Optimizing Hydrogen Production in the Co-Gasification Process: Comparison of Explainable Regression Models Using Shapley Additive Explanations.

The co-gasification of biomass and plastic waste offers a promising solution for producing hydrogen-rich syngas, addressing the rising demand for cleaner energy. However, optimizing this complex process to maximize hydrogen yield remains challenging, particularly when balancing diverse feedstocks and improving process efficiency. While machine learning (ML) has shown significant potential in simulating and optimizing such processes, there is no clear consensus on the most effective regression models for co-gasification, especially with limited experimental data.

The novel quinoline derivative SKA-346 as a K3.1 channel selective activator.

The calcium-activated K3.1 channel plays a crucial role in T-cell immune response. Genetic manipulation of T-cells to upregulate the expression of K channels has been shown to boost T-cell cytotoxicity in cancer.

Metaheuristics based dimensionality reduction with deep learning driven false data injection attack detection for enhanced network security.

Recent sensor, communication, and computing technological advancements facilitate smart grid use. The heavy reliance on developed data and communication technology increases the exposure of smart grids to cyberattacks. Existing mitigation in the electricity grid focuses on protecting primary or redundant measurements.

Iron-(Fe3+)-Dependent Reactivation of Telomerase Drives Colorectal Cancers.

Over-consumption of iron-rich red meat and hereditary or genetic iron overload are associated with an increased risk of colorectal carcinogenesis, yet the mechanistic basis of how metal-mediated signaling leads to oncogenesis remains enigmatic. Using fresh colorectal cancer samples we identify Pirin, an iron sensor, that overcomes a rate-limiting step in oncogenesis, by reactivating the dormant human telomerase reverse transcriptase (hTERT) subunit of the telomerase holoenzyme in an iron-(Fe3+)-dependent manner and thereby drives colorectal cancers. Chemical genetic screens combined with isothermal dose-response fingerprinting and mass spectrometry identified a small molecule SP2509 that specifically inhibits Pirin-mediated hTERT reactivation in colorectal cancers by competing with iron-(Fe3+) binding.

Tumour microenvironment programming by an RNA-RNA-binding protein complex creates a druggable vulnerability in IDH-wild-type glioblastoma.

Patients with IDH-wild-type glioblastomas have a poor five-year survival rate along with limited treatment efficacy due to immune cell (glioma-associated microglia and macrophages) infiltration promoting tumour growth and resistance. To enhance therapeutic options, our study investigated the unique RNA-RNA-binding protein complex LOC-DHX15. This complex plays a crucial role in driving immune cell infiltration and tumour growth by establishing a feedback loop between cancer and immune cells, intensifying cancer aggressiveness.

Dependency of NELF-E-SLUG-KAT2B epigenetic axis in breast cancer carcinogenesis.

Cancer cells undergo transcriptional reprogramming to drive tumor progression and metastasis. Using cancer cell lines and patient-derived tumor organoids, we demonstrate that loss of the negative elongation factor (NELF) complex inhibits breast cancer development through downregulating epithelial-mesenchymal transition (EMT) and stemness-associated genes. Quantitative multiplexed Rapid Immunoprecipitation Mass spectrometry of Endogenous proteins (qPLEX-RIME) further reveals a significant rewiring of NELF-E-associated chromatin partners as a function of EMT and a co-option of NELF-E with the key EMT transcription factor SLUG.

A convolutional neural network for face mask detection in IoT-based smart healthcare systems.

The new coronavirus that produced the pandemic known as COVID-19 has been going across the world for a while. Nearly every area of development has been impacted by COVID-19. There is an urgent need for improvement in the healthcare system.

Design of a Honey Badger Optimization Algorithm with a Deep Transfer Learning-Based Osteosarcoma Classification Model.

Osteosarcoma is one of the aggressive bone tumors with numerous histologic patterns. Histopathological inspection is a crucial criterion in the medical diagnosis of Osteosarcoma. Due to the advancement of computing power and hardware technology, pathological image analysis system based on artificial intelligence (AI) were more commonly used.

Deep Learning Approaches for Automatic Localization in Medical Images.

Recent revolutionary advances in deep learning (DL) have fueled several breakthrough achievements in various complicated computer vision tasks. The remarkable successes and achievements started in 2012 when deep learning neural networks (DNNs) outperformed the shallow machine learning models on a number of significant benchmarks. Significant advances were made in computer vision by conducting very complex image interpretation tasks with outstanding accuracy.

Computational Intelligence-Based Melanoma Detection and Classification Using Dermoscopic Images.

