A community-powered search of machine learning strategy space to find NMR property prediction models.

The rise of machine learning (ML) has created an explosion in the potential strategies for using data to make scientific predictions. For physical scientists wishing to apply ML strategies to a particular domain, it can be difficult to assess in advance what strategy to adopt within a vast space of possibilities. Here we outline the results of an online community-powered effort to swarm search the space of ML strategies and develop algorithms for predicting atomic-pairwise nuclear magnetic resonance (NMR) properties in molecules.

Facile one-pot synthesis of CuCN by pulsed laser ablation in nitrile solvents and mechanistic studies using quantum chemical calculations.

Binding energies of different nitrile solvents and their utilization for CuCN formation were investigated through quantum chemical calculations. A pulsed laser ablation in liquid (PLAL) method for CuCN synthesis was developed herein. Initially, the interaction between the pulsed laser and the Cu-target generated Cu-ions and electrons at the point of contact.

Constraints on Cosmic Strings Using Data from the Third Advanced LIGO-Virgo Observing Run.

We search for gravitational-wave signals produced by cosmic strings in the Advanced LIGO and Virgo full O3 dataset. Search results are presented for gravitational waves produced by cosmic string loop features such as cusps, kinks, and, for the first time, kink-kink collisions. A template-based search for short-duration transient signals does not yield a detection.

Deep learning-based prediction of future growth potential of technologies.

Research papers are a repository of information on the various elements that make up science and technology R&D activities. Generating knowledge maps based on research papers enables identification of specific areas of scientific and technical research as well as understanding of the flow of knowledge between those areas. Recently, as the number of electronic publishing and informatics archives along with the amount of accumulated knowledge related to science and technology has proliferated, the need to utilize the meta-knowledge obtainable from research papers has increased.

A merged molecular representation learning for molecular properties prediction with a web-based service.

Deep learning has brought a dramatic development in molecular property prediction that is crucial in the field of drug discovery using various representations such as fingerprints, SMILES, and graphs. In particular, SMILES is used in various deep learning models via character-based approaches. However, SMILES has a limitation in that it is hard to reflect chemical properties.

Dynamic Interactions of Fully Glycosylated SARS-CoV-2 Spike Protein with Various Antibodies.

The spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) presents a public health crisis, and the vaccines that can induce highly potent neutralizing antibodies are essential for ending the pandemic. The spike (S) protein on the viral envelope mediates human angiotensin-converting enzyme 2 (ACE2) binding and thus is the target of a variety of neutralizing antibodies. In this work, we built various S trimer-antibody complex structures on the basis of the fully glycosylated S protein models described in our previous work, and performed all-atom molecular dynamics simulations to get insight into the structural dynamics and interactions between S protein and antibodies.

Elliptic Flow of Electrons from Beauty-Hadron Decays in Pb-Pb Collisions at sqrt[s_{NN}]=5.02 TeV.

The elliptic flow of electrons from beauty hadron decays at midrapidity (|y|<0.8) is measured in Pb-Pb collisions at sqrt[s_{NN}]=5.02  TeV with the ALICE detector at the LHC.

Test of Lepton-Flavor Universality in B→K^{*}ℓ^{+}ℓ^{-} Decays at Belle.

We present a measurement of R_{K^{*}}, the branching fraction ratio B(B→K^{*}μ^{+}μ^{-})/B(B→K^{*}e^{+}e^{-}), for both charged and neutral B mesons. The ratio for the charged case R_{K^{*+}} is the first measurement ever performed. In addition, we report absolute branching fractions for the individual modes in bins of the squared dilepton invariant mass q^{2}.

Low-Level Brain Somatic Mutations Are Implicated in Schizophrenia.

Background: Somatic mutations arising from the brain have recently emerged as significant contributors to neurodevelopmental disorders, including childhood intractable epilepsy and cortical malformations. However, whether brain somatic mutations are implicated in schizophrenia (SCZ) is not well established.

Methods: We performed deep whole exome sequencing (average read depth > 550×) of matched dorsolateral prefrontal cortex and peripheral tissues from 27 patients with SCZ and 31 age-matched control individuals, followed by comprehensive and strict analysis of somatic mutations, including mutagenesis signature, substitution patterns, and involved pathways.

FCN3 functions as a tumor suppressor of lung adenocarcinoma through induction of endoplasmic reticulum stress.

In this study, we report a novel function of FCN3 (Ficolin 3), a secreted lectin capable of activating the complement pathway, as a tumor suppressor of lung adenocarcinoma (LUAD). First, the expression of FCN3 was strongly down-regulated in cancer tissues compared to matched normal lung tissues, and down-regulation of FCN3 was shown to be significantly correlated with increased mortality among LUAD patients. Interestingly, while ectopic expression of FCN3 led to cell cycle arrest and apoptosis in A549 and H23 cells derived from LUAD, the secreted form of the protein had no effect on the cells.

Elucidating molecular mechanisms of acquired resistance to BRAF inhibitors in melanoma using a microfluidic device and deep sequencing.

BRAF inhibitors (e.g., vemurafenib) are widely used to treat metastatic melanoma with the BRAF V600E mutation.

Evidence for X(3872)→J/ψπ^{+}π^{-} Produced in Single-Tag Two-Photon Interactions.

