Solar energy prediction through machine learning models: A comparative analysis of regressor algorithms.

Solar energy generated from photovoltaic panel is an important energy source that brings many benefits to people and the environment. This is a growing trend globally and plays an increasingly important role in the future of the energy industry. However, it intermittent nature and potential for distributed system use require accurate forecasting to balance supply and demand, optimize energy storage, and manage grid stability.

A Combined Computational and Experimental Approach to Studying Tropomyosin Kinase Receptor B Binders for Potential Treatment of Neurodegenerative Diseases.

Tropomyosin kinase receptor B (TrkB) has been explored as a therapeutic target for neurological and psychiatric disorders. However, the development of TrkB agonists was hindered by our poor understanding of the TrkB agonist binding location and affinity (both affect the regulation of disorder types). This motivated us to develop a combined computational and experimental approach to study TrkB binders.

Determination of Ca, P, K, Na, and Mg in Australian Retail Pasteurised Milk Using Inductively Coupled Plasma Atomic Emission Spectroscopy (ICP OES).

A rapid and simple inductively coupled plasma atomic emission spectrometry (ICP OES) method was developed and validated for the determination of macroelements including calcium (Ca), phosphorus (P), potassium (K), sodium (Na), and magnesium (Mg) in Australian retail pasteurised milk. The milk samples were digested using the mixture of 70% HNO and 30% HO (2 : 1, v/v) in an open-tube digester block at 120°C for 4 h. The validated ICP OES method showed good linearity for all elements ( > 0.

Appendiceal Diverticulitis Mimics Cecal Diverticulitis Diagnosed Preoperatively.

Appendiceal diverticulitis is an uncommon condition that clinically resembles acute appendicitis. However, it is an incidental finding in histopathological studies and is rarely diagnosed preoperatively by imaging studies. In this article, we present the clinical and imaging findings of a male patient presenting with right upper quadrant pain with a preoperative imaging diagnosis of appendiceal diverticulitis.

Fatty acid profiling of Western Australian pasteurised milk using gas chromatography-mass spectrometry.

The fatty acid composition of Western Australian commercial pasteurised milk was profiled using gas chromatography-mass spectrometry (GC-MS). A total of 31 fatty acids (FA) were identified in the milk samples. The majority of FA were medium-chain fatty acids (MCFA) with 6-13 carbon atoms and long-chain fatty acids (LCFA) with 14-20 carbon atoms.

Transoral endoscopic thyroidectomy vestibular approach versus conventional open thyroidectomy for the treatment of benign thyroid tumours: A prospective cohort study.

Introduction: Thyroid tumours are a common condition and open surgery is a conventional method for treating benign thyroid tumours when surgery is indicated. In this study, we evaluate the outcomes of benign thyroid tumour treatment using transoral endoscopic thyroidectomy via vestibular approach (TOETVA) and compare the results with those of conventional open thyroidectomy (COT).

Patients And Methods: We conducted a prospective cohort study between 100 patients who underwent TOETVA and 100 who underwent COT surgery for benign diseases from June 2018 to December 2021 in our hospital.

Geometric Graph Learning to Predict Changes in Binding Free Energy and Protein Thermodynamic Stability upon Mutation.

Accurate prediction of binding free energy changes upon mutations is vital for optimizing drugs, designing proteins, understanding genetic diseases, and cost-effective virtual screening. While machine learning methods show promise in this domain, achieving accuracy and generalization across diverse data sets remains a challenge. This study introduces Geometric Graph Learning for Protein-Protein Interactions (GGL-PPI), a novel approach integrating geometric graph representation and machine learning to forecast mutation-induced binding free energy changes.

Multiscale Laplacian Learning.

Machine learning has greatly influenced many fields, including science. However, despite of the tremendous accomplishments of machine learning, one of the key limitations of most existing machine learning approaches is their reliance on large labeled sets, and thus, data with limited labeled samples remains a challenge. Moreover, the performance of machine learning methods often severely hindered in case of diverse data, usually associated with smaller data sets or data associated with areas of study where the size of the data sets is constrained by high experimental cost and/or ethics.

Recurrent early-stage squamous cell carcinoma cervical cancer presenting with isolated ovary metastasis: a rare case report.

Introduction: Ovarian metastatic squamous carcinoma of the cervix is rare, accounting for about 0.4%. This study reports a single case of metastatic recurrent cervical cancer in the ovary.

