Green-light wavelength-selective organic solar cells for agrivoltaics: dependence of wavelength on photosynthetic rate.

There is a growing demand for the development of novel solar power systems that can simultaneously solve the problems associated with both energy generation and food supply in agriculture. Green-light wavelength-selective organic solar cells (OSCs), whose transmitted blue and red light can be utilized to promote plant growth were recently reported by our group. However, the influence of wavelength variation on the photosynthetic rate in green-light wavelength-selective OSCs remains unclear. In this study, we report on the design and synthesis of new electron-accepting π-conjugated molecules containing cyclopentene-annelated thiophene with a spiro-substituted 2,7-bis(2-ethylhexyl)fluorene (FT) unit (TT-FT-ID) as a green-light wavelength-selective nonfullerene acceptor along with a reference compound TT-T-ID. Photophysical measurements indicate that the introduction of the FT unit leads to an absorption band with a small full width at half maximum in films, leading to the ability to fine-tune the absorption length. Concerning the optimization of the conditions for the fabrication of the active layers, which are composed of a green-light wavelength-selective donor polymer of poly(3-hexylthiophene) (P3HT) and the new acceptors, Bayesian optimization based on Gaussian process regression was applied to minimize the experimental batches. The green-light wavelength-selective factor (SG) and the PCEs in the green-light region (PCE-GR) of the P3HT:TT-FT-ID-based device were determined to be 0.52 and 8.6%, respectively, which are higher values than those of the P3HT:TT-T-ID blend film. The P3HT:TT-FT-ID blend film increased the photosynthetic rate of green pepper compared to that of the P3HT:TT-T-ID blend film. These results indicate that the fine-tuning of the absorbance required for crop growth is an important issue in developing green-light wavelength-selective OSCs for agrivoltaics.

Bayesian Optimization-Assisted Screening to Identify Improved Reaction Conditions for Spiro-Dithiolane Synthesis.

Bayesian optimization (BO)-assisted screening was applied to identify improved reaction conditions toward a hundred-gram scale-up synthesis of 2,3,7,8-tetrathiaspiro[4.4]nonane (1), a key synthetic intermediate of 2,2-bis(mercaptomethyl)propane-1,3-dithiol [tetramercaptan pentaerythritol]. Starting from the initial training set (ITS) consisting of six trials sampled by random screening for BO, suitable parameters were predicted (78% conversion yield of spiro-dithiolane 1) within seven experiments. Moreover, BO-assisted screening with the ITS selected by Latin hypercube sampling (LHS) further improved the yield of 1 to 89% within the eight trials. The established conditions were confirmed to be satisfactory for a hundred grams scale-up synthesis of 1.

Artificial Intelligence Uncovered Clinical Factors for Cardiovascular Events in Myocardial Infarction Patients with Glucose Intolerance.

PURPOSE: Glucose intolerance (GI), defined as either prediabetes or diabetes, promotes cardiovascular events in patients with myocardial infarction (MI). Using the pooled clinical data from patients with MI and GI in the completed ABC and PPAR trials, we aimed to identify their clinical risk factors for cardiovascular events. METHODS: Using the limitless-arity multiple testing procedure, an artificial intelligence (AI)-based data mining method, we analyzed 415,328 combinations of < 4 clinical parameters. RESULTS: We identified 242 combinations that predicted the occurrence of hospitalization for (1) percutaneous coronary intervention for stable angina, (2) non-fatal MI, (3) worsening of heart failure (HF), and (4) all causes, and we analyzed combinations in 1476 patients. Among these parameters, the use of proton pump inhibitors (PPIs) or plasma glucose levels > 200 mg/dl after 2 h of a 75 g oral glucose tolerance test were linked to the coronary events of (1, 2). Plasma BNP levels > 200 pg/dl were linked to coronary and cardiac events of (1, 2, 3). Diuretics use, advanced age, and lack of anti-dyslipidemia drugs were linked to cardiovascular events of (1, 3). All of these factors were linked to (4). Importantly, each finding was verified by independently drawn Kaplan-Meier curves, indicating that the determined factors accurately affected cardiovascular events. CONCLUSIONS: In most previous MI patients with GI, progression of GI, PPI use, or high plasma BNP levels were linked to the occurrence of coronary stenosis or recurrent MI. We emphasize that use of AI may comprehensively uncover the hidden risk factors for cardiovascular events.

Plasma BNP Levels and Diuretics Use as Predictors of Cardiovascular Events in Patients with Myocardial Infarction and Impaired Glucose Tolerance.

