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PURPOSE We present 5-year results from CheckMate 227 Part 1, in which nivolumab plus ipilimumab improved overall survival (OS) versus chemotherapy in patients with metastatic non–small-cell lung cancer, regardless of tumor programmed death ligand 1 (PD-L1) status. METHODS Adults with stage IV/recurrent non–small-cell lung cancer without EGFR mutations or ALK alterations and with tumor PD-L1 ≥ 1% or < 1% (n = 1739) were randomly assigned. Patients with tumor PD-L1 ≥ 1% were randomly assigned to first-line nivolumab plus ipilimumab, nivolumab alone, or chemotherapy. Patients with tumor PD-L1 < 1% were randomly assigned to nivolumab plus ipilimumab, nivolumab plus chemotherapy, or chemotherapy. End points included exploratory 5-year results for efficacy, safety, and quality of life. RESULTS At a minimum follow-up of 61.3 months, 5-year OS rates were 24% versus 14% for nivolumab plus ipilimumab versus chemotherapy (PD-L1 ≥ 1%) and 19% versus 7% (PD-L1 < 1%). The median duration of response was 24.5 versus 6.7 months (PD-L1 ≥ 1%) and 19.4 versus 4.8 months (PD-L1 < 1%). Among patients surviving 5 years, 66% (PD-L1 ≥ 1%) and 64% (PD-L1 < 1%) were off nivolumab plus ipilimumab without initiating subsequent systemic anticancer treatment by the 5-year time point. Survival benefit continued after nivolumab plus ipilimumab discontinuation because of treatment-related adverse events, with a 5-year OS rate of 39% (combined PD-L1 ≥ 1% and < 1% populations). Quality of life in 5-year survivors treated with nivolumab plus ipilimumab was similar to that in the general US population through the 5-year follow-up. No new safety signals were observed. CONCLUSION With all patients off immunotherapy treatment for ≥ 3 years, nivolumab plus ipilimumab increased 5-year survivorship versus chemotherapy, including long-term, durable clinical benefit regardless of tumor PD-L1 expression. These data support nivolumab plus ipilimumab as an effective first-line treatment for patients with metastatic non–small-cell lung cancer. [Media: see text]

Species belonging to the genus are ubiquitous in nature and many of them form the normal microbial flora in human and animal bodies. A wide variety of infections on mucosal surfaces under certain conditions can be caused due to the sp: A total of 40 isolates of Candida, isolated from vaginal swabs among 476 patients with clinically suspected VVC. Antifungal susceptibility testing done using Fluconazole and Voriconazole. Virulence markers Phospholipase activity, Proteinase activity, Biofilm Productionwere detected by standard methods.: Out of 40 isolates, the following species were isolated, (52.5%) was the most common species isolated from vaginal swabs. The species isolated were (27.5%), (15%), (5%).Intense phospholipase activity was observed in 12 (30%) samples, there was no activity in 18 (45%) samples. A moderate phospholipase activity was observed in 6 (15%) isolates, poor activity in 4 (10%) samples.Proteinase activity was strongly positive in 33 isolates which was identified as and. Strong biofilm formation was observed in 9 isolates, moderate in 8, weak in 11 and negative in 12 isolates. All 40 isolates are susceptible to Fluconazole and Voriconazole.From this study it is observed that and are the major species of causing VVC. Virulence marker analysis such as that for phospholipase, proteinase and biofilm formation will help to analyse the pathogenicity and further research required to elucidate the mechanisms involved in these pathways.

Brain tumors are caused by abnormal cells developing in the human brain. The incidence of malignant brain tumors is relatively high and significantly influences humans and society. Magnetic Resonance Imaging (MRI) is an excellent non-invasive technique that produces high-quality brain images without damage. And it makes an adequate diagnosis and is considered the primary technical treatment. This type of method of tumor identification has some problems, there are less efficient for complex tumor stages and increases computation time, and segmentation is an inaccurate and unreliable result. To tackle this problem, this paper proposes Absolute Structure Threshold Segmentation Technique (ASTST) based on Deep Belief Convolution Neural Classifier (DBCNC) using Softmax activation function for brain tumor classification. The proposed method initially starts with the preprocessing step supported by completing the Gaussian and Bilateral Filter(GBF) using the brain images to remove the Noise, enhance the image size and color contrast level and enhance the frequency of the images to find the tumor-affected area. After preprocessing image is trained into Absolute Gabor with Canny Edge Selection (AGCES) technique to identify the edges without affecting the image quality. Then the Similarity Scaling Shapes Feature Selection (S3FS) method is used to analyze the most delicate features of brain tumors relatively to find the dimension to improve the accuracy. Based on the feature selection, the proposed DBCNC algorithm classifies the brain tumor as malignant or Normal. The proposed method improves prediction accuracy, sensitivity, specificity, and f-measure and minimizes time complexity and false rate. &nbsp

We report enantioselective one-carbon ring expansion of aziridines to make azetidines as a new-to-nature activity of engineered “carbene transferase” enzymes. A laboratory-evolved variant of cytochrome P450_(BM3), P411-AzetS, not only exerts unparalleled stereocontrol (99:1 er) over a [1,2]-Stevens rearrangement but also overrides the inherent reactivity of aziridinium ylides, cheletropic extrusion of olefins, to perform a [1,2]-Stevens rearrangement. By controlling the fate of the highly reactive aziridinium ylide intermediates, these evolvable biocatalysts promote a transformation which cannot currently be performed using other catalyst classes.

