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Progress in Alternative Strategies to Combat Antimicrobial Resistance: Focus on Antibiotics
Antibiotic resistance, and, in a broader perspective, antimicrobial resistance (AMR), continues to evolve and spread beyond all boundaries. As a result, infectious diseases have become more challenging or even impossible to treat, leading to an increase in morbidity and mortality. Despite the failure of conventional, traditional antimicrobial therapy, in the past two decades, no novel class of antibiotics has been introduced. Consequently, several novel alternative strategies to combat these (multi-) drug-resistant infectious microorganisms have been identified. The purpose of this review is to gather and consider the strategies that are being applied or proposed as potential alternatives to traditional antibiotics. These strategies include combination therapy, techniques that target the enzymes or proteins responsible for antimicrobial resistance, resistant bacteria, drug delivery systems, physicochemical methods, and unconventional techniques, including the CRISPR-Cas system. These alternative strategies may have the potential to change the treatment of multi-drug-resistant pathogens in human clinical settings.
Journal Article
Artificial Intelligence for big data : complete guide to automating big data solutions using artificial intelligence techniques
Annotation Build next-generation Artificial Intelligence systems with JavaKey FeaturesImplement AI techniques to build smart applications using Deeplearning4j Perform big data analytics to derive quality insights using Spark MLlibCreate self-learning systems using neural networks, NLP, and reinforcement learningBook DescriptionIn this age of big data, companies have larger amount of consumer data than ever before, far more than what the current technologies can ever hope to keep up with. However, Artificial Intelligence closes the gap by moving past human limitations in order to analyze data.With the help of Artificial Intelligence for big data, you will learn to use Machine Learning algorithms such as k-means, SVM, RBF, and regression to perform advanced data analysis. You will understand the current status of Machine and Deep Learning techniques to work on Genetic and Neuro-Fuzzy algorithms. In addition, you will explore how to develop Artificial Intelligence algorithms to learn from data, why they are necessary, and how they can help solve real-world problems.By the end of this book, you'll have learned how to implement various Artificial Intelligence algorithms for your big data systems and integrate them into your product offerings such as reinforcement learning, natural language processing, image recognition, genetic algorithms, and fuzzy logic systems.What you will learnManage Artificial Intelligence techniques for big data with JavaBuild smart systems to analyze data for enhanced customer experienceLearn to use Artificial Intelligence frameworks for big dataUnderstand complex problems with algorithms and Neuro-Fuzzy systemsDesign stratagems to leverage data using Machine Learning processApply Deep Learning techniques to prepare data for modelingConstruct models that learn from data using open source toolsAnalyze big data problems using scalable Machine Learning algorithmsWho this book is forThis book is for you if you are a data scientist, big data professional, or novice who has basic knowledge of big data and wish to get proficiency in Artificial Intelligence techniques for big data. Some competence in mathematics is an added advantage in the field of elementary linear algebra and calculus
Book
Insurance claims estimation and fraud detection with optimized deep learning techniques
Estimation and fraud detection in the case of insurance claims play a cardinal role in the insurance sector. With accurate estimation of insurance claims, insurers can have good risk perceptions and disburse compensation within proper time, while fraud prevention helps deter massive monetary loss from fraudulent activities. Financial fraud has done significant damage to the global economy, thus threatening the stability and efficiency of capital markets. Deep learning techniques have proven highly effective in addressing these challenges to analyse complex patterns and relationships in extensive datasets. Unlike traditional statistical methods, which often struggle with the intricate nature of insurance claims data, deep learning models performs well in handling diverse variables and factors influencing claim outcomes. To this extent, it explores the deep learning models like VGG 16 & 19, ResNet 50, and a custom 12 & 15-layer Convolutional Neural Network for accurate estimation of insurance claims and detection of fraud. The proposed work enhanced with Enhanced Hippopotamus Optimization Algorithm (EHOA) combined with a custom 12-layer CNN to optimize the hyperparameters and enhance the performance of the model. Overcoming challenges such as local minima and slow convergence, dynamic population adjustment, momentum-based updates, and hybrid fine-tuning are used with the EHOA. The experimental results reveal that the newly proposed EHOA-CNN-12 attains excellent accuracy (92%) and efficiency in comparison to other state-of-the-art approaches in claims estimation and fraud detection tasks.
