Miracle-dz/UniFAR-FTUs huggingface dataset:
Modification of Gene Expression and Protein Profiles by Exhaustive Exercise in Pathogen- stimulated and Un-stimulated Peripheral Blood Cells

Purpose: The capacity of whole blood cultures to produce cytokines in response to endotoxin (LPS) was studied in athletes before, 30 min after, 3 h after and 24 h after a half-marathon run. Methods: Eight well trained men and 8 well trained women (6 of them in the late luteal phase of their cycle) participated. EDTA blood was incubated with or without LPS for 1 h, and cytokine concentration and gene expression were determined. To quantify LPS-dependent release on a per monocyte basis (LDR), the mean values of the difference (delta) between cytokine concentration in stimulated and unstimulated cultures, normalized to monocyte numbers, were calculated. Results: LDR of TNF-α was significantly reduced by exercise with identical kinetic in men and women. TNF-α mRNA expression was slightly down-regulated following exercise (P<0.05), but significantly so only in women. LDR of IL-6 was also reduced, but with a faster kinetic in women than in men. Similarly, 30 min post-exercise; LDR and spontaneous release of IL-1ra were significantly less in women than men. Concomitantly, IL1ra mRNA was significantly elevated in unstimulated and in stimulated cultures in men only. IL-10 and IL-10 mRNA were significantly induced 30 min following exercise in absence of any detectable LDR. Women showed significantly lower 8 • Exhaustive exercise and LPS-dependent cytokine release EIR 19 2013 Correspondence to: Asghar Abbasi, Division of Exercise Immunology & Genetics, Institute of Clinical and Experimental Transfusion Medicine (IKET), University Hospital Tuebingen Otfried-Mueller -Str.4/1, 72076 Tuebingen, Germany Tel. +4970712981659, Fax. +497071295240, E-mail. Ashgar.Abbasi@med.uni-tuebingen.de levels than men. LDR and spontaneous release of IL-8 was enhanced in men and TGF-β1 in women. A significant up-regulation was seen in unstimulated IL-8 mRNA for women and LPS-stimulated IL-8 mRNA expression for men following exercise. Conclusion: Altogether, LPS-dependent ex vivo cytokine release was strongly influenced by exercise and these changes could only in part be attributed to changes in messenger RNA. Results for IL-1ra, IL-6 and IL-10 pointed to a less pronounced anti-inflammatory response in women as compared with men. Our results also indicate an early production of IL-10 by peripheral blood cells in response to exercise.
Cropping system intensification: vegetable pea can replace fallow between rainfed monsoon rice and irrigated spring rice

SUMMARY The Eastern Gangetic Plain is among the world's most intensively farmed regions, where rainfed and irrigated agriculture coexist. While the region and especially Bangladesh is a major producer of rice (Oryza sativa L. ssp. indica), there is potential to further develop sustainable rice production systems. Specifically, there is scope to include a replacement crop for the short fallow between rice crops in the dominant cropping pattern of rainfed monsoon rice harvest followed by irrigated spring rice. The aim of the current research was to identify a suitable cool-season legume crop – pea (Pisum sativum L.) or lentil (Lens culinaris Medik. ssp. culinaris) – that could be grown in the brief period between rice crops. The study comprised four crop sequence experiments comparing legume cultivars differing in maturity grown in between both long and short duration rice cultivars. These experiments were done at the Bangladesh Rice Research Institute regional station at Rajshahi over three cropping cycles. This was followed by an evaluation of pea vs. fallow between rice crops on three farmers’ fields in one cropping cycle. Here it is demonstrated that green pod vegetable pea is one of the best options to intensify the rainfed monsoon rice–fallow–spring irrigated rice cropping system, notwithstanding other remunerative rabi cropping options that could displace boro rice. The inclusion of an extra crop, pea as green pod vegetable, increased farm productivity by 1·4-fold over the dominant cropping sequence (rice–fallow–rice) and farm net income by fourfold. The study highlighted the advantages in total system productivity and monetary return of crop intensification with the inclusion of a pea crop between successive rice crops instead of a fallow period.
Expression of Prolactin Receptor mRNA in Lactotrophs and Somatotrophs of the Chicken Anterior Pituitary Gland