Objective: Conventional cervical pedicle screw insertion necessitates extensive paraspinal muscle dissection and retraction in order to achieve the lateral to medial angulation needed to achieve the optimal screw trajectory. Minimally invasive transmuscular approach can comfortably achieve this angulation without significant injury to the midline structures and its musculo-ligamentous attachments.Methods: Minimally invasive cervical pedicle screws were inserted in 4 fresh frozen cadaveric specimens. Pre-procedure and post-procedure CT scans were done to assess the pedicle dimensions, suitability for screw insertion and integrity of the screws. The same technique was applied in a clinical cohort of six cases – 3 cases of traumatic subluxation; one case of traumatic vertebral fracture and 2 cases of infective facet destruction (Koch’s); with successful outcomes.Results: Among the 38 screws in the cadaver specimens, a total of 11 screws (28.9%) had breached the pedicle wall (Lateral wall breach–9; Medial wall breach–2). Of the 9 screws (23.6%) that had a lateral breach into the vertebral canal, 4 (10.5%) each had Grade IIa breach and one (2.6%) had Grade III breach. The mean transverse screw angle in the subset of pedicle screws with no breach was 34.1 whereas it was 22.1 in screws that had lateral breach and 43.6 in those with medial breach. Among the 22 screws inserted in the clinical cohort of 6 cases, 4 screws (18.1%) had breached the pedicle wall. All the identified breaches were in the lateral wall (Grade IIa – 3; Grade IIb–1; Grade III–nil). Conclusion: Minimally invasive subaxial pedicle screw insertion can be utilized in cases where a robust posterior fixation is required, either in isolation or as an adjunct to anterior surgery, in cases where a direct posterior decompression is not warranted. It is a safe and effective approach which minimizes injury to the paraspinal structures and midline attachments.

The most prominent challenges in compressive sensing are seeking the domain where an image is represented sparsely and hence be faithfully recovered to obtain high-quality results. This paper introduces an approach for image compression and recovery. The proposed approach involves two phases: the initial step is the compression phase, and the second step is the recovery phase. Initially, the medical image is subjected to the compression module wherein the self-similarity and the 3-dimensional (3D) transform are adapted for compressing the image. Then, in the recovery phase, the compressive sensing recovery is performed based on structural similarity index measure (SSIM)-based collaborative sparsity measure (S-CoSM), and the novel optimization algorithm, named Taylor-based Sunflower optimization (Taylor-SFO) algorithm. An effective S-CoSM measure is designed by modifying the CoSM using the SSIM metric. The proposed Taylor-SFO will be designed by integrating the Taylor series with the sunflower optimization (SFO) algorithm. The performance of the proposed Taylor-SFO approach is evaluated for matrices SSIM of 0.9412 and peak signal to noise ratio of 57.57 dB.

For this study, functional precoalescence (internally crosslinked) and postcoalescence (crosslinkable) conventional latexes were made using seeded semi-continuous emulsion polymerization. Nanosize polymer latexes with or without crosslinkable functional groups were made using a modified microemulsion copolymerization process. Films cast from conventional and nanosize latex were characterized using specific end use tests and fundamental properties using dynamic mechanical analysis, modulated differential scanning calorimetry, and atomic force microscopy. This study compares conventional and nanosize latex with respect to the effect of the type and level of crosslinking, particle size and distribution on latex film formation and morphology, and end use and fundamental properties. Nanosize latex films in general have superior gloss, solvent resistance, and adhesion but inferior water resistance. Stress–strain Young’s modulus showed an increase as a function of increasing crosslinker level in the nanosize latex films.

Drugs are the critical elements in the health care system. They must be manufactured in the high-quality levels. End product testing by itself does not guarantee of the quality of the product. Quality assurance techniques must be used. In pharmaceutical industry, process validation performs this task, by ensuring that the process does what it purports to do. Validation is one of the important steps in achieving and maintaining the quality of the final product. If each step of production process is validated, we can assure that the final product is of the best quality. This study is intended to demonstrate and standardize the data that should be routinely included in the marketing authorization dossier describing evolution and validation of the manufacturing process and distinguish from the validation data which more properly fall under the remit of GMP inspection. During the study of critical process variables of sucralfate oral suspension were validated to demonstrate consistency of manufacturing process to produce the produce the product of desired quality. The validation studies were conducted which were intended for the use of commercial purpose so this validation study is concurrent type. All the in-process variables and finished product characteristics were monitored; the statistical analysis of data was carried out. Further from the results, it is inferred that the manufacturing process of sucralfate oral suspension was validated.

Results of our research on crosslinked latexes and polymer nanoparticles will be reported. Goals of this long-term program have been: comparing precoalescence and post-coalescence crosslinking; synthesizing polymer nanoparticle latexes (~ 20 to 25 nm) from a variety of acrylic monomers, including crosslinking and crosslinkable monomers; and blending nanoparticle latexes with compatible conventional (~ 120 to 130 nm) latexes and understanding their effects on film formation and on fundamental and empirical film properties. As we previously reported, blending conventional and nanolatexes in an 85/15 ratio (w/w of polymer solids) effects substantial changes of fundamental film properties. Best results included reducing minimum filming temperatures (MFT) while increasing Young’s moduli by factors of two to 17. Substantial improvements in empirical film properties such as gloss (up to 97 at 60°), hardness (increased by 2–5 pencils), block resistance, and adhesion to aluminum were also found. The previous report covered only a single blend ratio. Here we report studies of blends with ratios of conventional to nanoparticle latexes ranging from 92.5/7.5 to 30/70 (w/w). As before, the latexes and nanolatexes are BMA/BA copolymers with 0–4 wt.% of 1,3-butylene glycol dimethacrylate (precoalescence crosslinker) and 0–5 wt.% of diacetone acrylamide (for post-coalescence crosslinking with adipic dihydrazide). Films cast from these blends are subjected to everyday empirical coatings tests and are characterized using instrumental tests including dynamic mechanical analysis (DMA) and modulated differential scanning calorimetry (MDSC). Film formation and film morphology are studied using atomic force microscopy (AFM). The results confirm that blending nanoparticles at ratios up to 50/50 (w/w of polymer solids) can substantially improve certain properties of conventional latex films. For many properties, the optimum ratio is around 85/15 conventional/nano; a 92.5/7.5 ratio also affords substantial improvements. As coatings, the blends have near-zero VOC.

