Diurnal Evolution of Urban Heat Island and Its Impact on Air Quality by Using Ground Observations (SAFAR) over New Delhi

The paper presents a study of urban heat island (UHI) intensity and its impact on air quality by using the System of Air Quality Forecasting and Research (SAFAR) network observations over Delhi during the clear sky month of December of 2013 and 2015. It is found that in the month of December 2013 and 2015 the UHI shows a peak in late evening around 20:00 hrs. The concentration of PM2.5 shows a bimodal peak in the month of December of both the years 2013 and 2015 which is due to the enhanced anthropogenic activity during the traffic hours. The formation of UHI during the late evening traffic hours is due to the enhancement in the concentration of PM2.5 due to the enhanced anthropogenic activity with higher ground heat flux and lower PBLH and wind speed which leads to both the years 2013 and 2015 during the month of December. It is also found that UHI intensity shows a positive correlation (r = 0.57) with PM2.5 concentration and a negative correlation (r = -0.40) with wind speed and the PM2.5 concentration also shows a negative correlation (r = -0.57) with wind speed during December 2013. Whereas during December 2015 it has found that UHI intensity has a positive correlation (r = 0.65) with PM2.5 concentration and a negative correlation (r = -0.45) with wind speed and the PM2.5 concentration also shows a negative correlation (r = -0.57) with wind speed

Seasonal variation of urban heat island and its impact on air-quality using SAFAR observations at Delhi, India

This paper discussed the urban heat island (UHI) intensity and local air quality by using observational data of project of the System of Air Quality Forecasting and Research (SAFAR) over Delhi during the month of May and December 2013. It is found that UHI magnitudes ~2.2°C and ~1.5°C are formed at the evening traffic hours during May and December respectively. Also, intensity of UHI < 0°C over daytime is referred as Urban Cool Island (UCI) during May and December. The diurnal PM2.5 concentration shows a bimodal pattern with peaks at morning and evening traffic hours during May and December. The planetary boundary layer height (PBLH) values show higher in magnitude during the daytime and lower in magnitude during the night-time. Whereas, the Ground Heat Flux values are lower during the daytime and higher during the night-time. The wind speed shows lower values during the UHI and higher magnitudes during the UCI formation hours. Concentration of PM2.5 and wind speed shows a strong negative correlation during May (r = -0.56, p = 0.002) and December (r = -0.57, p = 0.001) at C V Raman (CVR) site, however, high values in the concentration of PM2.5 during the low wind speed favour the condition for the formation of UCI. The regression analysis indicated that PM2.5 plays a significant role in the daytime cooling and nighttime warming over the urban areas during the low wind speed condition

Exploiting attention for visual relationship detection

Visual relationship detection targets on predicting categories of predicates and object pairs, and also locating the object pairs. Recognizing the relationships between individual objects is important for describing visual scenes in static images. In this paper, we propose a novel end-to-end framework on the visual relationship detection task. First, we design a spatial attention model for specializing predicate features. Compared to a normal ROI-pooling layer, this structure significantly improves Predicate Classification performance. Second, for extracting relative spatial configuration, we propose to map simple geometric representations to a high dimension, which boosts relationship detection accuracy. Third, we implement a feature embedding model with a bi-directional RNN which considers subject, predicate and object as a time sequence. We evaluate our method on three tasks. The experiments demonstrate that our method achieves competitive results compared to state-of-the-art methods.</p

A Collection of Single-Domain Antibodies that Crowd Ricin Toxin’s Active Site

This work is licensed under a Creative Commons Attribution 4.0 International License.In this report, we used hydrogen exchange-mass spectrometry (HX-MS) to identify the epitopes recognized by 21 single-domain camelid antibodies (VHHs) directed against the ribosome-inactivating subunit (RTA) of ricin toxin, a biothreat agent of concern to military and public health authorities. The VHHs, which derive from 11 different B-cell lineages, were binned together based on competition ELISAs with IB2, a monoclonal antibody that defines a toxin-neutralizing hotspot (“cluster 3”) located in close proximity to RTA’s active site. HX-MS analysis revealed that the 21 VHHs recognized four distinct epitope subclusters (3.1–3.4). Sixteen of the 21 VHHs grouped within subcluster 3.1 and engage RTA α-helices C and G. Three VHHs grouped within subcluster 3.2, encompassing α-helices C and G, plus α-helix B. The single VHH in subcluster 3.3 engaged RTA α-helices B and G, while the epitope of the sole VHH defining subcluster 3.4 encompassed α-helices C and E, and β-strand h. Modeling these epitopes on the surface of RTA predicts that the 20 VHHs within subclusters 3.1–3.3 physically occlude RTA’s active site cleft, while the single antibody in subcluster 3.4 associates on the active site’s upper rim.National Institutes of Allergy and Infectious Diseases, National Institutes of Health (HHSN272201400021C

NODIS: Neural Ordinary Differential Scene Understanding

Semantic image understanding is a challenging topic in computer vision. It requires to detect all objects in an image, but also to identify all the relations between them. Detected objects, their labels and the discovered relations can be used to construct a scene graph which provides an abstract semantic interpretation of an image. In previous works, relations were identified by solving an assignment problem formulated as Mixed-Integer Linear Programs. In this work, we interpret that formulation as Ordinary Differential Equation (ODE). The proposed architecture performs scene graph inference by solving a neural variant of an ODE by end-to-end learning. It achieves state-of-the-art results on all three benchmark tasks: scene graph generation (SGGen), classification (SGCls) and visual relationship detection (PredCls) on Visual Genome benchmark