PURPOSE: Although impaired glucose tolerance (IGT) promotes cardiovascular events, our Alpha-glucosidase-inhibitor Blocks Cardiac Events in Patients with Myocardial Infarction and Impaired Glucose Tolerance (ABC) study showed that alpha-glucosidase inhibitors do not prevent cardiovascular events in patients with myocardial infarction (MI) and IGT. The aim of the present study was to identify potential clinical factors for cardiovascular events in patients with MI and IGT. METHODS: Using the limitless-arity multiple testing procedure, an artificial intelligence (AI)-based data mining method, we analyzed 385,391 combinations of fewer than four clinical parameters. RESULTS: We identified 380 combinations predicting the occurrence of (1) all-cause hospitalization, (2) hospitalization due to worsening of heart failure (HF), (3) hospitalization due to non-fatal MI, and (4) hospitalization due to percutaneous coronary intervention (PCI) and coronary artery bypass grafting (CABG) for stable angina among 385,391 combinations in 853 patients. Among these, either plasma BNP levels ≥ 200 pg/dl or diuretic use exclusively predicted (1) all-cause hospitalization, (2) hospitalization due to worsening of HF, and (3) hospitalization due to a non-fatal MI, with plasma BNP levels ≥ 200 pg/dl being the sole predictor of hospitalization due to PCI and CABG. Importantly, each finding was verified by independently drawn Kaplan-Meier curves, revealing the unexpected role of plasma BNP levels in the progression of coronary stenosis determined as the necessity of PCI and CABG for stable angina. CONCLUSIONS: In patients with MI and IGT, high plasma BNP levels predicted the occurrence of coronary stenosis, recurrent MI, and worsening of HF, whereas diuretic use did not predict the progression of coronary stenosis but non-fatal MI and worsening of HF.

Identifying Single Viruses Using Biorecognition Solid-State Nanopores.

Immunosensing is a bioanalytical technique capable of selective detections of pathogens by utilizing highly specific and strong intermolecular interactions between recognition probes and antigens. Here, we exploited the molecular mechanism in artificial nanopores for selective single-virus identifications. We designed hemagglutinin antibody mimicking oligopeptides with a weak affinity to influenza A virus. By functionalizing the pore wall surface with the synthetic peptides, we rendered specificity to virion-nanopore interactions. The ligand binding thereof was found to perturb translocation dynamics of specific viruses in the nanochannel, which facilitated digital typing of influenza by the resistive pulse bluntness. As amino acid sequence degrees of freedom can potentially offer variety of recognition ability to the molecular probes, this peptide nanopore approach can be used as a versatile immunosensor with single-particle sensitivity that promises wide applications in bioanalysis including bacterial and viral screening to infectious disease diagnosis.

Identification of Individual Bacterial Cells through the Intermolecular Interactions with Peptide-Functionalized Solid-State Pores.

Bioinspired pore sensing for selective detection of flagellated bacteria was investigated. The Au micropore wall surface was modified with a synthetic peptide designed from toll-like receptor 5 (TLR5) to mimic the pathogen-recognition capability. We found that intermolecular interactions between the TLR5-derived recognition peptides and flagella induce ligand-specific perturbations in the translocation dynamics of Escherichia coli, which facilitated the discrimination between the wild-type and flagellin-deletion mutant (ΔfliC) by the resistive pulse patterns thereby demonstrating the sensing of bacteria at a single-cell level. These results provide a novel concept of utilizing weak intermolecular interactions as a recognition probes for single-cell microbial identification.

Data Mining as a Powerful Tool for Creating Novel Drugs in Cardiovascular Medicine: The Importance of a "Back-and-Forth Loop" Between Clinical Data and Basic Research.

Cardiovascular diseases, which lead to cardiovascular events including death, progress with many deleterious pathophysiological sequels. If a cause-and-effect relationship follows a one-to-one relation, we can focus on a cause to treat an effect, but such a relation cannot be applied in cardiovascular diseases. To identify novel drugs in the cardiovascular field, we generally adopt two different strategies: induction and deduction. In the cardiovascular field, it is difficult to use deduction because cardiovascular diseases are caused by many factors, leading us to use induction. In this method, we consider all clinical data, such as medical records or genetic data, and identify a few candidates. Recent computational and mathematical advances enable us to use data-mining methods to uncover hidden relationships between many parameters and clinical outcomes. However, because these candidates are not identified as promoting or inhibiting factors, or as causal or consequent factors of cardiovascular diseases, we need to test them in basic research, and bring them back to the clinical field to test their efficacy in clinical trials. With such a "back-and-forth loop" between clinical observation and basic research, data-mining methods may provide novel strategies leading to new tools for clinicians, basic findings for researchers, and better outcomes for patients.