Heat treatment of products post additive manufacture are considered hugely important since the metallurgical condition post process is suboptimal. In the case of nickel-based superalloys, grain size, precipitate distribution and precipitate size are distinct from wrought equivalents. Appropriate heat treatment is required to ensure material performance. In this study, LPBF alloy 718, post-processed using a standard heat treatment, is explored under thermal and thermomechanical exposure conditions (with and without applied stress) to illustrate textural and microstructural evolution. The results show the instability of the LPBF microstructure in terms of grain size, precipitate density, and crystallographic orientation, illustrating the need for an appropriate heat treatment in relation to future service conditions. During thermal exposure only, the instability of the LPBF alloy microstructure was evident as the texture increased with time before decreasing and almost disappearing at the time of fracture. This contrasts with wrought alloy whose texture increases throughout creep testing and reaches a maximum at the time of fracture. An ideal microstructure for improved creep performance was identified and includes large equiaxed grains, elimination of texture, dissolution of Laves and δ phase and the precipitation of small carbides and γ’’ precipitates. Recommendations on how to heat treat LPBF alloy 718 to reach this microstructure are given. Overall, this work showed that LPBF components may become more performant than wrought and conventional equivalents.

Immunotherapeutic manipulation of the antitumor immune response offers an attractive strategy to target genomic instability in cancer. A subset of tumor-specific somatic mutations can be translated into immunogenic and HLA-bound epitopes called neoantigens, which can induce the activation of helper and cytotoxic T lymphocytes. However, cancer immunoediting and immunosuppressive mechanisms often allow tumors to evade immune recognition. Recent evidence also suggests that the tumor neoantigen landscape extends beyond epitopes originating from nonsynonymous single-nucleotide variants in the coding exome. Here we review emerging approaches for identifying, prioritizing, and immunologically targeting personalized neoantigens using polyvalent cancer vaccines and T-cell receptor gene therapy. Significance: Several major challenges currently impede the clinical efficacy of neoantigen-directed immunotherapy, such as the relative infrequency of immunogenic neoantigens, suboptimal potency and priming of de novo tumor-specific T cells, and tumor cell–intrinsic and –extrinsic mechanisms of immune evasion. A deeper understanding of these biological barriers could help facilitate the development of effective and durable immunotherapy for any type of cancer, including immunologically “cold” tumors that are otherwise therapeutically resistant.

Traditional didactic lectures is one of the commonly followed method of teaching learning in most of the medical colleges to teach undergraduate medical students till now. This method is a passive method of learning in which teacher will be talking and students has to listen, this type of teaching is dominating our MBBS curriculum. Students find these lecture classes very boring. Also after the implementation of new competency based medical education (CBME) by Medical council of India, medical education for undegraduates needs lots of recent improvements to provide satisfactory changing demands in medical practice. Physiology lecture classes was restructured with introduction of pre and post-test, knowledge before and after the lecture were obtained by comparing the test scores before and after to evaluate the enhancement of learning after the lectures. Also feedback regarding the introduction of pre & post -test from the students were taken for evaluating the effectiveness of lectures with the introduction of the test before and after the lecture classes. 1. To provide Pre-test & Post-test questions before lecture and after lecture classes on the topic; 2. To evaluate the enhancement of Learning after lecture based on post-test scores; 3. To evaluate the effectiveness of physiology lecture among students in relation to feedback obtained. The present study was conducted on 150 First year M.B.B.S during Physiology lectures, after obtaining Ethical clearance from the Instituitional Ethical Committee, Adichunchanagiri Institute of Medical Sciences, Adichunchanagiri university.I MBBS(150) students after obtaining consent for voluntary participation were asked to take the pre-test question s containing 20 multiple choice questions on topic “Cardiac cycle & Cardiac output” and the same 20 multiple choice questions were provided to the students at the end of the four hour lecture classes on the topic. Performance of the students with the salient concepts on the topic were assessed by the pre-test & post-test scores. Feedback from all students were obtained regarding perception of students on the use of pre and post-test in learning the salient concepts in physiology were obtained by administering a questionnaire.Pre-test score of the students obtained before the start of the lecture was 5.57±1.29 and the Post-test score of the students obtained after the end of four hour lecture classes on the topic was 15.26±9.7 (p < 0.0001) this was highly significant. Feedback obtained from the students was indicative of that most of the students felt that test was very interesting, innovative, they could focus more on the lecture classes, it helped them to learn the salient concepts in physiology, also most of them felt competitive aspect of the test contributed to their effectiveness and most of them wanted it during lectures. From the present study we would like to conclude that introducing pre and post-test in lectures could enhance learning among students. Also we could evaluate the effectiveness of lectures by introducing pre and post – test based on feedback obtained from the students.

Deep operator learning has emerged as a promising tool for reduced-order modelling and PDE model discovery. Leveraging the expressive power of deep neural networks, especially in high dimensions, such methods learn the mapping between functional state variables. While proposed methods have assumed noise only in the dependent variables, experimental and numerical data for operator learning typically exhibit noise in the independent variables as well, since both variables represent signals that are subject to measurement error. In regression on scalar data, failure to account for noisy independent variables can lead to biased parameter estimates. With noisy independent variables, linear models fitted via ordinary least squares (OLS) will show attenuation bias, wherein the slope will be underestimated. In this work, we derive an analogue of attenuation bias for linear operator regression with white noise in both the independent and dependent variables. In the nonlinear setting, we computationally demonstrate underprediction of the action of the Burgers operator in the presence of noise in the independent variable. We propose error-in-variables (EiV) models for two operator regression methods, MOR-Physics and DeepONet, and demonstrate that these new models reduce bias in the presence of noisy independent variables for a variety of operator learning problems. Considering the Burgers operator in 1D and 2D, we demonstrate that EiV operator learning robustly recovers operators in high-noise regimes that defeat OLS operator learning. We also introduce an EiV model for time-evolving PDE discovery and show that OLS and EiV perform similarly in learning the Kuramoto-Sivashinsky evolution operator from corrupted data, suggesting that the effect of bias in OLS operator learning depends on the regularity of the target operator., Comment: 23 pages, 10 figures

Wastewater management is the most discussed topic in this current era. To overcome the scarcity of water and wastewater treatment, reusage of water is the convenient way. By removing the organic pollutants, the water can be reused for domestic purposes. Pure and neodymium (Nd) doped ZrO2 was produced using facile co-precipitation technique. Along with doping, surfactant was also included to enhance the morphology of the prepared samples. The nanoparticles morphology was formed with less agglomeration as investigated by TEM analysis. The photocatalytic action was further examined using annular photoreactor. Both methylene blue (MB) and Rhodamine B (RhB) dyes degradation was examined. Neodymium doped ZrO2 was incorporated in the dye under visible light for 2 h. The light was irradiated for 20 min and sample solution was collected for every 20 min. The samples were analyzed for UV and degradation was confirmed. The enhancement of catalyst depended on the bandgap of the samples. The absorption peak for Rhodamine B and Methylene Blue dyes at 553 and 664 nm was decreased and confirmed the degradation of dyes. 2% Nd doped ZrO2 catalyst added MB dye sample showed 90% efficiency and exhibited 77% efficiency on Rhodamine B pollutant. The “k” values of 2% Nd doped ZrO2 was obtained as 0.0144 min−1 and 0.00944 min−1 for MB and RhB dyes. This candidate will be the potential one for futuristic water remediation process.

