Exploring SureChEMBL from a drug discovery perspective.

In the pharmaceutical industry, the patent protection of drugs and medicines is accorded importance because of the high costs involved in the development of novel drugs. Over the years, researchers have analyzed patent documents to identify freedom-to-operate spaces for novel drug candidates. To assist this, several well-established public patent document data repositories have enabled automated methodologies for extracting information on therapeutic agents.

TrG2P: A transfer-learning-based tool integrating multi-trait data for accurate prediction of crop yield.

Yield prediction is the primary goal of genomic selection (GS)-assisted crop breeding. Because yield is a complex quantitative trait, making predictions from genotypic data is challenging. Transfer learning can produce an effective model for a target task by leveraging knowledge from a different, but related, source domain and is considered a great potential method for improving yield prediction by integrating multi-trait data.

Towards early diagnosis of Alzheimer's disease: advances in immune-related blood biomarkers and computational approaches.

Alzheimer's disease has an increasing prevalence in the population world-wide, yet current diagnostic methods based on recommended biomarkers are only available in specialized clinics. Due to these circumstances, Alzheimer's disease is usually diagnosed late, which contrasts with the currently available treatment options that are only effective for patients at an early stage. Blood-based biomarkers could fill in the gap of easily accessible and low-cost methods for early diagnosis of the disease.

REFORMS: Consensus-based Recommendations for Machine-learning-based Science.

Machine learning (ML) methods are proliferating in scientific research. However, the adoption of these methods has been accompanied by failures of validity, reproducibility, and generalizability. These failures can hinder scientific progress, lead to false consensus around invalid claims, and undermine the credibility of ML-based science.

Application of a centrifugal disc fertilizer spreading system for UAVs in rice fields.

Unmanned aerial vehicle (UAV) granular fertilizer spreading technology has been gradually applied in agricultural production. However, in the process of spreading operation, the actual influence effect of each factor in field operation is still unclear. Based on the self-developed UAV fertilizer spreading system, this paper explores the effects of three factors, the baffle retraction (B), spreading disc speed (D), and UAV flight altitude (H), on the granular fertilizer spreading effect in the actual field scenarios through the orthogonal test and taking the coefficient of variation (Cv) and relative error of fertilizer application rate (λ) as the evaluation indexes.

Accuracy of Digital Orthodontic Treatment Planning: Assessing Aligner-Directed Tooth Movements and Exploring Inherent Intramaxillary Side Effects.

The attainment of precise posterior occlusion alignment necessitates a deeper understanding of the clinical efficacy of aligner therapy. This study aims to determine whether the treatment goals defined in the virtual planning of aligner therapy are effectively implemented in clinical practice, with a particular focus on the influence of distalization distances on potential vertical side effects. In this retrospective, non-interventional investigation, a cohort of 20 individuals undergoing Invisalign treatment was examined.

E-DQN-Based Path Planning Method for Drones in Airsim Simulator under Unknown Environment.

To improve the rapidity of path planning for drones in unknown environments, a new bio-inspired path planning method using E-DQN (event-based deep -network), referring to introducing event stream to reinforcement learning network, is proposed. Firstly, event data are collected through an airsim simulator for environmental perception, and an auto-encoder is presented to extract data features and generate event weights. Then, event weights are input into DQN (deep -network) to choose the action of the next step.

Using high-throughput phenotype platform MVS-Pheno to reconstruct the 3D morphological structure of wheat.

It is of great significance to study the plant morphological structure for improving crop yield and achieving efficient use of resources. Three dimensional (3D) information can more accurately describe the morphological and structural characteristics of crop plants. Automatic acquisition of 3D information is one of the key steps in plant morphological structure research.

Automated liver volumetry and hepatic steatosis quantification with magnetic resonance imaging proton density fat fraction.

Background: Preoperative imaging evaluation of liver volume and hepatic steatosis for the donor affects transplantation outcomes. However, computed tomography (CT) for liver volumetry and magnetic resonance spectroscopy (MRS) for hepatic steatosis are time consuming. Therefore, we investigated the correlation of automated 3D-multi-echo-Dixon sequence magnetic resonance imaging (ME-Dixon MRI) and its derived proton density fat fraction (MRI-PDFF) with CT liver volumetry and MRS hepatic steatosis measurements in living liver donors.

Three-Dimensional Modeling of Maize Canopies Based on Computational Intelligence.

The 3-dimensional (3D) modeling of crop canopies is fundamental for studying functional-structural plant models. Existing studies often fail to capture the structural characteristics of crop canopies, such as organ overlapping and resource competition. To address this issue, we propose a 3D maize modeling method based on computational intelligence.

Deep Learning Model for Prediction of Bronchopulmonary Dysplasia in Preterm Infants Using Chest Radiographs.

Bronchopulmonary dysplasia (BPD) is common in preterm infants and may result in pulmonary vascular disease, compromising lung function. This study aimed to employ artificial intelligence (AI) techniques to help physicians accurately diagnose BPD in preterm infants in a timely and efficient manner. This retrospective study involves two datasets: a lung region segmentation dataset comprising 1491 chest radiographs of infants, and a BPD prediction dataset comprising 1021 chest radiographs of preterm infants.

Synthesis and photocatalytic property of Au-TiO nanocomposites with controlled morphologies in microfluidic chips.