ParceLiNGAM: a causal ordering method robust against latent confounders.

We consider learning a causal ordering of variables in a linear nongaussian acyclic model called LiNGAM. Several methods have been shown to consistently estimate a causal ordering assuming that all the model assumptions are correct. But the estimation results could be distorted if some assumptions are violated. In this letter, we propose a new algorithm for learning causal orders that is robust against one typical violation of the model assumptions: latent confounders. The key idea is to detect latent confounders by testing independence between estimated external influences and find subsets (parcels) that include variables unaffected by latent confounders. We demonstrate the effectiveness of our method using artificial data and simulated brain imaging data.

In-silico-assisted derivatization of triarylboranes for the catalytic reductive functionalization of aniline-derived amino acids and peptides with H2.

Cheminformatics-based machine learning (ML) has been employed to determine optimal reaction conditions, including catalyst structures, in the field of synthetic chemistry. However, such ML-focused strategies have remained largely unexplored in the context of catalytic molecular transformations using Lewis-acidic main-group elements, probably due to the absence of a candidate library and effective guidelines (parameters) for the prediction of the activity of main-group elements. Here, the construction of a triarylborane library and its application to an ML-assisted approach for the catalytic reductive alkylation of aniline-derived amino acids and C-terminal-protected peptides with aldehydes and H2 is reported. A combined theoretical and experimental approach identified the optimal borane, i.e., B(2,3,5,6-Cl4-C6H)(2,6-F2-3,5-(CF3)2-C6H)2, which exhibits remarkable functional-group compatibility toward aniline derivatives in the presence of 4-methyltetrahydropyran. The present catalytic system generates H2O as the sole byproduct.

Metabolic syndrome is linked to the incidence of pancreatic cancer.

Background: Although previous studies have showed that metabolic syndrome is one of the contributors of pancreatic cancer, there is no clear consensus that early stages of metabolic syndrome are linked to increased incidence of pancreatic cancer. Therefore, we confirmed the linkage between metabolic syndrome and pancreatic cancer, and shown that even early stage of metabolic syndrome is linked to pancreatic cancer in the retrospective observational study. Methods: We recruited approximately 4.6 million Japanese in 2005 and followed up these subjects for more than 10 years. At the time of the enrollment, after obtaining clinical data with prescribed drugs and examining the presence or absence of metabolic syndrome (MetS), we followed up on these subjects with and without MetS to examine the incidence of pancreatic cancer. The modified criteria of the National Cholesterol Education Program Adult Treatment Panel III (NCEP/ATPIII) were used to define MetS. Findings: During the 40.7-month average follow-up period for 2,707,296 subjects with complete data for identifying MetS and important risk factors without pancreatic cancer before the enrollment, 87,857 suffered from pancreatic cancer. Pancreatic cancers occurred in 16,154 of 331,229 subjects (4.9%) in the MetS group and 71,703 of 2,376,067 patients (3.0%) in the non-MetS group (hazard ratio (HR), 1.37; 95% confidence interval [CI], 1.34-1.39; p < 0.0001 after the adjustment with age, smoking and sex). As the number of the constituent factors of MetS increased from one to five, the incidence of pancreatic cancer correspondingly increased (HR: 1.11, 1.23, 1.42, 1.66 and 2.03 using Cox proportional hazard models, p < 0.0001 each). When we defined MetS using the Japanese criteria, the results are in accord with the results using NCEP/ATPIII. Especially pre-metabolic syndrome (pre-MetS) in the Japanese criteria was tightly linked to the incidence of pancreatic cancers. Interpretation: MetS is confirmed to be linked to pancreatic cancer. Although we cannot conclude causality. We also demonstrated the link between pre-MetS and pancreatic cancer. Funding: The sponsors of the study were Japanese Heart Foundation and Japan Cardiovascular Research Foundation. This is also partially supported by Grants-in-Aid from the Ministry of Education, Culture, Sports, Science and Technology of Japan; and Grants-in-Aid from the Japan Agency for Medical Research and Development.

Predicting heart failure onset in the general population using a novel data-mining artificial intelligence method.