Wastewater management is the most discussed topic in this current era. To overcome the scarcity of water and wastewater treatment, reusage of water is the convenient way. By removing the organic pollutants, the water can be reused for domestic purposes. Pure and neodymium (Nd) doped ZrO2 was produced using facile co-precipitation technique. Along with doping, surfactant was also included to enhance the morphology of the prepared samples. The nanoparticles morphology was formed with less agglomeration as investigated by TEM analysis. The photocatalytic action was further examined using annular photoreactor. Both methylene blue (MB) and Rhodamine B (RhB) dyes degradation was examined. Neodymium doped ZrO2 was incorporated in the dye under visible light for 2 h. The light was irradiated for 20 min and sample solution was collected for every 20 min. The samples were analyzed for UV and degradation was confirmed. The enhancement of catalyst depended on the bandgap of the samples. The absorption peak for Rhodamine B and Methylene Blue dyes at 553 and 664 nm was decreased and confirmed the degradation of dyes. 2% Nd doped ZrO2 catalyst added MB dye sample showed 90% efficiency and exhibited 77% efficiency on Rhodamine B pollutant. The “k” values of 2% Nd doped ZrO2 was obtained as 0.0144 min−1 and 0.00944 min−1 for MB and RhB dyes. This candidate will be the potential one for futuristic water remediation process.

Lysosomal acid lipase (LAL) is a serine hydrolase that hydrolyzes cholesteryl ester (CE) and TGs delivered to the lysosomes into free cholesterol and fatty acids. LAL deficiency due to mutations in the LAL gene (LIPA) results in accumulation of TGs and cholesterol esters in various tissues of the body leading to pathological conditions such as Wolman's disease and CE storage disease (CESD). Here, we present the first crystal structure of recombinant human LAL (HLAL) to 2.6 A resolution in its closed form. The crystal structure was enabled by mutating three of the six potential glycosylation sites. The overall structure of HLAL closely resembles that of the evolutionarily related human gastric lipase (HGL). It consists of a core domain belonging to the classical α/β hydrolase-fold family with a classical catalytic triad (Ser-153, His-353, Asp-324), an oxyanion hole, and a "cap" domain, which regulates substrate entry to the catalytic site. Most significant structural differences between HLAL and HGL exist at the lid region. Deletion of the short helix, 238NLCFLLC244, at the lid region implied a possible role in regulating the highly hydrophobic substrate binding site from self-oligomerization during interfacial activation. We also performed molecular dynamic simulations of dog gastric lipase (lid-open form) and HLAL to gain insights and speculated a possible role of the human mutant, H274Y, leading to CESD.

Introduction: The emergence and global spread of carbapenem-resistant E. coli is of great concern to the health care facilities due to high rates of morbidity and mortality associated with them. It is necessary to know its epidemiology and the resistant pattern in a geographical area to formulate a antibiotic stewardship policy. Objective: To study the occurrence and mechanisms of carbapenem resistance in Escherichia coli. Materials and Methods: A total of 1172 clinical strains of E. coli obtained from various clinical specimens were screened for carbapenem resistance during the study period. Strains showing reduced susceptibility to imipenem &/or ertapenem &/or meropenem were included in the study. The resistance mechanisms were identified using various phenotypic methods. Results: Total of 53/1172 were found to be carbapenem resistant E. coli (CRE). The most common sample in which CRE were isolated was urine (n=26, 49.1%). A total of 50 isolates were confirmed as Metallobeta- lactamase (MBL) producers using Ezy MICTM strip. One of the three non carbapenemase producing isolate was positive for ESBL with porin loss and the other two isolates were positive for AmpC with porin loss. Conclusion: MBL production being the most common mechanism of carbapenem resistance, the study indicates the importance of regular monitoring of drug resistance in the hospital for an urgent action to be taken for antibiotics stewardship in the institute. Keywords: Escherichia coli, Carbapenem resistance, Super bug, Carba NP, MBL, Carbapenem resistance mechanisms.

In the present investigation the coriander plant volatiles for the tritrophic interactions on the egg parasitoid Trichogramma chilonis Ishii is evaluated. The GCMS profile study of the foliage extract of coriander in both hexane and dichloromethane showed presence of 2-Dodecenal-(E), (E)-Tetradec-2-enal, Octadecane, Nonacosane, Hexadecane, Pentadecane and Heneicosane, which are of biologically active fractions in insects. In laboratory study the maximum parasitization was recorded in hexane extract treated Corcyra card (31.04 eggs/card) compared to 28.16 eggs per card observed in untreated control. The data gathered on number of parasitized eggs hatched into adult indicated that in hexane extract treated Corcyra card had 91.19 per cent while parasitoid emergence was 29.30 eggs per card followed by 87.76 per cent in untreated control 24.90 eggs per card. The GCMS profile study of the foliage extract of coriander in both hexane and dichloromethane showed presence of 2-Dodecenal-(E), (E)-Tetradec-2-enal, Octadecane, Nonacosane, Hexadecane, Pentadecane and Heneicosane, which are of biologically active fractions in insects. The above findings reveal the possible role of such active chemicals in parasitoid compatibility of the intercrop.