Melanoma is a kind of skin cancer caused by the irregular development of pigment-producing cells. Since melanoma detection efficiency is limited to different factors such as poor contrast among lesions and nearby skin regions, and visual resemblance among melanoma and non-melanoma lesions, intelligent computer-aided diagnosis (CAD) models are essential. Recently, computational intelligence (CI) and deep learning (DL) techniques are utilized for effective decision-making in the biomedical field.

An investigation and comparison of machine learning approaches for intrusion detection in IoMT network.

Internet of Medical Things (IoMT) is network of interconnected medical devices (smart watches, pace makers, prosthetics, glucometer, etc.), software applications, and health systems and services. IoMT has successfully addressed many old healthcare problems.

Intelligent Deep-Learning-Enabled Decision-Making Medical System for Pancreatic Tumor Classification on CT Images.

Decision-making medical systems (DMS) refer to the design of decision techniques in the healthcare sector. They involve a procedure of employing ideas and decisions related to certain processes such as data acquisition, processing, judgment, and conclusion. Pancreatic cancer is a lethal type of cancer, and its prediction is ineffective with current techniques.

p53-NEIL1 co-abnormalities induce genomic instability and promote synthetic lethality with Chk1 inhibition in multiple myeloma having concomitant 17p13(del) and 1q21(gain).

Recurrent cytogenetic abnormalities are the main hallmark of multiple myeloma (MM) and patients having 2 or more high-risk prognostic events are associated with extremely poor outcome. 17p13(del) and 1q21(gain) are critical and independent high-risk cytogenetic markers, however, the biological significance underlying the poor outcome in MM patients having co-occurrence of both these chromosomal aberrations has never been interrogated. Herein, we identified that patients harbouring concomitant 17p13(del) with 1q21(gain) demonstrated the worst prognosis as compared to patients with single- (either 17p13(del) or 1q21(gain)) and with no chromosomal events (WT for both chromosomal loci); and they are highly enriched for genomic instability (GI) signature.

Design of Metaheuristic Optimization-Based Vascular Segmentation Techniques for Photoacoustic Images.

Biomedical imaging technologies are designed to offer functional, anatomical, and molecular details related to the internal organs. Photoacoustic imaging (PAI) is becoming familiar among researchers and industrialists. The PAI is found useful in several applications of brain and cancer imaging such as prostate cancer, breast cancer, and ovarian cancer.

Enhanced triacylglycerol catabolism by carboxylesterase 1 promotes aggressive colorectal carcinoma.

The ability to adapt to low-nutrient microenvironments is essential for tumor cell survival and progression in solid cancers, such as colorectal carcinoma (CRC). Signaling by the NF-κB transcription factor pathway associates with advanced disease stages and shorter survival in patients with CRC. NF-κB has been shown to drive tumor-promoting inflammation, cancer cell survival, and intestinal epithelial cell (IEC) dedifferentiation in mouse models of CRC.

Application of deep autoencoder as an one-class classifier for unsupervised network intrusion detection: a comparative evaluation.

The ever-increasing use of internet has opened a new avenue for cybercriminals, alarming the online businesses and organization to stay ahead of evolving thread landscape. To this end, intrusion detection system (IDS) is deemed as a promising defensive mechanism to ensure network security. Recently, deep learning has gained ground in the field of intrusion detection but majority of progress has been witnessed on supervised learning which requires adequate labeled data for training.

Small Molecule NF-κB Pathway Inhibitors in Clinic.

Nuclear factor kappa B (NF-κB) signaling is implicated in all major human chronic diseases, with its role in transcription of hundreds of gene well established in the literature. This has propelled research into targeting the NF-κB pathways for modulating expression of those genes and the diseases mediated by them. In-spite of the critical, but often promiscuous role played by this pathway and the inhibition causing adverse drug reaction, currently many biologics, macromolecules, and small molecules that modulate this pathway are in the market or in clinical trials.

Synthesis and pharmacological evaluation of novel N-aryl-3,4-dihydro-1'H-spiro[chromene-2,4'-piperidine]-1'-carboxamides as TRPM8 antagonists.

A novel series of N-aryl-3,4-dihydro-1'H-spiro[chromene-2,4'-piperidine]-1'-carboxamides was identified as transient receptor potential melastatin 8 (TRPM8) channel blockers through analogue-based rational design, synthesis and screening. Details of the synthesis, effect of aryl groups and their substituents on in-vitro potency were studied. The effects of selected functional groups on the 4-position of the chromene ring were also studied, which showed interesting results.