Nature-Inspired Search Method and Custom Waste Object Detection and Classification Model for Smart Waste Bin.

Waste management is one of the challenges facing countries globally, leading to the need for innovative ways to design and operationalize smart waste bins for effective waste collection and management. The inability of extant waste bins to facilitate sorting of solid waste at the point of collection and the attendant impact on waste management process is the motivation for this study. The South African University of Technology (SAUoT) is used as a case study because solid waste management is an aspect where SAUoT is exerting an impact by leveraging emerging technologies.

Scintillation light detection in the 6-m drift-length ProtoDUNE Dual Phase liquid argon TPC.

DUNE is a dual-site experiment for long-baseline neutrino oscillation studies, neutrino astrophysics and nucleon decay searches. ProtoDUNE Dual Phase (DP) is a 6   6   6 m liquid argon time-projection-chamber (LArTPC) that recorded cosmic-muon data at the CERN Neutrino Platform in 2019-2020 as a prototype of the DUNE Far Detector. Charged particles propagating through the LArTPC produce ionization and scintillation light.

Non-coding de novo mutations in chromatin interactions are implicated in autism spectrum disorder.

Three-dimensional chromatin interactions regulate gene expressions. The significance of de novo mutations (DNMs) in chromatin interactions remains poorly understood for autism spectrum disorder (ASD). We generated 813 whole-genome sequences from 242 Korean simplex families to detect DNMs, and identified target genes which were putatively affected by non-coding DNMs in chromatin interactions.

Repeated ketamine anesthesia during neurodevelopment upregulates hippocampal activity and enhances drug reward in male mice.

Early exposures to anesthetics can cause long-lasting changes in excitatory/inhibitory synaptic transmission (E/I imbalance), an important mechanism for neurodevelopmental disorders. Since E/I imbalance is also involved with addiction, we further investigated possible changes in addiction-related behaviors after multiple ketamine anesthesia in late postnatal mice. Postnatal day (PND) 16 mice received multiple ketamine anesthesia (35 mg kg, 5 days), and behavioral changes were evaluated at PND28 and PND56.

Analysis of the docking property of host variants of hACE2 for SARS-CoV-2 in a large cohort.

The recent novel coronavirus disease (COVID-19) outbreak, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is threatening global health. However, an understanding of the interaction of SARS-CoV-2 with human cells, including the physical docking property influenced by the host's genetic diversity, is still lacking. Here, based on germline variants in the UK Biobank covering 502,543 individuals, we revealed the molecular interactions between human angiotensin-converting enzyme 2 (hACE2), which is the representative receptor for SARS-CoV-2 entry, and COVID-19 infection.

Hypertriton Production in p-Pb Collisions at sqrt[s_{NN}]=5.02 TeV.

The study of nuclei and antinuclei production has proven to be a powerful tool to investigate the formation mechanism of loosely bound states in high-energy hadronic collisions. The first measurement of the production of _{Λ}^{3}H in p-Pb collisions at sqrt[s_{NN}]=5.02  TeV is presented in this Letter.

Identifying the Risk of Sepsis in Patients With Cancer Using Digital Health Care Records: Machine Learning-Based Approach.

Background: Sepsis is diagnosed in millions of people every year, resulting in a high mortality rate. Although patients with sepsis present multimorbid conditions, including cancer, sepsis predictions have mainly focused on patients with severe injuries.

Objective: In this paper, we present a machine learning-based approach to identify the risk of sepsis in patients with cancer using electronic health records (EHRs).

Plant phenotype relationship corpus for biomedical relationships between plants and phenotypes.

Medicinal plants have demonstrated therapeutic potential for applicability for a wide range of observable characteristics in the human body, known as "phenotype," and have been considered favorably in clinical treatment. With an ever increasing interest in plants, many researchers have attempted to extract meaningful information by identifying relationships between plants and phenotypes from the existing literature. Although natural language processing (NLP) aims to extract useful information from unstructured textual data, there is no appropriate corpus available to train and evaluate the NLP model for plants and phenotypes.

NGS data vectorization, clustering, and finding key codons in SARS-CoV-2 variations.

The rapid global spread and dissemination of SARS-CoV-2 has provided the virus with numerous opportunities to develop several variants. Thus, it is critical to determine the degree of the variations and in which part of the virus those variations occurred. Therefore, in this study, methods that could be used to vectorize the sequence data, perform clustering analysis, and visualize the results were proposed using machine learning methods.

Partially Globular Conformations from Random Charge Sequences.

Overall charged polymers with quenched charge sequences often adopt partially globular structures which result from the interplay between the disorder in charge sequences and thermal fluctuations. Simple energetic considerations show that structures consisting of alike (equal-size-equal-charge) globules are not favorable: the structures are intrinsically heterogeneous. We predict the globule distributions with the lowest energies in the size-charge space.

Polarization of Λ and Λ[over ¯] Hyperons along the Beam Direction in Pb-Pb Collisions at sqrt[s]_{NN}=5.02 TeV.