The recent developments in wireless communication technologies along with the costs of hardware allow both V2V and V2I communications for information exchange. Such a network is called Vehicular ad Hoc Network (VANET) which is very important for various road safety and non-safety related applications. However, Due to the wireless nature of communication in VANETs, it is also prone to various security attacks which are originally present in wireless networks. Hence to realize the highest potential of VANET, the network should be free from attackers, there by all the information exchanged in the network must be reliable should be originated from authenticated source. The system can be processed by the fuzzy logic method. It does not require any keys for data transmission. The system design is very efficient compared to other techniques. So the users prevent from road accident and traffic jam..

Carbon nanotube (CNT) is prepared by chemical vapor deposition method and their electrochemical behaviors for instance oxygen evolution (OER) and hydrogen evolution (HER) reaction have been successfully studied. In recent times, Pt-free electrocatalysts have been greatly attractive in electrochemical hydrogen evolution reactions for the replacement of fossil fuels and development of sustainable energy carriers. Chemical vapor deposition method was used as an efficient way to synthesize CNTs directly. The as prepared free-standing and multifunctional CNT electrodes are used for overall water splitting applications. In this work, we have designed CNT as electrode material as well as current collector using Ni-foil and Ni-foam substrate and their fundamental characterizations confirm the structural, morphological behaviors of CNTs. Moreover, the well-ordered growth of CNT was obtained in Ni-foam CNTs 1 and 2, whereas in the Ni foil CNTs 1 and 2 less growth of CNT and amorphous carbon sponge was exceeded, which was further confirmed by the SEM images. The achieved electrochemical HER results displayed that the Ni-foam-CNT-2 exhibited lower overpotential, smallest Tafel slope and lower resistance value of 110 mV, 240 mV/dec and 0.24 Ω respectively. Moreover, Ni-foam-CNT-2 revealed excellent stability with 86.6 % retention over 20 h. Hence, it is one of the cost-effective and reliable materials for electrochemical hydrogen evolution reaction. This work was supported by UGC-SAP, DST-FIST, DST-PURSE, MHRD-RUSA grants.

Plant disease is a continuing challenge for smallholder farmers, which has an impact on income and food production. Identifying the disease at starting stage and preventing it from spreading to other parts of the plant is a challenge even for the experts in the field. Experts can identify and diagnose the disease, but the process is time-consuming and a good observation of the infected part is needed. However, these experts are not available readily to smallholder farmers, who are a major part of our country. An adequate method is therefore required to detect plant leaf diseases at starting stage. The recent revolution in smartphone perforation and advancement in computer vision models has provided a way for computer vision applications in the agriculture. Convolutional Neural Networks (CNN) considered as state of the art in classification of images and have the ability to produce a conclusive diagnosis.In this article, a Transfer Learning approach is used, in which a pre trained model is used to train on pictures of different fruit plant leaves from the Plant Village dataset, covering various diseases as well as safe samples.

The aim of this study was to determine the relationship between pilot workload, performance, subjective fatigue, sleep duration, number of sectors and flight duration during short-haul operations. Ninety pilots completed a NASA Task Load Index, Psychomotor Vigilance Task and a Samn-Perelli fatigue scale on top-of-descent of each flight and wore an activity monitor throughout the study. Weak, but significant, correlations were revealed between workload and all factors. Subjective fatigue, number of sectors and lapses were significant predictors of workload. Pilots reported higher workload when fatigue increased, the number of sectors were higher, and objective performance was worse.

Using structure-guided design, several cell based assays, and microdosed positron emission tomography (PET) imaging, we identified a series of highly potent, selective, and brain-penetrant oxazole-4-carboxamide-based inhibitors of glycogen synthase kinase-3 (GSK-3). An isotopologue of our first-generation lead, [(3)H]PF-367, demonstrates selective and specific target engagement in vitro, irrespective of the activation state. We discovered substantial ubiquitous GSK-3-specific radioligand binding in Tg2576 Alzheimer’s disease (AD), suggesting application for these compounds in AD diagnosis and identified [(11)C]OCM-44 as our lead GSK-3 radiotracer, with optimized brain uptake by PET imaging in nonhuman primates. GSK-3β-isozyme selectivity was assessed to reveal OCM-51, the most potent (IC(50) = 0.030 nM) and selective (>10-fold GSK-3β/GSK-3α) GSK-3β inhibitor known to date. Inhibition of CRMP2(T514) and tau phosphorylation, as well as favorable therapeutic window against WNT/β-catenin signaling activation, was observed in cells.

Background The optimal duration of maintenance therapy is controversial in proliferative lupus nephritis (LN). Discordance between clinical parameters of renal remission and histological findings has made repeat biopsy a compulsory tool to confirm the histological remission (HR), but the timing is debatable. Aim of this study was to find the correlation of sustained complete clinical remission (CR) in sever lupus nephritis with histological findings on repeat kidney biopsy and appropriate duration of treatment in maintenance phase after achieving complete clinical remission. Methods Repeated kidney biopsy (biopsy 2) was performed on patients of biopsy proven (biopsy 1) proliferative LN who had been in CR for at least 2-years. The clinical and histologic findings of these groups (biopsy 1 and biopsy 2) were compared. Total 29 patients were included for the final analysis. Group 2 was further divided as per the duration of sustained CR (>48 months & Results Average time taken to achieve CR was 9(range 2–24) months. Average duration of follow up and maintenance therapy was 68 ± 17.8 and 62.5 ± 14.2 months respectively. In the repeat kidney biopsy, HR was observed in 93.1% patients. Immunofluorescence study (IF) was normal in 72% of the patients. Normal light microscopy (LM) findings were observed in 58% patients. Transformation from proliferative to nonproliferative LN was noted in 82% cases. Other than the duration of CR on maintenance therapy and blood pressure, rest of the variables failed to predict HR. In sustained remission for more than 48-months group, 100% patients achieved HR whereas only 84% in 24–48-months group. Conclusion Sustained CR on maintenance immunosuppressive therapy for more than 48-months duration predicts HR in repeat kidney biopsy findings in quiescent proliferative LN. Hence the minimum duration of maintenance therapy in proliferative LN should be at least for another 48 months after achieving CR.

