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This study addresses image denoising alongside the compression and reconstruction of hyperspectral images (HSIs) using deep learning techniques, since the research community is striving to produce effective results to utilize hyperspectral data. Here, the SqueezeNet architecture is trained with a Gaussian noise model to predict and discriminate noisy pixels of HSI to obtain a clean image as output. The denoised image is further processed by the tunable spectral filter (TSF), which is a dual-level prediction filter to produce a compressed image. Subsequently, the compressed image is analyzed through a dense attentional net (DAN) model for reconstruction by reverse dual-level prediction operation. All the proposed mechanisms are employed in Python and evaluated using a Ben-Gurion University-Interdisciplinary Computational Vision Laboratory (BGU-ICVL) dataset. The results of SqueezeNet architecture applied to the dataset produced the denoised output with a Peak Signal to Noise Ratio (PSNR) value of 45.43 dB. The TSF implemented to the denoised images provided compression with a Mean Square Error (MSE) value of 8.334. Subsequently, the DAN model executed and produced reconstructed images with a Structural Similarity Index Measure (SSIM) value of 0.9964 dB. The study proved that each stage of the proposed approach resulted in a quality output, and the developed model is more effective to further utilize the HSI. This model can be well utilized using HSI data for mineral exploration.

The binding specificities of an individual’s antibody repertoire contain a wealth of biological information. They harbor evidence of environmental exposures, allergies, ongoing or emerging autoimmune disease processes, and responses to immunomodulatory therapies, for example. Highly multiplexed methods to comprehensively interrogate antibody-binding specificities have therefore emerged in recent years as important molecular tools. Here, we provide a detailed protocol for performing ‘phage immunoprecipitation sequencing’ (PhIP-Seq), which is a powerful method for analyzing antibody-repertoire binding specificities with high throughput and at low cost. The methodology uses oligonucleotide library synthesis (OLS) to encode proteomic-scale peptide libraries for display on bacteriophage. These libraries are then immunoprecipitated, using an individual’s antibodies, for subsequent analysis by high-throughput DNA sequencing. We have used PhIP-Seq to identify novel self-antigens associated with autoimmune disease, to characterize the self-reactivity of broadly neutralizing HIV antibodies, and in a large international cross-sectional study of exposure to hundreds of human viruses. Compared with alternative array-based techniques, PhIP-Seq is far more scalable in terms of sample throughput and cost per analysis. Cloning and expression of recombinant proteins are not required (versus protein microarrays), and peptide lengths are limited only by DNA synthesis chemistry (up to 90-aa (amino acid) peptides versus the typical 8- to 12-aa length limit of synthetic peptide arrays). Compared with protein microarrays, however, PhIP-Seq libraries lack discontinuous epitopes and post-translational modifications. To increase the accessibility of PhIP-Seq, we provide detailed instructions for the design of phage-displayed peptidome libraries, their immunoprecipitation using serum antibodies, deep sequencing–based measurement of peptide abundances, and statistical determination of peptide enrichments that reflect antibody–peptide interactions. Once a library has been constructed, PhIP-Seq data can be obtained for analysis within a week.

The availability of a higher resolution fine spectral bandwidth in hyperspectral images (HSI) makes it easier to identify objects of interest in them. The inclusion of noise into the resulting collection of images is a limitation of HSI and has an adverse effect on post-processing and data interpretation. Denoising HSI data is thus necessary for the effective execution of post-processing activities like image categorization and spectral unmixing. Most of the existing models cannot handle many forms of noise simultaneously. When it comes to compression, available compression models face the problems of increased processing time and lower accuracy. To overcome the existing limitations, an image denoising model using an adaptive fusion network is proposed. The denoised output is then processed through a compression model which uses an optimized deep learning technique called \"chaotic Chebyshev artificial hummingbird optimization algorithm-based bidirectional gated recurrent unit\" (CCAO-BiGRU). All the proposed models were tested in Python and evaluated using the Indian Pines, Washington DC Mall and CAVE datasets. The proposed model underwent qualitative and quantitative analysis and showed a PSNR value of 82 in the case of Indian Pines and 78.4 for the Washington DC Mall dataset at a compression rate of 10. The study proved that the proposed model provides the knowledge about complex nonlinear mapping between noise-free and noisy HSI for obtaining the denoised images and also results in high-quality compressed output.