LNCS

Imprecision in timing can sometimes be beneficial: Metric interval temporal logic (MITL), disabling the expression of punctuality constraints, was shown to translate to timed automata, yielding an elementary decision procedure. We show how this principle extends to other forms of dense-time specification using regular expressions. By providing a clean, automaton-based formal framework for non-punctual languages, we are able to recover and extend several results in timed systems. Metric interval regular expressions (MIRE) are introduced, providing regular expressions with non-singular duration constraints. We obtain that MIRE are expressively complete relative to a class of one-clock timed automata, which can be determinized using additional clocks. Metric interval dynamic logic (MIDL) is then defined using MIRE as temporal modalities. We show that MIDL generalizes known extensions of MITL, while translating to timed automata at comparable cost

Metabolic adaptation drives arsenic trioxide resistance in acute promyelocytic leukemia.

Acquired genetic mutations can confer resistance to arsenic trioxide (ATO) in the treatment of acute promyelocytic leukemia (APL). However, such resistance-conferring mutations are rare and do not explain most disease recurrence seen in the clinic. We have generated stable ATO-resistant promyelocytic cell lines that are also less sensitive to ATRA and the combination of ATO and ATRA compared to the sensitive cell line. Characterization of these in-house generated resistant cell lines showed significant differences in immunophenotype, drug transporter expression, anti-apoptotic protein dependence, and PML-RARA mutation. Gene expression profiling revealed prominent dysregulation of the cellular metabolic pathways in these ATO resistant APL cell lines. Glycolytic inhibition by 2-DG was sufficient and comparable to the standard of care (ATO) in targeting the sensitive APL cell line. 2-DG was also effective in the in vivo transplantable APL mouse model; however, it did not affect the ATO resistant cell lines. In contrast, the resistant cell lines were significantly affected by compounds targeting the mitochondrial respiration when combined with ATO, irrespective of the ATO resistance-conferring genetic mutations or the pattern of their anti-apoptotic protein dependency. Our data demonstrate that the addition of mitocans in combination with ATO can overcome ATO resistance. We further show that this combination has the potential in the treatment of non-M3 AML and relapsed APL. The translation of this approach in the clinic needs to be explored further

Cure of Chronic Viral Infection and Virus-Induced Type 1 Diabetes by Neutralizing Antibodies

The use of neutralizing antibodies is one of the most successful methods to interfere with receptor–ligand interactions in vivo. In particular blockade of soluble inflammatory mediators or their corresponding cellular receptors was proven an effective way to regulate inflammation and/or prevent its negative consequences. However, one problem that comes along with an effective neutralization of inflammatory mediators is the general systemic immunomodulatory effect. It is, therefore, important to design a treatment regimen in a way to strike at the right place and at the right time in order to achieve maximal effects with minimal duration of immunosuppression or hyperactivation. In this review, we reflect on two examples of how short time administration of such neutralizing antibodies can block two distinct inflammatory consequences of viral infection. First, we review recent findings that blockade of IL-10/IL-10R interaction can resolve chronic viral infection and second, we reflect on how neutralization of the chemokine CXCL10 can abrogate virus-induced type 1 diabetes

Combined artificial bee colony algorithm and machine learning techniques for prediction of online consumer repurchase intention

A novel paradigm in the service sector i.e. services through the web is a progressive mechanism for rendering offerings over diverse environments. Internet provides huge opportunities for companies to provide personalized online services to their customers. But prompt novel web services introduction may unfavorably affect the quality and user gratification. Subsequently, prediction of the consumer intention is of supreme importance in selecting the web services for an application. The aim of study is to predict online consumer repurchase intention and to achieve this objective a hybrid approach which a combination of machine learning techniques and Artificial Bee Colony (ABC) algorithm has been used. The study is divided into three phases. Initially, shopping mall and consumer characteristic’s for repurchase intention has been identified through extensive literature review. Secondly, ABC has been used to determine the feature selection of consumers’ characteristics and shopping malls’ attributes (with > 0.1 threshold value) for the prediction model. Finally, validation using K-fold cross has been employed to measure the best classification model robustness. The classification models viz., Decision Trees (C5.0), AdaBoost, Random Forest (RF), Support Vector Machine (SVM) and Neural Network (NN), are utilized for prediction of consumer purchase intention. Performance evaluation of identified models on training-testing partitions (70-30%) of the data set, shows that AdaBoost method outperforms other classification models with sensitivity and accuracy of 0.95 and 97.58% respectively, on testing data set. This study is a revolutionary attempt that considers both, shopping mall and consumer characteristics in examine the consumer purchase intention.N/

Mechanism of cellular rejection in transplantation

The explosion of new discoveries in the field of immunology has provided new insights into mechanisms that promote an immune response directed against a transplanted organ. Central to the allograft response are T lymphocytes. This review summarizes the current literature on allorecognition, costimulation, memory T cells, T cell migration, and their role in both acute and chronic graft destruction. An in depth understanding of the cellular mechanisms that result in both acute and chronic allograft rejection will provide new strategies and targeted therapeutics capable of inducing long-lasting, allograft-specific tolerance