In this study, a one-step hydrothermal method was used to synthesize cadmium molybdenum oxide and revealed cationic cetyltrimethylammonium bromide surfactant effects on material preparation and energy storage characteristics. X-ray diffraction confirmed tetragonal-phase CdMoO4. Symmetric stretching modes of molybdenum oxide were confirmed from a Raman spectrum. A Fourier-transform infrared spectrum confirmed the presence of functional groups. Scanning electron microscopy images revealed a bunch of hierarchical microspheres of about 100–200 nm diameter. The specific capacitance achieved for CdMoO4, 0.1 M CTAB + CdMoO4, and 0.2 M CTAB + CdMoO4 were 200 F/g, 310 F/g, and 382 F/g, respectively, at 0.5 A/g. In addition, long-term cyclic stability for the best performing electrode (0.2 M CTAB + CdMoO4) material was investigated to explore cyclic performances of the supercapacitor. During the experiment, 86.01% capacity was retained after 5000 cycles at 5 A/g. The product activity is promising for high-efficiency supercapacitors due to the ease of production, environmentally friendly nature, and low cost of the synthesized material. This work was supported by RUSA, UGC-SAP, DST-FIST, DST-PURSE grants. The authors extend their appreciation to the Researchers supporting project number (RSP-2020/247) King Saud University, Riyadh, Saudi Arabia.

Approximation theorists have established best-in-class optimal approximation rates of deep neural networks by utilizing their ability to simultaneously emulate partitions of unity and monomials. Motivated by this, we propose partition of unity networks (POUnets) which incorporate these elements directly into the architecture. Classification architectures of the type used to learn probability measures are used to build a meshfree partition of space, while polynomial spaces with learnable coefficients are associated to each partition. The resulting hp-element-like approximation allows use of a fast least-squares optimizer, and the resulting architecture size need not scale exponentially with spatial dimension, breaking the curse of dimensionality. An abstract approximation result establishes desirable properties to guide network design. Numerical results for two choices of architecture demonstrate that POUnets yield hp-convergence for smooth functions and consistently outperform MLPs for piecewise polynomial functions with large numbers of discontinuities., Comment: 8 pages, 5 figures

A supercapacitor is an efficient energy storage system that acts as an excellent booster to deliver high power density required for batteries and fuel cells. Recently, composite material–based supercapacitors have attracted much more interest as promising greener and more capable candidates in energy-saving use. In this work, samarium cobalt oxide–decorated reduced graphene oxide (SmCoO₃/rGO) was prepared employing solvothermal route and used as reliable electrode material. The maximum specific capacity achieved was 30.80 mAh/g for 1 A/g of SmCoO₃/rGO nanocomposite with capacity retention of 86.95%@5A/g over 5000 charge discharge cycles. Better electrochemical performance of samarium and reduced graphene oxide nanostructures prevent the transfer of electrons through electrochemical active sites, creating electronic and structural diversity of electro active material. In addition, SmCoO₃/rGO/AC hybrid supercapacitor device that delivered good energy and power density of 52 W h/kg and 752 W/kg at 1 A/g was designed. 74.28% capacitive retention and 98.26% coulombic efficiency was maintained over 15,000 cycles. This work was supported by RUSA, UGC-SAP, DST-FIST, DST-PURSE grants. The authors extend their appreciation to the Researchers supporting project number (RSP-2020/247) King Saud University, Riyadh, Saudi Arabia.

Wastewater management is becoming a serious issue worldwide. To enhance the reuse of wastewater, one has to remove toxic pollutants present in it. High amount of dye is present in wastewater, and to remove these dyes is the large scope of this research. Herein, we report production of pure and Ce-doped copper ferrite via hydrothermal route. The synthesized nanoparticles were collected and analyzed by basic characterization techniques. The bandgap energy calculated for pure, 1% Ce, and 2% Ce-doped CuFe2O4 was found to be 2.77, 2.57, and 2.36eV, respectively. Reduction in bandgap was attributed to the doping element. The shape and size of pure and Ce-doped products were investigated using a scanning electron microscope. Agglomeration was observed in the pure copper ferrite sample. In the Ce-doped sample, agglomeration was clearly reduced and the 2% Ce-doped CuFe2O4 sample showed growth of small nanoparticles. They showed complete growth and were arranged in a uniform manner without agglomeration. The surface area of the 2% Ce-CuFe2O4 sample was found to be 65.89 m2/g with 7.02 nm pore diameter. The photocatalytic activity of the prepared material was observed for rhodamine B degradation. The pure and catalyst-added dye was exposed under visible light. The samples were tested for UV. The efficiency obtained for pure dye solution, pristine CuFe2O4-added, and 1% Ce and 2% Ce-doped CuFe2O4-added dye solutions were 48%, 50%, 66%, and 88% within 2 h of irradiation. The 2% Ce-doped CuFe2O4 sample showed excellent photocatalytic activity as the bandgap and morphology were enhanced by doping an appropriate ratio of Ce ions. This work was supported by UGC-SAP, DST-FIST, DST-PURSE, MHRD-RUSA grants.

Physics-informed neural network architectures have emerged as a powerful tool for developing flexible PDE solvers that easily assimilate data. When applied to problems in shock physics however, these approaches face challenges related to the collocation-based PDE discretization underpinning them. By instead adopting a least squares space-time control volume scheme, we obtain a scheme which more naturally handles: regularity requirements, imposition of boundary conditions, entropy compatibility, and conservation, substantially reducing requisite hyperparameters in the process. Additionally, connections to classical finite volume methods allows application of inductive biases toward entropy solutions and total variation diminishing properties. For inverse problems in shock hydrodynamics, we propose inductive biases for discovering thermodynamically consistent equations of state that guarantee hyperbolicity. This framework therefore provides a means of discovering continuum shock models from molecular simulations of rarefied gases and metals. The output of the learning process provides a data-driven equation of state which may be incorporated into traditional shock hydrodynamics codes.

