Predicting the properties of wheat flour from grains during debranning: A machine learning approach.

In the food industry, meeting food quality demands is challenging. The quality of wheat flour, one of the most commonly used ingredients, depends on the extent of debranning done to remove the aleurone layer before milling. Therefore, the end product management can be simplified by predicting the properties of wheat flour during the debranning stage.

Data-driven autonomous operation of VOCs removal system.

Removal of volatile organic compounds (VOCs) from the air has been an important issue in many industrial fields. Traditionally, the operation of VOCs removal systems has relied on fixed operating conditions determined by domain experts based on their expertise and intuition. In practice, this manual operation cannot respond immediately to changes in the system environment.

Improving chemical reaction yield prediction using pre-trained graph neural networks.

Graph neural networks (GNNs) have proven to be effective in the prediction of chemical reaction yields. However, their performance tends to deteriorate when they are trained using an insufficient training dataset in terms of quantity or diversity. A promising solution to alleviate this issue is to pre-train a GNN on a large-scale molecular database.

Retention Time Prediction through Learning from a Small Training Data Set with a Pretrained Graph Neural Network.

Graph neural networks (GNNs) have shown remarkable performance in predicting the retention time (RT) for small molecules. However, the training data set for a particular target chromatographic system tends to exhibit scarcity, which poses a challenge because the experimental process for measuring RT is costly. To address this challenge, transfer learning has been used to leverage an abundant training data set from a related source task.

Generative Modeling to Predict Multiple Suitable Conditions for Chemical Reactions.

In synthesis planning, it is important to determine suitable reaction conditions such that a chemical reaction proceeds as intended. Recent research attempts based on machine learning have proven to be effective in recommending reaction elements for specific categories regarding critical chemical context and operating conditions. However, existing methods can only make a single prediction per reaction and do not directly provide a complete specification of the reaction elements as the prediction.

Scalable graph neural network for NMR chemical shift prediction.

Graph neural networks (GNNs) have been proven effective in the fast and accurate prediction of nuclear magnetic resonance (NMR) chemical shifts of a molecule. Existing methods, despite their effectiveness, suffer from high space complexity and are therefore limited to relatively small molecules. In this work, we propose a scalable GNN for NMR chemical shift prediction.

Predicting perioperative outcomes of robot-assisted radical cystectomy: Data from the Asian Robot-Assisted Radical Cystectomy Consortium.

Objectives: To report the perioperative outcomes of robot-assisted radical cystectomy and elucidate their risk factors.

Methods: A review of the Asian Robot-Assisted Radical Cystectomy Consortium database from 2007 to 2020 was performed. The perioperative outcomes studied included complication rates, time to solid food intake, estimated blood loss, length of hospital stay, and 30-day readmission rates.

Uncertainty-aware prediction of chemical reaction yields with graph neural networks.

In this paper, we present a data-driven method for the uncertainty-aware prediction of chemical reaction yields. The reactants and products in a chemical reaction are represented as a set of molecular graphs. The predictive distribution of the yield is modeled as a graph neural network that directly processes a set of graphs with permutation invariance.

Molecular search by NMR spectrum based on evaluation of matching between spectrum and molecule.

Inferring molecular structures from experimentally measured nuclear magnetic resonance (NMR) spectra is an important task in many chemistry applications. Herein, we present a novel method implementing an automated molecular search by NMR spectrum. Given a query spectrum and a pool of candidate molecules, the matching score of each candidate molecule with respect to the query spectrum is evaluated by introducing a molecule-to-spectrum estimation procedure.

Evolutionary design of molecules based on deep learning and a genetic algorithm.

Evolutionary design has gained significant attention as a useful tool to accelerate the design process by automatically modifying molecular structures to obtain molecules with the target properties. However, its methodology presents a practical challenge-devising a way in which to rapidly evolve molecules while maintaining their chemical validity. In this study, we address this limitation by developing an evolutionary design method.

Perioperative Outcomes of Robot-Assisted Radical Cystectomy with Intracorporeal Versus Extracorporeal Urinary Diversion.

Purpose: This study was designed to investigate and compare the perioperative outcomes of intracorporeal urinary diversion (ICUD) versus extracorporeal urinary diversion (ECUD) following robotic-assisted radical cystectomy (RARC) in patients with localized bladder cancer from the Asian Robot-Assisted Radical Cystectomy (RARC) Consortium.

Methods: The Asian RARC registry was a multicenter registry involving nine centers in Asia. Consecutive patients who underwent RARC were included.

Compressed graph representation for scalable molecular graph generation.

Recently, deep learning has been successfully applied to molecular graph generation. Nevertheless, mitigating the computational complexity, which increases with the number of nodes in a graph, has been a major challenge. This has hindered the application of deep learning-based molecular graph generation to large molecules with many heavy atoms.

Predictive Modeling of NMR Chemical Shifts without Using Atomic-Level Annotations.

Recently, machine learning has been successfully applied to the prediction of nuclear magnetic resonance (NMR) chemical shifts. To build a prediction model, the existing methods require a training data set that comprises molecules whose NMR-active atoms are annotated with their chemical shifts. However, the laborious task of atomic-level annotation must be manually conducted by chemists.

Neural Message Passing for NMR Chemical Shift Prediction.

Fast and accurate prediction of NMR spectra enables automatic structure validation and elucidation of molecules on a large scale. In this Article, we propose an improved method of learning from an NMR database to predict the chemical shifts of NMR-active atoms of a new molecule. For this purpose, we use a message passing neural network that operates on the graph representation of a molecule.

Molecular Geometry Prediction using a Deep Generative Graph Neural Network.