We report the first evidence for X(3872) production in two-photon interactions by tagging either the electron or the positron in the final state, exploring the highly virtual photon region. The search is performed in e^{+}e^{-}→e^{+}e^{-}J/ψπ^{+}π^{-}, using 825  fb^{-1} of data collected by the Belle detector operated at the KEKB e^{+}e^{-} collider. We observe three X(3872) candidates, where the expected background is 0.

Transfer Learning from Simulation to Experimental Data: NMR Chemical Shift Predictions.

An accurate prediction of chemical shifts (δ) to elucidate molecular structures has been a challenging problem. Recently, noble machine learning architectures achieve accurate prediction performance, but the difficulty of building a huge chemical database limits the applicability of machine learning approaches. In this work, we demonstrate that the prior knowledge gained from the simulation database is successfully transferred into the problem of predicting an experimentally measured δ.

Expanding Our Understanding of COVID-19 from Biomedical Literature Using Word Embedding.

A better understanding of the clinical characteristics of coronavirus disease 2019 (COVID-19) is urgently required to address this health crisis. Numerous researchers and pharmaceutical companies are working on developing vaccines and treatments; however, a clear solution has yet to be found. The current study proposes the use of artificial intelligence methods to comprehend biomedical knowledge and infer the characteristics of COVID-19.

The impact of the pandemic declaration on public awareness and behavior: Focusing on COVID-19 google searches.

The unprecedented outbreaks of epidemics such as the coronavirus has caused major socio-economic changes. To analyze public risk awareness and behavior in response to the outbreak of epidemic diseases, this study focuses on RSV (Relative Search Volume) provided by Google Trends. This study uses the social big data provided by Google RSV to investigate how the WHO's pandemic declaration affected public awareness and behavior.

Structure, Dynamics, Receptor Binding, and Antibody Binding of the Fully Glycosylated Full-Length SARS-CoV-2 Spike Protein in a Viral Membrane.

The spike (S) protein of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) mediates host cell entry by binding to angiotensin-converting enzyme 2 (ACE2) and is considered the major target for drug and vaccine development. We previously built fully glycosylated full-length SARS-CoV-2 S protein models in a viral membrane including both open and closed conformations of the receptor-binding domain (RBD) and different templates for the stalk region. In this work, multiple μs-long all-atom molecular dynamics simulations were performed to provide deeper insights into the structure and dynamics of S protein and glycan functions.

Infectious disease outbreak prediction using media articles with machine learning models.

When a newly emerging infectious disease breaks out in a country, it brings critical damage to both human health conditions and the national economy. For this reason, apprehending which disease will newly emerge, and preparing countermeasures for that disease, are required. Many different types of infectious diseases are emerging and threatening global human health conditions.

Increased ribosomal protein levels and protein synthesis in the striatal synaptosome of Shank3-overexpressing transgenic mice.

The SH3 and multiple ankyrin repeat domains 3 (Shank3) protein is a core organizer of the macromolecular complex in excitatory postsynapses, and its defects cause numerous synaptopathies, including autism spectrum disorders. Although the function of Shank3 as a postsynaptic scaffold is adequately established, other potential mechanisms through which Shank3 broadly modulates the postsynaptic proteome remain relatively unexplored. In our previous quantitative proteomic analysis, six up-regulated ribosomal proteins were identified in the striatal synaptosome of Shank3-overexpressing transgenic (TG) mice.

Analysis of Learning Influence of Training Data Selected by Distribution Consistency.

This study suggests a method to select core data that will be helpful for machine learning. Specifically, we form a two-dimensional distribution based on the similarity of the training data and compose grids with fixed ratios on the distribution. In each grid, we select data based on the distribution consistency (DC) of the target class data and examine how it affects the classifier.

Serial Interval and Generation Interval for Imported and Local Infectors, Respectively, Estimated Using Reported Contact-Tracing Data of COVID-19 in China.

The emerging virus, COVID-19, has caused a massive outbreak worldwide. Based on the publicly available contact-tracing data, we identified 509 transmission chains from 20 provinces in China and estimated the serial interval (SI) and generation interval (GI) of COVID-19 in China. Inspired by different possible values of the time-varying reproduction number for the imported cases and the local cases in China, we divided all transmission events into three subsets: imported (the zeroth generation) infecting 1st-generation locals, 1st-generation locals infecting 2nd-generation locals, and other transmissions among 2+.

A Hybrid Approach to Industrial Augmented Reality Using Deep Learning-Based Facility Segmentation and Depth Prediction.

Typical AR methods have generic problems such as visual mismatching, incorrect occlusions, and limited augmentation due to the inability to estimate depth from AR images and attaching the AR markers onto physical objects, which prevents the industrial worker from conducting manufacturing tasks effectively. This paper proposes a hybrid approach to industrial AR for complementing existing AR methods using deep learning-based facility segmentation and depth prediction without AR markers and a depth camera. First, the outlines of physical objects are extracted by applying a deep learning-based instance segmentation method to the RGB image acquired from the AR camera.

Spectral Instability of Layered Mixed Halide Perovskites Results from Anion Phase Redistribution and Selective Hole Injection.

Despite the ability to precisely tune their bandgap energies, mixed halide perovskites (MHPs) suffer from significant spectral instability, which obstructs their utilization for the rational design of light-emitting diodes. Here, we investigate the origin of the electroluminescence peak shifts in layered MHPs containing bromide and iodide. X-ray diffraction and steady-state absorption measurements prove effective integration of iodide into the cubic lattice and the spatially uniform distribution of halides in the ambient environment.