Prolactin (PRL) is a hormone mainly secreted by the anterior pituitary gland. In birds, PRL exerts a variety of physiological functions in target tissues expressing the PRL receptor (PRLR). In chicken, the PRLR mRNA is abundant in the anterior pituitary gland, but its regional and cellular localization are unknown. In the present study, we investigated the expression of the PRLR mRNA in cephalic and caudal lobes of the chicken anterior pituitary gland. Real-time polymerase chain reaction (PCR) revealed high levels of PRLR mRNA in both cephalic and caudal lobes. In situ hybridization revealed that the PRLR mRNA was distributed in a wide area of both lobes, and co-localized with the PRL and growth hormone (GH) mRNAs in the cephalic and caudal lobes, respectively. These results suggest that PRL exerts autocrine/paracrine effects through PRLR on PRL-producing lactotrophs and GH-producing somatotrophs in the chicken anterior pituitary gland.
Somatic cell counts in bulk milk and their importance for milk processing

Bulk tank milk somatic cell counts are the indicator of the mammary gland health in the dairy herds and may be regarded as an indirect measure of milk quality. Elevated somatic cell counts are correlated with changes in milk composition The aim of this study was to assess the somatic cell counts that significantly affect the quality of milk and dairy products. We examined the somatic cell counts in bulk tank milk samples from 38 farms during the period of 6 months, from December to the May of the next year. The flow cytometry, Fossomatic was used for determination of somatic cell counts. In the same samples content of total proteins and lactose was determined by Milcoscan. Our results showed that average values for bulk tank milk samples were 273,605/ml from morning milking and 292,895/ml from evening milking. The average values for total proteins content from morning and evening milking are 3,31 and 3,34%, respectively. The average values for lactose content from morning and evening milking are 4,56 and 4,63%, respectively. The highest somatic cell count (516,000/ml) was detected in bulk tank milk sample from evening milk in the Winter and the lowest content of lactose was 4,46%. Our results showed that obtained values for bulk tank milk somatic cell counts did not significantly affected the content of total proteins and lactose.
Design, transformation and proliferation of VEGF111b recombinant isoform in Escherichia coli Top10 in order to produce recombinant drugs

: Background: The synthesis of recombinant proteins with the aim of inhibiting the tumor receptors is one of the new approaches in cancer treatment. The aim of this study was to design and clone the VEGF 111b recombinant isoform in pBudCE4.1 vector and assess its compatibility with E.coli Top10 in order to produce some recombinant drugs. Materials and Methods: The VEGF111b new isoform designed using gene sequences available in the databases and oligo 7 software was digested by BglII and KpnI enzymes. Then it was cloned at downstream of the EF-1 promoter in the pBudCE4.1vector. Isolation of recombinant bacteria was done in LBA medium with the concentration of Zeocin antibiotic (3% and 5%). In the final step, the recombinant vector was extracted using DNA gel extraction kit and VEGF111b recombinant fragment was confirmed by enzyme digestion and sequencing. Results: The ligation of 111b fragment in expected site was confirmed. The entire E.coli Top10 colonies were observed in Zeocin medium (5%) containing recombinant VEGF111b fragment and recombinant colonies (61.9%) were observed at Zeocin medium (3.0%). The existence of VEGF111b sequences in recombinant bacteria was confirmed by enzyme digestion and sequencing. Conclusion: The present study is an important step in the production process of VEGF111b recombinant protein and evaluating its anticancer effect. Moreover, the pBudCE4.1 vector containing 2 cloning sites and 8 enzyme excision sites is a good candidate for cloning and expression of recombinant proteins in E.coli TOP10.
MYC activity is required for maintenance of the Neuromesodermal Progenitor signalling network and for correct timing of segmentation clock gene oscillations