A molecule's geometry, also known as conformation, is one of a molecule's most important properties, determining the reactions it participates in, the bonds it forms, and the interactions it has with other molecules. Conventional conformation generation methods minimize hand-designed molecular force field energy functions that are often not well correlated with the true energy function of a molecule observed in nature. They generate geometrically diverse sets of conformations, some of which are very similar to the lowest-energy conformations and others of which are very different.

Efficient learning of non-autoregressive graph variational autoencoders for molecular graph generation.

With the advancements in deep learning, deep generative models combined with graph neural networks have been successfully employed for data-driven molecular graph generation. Early methods based on the non-autoregressive approach have been effective in generating molecular graphs quickly and efficiently but have suffered from low performance. In this paper, we present an improved learning method involving a graph variational autoencoder for efficient molecular graph generation in a non-autoregressive manner.

Conditional Molecular Design with Deep Generative Models.

Although machine learning has been successfully used to propose novel molecules that satisfy desired properties, it is still challenging to explore a large chemical space efficiently. In this paper, we present a conditional molecular design method that facilitates generating new molecules with desired properties. The proposed model, which simultaneously performs both property prediction and molecule generation, is built as a semisupervised variational autoencoder trained on a set of existing molecules with only a partial annotation.

Personalized prediction of drug efficacy for diabetes treatment via patient-level sequential modeling with neural networks.

Patients with type 2 diabetes mellitus are generally under continuous long-term medical treatment based on anti-diabetic drugs to achieve the desired glucose level. Thus, each patient is associated with a sequence of multiple records for prescriptions and their efficacies. Sequential dependencies are embedded in these records as personal factors so that previous records affect the efficacy of the current prescription for each patient.

The usefulness of flexible cystoscopy for preventing double-J stent malposition after laparoscopic ureterolithotomy.

Background: The aim of this study was to evaluate the role of flexible cystoscopy in preventing malpositioning of the ureteral stent after laparoscopic ureterolithotomy in male patients.

Methods: From April 2009 to June 2015, 97 male patients with stones >1.8 cm in the upper ureter underwent intracorporeal double-J stenting of the ureter after laparoscopic ureterolithotomy performed by four different surgeons.

Effect of laminin 332 on motility and invasion in bladder cancer.

We examined the correlation between laminin 332 and malignancy in bladder cancer patients, and, using a strain of invasive bladder cancer cells, determined whether laminin 332 causes bladder cancer motility and invasion. To investigate the correlation between laminin 332 g2 distribution and patient outcome, we performed a semiquantitative immunohistochemical analysis of 35 paraffin-embedded samples using the antibody D4B5, which is specific for the laminin 5 γ2 chain. To evaluate the role of laminin 332 in NBT-II cell motility and invasion, we used a scratch assay and the Boyden chamber chemoinvasion system.

Simultaneous determination of nine UV filters and four preservatives in suncare products by high-performance liquid chromatography.

HPLC method for quantitative determination of four preservatives and nine UV filters worldwide authorized in commercial suncare product was developed and validated, and then 101 samples of commercial suncare products were analyzed for the UV filters and preservatives using the proposed method. The mobile phase was acetonitrile-water containing 0.5% acetic acid using a gradient elution at a flow rate of 0.

Assembly of nanomaterials through highly ordered self-assembled monolayers and peptide-organic hybrid conjugates as templates.

Synthesis and processing techniques have now been established for obtaining high quality monodisperse nanocrystals of various metallic and semiconducting materials, fullerenes of distinct properties, single- and multi-wall carbon nanotubes, polymeric dendrimers with tailored functionalities, as well as other nanophase constructs. The next key step towards novel applications of nanostructured materials concerns their positioning, arrangement, and connection into functional networks without mutual aggregation. In this review, we highlight the recent progress of using anthracene- and pyrene-based self-assembling molecules with tunable energetic (pi-pi interactions, hydrogen bonding, dipole-dipole interactions) and variable geometries to create stable, highly ordered, and rigid self-assembled monolayer (SAM) templates with adjustable superlattices on crystalline substrates.

Extragastrointestinal stromal tumor presenting as a scrotal mass: an unusual case.

We describe an unusual case of extragastrointestinal stromal tumor (EGIST) presenting as a scrotal mass. A 71-year-old man presented with a gradually enlarging scrotal mass with a 20-year duration. Physical examination revealed a huge (as large as volleyball), round, nontender mass occupying the whole scrotum, which was resected completely.

Prostate-specific antigen adjusted for the transition zone volume as a second screening test: a prospective study of 248 cases.

Aim: This study was conducted to verify the effectiveness of prostate-specific antigen adjusted for the transition zone volume (PSATZ), and its availability as a second screening test for prostate cancer detection.

Materials And Methods: Total prostate-specific antigen (PSA) and free PSA was measured in male patients who visited our outpatient department for voiding difficulty or screening for prostate cancer. Patients who had an intermediate PSA level between 4.

Targeted-cryosurgical ablation of the prostate with androgen deprivation therapy: quality of life in high-risk prostate cancer patients.

Aim: To present preliminary results on health-related quality of life (QoL), prostate-associated symptoms and therapeutic effects of targeted-cryosurgical ablation of the prostate (TCSAP) with androgen deprivation therapy (ADT) in high-risk prostate cancer (PCa) patients.

Methods: Thirty-four men with high-risk PCa features underwent TCSAP, and ADT was added to improve the treatment outcomes. High-risk parameters were defined as either prostate-specific antigen (PSA) = or > 100ng/mL, or Gleason score = or > 8, or both.