The polarization of the Λ and Λ[over ¯] hyperons along the beam (z) direction, P_{z}, has been measured in Pb-Pb collisions at sqrt[s_{NN}]=5.02  TeV recorded with ALICE at the Large Hadron Collider (LHC). The main contribution to P_{z} comes from elliptic flow-induced vorticity and can be characterized by the second Fourier sine coefficient P_{z,s2}=⟨P_{z}sin(2φ-2Ψ_{2})⟩, where φ is the hyperon azimuthal emission angle and Ψ_{2} is the elliptic flow plane angle.

Gate-Tuned Gas Molecule Sensitivity of a Two-Dimensional Semiconductor.

In this work, we develop a gate-tunable gas sensor based on a MoS/hBN heterostructure field effect transistor. Through experimental measurements and numerical simulations, we systematically reveal a principle that relates the concentration of the target gas and sensing signals (Δ/) as a function of gate bias. Because a linear relationship between Δ/ and the gas concentration guarantees reliable sensor operation, the optimal gate bias condition for linearity was investigated.

Measurement of Two-Particle Correlations of Hadrons in e^{+}e^{-} Collisions at Belle.

The measurement of two-particle angular correlation functions in high-multiplicity e^{+}e^{-} collisions at sqrt[s]=10.52  GeV is reported. In this study, the 89.

Machine Learning Applications for Chemical Reactions.

Machine learning (ML) approaches have enabled rapid and efficient molecular property predictions as well as the design of new novel materials. In addition to great success for molecular problems, ML techniques are applied to various chemical reaction problems that require huge costs to solve with the existing experimental and simulation methods. In this review, starting with basic representations of chemical reactions, we summarized recent achievements of ML studies on two different problems; predicting reaction properties and synthetic routes.

A Significant Fluorescence Turn-On Probe for the Recognition of Al and Its Application.

An easy prepared probe, , was designed and synthesized, which displayed a significant fluorescence enhancement (over 38-fold) and obvious color change in the recognition of Al. The binding ratio of probe to Al was determined as 1:1, according to Job plot. The binding mechanism was fully clarified by the experiments, such as FT-IR spectrum, ESI-MS analysis, and H NMR titration.

System-Specific Separable Basis Based on Tucker Decomposition: Application to Density Functional Calculations.

For fast density functional calculations, a suitable basis that can accurately represent the orbitals within a reasonable number of dimensions is essential. Here, we propose a new type of basis constructed from Tucker decomposition of a finite-difference (FD) Hamiltonian matrix, which is intended to reflect the system information implied in the Hamiltonian matrix and satisfies orthonormality and separability conditions. By introducing the system-specific separable basis, the computation time for FD density functional calculations for seven two- and three-dimensional periodic systems was reduced by a factor of 2-71 times, while the errors in both the atomization energy per atom and the band gap were limited to less than 0.

SPICA Force Field for Proteins and Peptides.

A coarse-grained (CG) model for peptides and proteins was developed as an extension of the Surface Property fItting Coarse grAined (SPICA) force field (FF). The model was designed to examine membrane proteins that are fully compatible with the lipid membranes of the SPICA FF. A preliminary version of this protein model was created using thermodynamic properties, including the surface tension and density in the SPICA (formerly called SDK) FF.

MPI-GWAS: a supercomputing-aided permutation approach for genomewide association studies.

Permutation testing is a robust and popular approach for significance testing in genomic research that has the advantage of reducing inflated type 1 error rates; however, its computational cost is notorious in genome-wide association studies (GWAS). Here, we developed a supercomputing-aided approach to accelerate the permutation testing for GWAS, based on the message-passing interface (MPI) on parallel computing architecture. Our application, called MPI-GWAS, conducts MPI-based permutation testing using a parallel computing approach with our supercomputing system, Nurion (8,305 compute nodes, and 563,740 central processing units [CPUs]).

Cost-Effectiveness of Influenza Vaccination Strategies in Adults: Older Adults Aged ≥65 Years, Adults Aged 50-64 Years, and At-Risk Adults Aged 19-64 Years.

The high disease burden of influenza in elderly and chronically ill adults may be due to the suboptimal effectiveness and mismatch of the conventional trivalent influenza vaccine (TIV). This study evaluated the cost-effectiveness of quadrivalent (QIV), adjuvanted trivalent (ATIV), and high-dose quadrivalent (HD-QIV) vaccines versus TIV used under the current Korean National Immunization Program (NIP) in older adults aged ≥65 years. We also evaluated the cost-effectiveness of programs for at-risk adults aged 19-64 and adults aged 50-64.

Artificial intelligence-based decision support model for new drug development planning.

New drug development guarantees a very high return on success, but the success rate is extremely low. Pharmaceutical companies have attempted to use various strategies to increase the success rate of drug development, but this goal has been difficult to achieve. In this study, we developed a model that can guide effective decision-making at the planning stage of new drug development by leveraging machine learning.