Machine-learning algorithms for asthma,COPD, and lung cancer risk assessment using circulating microbial extracellular vesicle data and their application to assess dietary effects

Although mounting evidence suggests that the microbiome has a tremendous influence on intractable disease, the relationship between circulating microbial extracellular vesicles (EVs) and respiratory disease remains unexplored. Here, we developed predictive diagnostic models for COPD, asthma, and lung cancer by applying machine learning to microbial EV metagenomes isolated from patient serum and coded by their accumulated taxonomic hierarchy. All models demonstrated high predictive strength with mean AUC values ranging from 0.93 to 0.99 with various important features at the genus and phylum levels. Application of the clinical models in mice showed that various foods reduced high-fat diet-associated asthma and lung cancer risk, while COPD was minimally affected. In conclusion, this study offers a novel methodology for respiratory disease prediction and highlights the utility of serum microbial EVs as data-rich features for noninvasive diagnosis.Subject terms: Respiratory tract diseases, Machine learning, Predictive markers     

Ferromagnetic to Paramagnetic Transition in Spherical Spin Glass

Journal of Statistical Physics - We consider the spherical spin glass model defined by a combination of the pure 2-spin spherical Sherrington–Kirkpatrick Hamiltonian and the ferromagnetic...     

Synthesis of tris(β,β,γ-oximinoalkyl)amines from aliphatic nitro compounds and methyl vinyl ketone

A general strategy for the assembly of previously unknown tris(β,β,γ-oximinoalkyl)amines from aliphatic nitro compounds and methyl vinyl ketone is described. The strategy involves N,N-bis(siloxy)enamines as key intermediates. The latter are accessible by double silylation of alkylnitro compounds. Nickel(II) and copper(II) complexes of tris(β,β,γ-oximinoalkyl)amines are prepared and structurally characterized.     

Drug-likeness scoring based on unsupervised learning

Drug-likeness prediction is important for the virtual screening of drug candidates. It is challenging because the drug-likeness is presumably associated with the whole set of necessary properties to pass through clinical trials, and thus no definite data for regression is available. Recently, binary classification models based on graph neural networks have been proposed but with strong dependency of their performances on the choice of the negative set for training. Here we propose a novel unsupervised learning model that requires only known drugs for training. We adopted a language model based on a recurrent neural network for unsupervised learning. It showed relatively consistent performance across different datasets, unlike such classification models. In addition, the unsupervised learning model provides drug-likeness scores that well separate distributions with increasing mean values in the order of datasets composed of molecules at a later step in a drug development process, whereas the classification model predicted a polarized distribution with two extreme values for all datasets presumably due to the overconfident prediction for unseen data. Thus, this new concept offers a pragmatic tool for drug-likeness scoring and further can be applied to other biochemical applications.

A new quantification method of drug-likeness based on unsupervised learning. The method only uses drug molecules as training set without any non-drug-like molecules.     

Efficient deep blue organic light emitting diodes based on [2,7,7,13,13-pentamethyl-9-(10-phenylanthracen-9-yl)-7,13-dihydrobenzo[5,6]-s-indaceno[1,2-g]quinoline] derivatives

Abstract

In this study, as the ongoing effort to develop efficient blue OLEDs, two deep blue emitters based on indenoquinoline-substituted anthracene derivatives have been synthesized and characterized. Multilayer organic light emitting diodes were fabricated with the following sequence: indium-tin-oxide (ITO)/4,4’,4’’-tris[2-naphthyl(phenyl)amino (2-TNATA)/4,4’-bis(N-(1-naphthyl)-N-phenylamino) biphenyl (NPB)/Blue emitting materials/Bathophenanthroline (Bphen)/lithium quinolate (Liq)/Al. All the devices showed efficient blue emissions. Particularly, a device using ‘2,7,7,13,13-pentamethyl-9-(10-phenylanthracen-9-yl)?7,13-dihydrobenzo[5,6]-s-indaceno[1,2-g] quinoline’ as an emitter showed a maximum external quantum efficiency (EQE) of 4.92% with the Commission Internationale De L’Énclairage (CIE) coordinates of (0.15, 0.10) at 8.0?V.     

Thermoresponsive hybrid hydrogel of oxidized nanocellulose using a polypeptide crosslinker

Thermoresponsive hybrid nanocellulose hydrogels were prepared from a mixture of oxidized nanocellulose and elastin-like polypeptide (ELP). Positively charged ELP was used as a polymeric crosslinker for conjugation with negatively charged nanocellulose. Hydrogel formation was triggered by a simple increase in temperature, and the hydrogel was reversibly returned to the liquid phase by decreasing temperature. Surface potential measurement confirmed the electrostatic properties of oxidized nanocellulose and ELP molecules. The surface morphology of hydrogels was observed by atomic force microscopy and field emission-scanning electron microscopy. Conformational changes in the ELP/nanocellulose hybrid were characterized by circular dichroism. The ELP/nanocellulose hybrid hydrogel was noncytotoxic and suitable for encapsulating cells, indicating its potential for biomedical applications.     

Blue electroluminescent materials based on indeno[1,2-a]arene derivatives for Organic Light-Emitting Diodes

Abstract

In this study, as a continuous effort for searching efficient blue-emitting materials, we designed and synthesized materials based on indeno[1,2-a]arene. OLED devices using these materials were fabricated in the following sequence; ITO (180?nm)/N,N'-diphenyl-N,N'-(2-napthyl)-(1,1'-phenyl)-4,4'-diamine (NPB) (50?nm)/emitting materials (30?nm)/4,7-diphenyl-1,10-phenanthroline (Bphen) (30?nm)/Liq/Al (2/100?nm). Particularly, a device using 7,7-dimethyl-7H-indeno[1,2-a]pyrene as emitter showed maximum values of luminous efficiency, power efficiency, and external quantum efficiency of 1.10?cd/A, 0.49?lm/W, 1.47% at 20?mA/cm², respectively with CIE (x,y) coordinates of (0.15, 0.08) at 6.0V.