Empowering coffee farming using counterfactual recommendation based RNN driven IoT integrated soil quality command system.

Soil health is essential for whirling stale soil into rich coffee-growing land. By keeping healthy soil, coffee producers may improve plant growth, leaf health, buds, cherry and bean quality, and yield. Traditional soil monitoring is tedious, time-consuming, and error-prone. Enhancing the monitoring system using AI-based IoT technologies for quick and precise changes. Integrated soil fertility control system to optimize soil health, maximize efficiency, promote sustainability, and prevent crop threads using real-time data analysis to turn infertile land into fertile land. The RNN-IoT approach uses IoT sensors in the coffee plantation to collect real-time data on soil temperature, moisture, pH, nutrient levels, weather, CO2 levels, EC, TDS, and historical data. Data transmission using a wireless cloud platform. Testing and training using recurrent neural networks (RNNs) and gated recurrent units gathered data for predicting soil conditions and crop hazards. Researchers are carrying out detailed qualitative testing to evaluate the proposed RNN-IoT approach. Utilize counterfactual recommendations for developing alternative strategies for irrigation, fertilization, fertilizer regulation, and crop management, taking into account the existing soil conditions, forecasts, and historical data. The accuracy is evaluated by comparing it to other deep learning algorithms. The utilization of the RNN-IoT methodology for soil health monitoring enhances both efficiency and accuracy in comparison to conventional soil monitoring methods. Minimized the ecological impact by minimizing water and fertilizer utilization. Enhanced farmer decision-making and data accessibility with a mobile application that provides real-time data, AI-generated suggestions, and the ability to detect possible crop hazards for swift action.

Archimedes optimisation algorithm quantum dilated convolutional neural network for road extraction in remote sensing images.

Roads are closely intertwined with human existence, and the process of extracting road networks has emerged as the most prominent task in remote sensing (RS). The automated road interpretation process of remote sensing images (RSI) efficiently acquires road network data at a reduced expense in comparison to the traditional visual interpretation of RSI. However the manifestation of RSI is completely distinct because of the great difference in length, width, material, and shape of road networks in dissimilar areas. Thus, the extraction of road network data in RSI is still a complex issue. In recent times, DL-based approaches have projected a famous development in image segmentation outcomes, but a lot of them still could not retain boundary data and attain high-resolution road segmentation maps while processing the RSI. Traditional convolutional neural networks (CNNs) demonstrate impressive performance in road extract tasks; however, they frequently encounter difficulties in capturing intricate details and contextual information. The study introduces a novel method, named Archimedes Optimisation Algorithm, Quantum Dilated Convolutional Neural Network for Road Extraction (AOA-QDCNNRE), to tackle the challenges encountered in remote sensing images. The AOA-QDCNNRE technique aims to generate a high-resolution road segmentation map using DL with a hyperparameter tuning process. The AOA-QDCNNRE technique primarily relies on the QDCNN model, which integrates quantum technology (QC) with dilated convolutions to augment the network's capacity to capture local as well as global contextual information. In addition, the incorporation of the dilated convolutional technique effectively enhances the receptive field without sacrificing spatial resolution, enabling the extraction of precise road features. To develop the road extraction outcomes of the QDCNN approach, the AOA-based hyperparameter tuning process can be exploited. The AOA-QDCNNRE system's simulation results can be tested on benchmark databases, and the results indicate that the AOA-QDCNNRE method surpasses recent algorithms.

Dipper throated optimization with deep convolutional neural network-based crop classification for remote sensing image analysis.

