejunum in a concentration dependent manner with complete inhibition at 20.48 mg/mL of the extract. Conclusion: The extract of Caralluma dalzielii possesses antidiarrheal activities which may be related to its antimotility, antioxidant and antispasmodic properties.
Fully Gibbs Sampling Algorithms for Bayesian Variable Selection in Latent Regression Models

This study proposed efficient Gibbs sampling algorithms for variable selection in a latent regression model under a unidimensional two-parameter logistic item response theory model. Three types of shrinkage priors were employed to obtain shrinkage estimates: double-exponential (i.e., Laplace), horseshoe, and horseshoe+ priors. These shrinkage priors were compared to a uniform prior case in both simulation and real data analysis. The simulation study revealed that two types of horseshoe priors had a smaller root mean square errors and shorter 95% credible interval lengths than double-exponential or uniform priors. In addition, the horseshoe prior+ was slightly more stable than the horseshoe prior. The real data example successfully proved the utility of horseshoe and horseshoe+ priors in selecting effective predictive covariates for math achievement. In the final section, we discuss the benefits and limitations of the three types of Bayesian variable selection methods.
Blockchain-Based Event Detection and Trust Verification Using Natural Language Processing and Machine Learning

Information sharing is one of the huge topics in social media platform regarding the daily news related to events or disasters happens in nature or its human-made. The automatic urgent need identification and sharing posts and information delivery with a short response are essential tasks in this area. The key goal of this research is developing a solution for management of disasters and emergency response using social media platforms as a core component. This process focuses on text analysis techniques to improve the process of authorities in terms of emergency response and filter the information using the automatically gathered information to support the relief efforts. Specifically, we used state-of-art Machine Learning (ML), Deep Learning (DL), and Natural Language Processing (NLP) based on supervised and unsupervised learning using social media datasets to extract real-time content related to the emergency events to comfort the fast response in a critical situation. Similarly, the blockchain framework used in this process for trust verification of the detected events and eliminating the single authority on the system. The main reason of using the integrated system is to improve the system security and transparency to avoid sharing the wrong information related to an event in social media.
The Host-Guest Complexes of Various Carbon Nanotubes (CNTs) and Boron Nitride Nanotubes (BNNTs) with Water at DFT Levels

We have investigated the various nanotube (NT)-water complexes as important host-guest complexes via the DFT method using B3LYP/6-31G* and M06/6-31G* levels of theory. These NTs include single-walled and double-walled carbon nanotubes (SWCNT and DWCNT, respectively). In addition, the boron nitride nanotube (BNNT) and tip-functionalized CNTs are also designed. All geometries turn out as minima on their energy surfaces. Calculated structural and thermodynamic parameters, along with atoms in molecules (AIM) and natural bond orbital (NBO) analyses, indicate that water inside the SWCNTs shows a higher interaction with NT where the nature of interactions is partially electrostatic-partially covalent. Therefore, the SWCNTs turn out as the best candidates for carrying and storage the water molecules.
Differential expression of genes in follicular cells of swines 1

The main purpose of the present study was to identify for candidate genes related to ovulation in swines. To do so, it was investigated in ovarian follicular cells through quantitative real-time PCR the differential expression of the following genes: steroidogenic acute regulator (STAR), GATA-binding protein 4 (GATA), prostaglandin F2α (PGF2α), progesterone receptor (P4R), follicle-stimulating hormone receptor (FSHR), and cytochrome P450 aromatase (CYP19). These genes encode hormone receptors (FSHR and P4R), hormone (PGF2α), steroidogenic proteins (STAR and CYP19) and transcription factor (GATA). Folicular cells were collected from sows with high and low number of piglets/litters during the follicular phase of the estrus cycle. There was difference in transcript abundance among low and high prolific sows for the STAR, GATA, PGF2α, P4R and CYP19 genes. For the FSHR gene, the fold change was not considered to be significantly different. Because in the present study only the transcript level of the above mentioned genes was analyzed, no inference can be made regarded to protein translation or activity. Therefore, gene sequence trials and other functional studies will be necessary to complement the present results, allowing a better understanding on biological complexity of these genes and their use as markers for prolificity in swines.
Partial predation on tropical gorgonians

