Gas-sensitive properties of thin film heterojunction structures based on Fe2O3-In2O3 nanocomposites

This paper reports an investigation of the gas-sensitive properties of thin film based on the double-layers Fe2O3/In2O3 and Fe2O3-In2O3/In2O3 towards gases with different chemical nature (C2H5OH, CH4, CO, NH3, NO2, O3). As it was found, the -Fe2O3-In2O3 composite (Fe:In = 9:1) is more sensitive to O3; on the contrary, the -Fe2O3-In2O3 system (9:1), possesses an higher sensitivity to NO2. The optimal temperature for detecting both gases is in the range 70 - 100C. Sensors based on the -Fe2O3/In2O3 heterostructure show the maximum response to C2H5OH at considerably higher temperatures (250-300C), but this layer is practically insensitive to other reducing gases like CH4, CO and NH3 in the same temperature range. An explanation of the different gas-sensitive behavior for the these samples resulted from the particular features of their structure and phase stat

Predictability improvement of Scheduled Flights Departure Time Variation using Supervised Machine Learning

The departure time uncertainty exacerbates the inaccuracy of arrival time estimation and demand for arrival slots, particularly for movements to capacity constrained airports. The Estimated Take-Off Time (ETOT) or Estimated Departure Time(ETD) for each individual flight is currently derived from Air Traffic Flow Management System (ATFMS), which are solely determined based on individual flight plan Estimated Off Block Time(EOBT) or subsequent delays updated by Airline. Even if normal weather conditions prevail, aircraft departure times will differ from ETOTs determined by the ATFMS due to a number of factors such as congestion, early/delayed inbound flight (linked flights), reactionary delays and air traffic flow management slot changes. This paper presents a model that predicts departure time variance based on the previous leg departure time using a combination of exponential moving average and machine learning methods. The model correctly classifies the departure time (Early, On Time, Delay) based on the previous leg departure state, allowing the ATFM system to measure the arrival time of a capacity constrained airport with greater accuracy and better assess demand requirements. The results show that the proposed model with M5P Regression tree provides the best results, with Mean Absolute Error and Root Mean Square Error (RMSE) of 3.43 and 4.83, respectively, indicating a 50% improvement over previous research findings. Whereas, with logistic regression, the classification of departure time (Early, On Time, Delay) is achieved a better accuracy of 91 %, which is higher than previous works

Prediction of Gate In Time of Scheduled Flights and Schedule Conformance using Machine Learning-based Algorithms

Prediction of Gate to Gate block time for scheduled flights is considered as one of the challenging tasks in Air Traffic Flow Management (ATFM)system. Establishing an effective and practically reliable model to manage the problem of block time variation is a significant work. The airlines do tend to pad or inflate block time to Actual Block time to calculate Schedule block times which is approved by aviation regulator. This will lead to flaws in air traffic flow strategic decision-making and in turn affect the efficiency, estimation and undesirable delays, which leads to traffic congestion and inefficient ground delay programs. This study evaluates the effectiveness of nonlinear and time varying regression models to predict block time with minimal attributes in order to solve the problem of difficulty in predicting the block time variation. The key research outcome of this paper is to trace the temporal variations of flying time for different aircraft types and to predict the variation of actual arrival time from the scheduled arrival time at the destination airport. Ultimately, a combination of M5P regression model and logistic regression model is proposed to predict early, delayed and on-time conformity with approved schedules. Analysis based on a realistic data set of a domestic airport pair (Mumbai International Airport and New Delhi International Airport) in India shows that the proposed model is able to predict in block time at the time of departure with an accuracy of minutes for of test instances. As a result of the scheduled arrival time performance (early, delayed and timely) has been classified accurately using Logistic regression Classifier of machine learning. The test results show that the proposed model uses a minimum number of attributes and less computational time to more accurately predict the actual arrival time and scheduled arrival performance without details on the weather

Characterization of human T cell receptor repertoire data in eight thymus samples and four related blood samples