Problem: With the rapid advancement of remote sensing technology is that the need for efficient and accurate crop classification methods has become increasingly important. This is due to the ever-growing demand for food security and environmental monitoring. Traditional crop classification methods have limitations in terms of accuracy and scalability, especially when dealing with large datasets of high-resolution remote sensing images. This study aims to develop a novel crop classification technique, named Dipper Throated Optimization with Deep Convolutional Neural Networks based Crop Classification (DTODCNN-CC) for analyzing remote sensing images. The objective is to achieve high classification accuracy for various food crops. Methods: The proposed DTODCNN-CC approach consists of the following key components. Deep convolutional neural network (DCNN) a GoogleNet architecture is employed to extract robust feature vectors from the remote sensing images. The Dipper throated optimization (DTO) optimizer is used for hyper parameter tuning of the GoogleNet model to achieve optimal feature extraction performance. Extreme Learning Machine (ELM): This machine learning algorithm is utilized for the classification of different food crops based on the extracted features. The modified sine cosine algorithm (MSCA) optimization technique is used to fine-tune the parameters of ELM for improved classification accuracy. Results: Extensive experimental analyses are conducted to evaluate the performance of the proposed DTODCNN-CC approach. The results demonstrate that DTODCNN-CC can achieve significantly higher crop classification accuracy compared to other state-of-the-art deep learning methods. Conclusion: The proposed DTODCNN-CC technique provides a promising solution for efficient and accurate crop classification using remote sensing images. This approach has the potential to be a valuable tool for various applications in agriculture, food security, and environmental monitoring.

Xpert Carba-R Assay on Flagged Blood Culture Samples: Clinical Utility in Intensive Care Unit Patients with Bacteremia Caused by Enterobacteriaceae.

Introduction and background: Rapid molecular diagnostics to predict carbapenem resistance well before the availability of routine drug sensitivity testing (DST) can serve as an antimicrobial stewardship tool in the context of high rates of Carbapenem-resistant Enterobacteriaceae (CRE). Materials and methods: A retrospective observational study of patients more than 18 years of age on whom Xpert Carba-R (FDA approved for rectal swab specimen) was done on gram-negative bacteria (GNB) flagged blood culture samples, in an Indian intensive care unit between January 2015 and November 2018. We analyzed the performance of Xpert Carba-R in comparison with routine DST. Results: A total of 164 GNBs were isolated from 160 patients. Klebsiella pneumoniae and Escherichia coli were the predominant isolates. Carba-R was positive in 35.36% of samples and 45.34% were carbapenem-resistant (CR) on routine DST. The distribution of the CR gene was: Oxacillinase (OXA) (50%), NDM (32.7%) followed by OXA and NDM co-expression (15.51%). The sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, positive predictive value, and negative predictive value of Carba-R were 90.74, 93.15, 13.25, 0.10, 83.58 and 96.31% for Enterobacteriaceae. The median time to obtain the Carba-R report was 30 hours 34 minutes vs 74 hours and 20 minutes for routine DST. Based on the Carba-R report, 9.72% of patients had escalation and 27.08% had de-escalation of antibiotics. Conclusion: Xpert Carba-R serves as a rapid diagnostic tool for predicting carbapenem resistance in intensive care unit patients with bacteremia caused by Enterobacteriaceae. How to cite this article: Rajendran S, Gopalakrishnan R, Tarigopula A, Kumar DS, Nambi PS, Sethuraman N, et al. Xpert Carba-R Assay on Flagged Blood Culture Samples: Clinical Utility in Intensive Care Unit Patients with Bacteremia Caused by Enterobacteriaceae. Indian J Crit Care Med 2023;27(9):655-662.

Computational linguistics based text emotion analysis using enhanced beetle antenna search with deep learning during COVID-19 pandemic.

Computational intelligence and nature-inspired computing have changed the way biologically and linguistically driven computing paradigms are made. In the last few decades, they have been used more and more to solve optimisation problems in the real world. Computational linguistics has its roots in linguistics, but most of the studies being done today are led by computer scientists. Data-driven and machine-learning methods have become more popular than handwritten language rules, which shows this shift. This study uses a new method called Computational Linguistics-based mood Analysis using Enhanced Beetle Antenna Search with deep learning (CLSA-EBASDL) to tackle the important problem of mood analysis during the COVID-19 pandemic. We sought to determine how people felt about the COVID-19 pandemic by studying social media texts. The method is made up of three main steps. First, data pre-processing changes raw data into a shape that can be used. After that, word embedding is done using the 'bi-directional encoder representations of transformers (BERT) process. An attention-based bidirectional long short-term memory (ABiLSTM) network is at the heart of mood classification. The Enhanced Beetle Antenna Search (EBAS) method, in particular, fine-tunes hyperparameters so that the ABiLSTM model works at its best. Many tests show that the CLSA-EBASDL method works better than others. Comparative studies show that it works, making it the best method for analysing opinion during the COVID-19 pandemic.