Cyphoma gibbosum (Gastropoda, Ovulidae), a generalized predator on tropical gorgonians, forages selectively among gorgonian species on shallow reefs at St. Croix, U.S. Virgin Islands. Mean residence times of marked snails on prey taxa ranged from 1.8 d on Eunicea spp. to 4.7 and 10 d on Gorgonia ventalina and Plexaurella dichotoma respectively. Juveniles were sedentary and remained on single hosts throughout the study period. An analysis of wounds caused by C. gibbosum on gorgonians revealed that colonies of some taxa were consistently stripped of tissue, baring the underlying proteinaceous axis, while others were only superficially grazed. Wounds baring the axial skeleton were considerably more severe than superficial wounds because the bared skeleton could be colonized by algae causing subsequent tissue regeneration to be inhibited. Within all species, wounds were not restricted to any single portion of the colony. However, most wounds occurred within the central regions of colonies and the lowest portions of colonies were seldom damaged. The short residence times and superficial grazing on some taxa such as Eunicea spp. and Muncea spp. may be a consequence of heavy spicular armor over polyps and over the proteinaceous axis in those taxa. Deep wounds to the axial skeleton occurred only on taxa with smaller spicules and a smaller proportion of spicules in the coenenchyme by dry weight. Residence time on a prey species was inversely proportional to both maxlmum spicule length and percent composition of spicules. Short residence times and shallow wounds on colonies of h e a d y splculed species may be a response by C. gibbosum to foraging on suboptimal, heavlly defended resources Despite the short residence times, these potentially suboptimal prey taxa were regularly visited and preyed upon. This suggests that factors other than spicular architecture, such as gorgonian chemistry, may also play a role in the foraging of C. gibbosum.
Theoretical analysis of generalized Sagnac effect in the standard synchronization

The Sagnac effect has been shown in inertial frames as well as rotating frames. We solve the problem of the generalized Sagnac effect in the standard synchronization of clocks. The speed of a light beam that traverses an optical fiber loop is measured with respect to the proper time of the light detector, and is shown to be other than the constant c, though it appears to be c if measured by the time standard-synchronized. The fiber loop, which can have an arbitrary shape, is described by an infinite number of straight lines such that it can be handled by the general framework of Mansouri and Sexl (MS). For a complete analysis of the Sagnac effect, the motion of the laboratory should be taken into account. The MS framework is introduced to deal with its motion relative to a preferred reference frame. Though the one-way speed of light is other than c, its two-way speed is shown to be c with respect to the proper time. The theoretical analysis of the generalized Sagnac effect corresponds to the experimental .
GPER 1 is regulated by insulin in cancer cells and cancer-associated fibroblasts

Elevated insulin levels have been associated with an increased cancer risk as well as with aggressive and metastatic cancer phenotypes characterized by a poor prognosis. Insulin stimulates the proliferation, migration, and invasiveness of cancer cells through diverse transduction pathways, including estrogen signaling. As G protein estrogen receptor 1 (GPER1) mediates rapid cell responses to estrogens, we evaluated the potential of insulin to regulate GPER1 expression and function in leiomyosarcoma cancer cells (SKUT-1) and breast cancer-associated fibroblasts (CAFs), which were used as a model system. We found that insulin transactivates the GPER1 promoter sequence and increases the mRNA and protein expression of GPER1 through the activation of the PRKCD/MAPK1/c-Fos/AP1 transduction pathway, as ascertained by means of specific pharmacological inhibitors and gene-silencing experiments. Moreover, cell migration triggered by insulin occurred through GPER1 and its main target gene CTGF, whereas the insulin-induced expression of GPER1 boosted cell-cycle progression and the glucose uptake stimulated by estrogens. Notably, a positive correlation between insulin serum levels and GPER1 expression was found in cancer fibroblasts obtained from breast cancer patients. Altogether, our data indicate that GPER1 may be included among the complex network of transduction signaling triggered by insulin that drives cells toward cancer progression.
Endophytic Fungi Controls the Physicochemical
Status of Maize Crop under Salt Stress

Salt stress is one of the abiotic stresses which negatively affects the growth and yield of different plants world-wide. The purpose of the present investigation was to analyze the physiological and biochemical parameters of maize plants exposed to salt stress with or without fungal association. The results indicated that salinity negatively affected the growth attributes, biochemical parameters and ionic status in maize. In contrast, WSQ association has improved the growth parameters, antioxidant enzyme activity, IAA and metabolites, N, P, Ca, K and Mg concentrations in maize. Moreover, WSQ association reduced lipid-peroxidation, Na, Cl, Na/K and Ca/K ratio in maize during salinity stress. The results of the present study concluded that the endophyticfungal strain WSQ can serve as a good strategic-tool to improve maize growth under salt-stress.
Detection of Non-Stationary GW Signals in High Noise From Cohen’s Class of Time–Frequency Representations Using Deep Learning