The Myc transcriptional regulators are implicated in a range of cellular functions, including proliferation, cell cycle progression, metabolism and pluripotency maintenance. Here, we investigated the expression, regulation and function of Myc during mouse embryonic axis elongation and segmentation. Expression of both cMyc and MycN in the domains where neuromesodermal progenitors (NMPs) and underlying caudal pre-somitic mesoderm (cPSM) cells reside is coincident WNT and FGF signals; factors known to maintain progenitors in an undifferentiated state. Pharmacological inhibition of MYC activity, downregulates expression of WNT/FGF components. In turn, we find that cMyc expression is WNT, FGF and NOTCH regulated, placing it centrally in the signalling circuit that operates in the tail end that both sustains progenitors and drives maturation of the PSM into somites. Interfering with MYC function in the PSM, where it displays oscillatory expression, delays the timing of segmentation clock oscillations and thus of somite formation. In summary, we identify Myc as a component that links NMP maintenance and PSM maturation during the body axis elongation stages of mouse embryogenesis. Summary Statement MYC operates in a positive feedback loop with WNT/FGF signals to maintain the progenitors which facilitate body axis elongation while its activity is crucial for timing of the segmentation clock.
Molecular cloning and expression analysis of a putative E class MADS-box gene, GbSEP, from Ginkgo biloba.

MADS-box gene family plays an important role in the development of floral organs in plants. A full-length cDNA of MADS-box gene, named as GbSEP(Genbank accession numberKJ630504), was cloned by RT-PCR from female flower of Ginkgo biloba. The cDNA sequence of GbSEP contained an ORF of 756 bp encoding 252 amino acid residues. The comparison between cDNA and genomic sequences of GbSEP indicated no intron in the genomic DNA sequence of this gene. Protein multi-alignment analysis indicated that GbSEP shared high homology with other SEP family proteins from other plants including Pinus radiate (PrMADS3), Pinus resinosa (PrMADS1), Arabidopsis thaliana (AtSEP4), Alpinia hainanensis (AhSEP3), Lotus japonicas (LjSEP), Populus tomentosa (PtSEP), and Mangifera indica (MiSEP3). Moreover, GbSEP contained a MADS-box and a K-box domain, typical conservative domains of plant MADS-box proteins. Phylogenetic tree clearly showed that GbSEP is classed into SEP subgroup of E class MADS-box protein family. Real-time PCR analysis revealed that expression levels of GbSEP gene in female and male flower were significantly higher than those in root, stem, and leaves, and that GbSEP expression level increased along with time of flower development, suggesting that GbSEP might be involved in development of reproductive organs in G. biloba.
Defining the paediatric nasopharyngeal microbiota during PCV13 vaccine implementation

During pneumococcal conjugate vaccine (PCV) implementation, studies have shown a reduction in the carriage of serotypes included within the vaccine and differing reports of the indirect effects this reduction has had on other species of the nasopharynx. This study contributes to and expands upon this area of work, focusing on PCV13 implementation, which occurred four years after the introduction of PCV7, and its effects on pneumococcal carriage and other species carried in the upper respiratory tract. Between 2006/07 and 2012/13, PCV13 vaccine serotypes reduced in carriage with a corresponding increase in PCV13 non-vaccine serotypes, and an overall consistent rate of pneumococcal carriage. No change in carriage rate was detected with any of the bacterial species detected at the species level. Carriage of H. influenzae was determined to be predominately non-typeable H. influenzae (NTHi) and demonstrated clonal diversity when typed using MLST. Antibiotic resistance genes were detected in around 10% of pneumococcal and H. influenzae isolates, with ermB and blaTEM genes being most prevalent for each species respectively. Virulence factor profiling resulted in matching gene profiles being ST dependent for S. pneumoniae and H. influenzae. Pneumococcal serotype 15A/ST63 has emerged as being a prevalent strain post-PCV13 with the propensity to contribute to both antibiotic resistance and virulence. This thesis provides information relevant to vaccine policy based on carriage data and expands upon the neglected area of using next generation sequencing and molecular characterisation of carriage isolates.
Design of periodic scheduling and control for networked systems under random data loss