The application of deep learning toward discovery of data-driven models requires careful application of inductive biases to obtain a description of physics which is both accurate and robust. We present here a framework for discovering continuum models from high fidelity molecular simulation data. Our approach applies a neural network parameterization of governing physics in modal space, allowing a characterization of differential operators while providing structure which may be used to impose biases related to symmetry, isotropy, and conservation form. We demonstrate the effectiveness of our framework for a variety of physics, including local and nonlocal diffusion processes and single and multiphase flows. For the flow physics we demonstrate this approach leads to a learned operator that generalizes to system characteristics not included in the training sets, such as variable particle sizes, densities, and concentration., 37 pages, 15 figures

Second-order optimizers hold intriguing potential for deep learning, but suffer from increased cost and sensitivity to the non-convexity of the loss surface as compared to gradient-based approaches. We introduce a coordinate descent method to train deep neural networks for classification tasks that exploits global convexity of the cross-entropy loss in the weights of the linear layer. Our hybrid Newton/Gradient Descent (NGD) method is consistent with the interpretation of hidden layers as providing an adaptive basis and the linear layer as providing an optimal fit of the basis to data. By alternating between a second-order method to find globally optimal parameters for the linear layer and gradient descent to train the hidden layers, we ensure an optimal fit of the adaptive basis to data throughout training. The size of the Hessian in the second-order step scales only with the number weights in the linear layer and not the depth and width of the hidden layers; furthermore, the approach is applicable to arbitrary hidden layer architecture. Previous work applying this adaptive basis perspective to regression problems demonstrated significant improvements in accuracy at reduced training cost, and this work can be viewed as an extension of this approach to classification problems. We first prove that the resulting Hessian matrix is symmetric semi-definite, and that the Newton step realizes a global minimizer. By studying classification of manufactured two-dimensional point cloud data, we demonstrate both an improvement in validation error and a striking qualitative difference in the basis functions encoded in the hidden layer when trained using NGD. Application to image classification benchmarks for both dense and convolutional architectures reveals improved training accuracy, suggesting possible gains of second-order methods over gradient descent., 10 pages, 4 figures

Quantum confinement is the spatial confinement of electron–hole pairs (excitons) in one or more dimensions within a material, and also electronic energy levels are discrete. It is due to the confinement of the electronic wave function to the physical dimensions of the particles. In this effect can be divided into three ways, 1D confinement (free carrier in a plane), quantum wells; 2D confinement (carriers are free to move down), quantum wire; and 3D confinement (carriers are confined in all directions), which are discussed in details. In addition the formation mechanism of exciton and quantum confinement behavior of strong, moderate, and weak confinement have been discussed below. MHRD-SPARC (ID: 890/2019) & UKIERI by the Governments of India and UK.

Adapalene-loaded transfersome gel containing vitamin C as a combination therapy for the management of acne vulgaris was developed in the present study. The transfersome was prepared by reverse-phase evaporation, and the effect of various process parameters were investigated by the Design of Experiment (DOE) approach and optimized based on the particle size (PS), polydispersity index (PDI), zeta potential (ZP), and entrapment efficiency (EE). The selected tranfersomes were further evaluated for their thermal behavior and morphology by transmission electron microscopy and turbidity measurements and incorporated into a gel with/without vitamin C. The gel was evaluated and compared with the marketed product (Adiff gel) for various physicochemical parameters, and in vivo studies in testosterone-induced rat models of acne. The prepared transfersomes had PS in the range of 280 to 400 nm, PDI values of 0.416 to 0.8, ZP of − 38 to − 20 mV, and % EE of 32 to 70%. DSC studies confirmed a positive interaction of the components in the transfersome. Surface morphology confirmed that the vesicles were spherical, unilamellar, and discrete. A relative deformability study showed higher elasticity of the transfersomes compared with Adiff aqs gel. Ascorbyl-6-palmitate in adapalene-loaded transfersome gel containing vitamin C (ADVTG) was found to have a good antioxidant free radical–scavenging activity. An in vitro drug release study showed that the sustained release of the transfersomal formulations was attributed to the flexibility of the vesicles by which penetration was increased. ADVTG was found to be promising in treating acne compared with the marketed product.

Studies on indole-3-carboxylic acid derivatives as direct activators of human adenosine monophosphate-activated protein kinase (AMPK) α1β1γ1 isoform have culminated in the identification of PF-06409577 (1), PF-06885249 (2), and PF-06679142 (3) as potential clinical candidates. Compounds 1-3 are primarily cleared in animals and humans via glucuronidation. Herein, we describe the biosynthetic preparation, purification, and structural characterization of the glucuronide conjugates of 1-3. Spectral characterization of the purified glucuronides M1, M2, and M3 indicated that they were acyl glucuronide derivatives. In vitro pharmacological evaluation revealed that all three acyl glucuronides retained selective activation of β1-containing AMPK isoforms. Inhibition of de novo lipogenesis with representative parent carboxylic acids and their respective acyl glucuronide conjugates in human hepatocytes demonstrated their propensity to activate cellular AMPK. Cocrystallization of the AMPK α1β1γ1 isoform with 1-3 and M1-M3 provided molecular insights into the structural basis for AMPK activation by the glucuronide conjugates.

Many diseases are believed to be driven by pathological levels of reactive oxygen species (ROS) and oxidative stress has long been recognized as a driver for inflammatory disorders. Apoptosis signal-regulating kinase 1 (ASK1) has been reported to be activated by intracellular ROS and its inhibition leads to a down regulation of p38-and JNK-dependent signaling. Consequently, ASK1 inhibitors may have the potential to treat clinically important inflammatory pathologies including renal, pulmonary and liver diseases. Analysis of the ASK1 ATP-binding site suggested that Gln756, an amino acid that rarely occurs at the GK+2 position, offered opportunities for achieving kinase selectivity for ASK1 which was applied to the design of a parallel medicinal chemistry library that afforded inhibitors of ASK1 with nanomolar potency and excellent kinome selectivity. A focused optimization strategy utilizing structure-based design resulted in the identification of ASK1 inhibitors with low nanomolar potency in a cellular assay, high selectivity when tested against kinase and broad pharmacology screening panels, and attractive physicochemical properties. The compounds we describe are attractive tool compounds to inform the therapeutic potential of ASK1 inhibition.

