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Exploring entropy measures with topological indices on colorectal cancer drugs using curvilinear regression analysis and machine learning approaches

A topological index is a numerical value derived from the structure of a molecule or graph that provides useful information about the molecule’s physical, chemical, or biological properties. These indices are especially important in chemo-informatics and QSAR/QSPR (Quantitative Structure-Activity Relationship/Quantitative Structure-Property Relationship) studies, where they are used to predict a wide range of properties without the need for experimental measurements. In essence, a topological index is a way to quantify the molecular structure in a form that can be used in mathematical models to estimate the molecule’s behavior, activity, or properties. In terms of chemical graph theory and chemo-informatics, entropy-based indices quantify the structural complexity or disorder in a molecule’s connectivity. These indices are useful for modeling and predicting molecular properties and biological activities. In this paper, we established a QSPR analysis of colorectal drugs between entropy indices and their physical properties and developed a relationship. Through a comprehensive analysis of these drugs, we gain essential insights into their molecular properties, which are vital for predicting their behavior and effectiveness in treating colorectal cancer. These models are compared with existing degree-based models, highlighting the superior performance of our approach. The QSPR study is performed using curvilinear regression models including linear, quadratic, cubic exponential and logarithmic models. Additionally, we propose the integration of machine learning (ML) techniques to further enhance the predictive accuracy and robustness of our models. By leveraging advanced ML algorithms, we aim to uncover more complex, non-linear relationships between topological indices and drug efficacy, potentially leading to more accurate predictions and better-informed drug design strategies.

Bacteriome and mycobiome profiling of liquid feed for finisher pigs on commercial pig farms

Abstract The aim was to assess the quality of liquid feed for grow-finisher pigs across commercial pig farms by profiling the bacteriome and mycobiome of samples and determining biogenic amine concentrations. Amplicon sequencing of liquid feed samples revealed that bacterial and fungal community structures were influenced by the farm of origin and sampling location (mixing tank/trough) on a given farm. Decreases in alpha-diversity of liquid feed between the mixing tank and the troughs corresponded with increased relative abundances of bacteria, particularly Lactobacillus, Weissella and Leuconostoc, as well as yeasts, including Kazachstania and Dipodascus, indicative of spontaneous fermentation. The concentration of biogenic amines, resulting from amino acid loss from the feed, which likely plays a role in poorer feed efficiency, also increased between the mixing tank and the troughs. The highest biogenic amine concentrations in the feed were found on the farm that had the highest lactic acid bacteria (LAB) and yeast counts. Both Lactobacillus and Kazachstania were correlated with biogenic amine concentrations in liquid feed, highlighting the unexplored role that LAB and yeast may play in amino acid decarboxylation and biogenic amine formation in liquid feed. Factors including the use of liquid co-products in diets also impacted the liquid feed microbiome.

A Radiolabeled Photoswitchable G Protein-Coupled Receptor Antagonist Enlightens Ligand Binding Kinetics Associated with Photoswitching

Enhancing microbial-induced calcium carbonate precipitation efficiency in calcareous sands through ferric ion additives: A comprehensive experimental investigation

In recent years, the reinforcement of calcareous sands using the microbially induced calcium carbonate precipitation (MICP) method has emerged as a prominent research area. Nevertheless, a significant drawback of the MICP method is that multiple treatments with the cementing solution are required to achieve the desired improvement effect. To address this limitation, this study proposes an optimized MICP strategy through adding the ferric ion into cementing solutions. The effectiveness of the proposed method was investigated by analyzing the precipitation of CaCO3, unconfined compressive strength (UCS) and permeability coefficient through aqueous solution test and sand column reinforcement test. Experimental results revealed that ferric ion incorporation significantly altered CaCO3 crystal morphology and particle size distribution in aqueous solution test. In sand column tests, specimens treated with cementing solution with ferric ion achieved the UCS of 2.83 MPa after five injection cycles, representing a 15-fold increase compared to conventional MICP-treated specimens under the same test conditions. At the same time, permeability coefficients decreased by two orders of magnitude relative to untreated sand. The micro-structure analysis showed that ferric ions were involved in the reaction to generate a clogging precipitate, which changed the distribution of bio-CaCO3 in the pores of the soil, thereby improving the cementation efficiency. These findings indicate that the addition of ferric ion can overcome the shortcoming of frequent treatment of cementing solution in MICP-reinforced calcareous sand, and provide new insights for the development of effective biological grouting strategies.

Exploring explainable machine learning algorithms to model predictors of tobacco use among men in Sub Sahara Africa between 2018 and 2023

μMap-FFPE: A High-Resolution Protein Proximity Labeling Platform for Formalin-Fixed Paraffin-Embedded Tissue Samples

Workpiece surface defect detection based on YOLOv11 and edge computing

The rapid development of modern industry has significantly raised the demand for workpieces. To ensure the quality of workpieces, workpiece surface defect detection has become an indispensable part of industrial production. Most workpiece surface defect detection technologies rely on cloud computing. However, transmitting large volumes of data via wireless networks places substantial computational burdens on cloud servers, significantly reducing defect detection speed. Therefore, to enable efficient and precise detection, this paper proposes a workpiece surface defect detection method based on YOLOv11 and edge computing. First, the NEU-DET dataset was expanded using random flipping, cropping, and the self-attention generative adversarial network (SA-GAN). Then, the accuracy indicators of the YOLOv7–YOLOv11 models were compared on NEU-DET and validated on the Tianchi aluminium profile surface defect dataset. Finally, the cloud-based YOLOv11 model, which achieved the highest accuracy, was converted to the edge-based YOLOv11-RKNN model and deployed on the RK3568 edge device to improve the detection speed. Results indicate that YOLOv11 with SA-GAN achieved mAP@0.5 improvements of 7.7%, 3.1%, 5.9%, and 7.0% over YOLOv7, YOLOv8, YOLOv9, and YOLOv10, respectively, on the NEU-DET dataset. Moreover, YOLOv11 with SA-GAN achieved an 87.0% mAP@0.5 on the Tianchi aluminium profile surface defect dataset, outperforming the other models again. This verifies the generalisability of the YOLOv11 model. Additionally, quantising and deploying YOLOv11 on the edge device reduced its size from 10,156 kB to 4,194 kB and reduced its single-image detection time from 52.1ms to 33.6ms, which represents a significant efficiency enhancement.