This paper deals with Networked Control Systems (NCSs) whose shared networks have limited communication capacity and are prone to data losses. We assume that among (N) plants, only (M < N) plants can communicate with their controllers at any time instant. In addition, a control input, at any time instant, is lost in a channel with a probability (p). Our contributions are threefold. First, we identify necessary and sufficient conditions on the open-loop and closed-loop dynamics of the plants that ensure existence of purely time-dependent periodic scheduling sequences under which stability of each plant is preserved for all admissible data loss signals. Second, given the open-loop and closed-loop dynamics of the plants, relevant parameters of the shared network and a period for the scheduling sequence, we present an algorithm that verifies our stability conditions and if satisfied, designs stabilizing scheduling sequences. Otherwise, the algorithm reports non-existence of a stabilizing periodic scheduling sequence with the given period and stability margins. Third, given the plant matrices, the parameters of the network and a period for the scheduling sequence, we present an algorithm that designs static state-feedback controllers such that our stability conditions are satisfied. The main apparatus for our analysis is a switched systems representation of the individual plants in an NCS whose switching signals are time-inhomogeneous Markov chains. Our stability conditions rely on the existence of sets of symmetric and positive definite matrices that satisfy certain (in)equalities.
Egok360: A 360 Egocentric Kinetic Human Activity Video Dataset

Recently, there has been a growing interest in wearable sensors which provides new research perspectives for 360 ° video analysis. However, the lack of 360 ° datasets in literature hinders the research in this field. To bridge this gap, in this paper we propose a novel Egocentric (first-person) 360° Kinetic human activity video dataset (EgoK360). The EgoK360 dataset contains annotations of human activity with different sub-actions, e.g., activity Ping-Pong with four sub-actions which are pickup-ball, hit, bounce-ball and serve. To the best of our knowledge, EgoK360 is the first dataset in the domain of first-person activity recognition with a 360° environmental setup, which will facilitate the egocentric 360 ° video understanding. We provide experimental results and comprehensive analysis of variants of the two-stream network for 360 egocentric activity recognition. The EgoK360 dataset can be downloaded from https://egok360.github. io/.
Formation ofsulphidopeptide- leukotrienes bycell-cell interaction causescoronaryvasoconstriction inisolated, cell-perfused heart ofrabbit

1 We havestudied thetranscellular biosynthesis ofbioactive leukotrienes (LTs), generated upon blood cell-vascular wallinteractions andtheir functional consequences,inthespontaneously beating, cellperfused, heart oftherabbit. Rabbit isolated hearts wereperfused under recirculating conditions (50ml) with5x 106cells ofunpurified (buffy coat) orpurified humanneutrophils (PMNL),andchallenged with 0.5pM A23187for30min.Coronary perfusion pressure(CPP), heart rate(HR),left ventricular end-diastolic pressure(LVEDP)andleft ventricular pressure(LVP)were monitored continuously. Leukotriene formation was measured byspecific enzyme-immunoassay andconfirmed byreversed phase h.p.l.c. andu.v. spectral analysis. 2 Basal CPPvalues averaged 44± 1.4mmHg;A23187triggered a markedincrease inCPPbothinthe presenceofbuffy coatcells (+100%abovebasal) andPMNL (+270%abovebasal); thelatter change inCPP was accompanied bya rise inLVEDP (+138%abovebasal). 3 Theincrease inCPPwas preceded bya statistically significant rise iniLTC4-D4 concentration inthe circulating buffer. Pretreatment withtwostructurally unrelated LTD4receptor antagonists, LY171883 andSKF104353 (10fM), fully prevented theincrease inCPPandLVEDP.A similar protection was also observed whentherabbit heart was perfused withPMNL that hadbeenpretreated withMK886(1JLM), a potentinhibitor ofleukotriene biosynthesis. 4 Theincreased coronarytonewas accompanied byamarkedrelease oflactate dehydrogenase (LDH), a marker ofischaemic damage; pretreatment oftheheart withtheLTD4receptor antagonists aswell as ofthePMNL withMK886resulted ina complete suppression ofLDH activity release. 5 Positive identification ofLTC4-D4intheperfusates was obtained anda significant correlation observed between theCPPvalues andiLTC4-D4 concentrations. 6 Thisstudy suggests thatchallenge ofPMNL presentwithin thecoronary vasculature, causes a LTD4-dependent coronaryvasoconstriction, favoured byan efficient uptake ofPMNL-derived LTA4by endothelial cells. Theactivation ofthe5-lipoxygenase pathway inthecontextoftight interactions between bloodcells andcoronaryvasculature, issuggested tohavean important outcomeinthe alterations ofcoronaryflowandcardiac contractility.
What Is More Likely to Happen Next? Video-and-Language Future Event Prediction