T cell receptor (TCR) is a heterodimer consisting of TCR alpha and TCR beta chains that are generated by somatic recombination of multiple gene segments. Nascent TCR repertoire undergoes thymic selections where non-functional and potentially autoreactive receptors are removed. During the last years, the development of high-throughput sequencing technology has allowed a large scale assessment of TCR repertoire and multiple analysis tools are now also available. In our recent manuscript, Human thymic T cell repertoire is imprinted with strong convergence to shared sequences [1], we show highly overlapping thymic TCR repertoires in unrelated individuals. In the current Data in Brief article, we provide a more detailed characterization of the basic features of these thymic and related peripheral blood TCR repertoires. The thymus samples were collected from eight infants undergoing corrective cardiac surgery, two of whom were monozygous twins [2]. In parallel with the surgery, a small aliquot of peripheral blood was drawn from four of the donors. Genomic DNA was extracted from mechanically released thymocytes and circulating leukocytes. The sequencing of TCR alpha and TCR beta repertoires was performed at ImmunoSEQ platform (Adaptive Biotechnologies). The obtained repertoire data were analysed applying relevant features from immunoSEQ (R) 3.0 Analyzer (Adaptive Biotechnologies) and a freely available VDJTools software package for programming language R [3]. The current data analysis displays the basic features of the sequenced repertoires including observed TCR diversity, various descriptive TCR diversity measures, and V and J gene usage. In addition, multiple methods to calculate repertoire overlap between two individuals are applied. The raw sequence data provide a large database of reference TCRs in healthy individuals at an early developmental stage. The data can be exploited to improve existing computational models on TCR repertoire behaviour as well as in the generation of new models. (C) 2021 The Authors. Published by Elsevier Inc.Peer reviewe

Human thymic T cell repertoire is imprinted with strong convergence to shared sequences

A highly diverse repertoire of T cell antigen receptors (TCR) is created in the thymus by recombination of gene segments and the insertion or deletion of nucleotides at the junctions. Using next-generation TCR sequencing we define here the features of recombination and selection in the human TCR alpha and TCR beta locus, and show that a strikingly high proportion of the repertoire is shared by unrelated individuals. The thymic TCRa nucleotide repertoire was more diverse than TCR beta, with 4.1 x 10(6) vs. 0.81 x 10(6) unique clonotypes, and contained nonproductive clonotypes at a higher frequency (69.2% vs. 21.2%). The convergence of distinct nucleotide clonotypes to the same amino acid sequences was higher in TCRa than in TCR beta repertoire (1.45 vs. 1.06 nucleotide sequences per amino acid sequence in thymus). The gene segment usage was biased, and generally all individuals favored the same genes in both TCR alpha and TCR beta loci. Despite the high diversity, a large fraction of the repertoire was found in more than one donor. The shared fraction was bigger in TCR alpha than TCR beta repertoire, and more common in in-frame sequences than in nonproductive sequences. Thus, both biases in rearrangement and thymic selection are likely to contribute to the generation of shared repertoire in humans.Peer reviewe

Hydrogen Through Water Electrolysis and Biomass Gasification for Application in Fuel Cells

Hydrogen is considered to be one of the most promising green energy carrier in the energy storage and conversion scenario. Although it is abundant on Earth in the form of compounds, its occurrence in free form is extremely low. Thus, it has to be produced by reforming processes, steam reforming (SR), partial oxidation (POX) and auto-thermal reforming (ATR) mainly from fossil fuels for high throughput with high energy requirements, pyrolysis of biomass and electrolysis. Electrolysis is brought about by passing electric current though two electrodes to evolve water into its constituent parts, viz. hydrogen and oxygen, respectively. Hydrogen produced by non-noble metal catalysts for both anode and cathode is therefore cost-effective and can be integrated into fuel cells for direct chemical energy conversion into electrical energy electricity, thus meeting the sustainable and renewable use with low carbon footprint

Comparative distribution of human and avian type sialic acid influenza receptors in the pig

<p>Abstract</p> <p>Background</p> <p>A major determinant of influenza infection is the presence of virus receptors on susceptible host cells to which the viral haemagglutinin is able to bind. Avian viruses preferentially bind to sialic acid α2,3-galactose (SAα2,3-Gal) linked receptors, whereas human strains bind to sialic acid α2,6-galactose (SAα2,6-Gal) linked receptors. To date, there has been no detailed account published on the distribution of SA receptors in the pig, a model host that is susceptible to avian and human influenza subtypes, thus with potential for virus reassortment. We examined the relative expression and spatial distribution of SAα2,3-GalG(1-3)GalNAc and SAα2,6-Gal receptors in the major organs from normal post-weaned pigs by binding with lectins <it>Maackia amurensis agglutinins </it>(MAA II) and <it>Sambucus nigra agglutinin </it>(SNA) respectively.</p> <p>Results</p> <p>Both SAα2,3-Gal and SAα2,6-Gal receptors were extensively detected in the major porcine organs examined (trachea, lung, liver, kidney, spleen, heart, skeletal muscle, cerebrum, small intestine and colon). Furthermore, distribution of both SA receptors in the pig respiratory tract closely resembled the published data of the human tract. Similar expression patterns of SA receptors between pig and human in other major organs were found, with exception of the intestinal tract. Unlike the limited reports on the scarcity of influenza receptors in human intestines, we found increasing presence of SAα2,3-Gal and SAα2,6-Gal receptors from duodenum to colon in the pig.</p> <p>Conclusions</p> <p>The extensive presence of SAα2,3-Gal and SAα2,6-Gal receptors in the major organs examined suggests that each major organ may be permissive to influenza virus entry or infection. The high similarity of SA expression patterns between pig and human, in particular in the respiratory tract, suggests that pigs are not more likely to be potential hosts for virus reassortment than humans. Our finding of relative abundance of SA receptors in the pig intestines highlights a need for clarification on the presence of SA receptors in the human intestinal tract.</p