Machine learning-based identification of key factors and spatial heterogeneity analysis of urban flooding: a case study of the central urban area of Ordos

Enzymatic Stereoselective Nucleophilic Cyclization Governs Atypical Spirotetronate Assembly in Lucensimycin A Biosynthesis

Distribution of chronic wasting disease (CWD) prions in tissues from experimentally exposed coyotes (Canis latrans)

Cervids susceptible to chronic wasting disease (CWD) are sympatric with multiple other animal species that can interact with infectious prions. Several reports have described the susceptibility of other species to CWD prions, or their potential to transport them. One of these species is the coyote (Canis latrans), which has been previously shown to pass transmission-relevant prion titers in their feces for at least three days after ingesting prion-positive brain material. The current study followed up on these findings and evaluated the distribution of prions in multiple tissues from the same coyotes. Our results show that prions persist in the digestive tract of prion-exposed coyotes five days after exposure. Moreover, prion seeding activity was identified in other tissues, including lymph nodes and lungs. These results provide additional information about the dynamics of CWD prions in the environment and show the initial fate of prions after ingestion by a canid species that is a carnivorous predator and scavenger.

Electricity usage prediction using developed human evolutionary optimization algorithm and Xception neural network

A 54.6 GHz Clock Transition in Ho³⁺ Electron Spin Qubits Assembled into a Metal–Organic Framework

Focal adhesion-related non-ciliary functions of CEP290

Nearly all differentiated mammalian cells possess primary cilia on their surface. Ciliary dysfunction causes ciliopathy in humans. Centrosomal protein 290 (CEP290), a ciliary protein implicated in ciliopathies, localizes to the ciliary base and the centrosome in ciliated cells. CEP290-related ciliopathies arise from molecular dysfunctions of the CEP290 molecule, exhibiting a diverse range of symptoms. Thus far, these disorders have been attributed to cilia-specific functional abnormalities of CEP290, reflecting the conventional view of its primary role within cilia. However, CEP290 is also expressed in proliferating non-ciliated cells and localizes to the centrosome, suggesting potential cilia-independent functions of CEP290 in the pathophysiology of these disorders. In this study, we investigated the cilia-independent functions of CEP290 in non-ciliated cells. Our findings reveal that the loss of Cep290 function impairs microtubule elongation due to malfunction of the microtubule organizing center. Notably, CEP290 forms a complex with adenomatous polyposis coli (APC), a protein that localizes to the centrosome and associates with microtubules. Importantly, reduced focal adhesion formation appears to underlie the phenotypic abnormalities observed in Cep290 knockout cells, including impaired collective cell migration, altered cell morphology, and reduced adhesive capacity to the extracellular matrix. The APC-CEP290 complex plays a consistent and crucial role in stabilizing a focal adhesion molecule, paxillin, at the leading edge in non-ciliated cells. These findings provide a novel framework for understanding the molecular mechanisms underlying ciliopathies, highlighting the importance of CEP290’s cilia-independent functions.

Implementing and evaluating the quality 4.0 PMQ framework for process monitoring in automotive manufacturing

Single-Component High-Performance Adhesives Enabled by Synergistic Supramolecular and Covalent Polymerization

Correction: All is not loss: Plant biodiversity in the anthropocene

Concept and preliminary structural analysis of a crater-covering dome for future lunar habitats

General Modular and Convergent Approach to Diversely Functionalized Allylic Systems

Papillary thyroid microcarcinoma and papillary thyroid carcinoma: Clinical characteristics and stratification of treatment strategies

Aim Exploring the clinical differences between papillary thyroid micarcinoma (PTMC) and papillary thyroid carcinoma (PTC), optimizing clinical decision-making pathways, and reducing excessive medical behavior while ensuring therapeutic efficacy. Method Patients diagnosed with PTMC or PTC by pathological histology from May 2023 to May 2024 at Jinan Shizhong District People’s Hospital were retrospectively analyzed. PTMC refers to thyroid papillary carcinoma with a maximum diameter of ≤1 cm. Results There were 186 patients (PTMC group) whose maximum tumor diameter was ≤ 1 cm and 45 patients (PTC group) whose maximum tumor diameter was > 1 cm. The patient’s age was (45.97 ± 10.63) years for the PTMC group and (45.31 ± 11.55) years for the PTC group. No statistically significant differences existed between the two groups in sex, age, BRAF V600E gene mutation, tumor multifocality, and capsular invasion (P > 0.05). Between the two groups, there were statistically significant (P < 0.05) differences in TNM staging, the thyroid imaging reporting and data system (TI-RADS) staging, and cervical lymph node metastasis. Conclusions Thyroid surgery, thermal ablation, and active monitoring are different approaches in the stratified treatment of PTMC and PTC. To avoid overtreatment and improve the quality of life of the patients, personalized treatment plans should be developed according to the test results of TNM stage, TI-RADS classification, and cervical lymph node metastasis.

Attribution-based interpretable classification neural network with global and local perspectives