Analysis of non-stationary signals in a noisy environment is a challenging research topic in many fields often requiring simultaneous signal decomposition in the time and frequency domain. This paper proposes a method for the classification of noisy non-stationary time-series signals based on Cohen’s class of their time-frequency representations (TFRs) and deep learning algorithms. We demonstrated the proposed approach on the example of detecting gravitational-wave (GW) signals in intensive real-life, non-stationary, non-white, and non-Gaussian noise. For this purpose, we prepared a dataset based on the actual data from the Laser Interferometer Gravitational-Wave Observatory (LIGO) detector and the synthetic GW signals obtained by realistic simulations. Next, 12 different TFRs from Cohen’s class were calculated from the original noisy time-series data and used to train three state-of-the-art convolutional neural network (CNN) architectures: ResNet-101, Xception, and EfficientNet. The obtained classification results are compared to those achieved by the base model trained on the original time series. Analysis of the results suggests that the proposed approach combining deep CNN architectures with Cohen’s class TFRs yields high values of performance metrics and significantly improves the classification performance compared to the base model. The TFR-CNN models achieve the values of the classification accuracy of up to 97.10%, the area under the receiver operating characteristic curve (ROC AUC) of up to 0.9885, the recall of up to 95.87%, the precision of up to 99.51%, the F1 score of up to 97.03%, and the area under the precision-recall curve (PR AUC) of up to 0.9920. This classification performance is obtained on the dataset in which the signal-to-noise ratio (SNR) values of the raw, noisy time-series signals range from −123.46 to −2.27 dB. Therefore, this study suggests that using alternative TFRs of Cohen’s class can improve the deep learning-based detection of non-stationary GW signals in an intensive noise environment. Moreover, the proposed approach can also be a viable solution for deep learning-based analysis of numerous other noisy non-stationary signals in different practical applications.
High-resolution NWP forecast precipitation comparison over complex terrain of the Sierras de Córdoba during RELAMPAGO-CACTI.

Sierras de Córdoba (Argentina) is characterized by the occurrence of extreme precipitation events during the austral warm season. Heavy precipitation in the region has a large societal impact, causing flash floods. This motivates the forecast performance evaluation of 24-hour accumulated precipitation and vertical profiles of atmospheric variables from different numerical weather prediction (NWP) models with the final aim of helping water management in the region. The NWP models evaluated include the Global Forecast System (GFS) which parameterizes convection, and convection-permitting simulations of the Weather Research and Forecasting Model (WRF) configured by three institutions: University of Illinois at Urbana–Champaign (UIUC), Colorado State University (CSU) and National Meteorological Service of Argentina (SMN). These models were verified with daily accumulated precipitation data from rain gauges and soundings during the RELAMPAGO-CACTI field campaign. Generally all configurations of the higher-resolution WRFs outperformed the lower-resolution GFS based on multiple metrics. Among the convection-permitting WRF models, results varied with respect to rainfall threshold and forecast lead time, but the WRFUIUC mostly performed the best. However, elevation dependent biases existed among the models that may impact the use of the data for different applications. There is a dry (moist) bias in lower (upper) pressure levels which is most pronounced in the GFS. For Córdoba an overestimation of the northern flow forecasted by the NWP configurations at lower levels was encountered. These results show the importance of convection-permitting forecasts in this region, which should be complementary to the coarser-resolution global model forecasts to help various users and decision makers.
Creating a Big Data Resource from the Faces of Wikipedia

We present the Faces of Wikipedia data set in which we have used Wikipedia to create a large database of identities and faces. To automatically extract faces for over 50,000 identities we have developed a state of the art face extraction pipeline and a novel facial co-reference technique. Our approach is based on graphical models and uses the text of Wikipedia pages, face attributes and similarities, as well as clues from various other sources. Our method resolves the name-face association problem jointly for all detected faces on a Wikipedia page. We provide this dataset to the community for further research in various forms including: manually labeled faces, automatically labeled faces using our co-reference technique, raw and processed faces as well as text and meta data features for further evaluations of extraction and co-reference methods.