We present an efficient approach for the consecutive synthesis of Au-TiO nanocomposites with controlled morphologies in a microfluidic chip. The seed-mediated growth method was employed to synthesize Au nanorods as the core, and TiO layers of varying thicknesses were deposited on the surface or tip of the Au nanorods. Au-TiO nanocomposites with core-shell, dumbbell, and dandelion-like structures can be precisely synthesized in a one-step manner within the microfluidic chip by finely tuning the flow rate of NaHCO and the amount of hexadecyl trimethyl ammonium bromide.

Evaluating the use of unmanned aerial vehicles for spray applications in mountain Nanguo pear orchards.

Background: Nanguo pear is a distinctive pear variety in northeast China, grown mainly in mountainous areas. Due to terrain limitations, ground-based pesticide application equipment is difficult to use. This limitation could be overcome by using unmanned aerial vehicles (UAVs) for pesticide application in Nanguo pear orchards.

Pediatric obstructive sleep apnea diagnosis: leveraging machine learning with linear discriminant analysis.

Objective: The objective of this study was to investigate the effectiveness of a machine learning algorithm in diagnosing OSA in children based on clinical features that can be obtained in nonnocturnal and nonmedical environments.

Patients And Methods: This study was conducted at Beijing Children's Hospital from April 2018 to October 2019. The participants in this study were 2464 children aged 3-18 suspected of having OSA who underwent clinical data collection and polysomnography(PSG).

Satellite Remote Sensing: A Tool to Support Harmful Algal Bloom Monitoring and Recreational Health Advisories in a California Reservoir.

Cyanobacterial harmful algal blooms (cyanoHABs) can harm people, animals, and affect consumptive and recreational use of inland waters. Monitoring cyanoHABs is often limited. However, chlorophyll- (chl-) is a common water quality metric and has been shown to have a relationship with cyanobacteria.

Optical biosensor based on weak measurement for ultra-sensitive detection of calreticulin in human serum.

A novel real-time optical phase sensing method based on the Mach-Zehnder interference principle has been proposed for the detection of calreticulin (CRT) levels in human serum samples. In this approach, anti-CRT antibodies are utilized to capture CRT molecules in serum, leading to a phase shift in both the measuring and reference arms of the system. By employing the concept of weak amplification within the framework of weak measurements, it becomes feasible to continuously monitor the response of CRT in real-time, allowing for the precise determination of serum CRT content at the picomolar level.

Research on the influence factors of sustainable development of plateau characteristic agriculture based on DEMATEL and AISM combined model.

Under the background of the continuous progress of China's agricultural reform, the development of characteristic agriculture is an important field of agricultural development in the country and even the world. Yunnan has unique advantages in geography, location, climate, and human resources, and has unique conditions for the development of agriculture with plateau characteristic. However, the sustainable development of agriculture with plateau characteristic is affected and restricted by many factors.

Design and experiments with a SLAM system for low-density canopy environments in greenhouses based on an improved Cartographer framework.

To address the problem that the low-density canopy of greenhouse crops affects the robustness and accuracy of simultaneous localization and mapping (SLAM) algorithms, a greenhouse map construction method for agricultural robots based on multiline LiDAR was investigated. Based on the Cartographer framework, this paper proposes a map construction and localization method based on spatial downsampling. Taking suspended tomato plants planted in greenhouses as the research object, an adaptive filtering point cloud projection (AF-PCP) SLAM algorithm was designed.

Oxidation Analysis of l-Cysteine with a Chiral Sensor Based on Quantum Weak Measurement.

l-Cysteine, distinguished by its possession of reactive sulfhydryl groups within its molecular structure, plays a significant role in both biological systems and the pharmaceutical industry. It stands not only as a natural component integral to the constitution of glutathione but also as the principal precursor for the synthesis of l-cystine through an oxidation reaction. This study endeavors to introduce a novel approach to l-cysteine analysis, capitalizing on its optical activity, whereby an optical rotation detection system grounded in the principles of quantum weak measurement is proffered.

Personalized lattice-structured prosthesis as a graftless solution for mandible reconstruction and prosthetic restoration: A finite element analysis.

Oral cavity tumors are a prevalent cause of mandible reconstruction surgeries. The mandible is vital for functions like oralization, respiration, mastication, and deglutition. Current mandible reconstruction methods have low success rates due to complications like plate fracture or exposure, infections, and screw loosening.

Determination of lead content in oilseed rape leaves in silicon-free and silicon environments based on deep transfer learning and fluorescence hyperspectral imaging.

The ability of fluorescence hyperspectral imaging to predict heavy metal lead (Pb) concentration in oilseed rape leaves was studied in silicon-free and silicon environments. Further, the transfer stacked convolution auto-encoder (T-SCAE) algorithm was proposed based on the stacked convolution auto-encoder (SCAE) algorithm. Fluorescence hyperspectral images of oilseed rape leaves under different Pb stress contents were obtained in the silicon-free and silicon environments.

Matrisome and Immune Pathways Contribute to Extreme Vascular Outcomes in Williams-Beuren Syndrome.

Background: Supravalvar aortic stenosis (SVAS) is a characteristic feature of Williams-Beuren syndrome (WBS). Its severity varies: ~20% of people with Williams-Beuren syndrome have SVAS requiring surgical intervention, whereas ~35% have no appreciable SVAS. The remaining individuals have SVAS of intermediate severity.

Agricultural machinery automatic navigation technology.

In this paper, we review, compare, and analyze previous studies on agricultural machinery automatic navigation and path planning technologies. First, the paper introduces the fundamental components of agricultural machinery autonomous driving, including automatic navigation, path planning, control systems, and communication modules. Generally, the methods for automatic navigation technology can be divided into three categories: Global Navigation Satellite System (GNSS), Machine Vision, and Laser Radar.