Journal Article
Exploring mechanical behavior at interfaces of laser powder bed fusion (LPBF) deposits on wrought Inconel 718: an indentation-based approach
Purpose
This study used an indentation-based mechanical testing framework for the mechanical characterization of laser powder bed fusion (LPBF) processed Inconel 718 on a wrought Inconel 718 substrate. The purpose of the paper is to investigate the effectiveness of the indentation-based approach for localized mechanical evaluation.
Design/methodology/approach
The LPBF-processed wrought substrate was sectioned into three sections for microstructural and mechanical characterization. A 3D heat source model was used for the thermal analysis of the interface region. The developed interface region is probed using the Knoop hardness indenter in different orientations to determine the textural anisotropy and mechanical behavior of the region.
Findings
LPBF process develops a melted interface zone (MIZ) at the deposition-substrate interface. The MIZ exhibited a coarse grain structure region along with a larger primary dendritic arm spacing (PDAS), signifying a slower cooling rate. FE modeling of the LPBF process reveals heat accumulation in the substrate along with intrinsic heat treatment (IHT) induced due to layer-wise processing. The obtained yield locus shows strong anisotropy in the deposition region, whereas reduced anisotropy with a nearly uniform ellipse locus for the MIZ regions. This reduced anisotropy is attributable to IHT and heat accumulation in the substrate.
Originality/value
An alternative localized mechanical characterization tool has been investigated in this work. The approach proved sensitive to thermal variations during LPBF processing in an isolated region which extends its suitability to variable geometry parts. Moreover, the approach could serve as a screening tool for parts made from dissimilar metals.
Journal Article
Intraoperative molecular imaging: 3rd biennial clinical trials update
This third biennial intraoperative molecular imaging (IMI) conference shows how optical contrast agents have been applied to develop clinically significant endpoints that improve precision cancer surgery.
National and international experts on IMI presented ongoing clinical trials in cancer surgery and preclinical work. Previously known dyes (with broader applications), new dyes, novel nonfluorescence-based imaging techniques, pediatric dyes, and normal tissue dyes were discussed.
Principal investigators presenting at the Perelman School of Medicine Abramson Cancer Center's third clinical trials update on IMI were selected to discuss their clinical trials and endpoints.
Dyes that are FDA-approved or currently under clinical investigation in phase 1, 2, and 3 trials were discussed. Sections on how to move benchwork research to the bedside were also included. There was also a dedicated section for pediatric dyes and nonfluorescence-based dyes that have been newly developed.
IMI is a valuable adjunct in precision cancer surgery and has broad applications in multiple subspecialties. It has been reliably used to alter the surgical course of patients and in clinical decision making. There remain gaps in the utilization of IMI in certain subspecialties and potential for developing newer and improved dyes and imaging techniques.
Journal Article
CRISPR/Cas genome editing in tomato improvement: Advances and applications
The narrow genetic base of tomato poses serious challenges in breeding. Hence, with the advent of clustered regularly interspaced short palindromic repeat (CRISPR)-associated protein9 (CRISPR/Cas9) genome editing, fast and efficient breeding has become possible in tomato breeding. Many traits have been edited and functionally characterized using CRISPR/Cas9 in tomato such as plant architecture and flower characters (e.g. leaf, stem, flower, male sterility, fruit, parthenocarpy), fruit ripening, quality and nutrition (e.g., lycopene, carotenoid, GABA, TSS, anthocyanin, shelf-life), disease resistance (e.g. TYLCV, powdery mildew, late blight), abiotic stress tolerance (e.g. heat, drought, salinity), C-N metabolism, and herbicide resistance. CRISPR/Cas9 has been proven in introgression of de novo domestication of elite traits from wild relatives to the cultivated tomato and vice versa. Innovations in CRISPR/Cas allow the use of online tools for single guide RNA design and multiplexing, cloning (e.g. Golden Gate cloning, GoldenBraid, and BioBrick technology), robust CRISPR/Cas constructs, efficient transformation protocols such as Agrobacterium , and DNA-free protoplast method for Cas9-gRNAs ribonucleoproteins (RNPs) complex, Cas9 variants like PAM-free Cas12a, and Cas9-NG/XNG-Cas9, homologous recombination (HR)-based gene knock-in (HKI) by geminivirus replicon, and base/prime editing (Target-AID technology). This mini-review highlights the current research advances in CRISPR/Cas for fast and efficient breeding of tomato.