A series of edaravone derivatives and the corresponding Cu(II) complexes were synthesized and characterized using spectroscopic and analytical techniques such as IR, UV, NMR and elemental analysis. Antioxidant activities of all compounds were examined using free radical scavenging methods such as hydrogen peroxide scavenging activity (HPSA), 1,1-diphenyl-2-picrylhydrazyl (DPPH) and 2-2′-azino-bis-(3-ethylbenzothiazoline-6-sulfonate) (ABTS) assays. All of the tested compounds exhibited good antioxidant activity. Further, the frontier orbital energy levels, as well as various chemical properties, were determined using the density functional theory (DFT) calculations. The MEP maps of all of the derivatives were plotted to identify the nucleophilic and electrophilic reactive sites. Further, binding energies of all of the organic compounds with the protein tyrosinase was investigated to determine their potential anti-melanogenic applications. The selected ligand, L6 was subjected to molecular dynamics simulation analysis to determine the stability of the ligand–protein complex. The MD simulation was performed (150 ns) to estimate the stability of the tyrosinase–L6 complex. Other key parameters, such as, RMSD, RMSF, Rg, hydrogen bonds, SASA and MMPBSA were also analyzed to understand the interaction of L6 with the tyrosinase protein.

Chronic inflammation is an underlying feature of respiratory diseases such as chronic obstructive pulmonary disease (COPD). Novel therapies that target the inflammatory mechanisms driving acute exacerbations of COPD are required. The ST2 receptor, which binds the alarmin interleukin (IL)-33 to initiate an inflammatory response, is a potential target. Astegolimab, a fully human immunoglobulin G2 monoclonal antibody, which binds with high affinity to ST2 to prevent binding of IL-33, is a potential therapy for COPD. However, targeting inflammatory pathways that form part of the immune system may have unintended consequences, such as implications for the response to infection and cardiovascular function. Therefore, an understanding of astegolimab’s safety profile in clinical use is essential. This narrative review summarizes clinical safety data from published clinical trials of astegolimab with a focus on adverse events of interest, including infections and cardiac events. Astegolimab was shown to be well tolerated in > 580 patients with asthma, atopic dermatitis, COPD, and severe COVID-19 pneumonia who took part in Phase II trials. The frequency of adverse events (AEs) and serious AEs was similar between the astegolimab and placebo arms in each trial (AEs: 41–81% vs. 58–77%; serious AEs: 3–29% vs. 0–41%, respectively). The number of deaths was similar between treatment arms and there were no astegolimab-related deaths. Astegolimab did not increase the risk of infection or major adverse cardiac events. Ongoing Phase IIb and Phase III trials of astegolimab in patients with COPD who have a history of frequent acute exacerbation(s) of COPD will provide a future opportunity to confirm the safety profile of astegolimab.

The swift rise of digitization in legal documentation has opened doors for artificial intelligence to revolutionize various tasks within the legal domain. Among these tasks is the segmentation of legal documents using rhetorical labels. This process, known as rhetorical role labeling, involves assigning labels (such as Final Judgment, Argument, Fact, etc.) to sentences within a legal case document. This task can be down streamed to various major legal analytics problems such as summarization of legal documents, readability of lengthy case documents, document similarity estimation, etc. The mentioned task of semantic segmentation of documents via labels is challenging as the legal documents are lengthy, unstructured and the labels are subjective in nature. Various previous works on automatic rhetorical role labeling was carried out using methods like conditional random fields with handcrafted features, etc. This research focuses on analyzing case documents from two different legal systems: the High Court of Kerala and the High Court of Justice in the United Kingdom. Through rigorous experimentation with a range of deep learning models, this study highlights the robustness and efficacy of deep learning methods in accurately labeling rhetorical roles within legal texts. Additionally, comprehensive annotation of legal case documents from the UK and analysis of inter-annotator agreement are conducted. The overarching objective of this research is to design systems that facilitate a deeper comprehension of the organizational structure inherent in legal case documents.

Schiff bases and their metal complexes are widely used in the area of pharmacology owing to their anti-microbial properties. In this article, we detail the synthesis of two metal complexes, viz. Cu(II) and Zn(II) complexes of a salicylaldimine Schiff base formed from orthophenylene diamine (o-phdn) and salicylaldehyde. The complexes 1 [Cu(sal-o-phdn)] and 2 [Zn(sal-o-phdn)] are characterized by spectroscopic techniques like Fourier Transform Infrared spectroscopy (FT IR) and UV-Visible spectroscopic studies. The biological activity of the two complexes along with the Schiff base resistant to two gram positive and two gram negative bacteria is measured by Disc diffusion method. The complexes are found to be good candidates against certain microorganisms.