Application of Pharmacokinetic/Pharmacodynamic Modeling to Bridge Mouse Antitumor Efficacy and Monkey Toxicology Data for Determining the Therapeutic Index of an Interleukin-10 Fc Fusion Protein.

Pharmacokinetic/pharmacodynamic (PK/PD) modeling was performed to quantitatively integrate preclinical pharmacology and toxicology data for determining the therapeutic index (TI) of an interleukin-10 (IL-10) fragment crystallizable (Fc) fusion protein. Mouse Fc fused with mouse IL-10 (mFc-mIL-10) was studied in mice for antitumor efficacy, and the elevation of interleukin-18 (IL-18) was examined as a PD biomarker. The in vivo mFc-mIL-10 EC50 for the IL-18 induction was estimated to be 2.4 nM, similar to the in vitro receptor binding affinity (Kd) of 3.2 nM. The IL-18 induction was further evaluated in cynomolgus monkeys, where the in vivo induction EC50 by a human IL-10 human Fc-fusion protein (hFc-hIL-10) was 0.08 nM vs. 0.3 nM measured as the in vitro Kd. The extent of the IL-18 induction correlated with mouse antitumor efficacy and was used to connect mouse efficacy to that in monkeys. The PD-based efficacious dose projected in monkeys was comparable to the results obtained using a PK-based method in which mouse efficacious exposure was targeted and corrected for affinity differences between the species. Furthermore, PK/PD relationships were developed for anemia and thrombocytopenia in monkeys treated with hFc-hIL-10, with thrombocytopenia predicted to be dose-limiting toxicity. Using quantitative pharmacology and toxicology information obtained through modeling work in the same species, the TI of hFc-hIL-10 in monkeys was determined to be 2.4 (vs. PD-based efficacy) and 1.2-3 (vs. PK-based efficacy), indicating a narrow safety margin. The model-based approaches were proven valuable to the developability assessment of the IL-10 Fc-fusion protein.

Target-Mediated Drug Disposition Affects the Pharmacokinetics of Interleukin-10 Fragment Crystallizable Fusion Proteins at Pharmacologically Active Doses.

Fragment crystallizable (Fc) fusion is commonly used for extending the half-life of biotherapeutics such as cytokines. In this work, we studied the pharmacokinetics of Fc-fused interleukin-10 (IL-10) proteins that exhibited potent antitumor activity in mouse syngeneic tumor models. At pharmacologically active doses of ≥0.1 mg/kg, both mouse Fc-mouse IL-10 and human Fc-human IL-10, constructed as the C terminus of the Fc domain fused with IL-10 via a glycine-serine polypeptide linker, exhibited nonlinear pharmacokinetics after intravenous administration to mice at the doses of 0.05, 0.5, and 5 mg/kg. With a nominal dose ratio of 1:10:100; the ratio of the area under the curve for mouse Fc-mouse IL-10 and human Fc-human IL-10 was 1:181:1830 and 1:75:633, respectively. In contrast, recombinant mouse or human IL-10 proteins exhibited linear pharmacokinetics in mice. Compartmental analysis, using the Michaelis-Menten equation with the in vitro IL-10 receptor alpha binding affinity inputted as the Km, unified the pharmacokinetic data across the dose range. Additionally, nontarget-mediated clearance estimated for fusion proteins was ∼200-fold slower than that for cytokines, causing the manifestation of target-mediated drug disposition (TMDD) in the fusion protein pharmacokinetics. The experimental data generated with a mouse IL-10 receptor alpha-blocking antibody and a human Fc-human IL-10 mutant with a reduced receptor binding affinity showed significant improvements in pharmacokinetics, supporting TMDD as the cause of nonlinearity. Target expression and its effect on pharmacokinetics must be determined when considering using Fc as a half-life extension strategy, and pharmacokinetic evaluations need to be performed at a range of doses covering pharmacological activity. SIGNIFICANCE STATEMENT: Target-mediated drug disposition can manifest to affect the pharmacokinetics of a fragment crystallizable (Fc)-fused cytokine when the nontarget-mediated clearance of the cytokine is decreased due to neonatal Fc receptor-mediated recycling and molecular weight increases that reduce the renal clearance. The phenomenon was demonstrated with interleukin-10 Fc-fusion proteins in mice at pharmacologically active doses. Future drug designs using Fc as a half-life extension approach for cytokines need to consider target expression and its effect on pharmacokinetics at relevant doses.