Simulations of homogeneously sheared turbulence (HST) are conducted until a universal self-similar state is established at the long non-dimensional time $\unicode[STIX]{x1D6E4}t=20$, where $\unicode[STIX]{x1D6E4}$ is the shear rate. The simulations are enabled by a new robust and discretely conservative algorithm. The method solves the governing equations in physical space using the so-called shear-periodic boundary conditions. Convection by the mean homogeneous shear flow is treated implicitly in a split step approach. An iterative Crank–Nicolson time integrator is chosen for robustness and stability. The numerical strategy captures without distortion the Kelvin modes, rotating waves that are fundamental to homogeneously sheared flows and are at the core of rapid distortion theory. Three direct numerical simulations of HST with the initial Taylor scale Reynolds number $Re_{\unicode[STIX]{x1D706}0}=29$ and shear numbers of $S_{0}^{\ast }=\unicode[STIX]{x1D6E4}q^{2}/\unicode[STIX]{x1D716}=3$, 15 and 27 are performed on a $2048\times 1024\times 1024$ grid. Here, $\unicode[STIX]{x1D716}$ is the dissipation rate and $1/2q^{2}$ is the turbulent kinetic energy. The long integration time considered allows the establishment of a self-similar state observed in experiments but often absent from simulations conducted over shorter times. The asymptotic state appears to be universal with a long time production to dissipation rate ${\mathcal{P}}/\unicode[STIX]{x1D716}\sim 1.5$ and shear number $S^{\ast }\sim 10$ in agreement with experiments. While the small scales exhibit strong anisotropy increasing with initial shear number, the skewness of the transverse velocity derivative decreases with increasing Reynolds number.

We present a verification study of three simulation techniques for fluid–particle flows, including an Euler–Lagrange approach (EL) inspired by Jackson's seminal work on fluidized particles, a quadrature–based moment method based on the anisotropic Gaussian closure (AG), and the traditional two-fluid model. We perform simulations of two problems: particles in frozen homogeneous isotropic turbulence (HIT) and cluster-induced turbulence (CIT). For verification, we evaluate various techniques for extracting statistics from EL and study the convergence properties of the three methods under grid refinement. The convergence is found to depend on the simulation method and on the problem, with CIT simulations posing fewer difficulties than HIT. Specifically, EL converges under refinement for both HIT and CIT, but statistics exhibit dependence on the postprocessing parameters. For CIT, AG produces similar results to EL. For HIT, converging both TFM and AG poses challenges. Overall, extracting converged, parameter-independent Eulerian statistics remains a challenge for all methods. © 2017 American Institute of Chemical Engineers AIChE J, 63: 5396–5412, 2017

Diabetic nephropathy remains an area of high unmet medical need, with current therapies that slow down, but do not prevent, the progression of disease. A reduced phosphorylation state of adenosine monophosphate-activated protein kinase (AMPK) has been correlated with diminished kidney function in both humans and animal models of renal disease. Here, we describe the identification of novel, potent, small molecule activators of AMPK that selectively activate AMPK heterotrimers containing the β1 subunit. After confirming that human and rodent kidney predominately express AMPK β1, we explore the effects of pharmacological activation of AMPK in the ZSF1 rat model of diabetic nephropathy. Chronic administration of these direct activators elevates the phosphorylation of AMPK in the kidney, without impacting blood glucose levels, and reduces the progression of proteinuria to a greater degree than the current standard of care, angiotensin-converting enzyme inhibitor ramipril. Further analyses of urine biomarkers and kidney tissue gene expression reveal AMPK activation leads to the modulation of multiple pathways implicated in kidney injury, including cellular hypertrophy, fibrosis, and oxidative stress. These results support the need for further investigation into the potential beneficial effects of AMPK activation in kidney disease.

Adenosine monophosphate-activated protein kinase (AMPK), a serine/threonine heterotrimeric protein kinase, is a critical regulator of cellular and whole body energy homeostasis. There are twelve known AMPK isoforms that are differentially expressed in tissues and species. Dysregulation of AMPK signaling is associated with a multitude of human pathologies. Hence isoform-selective activators of AMPK are actively being sought for the treatment of cardiovascular and metabolic diseases. The present review summarizes the status of direct AMPK activators from the patent and published literature.

Crystals of phosphorylated JAK1 kinase domain were initially generated in complex with nucleotide (ADP) and magnesium. The tightly bound Mg2+-ADP at the ATP-binding site proved recalcitrant to ligand displacement. Addition of a molar excess of EDTA helped to dislodge the divalent metal ion, promoting the release of ADP and allowing facile exchange with ATP-competitive small-molecule ligands. Many kinases require the presence of a stabilizing ligand in the ATP site for crystallization. This procedure could be useful for developing co-crystallization systems with an exchangeable ligand to enable structure-based drug design of other protein kinases.

Data fields sampled on irregularly spaced points arise in many applications in the sciences and engineering. For regular grids, Convolutional Neural Networks (CNNs) have been successfully used to gaining benefits from weight sharing and invariances. We generalize CNNs by introducing methods for data on unstructured point clouds based on Generalized Moving Least Squares (GMLS). GMLS is a non-parametric technique for estimating linear bounded functionals from scattered data, and has recently been used in the literature for solving partial differential equations. By parameterizing the GMLS estimator, we obtain learning methods for operators with unstructured stencils. In GMLS-Nets the necessary calculations are local, readily parallelizable, and the estimator is supported by a rigorous approximation theory. We show how the framework may be used for unstructured physical data sets to perform functional regression to identify associated differential operators and to regress quantities of interest. The results suggest the architectures to be an attractive foundation for data-driven model development in scientific machine learning applications.

Data fields sampled on irregularly spaced points arise in many applications in the sciences and engineering. For regular grids, Convolutional Neural Networks (CNNs) have been successfully used to gaining benefits from weight sharing and invariances. We generalize CNNs by introducing methods for data on unstructured point clouds based on Generalized Moving Least Squares (GMLS). GMLS is a non-parametric technique for estimating linear bounded functionals from scattered data, and has recently been used in the literature for solving partial differential equations. By parameterizing the GMLS estimator, we obtain learning methods for operators with unstructured stencils. In GMLS-Nets the necessary calculations are local, readily parallelizable, and the estimator is supported by a rigorous approximation theory. We show how the framework may be used for unstructured physical data sets to perform functional regression to identify associated differential operators and to regress quantities of interest. The results suggest the architectures to be an attractive foundation for data-driven model development in scientific machine learning applications.