We aimed to identify combinations of clinical factors that predict heart failure (HF) onset using a novel limitless-arity multiple-testing procedure (LAMP). We also determined if increases in numbers of predictive combinations of factors increases the probability of developing HF. We recruited people without HF who received health check-ups in 2010, who were followed annually for 4 years. Using 32,547 people, LAMP was performed to identify combinations of factors of fewer than four factors that could predict the onset of HF. The ability of the method to predict the probability of HF onset based on the number of matching predictive combinations of factors was determined in 275,658 people. We identified 549 combinations of factors for the onset of HF. Then we classified 275,658 people into six groups who had 0, 1-50, 51-100, 101-150, 151-200 or 201-250 predictive combinations of factors for the onset of HF. We found that the probability of HF progressively increased as the number of predictive combinations of factors increased. We identified combinations of variables that predict HF onset. An increased number of matching predictive combinations for the onset of HF increased the probability of HF onset.

High-precision rapid testing of omicron SARS-CoV-2 variants in clinical samples using AI-nanopore.

A digital platform that can rapidly and accurately diagnose pathogenic viral variants, including SARS-CoV-2, will minimize pandemics, public anxiety, and economic losses. We recently reported an artificial intelligence (AI)-nanopore platform that enables testing for Wuhan SARS-CoV-2 with high sensitivity and specificity within five minutes. However, which parts of the virus are recognized by the platform are unknown. Similarly, whether the platform can detect SARS-CoV-2 variants or the presence of the virus in clinical samples needs further study. Here, we demonstrated the platform can distinguish SARS-CoV-2 variants. Further, it identified mutated Wuhan SARS-CoV-2 expressing spike proteins of the delta and omicron variants, indicating it discriminates spike proteins. Finally, we used the platform to identify omicron variants with a sensitivity and specificity of 100% and 94%, respectively, in saliva specimens from COVID-19 patients. Thus, our results demonstrate the AI-nanopore platform is an effective diagnostic tool for SARS-CoV-2 variants.

Bayesian optimization with constraint on passed charge for multiparameter screening of electrochemical reductive carboxylation in a flow microreactor.

Multiparameter screening of reductive carboxylation in an electrochemical flow microreactor was performed using a Bayesian optimization (BO) strategy. The developed algorithm features a constraint on passed charge for the electrochemical reaction, which led to suitable conditions being instantaneously found for the desired reaction. Analysis of the BO-suggested conditions underscored the physicochemical validity.

Bayesian optimization-driven parallel-screening of multiple parameters for the flow synthesis of biaryl compounds.

Traditional optimization methods using one variable at a time approach waste time and chemicals and assume that different parameters are independent from one another. Hence, a simpler, more practical, and rapid process for predicting reaction conditions that can be applied to several manufacturing environmentally sustainable processes is highly desirable. In this study, biaryl compounds were synthesized efficiently using an organic Brønsted acid catalyst in a flow system. Bayesian optimization-assisted multi-parameter screening, which employs one-hot encoding and appropriate acquisition function, rapidly predicted the suitable conditions for the synthesis of 2-amino-2'-hydroxy-biaryls (maximum yield of 96%). The established protocol was also applied in an optimization process for the efficient synthesis of 2,2'-dihydroxy biaryls (up to 97% yield). The optimized reaction conditions were successfully applied to gram-scale synthesis. We believe our algorithm can be beneficial as it can screen a reactor design without complicated quantification and descriptors.

Identification of Bacterial Drug-Resistant Cells by the Convolutional Neural Network in Transmission Electron Microscope Images.

The emergence of bacteria that are resistant to antibiotics is common in areas where antibiotics are used widely. The current standard procedure for detecting bacterial drug resistance is based on bacterial growth under antibiotic treatments. Here we describe the morphological changes in enoxacin-resistant Escherichia coli cells and the computational method used to identify these resistant cells in transmission electron microscopy (TEM) images without using antibiotics. Our approach was to create patches from TEM images of enoxacin-sensitive and enoxacin-resistant E. coli strains, use a convolutional neural network for patch classification, and identify the strains on the basis of the classification results. The proposed method was highly accurate in classifying cells, achieving an accuracy rate of 0.94. Using a gradient-weighted class activation mapping to visualize the region of interest, enoxacin-resistant and enoxacin-sensitive cells were characterized by comparing differences in the envelope. Moreover, Pearson's correlation coefficients suggested that four genes, including lpp, the gene encoding the major outer membrane lipoprotein, were strongly associated with the image features of enoxacin-resistant cells.

Analysis on Microstructure-Property Linkages of Filled Rubber Using Machine Learning and Molecular Dynamics Simulations.