Journal Article
A Review of CeO2 Supported Catalysts for CO2 Reduction to CO through the Reverse Water Gas Shift Reaction
The catalytic conversion of CO2 to CO by the reverse water gas shift (RWGS) reaction followed by well-established synthesis gas conversion technologies could be a practical technique to convert CO2 to valuable chemicals and fuels in industrial settings. For catalyst developers, prevention of side reactions like methanation, low-temperature activity, and selectivity enhancements for the RWGS reaction are crucial concerns. Cerium oxide (ceria, CeO2) has received considerable attention in recent years due to its exceptional physical and chemical properties. This study reviews the use of ceria-supported active metal catalysts in RWGS reaction along with discussing some basic and fundamental features of ceria. The RWGS reaction mechanism, reaction kinetics on supported catalysts, as well as the importance of oxygen vacancies are also explored. Besides, recent advances in CeO2 supported metal catalyst design strategies for increasing CO2 conversion activity and selectivity towards CO are systematically identified, summarized, and assessed to understand the impacts of physicochemical parameters on catalytic performance such as morphologies, nanosize effects, compositions, promotional abilities, metal-support interactions (MSI) and the role of selected synthesis procedures for forming distinct structural morphologies. This brief review may help with future RWGS catalyst design and optimization.
Journal Article
HDL-ACO hybrid deep learning and ant colony optimization for ocular optical coherence tomography image classification
Optical Coherence Tomography (OCT) plays a crucial role in diagnosing ocular diseases, yet conventional CNN-based models face limitations such as high computational overhead, noise sensitivity, and data imbalance. This paper introduces HDL-ACO, a novel Hybrid Deep Learning (HDL) framework that integrates Convolutional Neural Networks with Ant Colony Optimization (ACO) to enhance classification accuracy and computational efficiency. The proposed methodology involves pre-processing the OCT dataset using discrete wavelet transform and ACO-optimized augmentation, followed by multiscale patch embedding to generate image patches of varying sizes. The hybrid deep learning model leverages ACO-based hyperparameter optimization to enhance feature selection and training efficiency. Furthermore, a Transformer-based feature extraction module integrates content-aware embeddings, multi-head self-attention, and feedforward neural networks to improve classification performance. Experimental results demonstrate that HDL-ACO outperforms state-of-the-art models, including ResNet-50, VGG-16, and XGBoost, achieving 95% training accuracy and 93% validation accuracy. The proposed framework offers a scalable, resource-efficient solution for real-time clinical OCT image classification.
Journal Article
Business Model Innovation for Inclusive Health Care Delivery at the Bottom of the Pyramid
This article investigates business models innovation for delivering health care at the base of the pyramid (BoP). The examination of six health care organizational cases suggests that co-creation of patient needs, community engagement, continuous involvement of customers, innovative medical technology, focus on human resources for health, strategic partnerships, economies of scale, and cross-subsidization are business model innovation strategies that enable inclusive health care delivery. Based on these findings, we propose a four-dimensional framework. A process of value discovery, leading BoP patients and communities to recognize a health need and seek for an acceptable treatment, precedes the identification of a successful value proposition. Value creation and value appropriation then follow to warrant patient affordability and organizational sustainability. A “business model mechanism” for BoP health care hence emerges, where interdependencies among these dimensions are highlighted. This article sheds new light on how market-based approaches can improve equitable health care access and hence contribute to poverty alleviation.
Journal Article