Hyperthermia induced by transient receptor potential vanilloid-1 (TRPV1) antagonists in human clinical trials: Insights from mathematical modeling and meta-analysis

Antagonists of the transient receptor potential vanilloid-1 (TRPV1) channel alter body temperature (Tb) in laboratory animals and humans: most cause hyperthermia; some produce hypothermia; and yet others have no effect. TRPV1 can be activated by capsaicin (CAP), protons (low pH), and heat. First-generation (polymodal) TRPV1 antagonists potently block all three TRPV1 activation modes. Second-generation (mode-selective) TRPV1 antagonists potently block channel activation by CAP, but exert different effects (e.g., potentiation, no effect, or low-potency inhibition) in the proton mode, heat mode, or both. Based on our earlier studies in rats, only one mode of TRPV1 activation - by protons - is involved in thermoregulatory responses to TRPV1 antagonists. In rats, compounds that potently block, potentiate, or have no effect on proton activation cause hyperthermia, hypothermia, or no effect on Tb, respectively. A Tb response occurs when a TRPV1 antagonist blocks (in case of hyperthermia) or potentiates (hypothermia) the tonic TRPV1 activation by protons somewhere in the trunk, perhaps in muscles, and - via the acido-antithermogenic and acido-antivasoconstrictor reflexes - modulates thermogenesis and skin vasoconstriction. In this work, we used a mathematical model to analyze Tb data from human clinical trials of TRPV1 antagonists. The analysis suggests that, in humans, the hyperthermic effect depends on the antagonist's potency to block TRPV1 activation not only by protons, but also by heat, while the CAP activation mode is uninvolved. Whereas in rats TRPV1 drives thermoeffectors by mediating pH signals from the trunk, but not Tb signals, our analysis suggests that TRPV1 mediates both pH and thermal signals driving thermoregulation in humans. Hence, in humans (but not in rats), TRPV1 is likely to serve as a thermosensor of the thermoregulation system. We also conducted a meta-analysis of Tb data from human trials and found that polymodal TRPV1 antagonists (ABT-102, AZD1386, and V116517) increase Tb, whereas the mode-selective blocker NEO6860 does not. Several strategies of harnessing the thermoregulatory effects of TRPV1 antagonists in humans are discussed

Rapid death of duck cells infected with influenza: a potential mechanism for host resistance to H5N1

Aquatic birds are the natural reservoir for most subtypes of influenza A, and a source of novel viruses with the potential to cause human pandemics, fatal zoonotic disease or devastating epizootics in poultry. It is well recognised that waterfowl typically show few clinical signs following influenza A infection, in contrast, terrestrial poultry such as chickens may develop severe disease with rapid death following infection with highly pathogenic avian influenza. This study examined the cellular response to influenza infection in primary cells derived from resistant (duck) and susceptible (chicken) avian hosts. Paradoxically, we observed that duck cells underwent rapid cell death following infection with low pathogenic avian H2N3, classical swine H1N1 and ‘classical' highly pathogenic H5N1 viruses. Dying cells showed morphological features of apoptosis, increased DNA fragmentation and activation of caspase 3/7. Following infection of chicken cells, cell death occurred less rapidly, accompanied by reduced DNA fragmentation and caspase activation. Duck cells produced similar levels of viral RNA but less infectious virus, in comparison with chicken cells. Such rapid cell death was not observed in duck cells infected with a contemporary Eurasian lineage H5N1 fatal to ducks. The induction of rapid death in duck cells may be part of a mechanism of host resistance to influenza A, with the loss of this response leading to increased susceptibility to emergent strains of H5N1. These studies provide novel insights that should help resolve the long-standing enigma of host–pathogen relationships for highly pathogenic and zoonotic avian influenza