Geometric graph learning with extended atom-types features for protein-ligand binding affinity prediction.

Understanding and accurately predicting protein-ligand binding affinity are essential in the drug design and discovery process. At present, machine learning-based methodologies are gaining popularity as a means of predicting binding affinity due to their efficiency and accuracy, as well as the increasing availability of structural and binding affinity data for protein-ligand complexes. In biomolecular studies, graph theory has been widely applied since graphs can be used to model molecules or molecular complexes in a natural manner.

Poly(l-Histidine)-Mediated On-Demand Therapeutic Delivery of Roughened Ceria Nanocages for Treatment of Chemical Eye Injury.

Development of topical bioactive formulations capable of overcoming the low bioavailability of conventional eye drops is critically important for efficient management of ocular chemical burns. Herein, a nanomedicine strategy is presented to harness the surface roughness-controlled ceria nanocages (SRCNs) and poly(l-histidine) surface coatings for triggering multiple bioactive roles of intrinsically therapeutic nanocarriers and promoting transport across corneal epithelial barriers as well as achieving on-demand release of dual drugs [acetylcholine chloride (ACh) and SB431542] at the lesion site. Specifically, the high surface roughness helps improve cellular uptake and therapeutic activity of SRCNs while exerting a negligible impact on good ocular biocompatibility of the nanomaterials.

Novel Molecular Representations Using Neumann-Cayley Orthogonal Gated Recurrent Unit.

Advances in deep neural networks (DNNs) have made a very powerful machine learning method available to researchers across many fields of study, including the biomedical and cheminformatics communities, where DNNs help to improve tasks such as protein performance, molecular design, drug discovery, etc. Many of those tasks rely on molecular descriptors for representing molecular characteristics in cheminformatics. Despite significant efforts and the introduction of numerous methods that derive molecular descriptors, the quantitative prediction of molecular properties remains challenging.

Highly Retina-Permeating and Long-Acting Resveratrol/Metformin Nanotherapeutics for Enhanced Treatment of Macular Degeneration.

The development of therapeutics for effective treatments of retinal diseases is significantly constrained by various biological barriers. We herein report a nanomedicine strategy to develop nanotherapeutics featured with not only high retinal permeability but also sustained bioactive delivery. Specifically, the nanotherapeutics are rationally designed via aminolysis of resveratrol-encapsulated polycaprolactone nanoparticles (R@PCL NPs), followed by the formation of amide linkages with carboxyl-terminated transacting activator of transcription cell penetrating peptide (T) and metformin (M).

eGAC3D: enhancing depth adaptive convolution and depth estimation for monocular 3D object pose detection.

Many alternative approaches for 3D object detection using a singular camera have been studied instead of leveraging high-precision 3D LiDAR sensors incurring a prohibitive cost. Recently, we proposed a novel approach for 3D object detection by employing a ground plane model that utilizes geometric constraints named GAC3D to improve the results of the deep-based detector. GAC3D adopts an adaptive depth convolution to replace the traditional 2D convolution to deal with the divergent context of the image's feature, leading to a significant improvement in both training convergence and testing accuracy on the KITTI 3D object detection benchmark.

Graphene woven fabric-polydimethylsiloxane piezoresistive films for smart multi-stimuli responses.

The outstanding properties of graphene, including its electromechanical property, could be engineered for wearable electronic sensor platforms. The tubular graphene weaved into a mesh or graphene woven fabrics (GWF) has been reported as one of the most sensitive materials for deformation detection, as well as a promising temperature sensor. Herein, we present the performance of our developed flexible, stretchable, and multiple sensitive sensors fabricated from GWF embedded in polydimethylsiloxane (PDMS) film substrate.

EISA-Score: Element Interactive Surface Area Score for Protein-Ligand Binding Affinity Prediction.

Molecular surface representations have been advertised as a great tool to study protein structure and functions, including protein-ligand binding affinity modeling. However, the conventional surface-area-based methods fail to deliver a competitive performance on the energy scoring tasks. The main reason is the lack of crucial physical and chemical interactions encoded in the molecular surface generations.

Oxidation-mediated scaffold engineering of hyaluronic acid-based microcarriers enhances corneal stromal regeneration.