A better understanding of the microstructure-property relationship can be achieved by sampling and analyzing a microstructure leading to a desired material property. During the simulation of filled rubber, this approach includes extracting common aggregates from a complex filler morphology consisting of hundreds of filler particles. However, a method for extracting a core structure that determines the rubber mechanical properties has not been established yet. In this study, we analyzed complex filler morphologies that generated extremely high stress using two machine learning techniques. First, filler morphology was quantified by persistent homology and then vectorized using persistence image as the input data. After that, a binary classification model involving logistic regression analysis was developed by training a dataset consisting of the vectorized morphology and stress-based class. The filler aggregates contributing to the desired mechanical properties were extracted based on the trained regression coefficients. Second, a convolutional neural network was employed to establish a classification model by training a dataset containing the imaged filler morphology and class. The aggregates strongly contributing to stress generation were extracted by a kernel. The aggregates extracted by both models were compared, and their shapes and distributions producing high stress levels were discussed. Finally, we confirmed the effects of the extracted aggregates on the mechanical property, namely the validity of the proposed method for extracting stress-contributing fillers, by performing coarse-grained molecular dynamics simulations.

Deep Learning-Enhanced Nanopore Sensing of Single-Nanoparticle Translocation Dynamics.

Noise is ubiquitous in real space that hinders detection of minute yet important signals in electrical sensors. Here, the authors report on a deep learning approach for denoising ionic current in resistive pulse sensing. Electrophoretically-driven translocation motions of single-nanoparticles in a nano-corrugated nanopore are detected. The noise is reduced by a convolutional auto-encoding neural network, designed to iteratively compare and minimize differences between a pair of waveforms via a gradient descent optimization. This denoising in a high-dimensional feature space is demonstrated to allow detection of the corrugation-derived wavy signals that cannot be identified in the raw curves nor after digital processing in frequency domains under the given noise floor, thereby enabled in-situ tracking to electrokinetic analysis of fast-moving single- and double-nanoparticles. The ability of the unlabeled learning to remove noise without compromising temporal resolution may be useful in solid-state nanopore sensing of protein structure and polynucleotide sequence.

A photoswitchable fluorescent protein for hours-time-lapse and sub-second-resolved super-resolution imaging.

Reversibly photoswitchable fluorescent proteins (RSFPs) are a class of fluorescent proteins whose fluorescence can be turned on and off by light irradiation. RSFPs have become essential tools for super-resolution (SR) imaging. Because most SR imaging techniques require high-power-density illumination, mitigating phototoxicity in cells due to intense light irradiation has been a challenge. Although we previously developed an RSFP named Kohinoor to achieve SR imaging with low phototoxicity, the photoproperties were insufficient to move a step further to explore the cellular dynamics by SR imaging. Here, we show an improved version of RSFP, Kohinoor2.0, which is suitable for SR imaging of cellular processes. Kohinoor2.0 shows a 2.6-fold higher fluorescence intensity, 2.5-fold faster chromophore maturation and 1.5-fold faster off-switching than Kohinoor. The analysis of the pH dependence of the visible absorption band revealed that Kohinoor2.0 and Kohinoor were in equilibria among multiple fluorescently bright and dark states, with the mutations introduced into Kohinoor2.0 bringing about a higher stabilization of the fluorescently bright states compared to Kohinoor. Using Kohinoor2.0 with our SR imaging technique, super-resolution polarization demodulation/on-state polarization angle narrowing, we conducted 4-h time-lapse SR imaging of an actin filament network in mammalian cells with a total acquisition time of 480 s without a noticeable indication of phototoxicity. Furthermore, we demonstrated the SR imaging of mitochondria dynamics at a time resolution of 0.5 s, in which the fusion and fission processes were clearly visualized. Thus, Kohinoor2.0 is shown to be an invaluable RSFP for the SR imaging of cellular dynamics.

Detecting Single Molecule Deoxyribonucleic Acid in a Cell Using a Three-Dimensionally Integrated Nanopore.

Amplification-free genome analysis can revolutionize biology and medicine by uncovering genetic variations among individuals. Here, the authors report on a 3D-integrated nanopore for electrolysis to in situ detection of single-molecule DNA in a cell by ionic current measurements. It consists of a SiO2 multipore sheet and a SiNx nanopore membrane stacked vertically on a Si wafer. Single cell lysis is demonstrated by 106  V m-1 -level electrostatic field focused at the multinanopore. The intracellular molecules are then directly detected as they move through a sensing zone, wherein the authors find telegraphic current signatures reflecting folding degrees of freedom of the millimeter-long polynucleotides threaded through the SiNx nanopore. The present device concept may enable on-chip single-molecule sequencing to multi-omics analyses at a single-cell level.