MapReduce-based big data classification model using feature subset selection and hyperparameter tuned deep belief network.

In recent times, big data classification has become a hot research topic in various domains, such as healthcare, e-commerce, finance, etc. The inclusion of the feature selection process helps to improve the big data classification process and can be done by the use of metaheuristic optimization algorithms. This study focuses on the design of a big data classification model using chaotic pigeon inspired optimization (CPIO)-based feature selection with an optimal deep belief network (DBN) model. The proposed model is executed in the Hadoop MapReduce environment to manage big data. Initially, the CPIO algorithm is applied to select a useful subset of features. In addition, the Harris hawks optimization (HHO)-based DBN model is derived as a classifier to allocate appropriate class labels. The design of the HHO algorithm to tune the hyperparameters of the DBN model assists in boosting the classification performance. To examine the superiority of the presented technique, a series of simulations were performed, and the results were inspected under various dimensions. The resultant values highlighted the supremacy of the presented technique over the recent techniques.

Quantification of surrogate monoclonal antibodies in mouse serum using LC-MS/MS.

Background: Surrogate monoclonal antibodies (mAbs) used in preclinical in vivo studies can be challenging to quantify due to lack of suitable immunoaffinity reagents or unavailability of the mAb protein sequence. Generic immunoaffinity reagents were evaluated to develop sensitive LC-MS/MS assays. Peptides of unknown sequence can be used for selective LC-MS quantification. Results: anti-mouse IgG1 was found to be an effective immunoaffinity reagent, enabling quantification of mouse IgG1 mAbs in mouse serum. Selective peptides of unknown sequence were applied for multiplex LC-MS quantification of two rat mAbs co-dosed in mouse. Conclusion: Generic anti-mouse IgG subtype-specific antibodies can be used to improve assay sensitivity and peptides of unknown sequence can be used to quantify surrogate mAbs when the mAb protein sequence in unavailable.

Chest radiography in acute aortic syndrome: pearls and pitfalls.

Acute aortic syndrome is a group of life-threatening diseases of the thoracic aorta that usually present to the emergency department. It includes aortic dissection, aortic intramural hematoma, and penetrating aortic ulcer. Rare aortic pathologies of aorto-esophageal fistula and mycotic aneurysm may also be included in this list. All these conditions require urgent treatment with complex clinical care and management. Most patients who present with chest pain are evaluated with a chest radiograph in the emergency department. It is important that maximum diagnostic information is extracted from the chest radiograph as certain signs on the chest radiograph are extremely useful in pointing towards the diagnosis of acute aortic syndrome.

Quantification of human mAbs in mouse tissues using generic affinity enrichment procedures and LC-MS detection.

BACKGROUND: The disease state can modulate the penetration of large antibody-sized therapeutic molecules into affected tissues. Suitable bioanalytical methods are required for the quantitative analysis of drug tissue levels to enable a better understanding of the parameters influencing drug penetration and target engagement. RESULTS: Described is a sensitive and selective LC-MS/MS assay for the quantification of human mAb molecules in mouse tissues. By homogenizing tissues directly into serum, a common serum calibration curve can be used for multiple tissues. A generic procedure was used for affinity enrichment. An analytical range of 20 - 20,000 ng/ml was achieved in serum. CONCLUSION: The method described here can be applied for the quantitative analysis of mAb and Fc-fusion therapeutic molecules in a variety of animal tissue matrices.

Validation of a ligand binding assay using dried blood spot sampling.

Dried blood spots (DBS) technology has been introduced as a microsampling alternative to traditional plasma or serum sampling for pharmacokinetics or toxicokinetics evaluation. The application of DBS has been established for many small molecule drugs at discovery, nonclinical, and clinical stages. However, the application of DBS for large molecule therapeutics development is not yet well-established. This article describes the method validation of a ligand binding assay (LBA) for DBS sampling of a therapeutic monoclonal antibody-AMG 162 (Denosumab). The original serum LBA was modified for the DBS method. A fit-for-purpose method validation was performed to evaluate accuracy and precision, selectivity, dilutional linearity, and stability. In addition, the parameters relevant to DBS, such as spot volume, extraction recovery, whole blood stability, and hematocrit effects, were evaluated. The validation results demonstrated assay robustness with inter-assay precision of ≤ 19%, inter-assay accuracy of ≤ 9%, and total error of ≤ 24%. Selectivity, extraction recovery, dilutional linearity, and stability were demonstrated. The validation results revealed some limitations of the possible effect of blood hematocrit on therapeutic concentration measurements and the caution required using whole blood for standards and quality controls preparation. This is the first article to describe a thorough method validation of an LBA using DBS for a therapeutic monoclonal antibody. The lessons learned can serve as a model process for future method validation of other LBAs for large molecule therapeutics or biomarkers using the DBS sampling method.