The functional design of scaffolding biomaterials with potent capabilities of promoting cell adhesion and proliferation is critically important for tissue repair and regeneration. Here, we exploit the effects of oxidation level of aldehyde hyaluronic acid (oHA) on gelatin microcarriers for repairing corneal injuries. Specifically, high oxidation levels can endow the microcarrier surface with large oHA grafting amount, smooth topography, and strong stiffness, consequently formulating biocompatible scaffolding materials with superior affinities for keratocyte attachment and growth.

Thermogels containing sulfated hyaluronan as novel topical therapeutics for treatment of ocular surface inflammation.

The development of long lasting therapeutic agents is critically important for efficient treatment of chronic diseases. We herein report a rational strategy to develop a therapeutic thermogel featured with prolonged anti-inflammatory and corneal-protective effects. Specifically, a hyaluronic acid with different sulfation degrees and an amine-terminated poly(-isopropylacrylamide) are conjugated to achieve the thermogels.

GAC3D: improving monocular 3D object detection with ground-guide model and adaptive convolution.

Monocular 3D object detection has recently become prevalent in autonomous driving and navigation applications due to its cost-efficiency and easy-to-embed to existent vehicles. The most challenging task in monocular vision is to estimate a reliable object's location cause of the lack of depth information in RGB images. Many methods tackle this ill-posed problem by directly regressing the object's depth or take the depth map as a supplement input to enhance the model's results.

Visible-Light-Assisted Photoelectrochemical Biosensing of Uric Acid Using Metal-Free Graphene Oxide Nanoribbons.

In this study, we demonstrate the visible-light-assisted photoelectrochemical (PEC) biosensing of uric acid (UA) by using graphene oxide nanoribbons (GONRs) as PEC electrode materials. Specifically, GONRs with controlled properties were synthesized by the microwave-assisted exfoliation of multi-walled carbon nanotubes. For the detection of UA, GONRs were adopted to modify either a screen-printed carbon electrode (SPCE) or a glassy carbon electrode (GCE).

The characteristics of coalbed water and coal in a coal seam situated in the Red River Basin, Vietnam.

An in-depth understanding of the hydrogeochemical characteristics of coal mines is helpful in establishing an effective and successful exploration program of coalbed methane (CBM). This study provides a comprehensive analysis of hydrogeological characteristics, characteristics of coalbed water, and characteristics of the coal sample from a coal seam located in the Red River Basin (RRB). These physicochemical characteristics along with the microbial composition of coalbed water were critically analyzed.

Unveiling the molecular mechanism of SARS-CoV-2 main protease inhibition from 137 crystal structures using algebraic topology and deep learning.

Currently, there is neither effective antiviral drugs nor vaccine for coronavirus disease 2019 (COVID-19) caused by acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Due to its high conservativeness and low similarity with human genes, SARS-CoV-2 main protease (M) is one of the most favorable drug targets. However, the current understanding of the molecular mechanism of M inhibition is limited by the lack of reliable binding affinity ranking and prediction of existing structures of M-inhibitor complexes.

Algebraic graph-assisted bidirectional transformers for molecular property prediction.

The ability of molecular property prediction is of great significance to drug discovery, human health, and environmental protection. Despite considerable efforts, quantitative prediction of various molecular properties remains a challenge. Although some machine learning models, such as bidirectional encoder from transformer, can incorporate massive unlabeled molecular data into molecular representations via a self-supervised learning strategy, it neglects three-dimensional (3D) stereochemical information.

Tailoring therapeutic properties of silver nanoparticles for effective bacterial keratitis treatment.

The formulation of nanoparticles with intrinsically therapeutic properties in a tailorable and appropriate manner is critical in nanomedicine for effective treatments of infectious diseases. Here, we present a biomedical strategy to formulate silver nanoparticles (AgNPs) as intrinsically therapeutic agents for the treatment of Staphylococcus aureus (S. aureus) keratitis.

AweGNN: Auto-parametrized weighted element-specific graph neural networks for molecules.

While automated feature extraction has had tremendous success in many deep learning algorithms for image analysis and natural language processing, it does not work well for data involving complex internal structures, such as molecules. Data representations via advanced mathematics, including algebraic topology, differential geometry, and graph theory, have demonstrated superiority in a variety of biomolecular applications, however, their performance is often dependent on manual parametrization. This work introduces the auto-parametrized weighted element-specific graph neural network, dubbed AweGNN, to overcome the obstacle of this tedious parametrization process while also being a suitable technique for automated feature extraction on these internally complex biomolecular data sets.