In chronic obstructive pulmonary disease (COPD), acute exacerbation of COPD requiring hospital admission is associated with mortality and healthcare costs. The ERICA study assessed multiple clinical measures in people with COPD, including the short physical performance battery (SPPB), a simple test of physical function with 3 components (gait speed, balance and sit-to-stand). We tested the hypothesis that SPPB score would relate to risk of hospital admissions and length of hospital stay. Data were analysed from 714 of the total 729 participants (434 men and 280 women) with COPD. Data from this prospective observational longitudinal study were obtained from 4 secondary and 1 tertiary centres from England, Scotland, and Wales. The main outcome measures were to estimate the risk of hospitalisation with acute exacerbation of COPD (AECOPD and length of hospital stay derived from hospital episode statistics (HES). In total, 291 of 714 individuals experienced 762 hospitalised AECOPD during five-year follow up. Poorer performance of SPPB was associated with both higher rate (IRR 1.08 per 1 point decrease, 95% CI 1.01 to 1.14) and increased length of stay (IRR 1.18 per 1 point decrease, 95% CI 1.10 to 1.27) for hospitalised AECOPD. For the individual sit-to-stand component of the SPPB, the association was even stronger (IRR 1.14, 95% CI 1.02 to 1.26 for rate and IRR 1.32, 95% CI 1.16 to 1.49 for length of stay for hospitalised AECOPD). The SPPB, and in particular the sit-to-stand component can both evaluate the risk of H-AECOPD and length of hospital stay in COPD. The SPPB can aid in clinical decision making and when prioritising healthcare resources.

Astegolimab, a fully human immunoglobulin G2 monoclonal antibody, binds with high affinity to ST2, the interleukin‐33 receptor, thereby blocking ST2/interleukin‐33 binding and subsequent inflammatory cascades involved in inflammatory diseases. Here, we present three randomized, double‐blind, placebo‐controlled, Phase I studies evaluating the safety, tolerability, pharmacokinetics, and immunogenicity of single‐ascending doses of astegolimab in healthy participants and patients with mild atopic asthma (NCT01928368), multiple‐ascending doses in healthy participants (NCT02170337), and single‐ascending doses in healthy Japanese and White adults. Overall, 152 participants were enrolled, randomized, and treated with single‐ or multiple‐ascending doses of astegolimab (n = 112) or placebo (n = 40) subcutaneously (2.1–560 mg) or intravenously (210 or 700 mg). No deaths, serious adverse events, or discontinuations due to adverse events occurred during the studies. No clinically meaningful differences in incidence of TEAEs were observed between treatment arms. Pharmacokinetic exposure increased more than dose proportionally over 2.1–420 mg for single‐ascending doses but were approximately dose proportional for single‐ and multiple‐ascending doses ≥ 70 mg following subcutaneous administration. No pharmacokinetic differences were observed based on ethnicity between Japanese and White participants following body weight adjustments. Incidence of antidrug antibodies to astegolimab in healthy participants in the single‐ and multiple‐ascending dose studies was 14%–23% and 33%–50% for subcutaneous and intravenous administration, respectively. Astegolimab was well tolerated in these Phase I studies with no safety concerns identified. Thus, further assessment of astegolimab in targeted patient populations was justified; the Phase IIb ALIENTO and Phase III ARNASA trials in patients with chronic obstructive pulmonary disease are ongoing.

Cardiovascular disease is a major cause of morbidity and mortality in COPD patients. Systemic inflammation associated with COPD, is often hypothesised as a causal factor. p38 mitogen-activated protein kinases play a key role in the inflammatory pathogenesis of COPD and atherosclerosis. This study sought to evaluate the effects of losmapimod, a p38 mitogen-activated protein kinase (MAPK) inhibitor, on vascular inflammation and endothelial function in chronic obstructive pulmonary disease (COPD) patients with systemic inflammation (defined by plasma fibrinogen >2·8g/l). This was a randomised, double-blind, placebo-controlled, Phase II trial that recruited COPD patients with plasma fibrinogen >2.8g/l. Participants were randomly assigned by an online program to losmapimod 7·5mg or placebo tablets twice daily for 16 weeks. Pre- and post-dose 18F-Fluorodeoxyglucose positron emission tomography co-registered with computed tomography (FDG PET/CT) imaging of the aorta and carotid arteries was performed to quantify arterial inflammation, defined by the tissue-to-blood ratio (TBR) from scan images. Endothelial function was assessed by brachial artery flow-mediated dilatation (FMD). We screened 160 patients, of whom, 36 and 37 were randomised to losmapimod or placebo. The treatment effect of losmapimod compared to placebo was not significant, at -0·05 for TBR (95% CI: -0·17, 0·07), p = 0·42, and +0·40% for FMD (95% CI: -1·66, 2·47), p = 0·70. The frequency of adverse events reported was similar in both treatment groups. In this plasma fibrinogen-enriched study, losmapimod had no effect on arterial inflammation and endothelial function at 16 weeks of treatment, although it was well tolerated with no significant safety concerns. These findings do not support the concept that losmapimod is an effective treatment for the adverse cardiovascular manifestations of COPD.