Given a video with aligned dialogue, people can often infer what is more likely to happen next. Making such predictions requires not only a deep understanding of the rich dynamics underlying the video and dialogue, but also a significant amount of commonsense knowledge. In this work, we explore whether AI models are able to learn to make such multimodal commonsense next-event predictions. To support research in this direction, we collect a new dataset, named Video-and-Language Event Prediction (VLEP), with 28,726 future event prediction examples (along with their rationales) from 10,234 diverse TV Show and YouTube Lifestyle Vlog video clips. In order to promote the collection of non-trivial challenging examples, we employ an adversarial human-and-model-in-the-loop data collection procedure. We also present a strong baseline incorporating information from video, dialogue, and commonsense knowledge. Experiments show that each type of information is useful for this challenging task, and that compared to the high human performance on VLEP, our model provides a good starting point but leaves large room for future work. Our dataset and code are available at: this https URL
Improved predictive deep temporal neural networks with trend filtering

Forecasting with multivariate time series, which aims to predict future values given previous and current several univariate time series data, has been studied for decades, with one example being ARIMA. Because it is difficult to measure the extent to which noise is mixed with informative signals within rapidly fluctuating financial time series data, designing a good predictive model is not a simple task. Recently, many researchers have become interested in recurrent neural networks and attention-based neural networks, applying them in financial forecasting. There have been many attempts to utilize these methods for the capturing of long-term temporal dependencies and to select more important features in multivariate time series data in order to make accurate predictions. In this paper, we propose a new prediction framework based on deep neural networks and a trend filtering, which converts noisy time series data into a piecewise linear fashion. We reveal that the predictive performance of deep temporal neural networks improves when the training data is temporally processed by a trend filtering. To verify the effect of our framework, three deep temporal neural networks, state of the art models for predictions in time series finance data, are used and compared with models that contain trend filtering as an input feature. Extensive experiments on real-world multivariate time series data show that the proposed method is effective and significantly better than existing baseline methods.
PrivNet: Safeguarding Private Attributes in Transfer Learning for Recommendation

Transfer learning is an effective technique to improve a target recommender system with the knowledge from a source domain. Existing research focuses on the recommendation performance of the target domain while ignores the privacy leakage of the source domain. The transferred knowledge, however, may unintendedly leak private information of the source domain. For example, an attacker can accurately infer user demographics from their historical purchase provided by a source domain data owner. This paper addresses the above privacy-preserving issue by learning a privacy-aware neural representation by improving target performance while protecting source privacy. The key idea is to simulate the attacks during the training for protecting unseen users’ privacy in the future, modeled by an adversarial game, so that the transfer learning model becomes robust to attacks. Experiments show that the proposed PrivNet model can successfully disentangle the knowledge benefitting the transfer from leaking the privacy.
Predicting Biomedical Interactions with Higher-Order Graph Convolutional Networks

Biomedical interaction networks have incredible potential to be useful in the prediction of biologically meaningful interactions, identification of network biomarkers of disease, and the discovery of putative drug targets. Recently, graph neural networks have been proposed to effectively learn representations for biomedical entities and achieved state-of-the-art results in biomedical interaction prediction. These methods only consider information from immediate neighbors but cannot learn a general mixing of features from neighbors at various distances. In this paper, we present a higher-order graph convolutional network (HOGCN)to aggregate information from the higher-order neighborhood for biomedical interaction prediction. Specifically, HOGCN collects feature representations of neighbors at various distances and learns their linear mixing to obtain informative representations of biomedical entities. Experiments on four interaction networks, including protein-protein, drug-drug, drug-target, and gene-disease interactions, show that HOGCN achieves more accurate and calibrated predictions. HOGCN performs well on noisy, sparse interaction networks when feature representations of neighbors at various distances are considered. Moreover, a set of novel interaction predictions are validated by literature-based case studies.
Who's in the Gang? Revealing Coordinating Communities in Social Media

Political astroturfing and organised trolling are online malicious behaviours with significant real-world effects. Common approaches examining these phenomena focus on broad campaigns rather than the small groups responsible. To reveal networks of cooperating accounts, we propose a novel temporal window approach that relies on account interactions and metadata alone. It detects groups of accounts engaging in behaviours that, in concert, execute different goal-based strategies, which we describe. Our approach is validated against two relevant datasets with ground truth data. See https://github.com/weberdc/find_hccs for code and data.