Rapid affinity measurement of protein-protein interactions in a microfluidic platform.

A rapid screening method has been developed to determine binding affinities for protein-ligand interactions using the Gyrolab workstation, a commercial microfluidic platform developed to accurately and precisely quantify proteins in solution. This method was particularly suited for assessing the high-affinity interactions that have become typical of therapeutic antibody-antigen systems. Five different commercially available antibodies that bind digoxin and a digoxin-bovine serum albumin (BSA) conjugate with high affinity were rigorously evaluated by this method and by the more conventional kinetic exclusion assay (KinExA) method. Binding parameter values obtained using Gyrolab were similar to those recovered from KinExA. However, the total experimental time for 20 binding affinity titrations, with each titration covering 12 data points in duplicate, took approximately 4h by the Gyrolab method, which reduced the experimental duration by more than 10-fold when compared with the KinExA method. This rapid binding analysis method has significant applications in the screening and affinity ranking selection of antibodies from a very large pool of candidates spanning a wide range of binding affinities from the low pM to µM range.

Activity of the isolated HIV RNase H domain and specific inhibition by N-hydroxyimides.

This report describes a procedure to generate enzymatically active, isolated HIV RNase H domain. In contrast to previously described preparations, the RNA cleavage activity of the untagged RNase H domain was surprisingly similar to that of the full-length HIV-RT protein. Signature cleavages at 18 and 9 nucleotides downstream of a recessed RNA 5'-end were retained with the isolated RNase H domain. Activity was strongly decreased by deletion of 3 amino acids from the C-terminus, consistent with an important structural or functional role of the C-terminal alpha-helix. A prototype N-hydroxyimide (2-hydroxy-4H-isoquinoline-1,3-dione) was found to inhibit the activity of the isolated HIV RNase H domain as well as the RNase H activity of full-length HIV reverse transcriptase. In contrast, the compound did not significantly inhibit the structurally closely related Escherichia coli RNase HI. Specific binding of N-hydroxyimide compounds to the isolated RNase H domain was observed by protein fluorescence quenching.

Two-metal ion mechanism of RNA cleavage by HIV RNase H and mechanism-based design of selective HIV RNase H inhibitors.

Human immunodeficiency virus (HIV) RNase H activity is essential for the synthesis of viral DNA by HIV reverse transcriptase (HIV-RT). RNA cleavage by RNase H requires the presence of divalent metal ions, but the role of metal ions in the mechanism of RNA cleavage has not been resolved. We measured HIV RNase H activity associated with HIV-RT protein in the presence of different concentrations of either Mg2+, Mn2+, Co2+ or a combination of these divalent metal ions. Polymerase-independent HIV RNase H was similar to or more active with Mn2+ and Co2+ compared with Mg2+. Activation of RNase H by these metal ions followed sigmoidal dose-response curves suggesting cooperative metal ion binding. Titration of Mg2+-bound HIV RNase H with Mn2+ or Co2+ ions generated bell-shaped activity dose-response curves. Higher activity could be achieved through simultaneous binding of more than one divalent metal ion at intermediate Mn2+ and Co2+ concentrations, and complete replacement of Mg2+ occurred at higher Mn2+ or Co2+ concentrations. These results are consistent with a two-metal ion mechanism of RNA cleavage as previously suggested for a number of polymerase-associated nucleases. In contrast, the structurally highly homologous RNase HI from Escherichia coli is most strongly activated by Mg2+, is significantly inhibited by submillimolar concentrations of Mn2+ and most probably cleaves RNA via a one-metal ion mechanism. Based on this difference in active site structure, a series of small molecule N-hydroxyimides was identified with significant enzyme inhibitory potency and selectivity for HIV RNase H.