A comprehensive evaluation of multicategory classification methods for microbiomic data

BACKGROUND: Recent advances in next-generation DNA sequencing enable rapid high-throughput quantitation of microbial community composition in human samples, opening up a new field of microbiomics. One of the promises of this field is linking abundances of microbial taxa to phenotypic and physiological states, which can inform development of new diagnostic, personalized medicine, and forensic modalities. Prior research has demonstrated the feasibility of applying machine learning methods to perform body site and subject classification with microbiomic data. However, it is currently unknown which classifiers perform best among the many available alternatives for classification with microbiomic data. RESULTS: In this work, we performed a systematic comparison of 18 major classification methods, 5 feature selection methods, and 2 accuracy metrics using 8 datasets spanning 1,802 human samples and various classification tasks: body site and subject classification and diagnosis. CONCLUSIONS: We found that random forests, support vector machines, kernel ridge regression, and Bayesian logistic regression with Laplace priors are the most effective machine learning techniques for performing accurate classification from these microbiomic data

EGFR deficiency leads to impaired self-renewal and pluripotency of mouse embryonic stem cells

Background Self-renewal and pluripotency are considered as unwavering features of embryonic stem cells (ESCs). How ESCs regulate the self-renewal and differentiation is a central question in development and regenerative medicine research. Epidermal growth factor receptor (EGFR) was identified as a critical regulator in embryonic development, but its role in the maintenance of ESCs is poorly understood. Methods Here, EGFR was disrupted by its specific inhibitor AG1478 in mouse ESCs (mESCs), and its self-renewal and pluripotency were characterized according to their proliferation, expression of pluripotency markers, embryoid body (EB) formation, and mRNA expression patterns. We also used another EGFR inhibitor (gefitinib) and RNA interference assay to rule out the possibility of non-specific effects of AG1478. Results EGFR inhibition by AG1478 treatment in mESCs markedly reduced cell proliferation, caused cell cycle arrest at G0/G1 phase, and altered protein expressions of the cell cycle regulatory genes (CDK2 (decreased 11.3%) and proliferating cell nuclear antigen (decreased 25.2%)). The immunoreactivities and protein expression of pluripotency factors (OCT4 (decreased 26.9%)) also dramatically decreased, while the differentiation related genes (GATA4 (increased 1.6-fold)) were up-regulated in mESCs after EGFR inhibition. Meanwhile, EGFR inhibition in mESCs disrupted EB formation, indicating its impaired pluripotency. Additionally, the effects observed by EGFR inhibition with another inhibitor gefitinib and siRNA were consistent with those observed by AG1478 treatment in mESCs. These effects were manifested in the decreased expression of proliferative and pluripotency-related genes and the increased expression of genes involved in differentiation. Moreover, RNA-seq analysis displayed that transcript profiling was changed significantly after EGFR inhibition by AG1478. A large number of differentially expressed genes were involved in cell cycle, apoptotic process, epigenetic modification, and metabolic process, which were related to self-renewal and pluripotency, confirming that EGFR deficiency impaired self-renewal and pluripotency in mESCs. Conclusions Taken together, our results demonstrated the importance of EGFR in guarding the stemness of mESCs

Facile Fabrication of Hierarchical MOF–Metal Nanoparticle Tandem Catalysts for the Synthesis of Bioactive Molecules

Multifunctional metal–organic frameworks (MOFs) that possess permanent porosity are promising catalysts in organic transformation. Herein, we report the construction of a hierarchical MOF functionalized with basic aliphatic amine groups and polyvinylpyrrolidone-capped platinum nanoparticles (Pt NPs). The postsynthetic covalent modification of organic ligands increases basic site density in the MOF and simultaneously introduces mesopores to create a hierarchically porous structure. The multifunctional MOF is capable of catalyzing a sequential Knoevenagel condensation–hydrogenation–intramolecular cyclization reaction. The unique selective reduction of the nitro group to intermediate hydroxylamine by Pt NPs supported on MOF followed by intramolecular cyclization with a cyano group affords an excellent yield (up to 92%) to the uncommon quinoline N-oxides over quinolines. The hierarchical MOF and polyvinylpyrrolidone capping agent on Pt NPs synergistically facilitate the enrichment of substrates and thus lead to high activity in the reduction–intramolecular cyclization reaction. The bioactivity assay indicates that the synthesized quinoline N-oxides evidently inhibit the proliferation of lung cancer cells. Our findings demonstrate the feasibility of MOF-catalyzed direct synthesis of bioactive molecules from readily available compounds under mild conditions

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery

Investigation of ZnTiO3/TiO2 composites and their application in photocatalysis

In this work, we report that ZnTiO3/TiO2 composites, which were synthesized by hydrothermal method possessed photocatalytic and potential spraying properties. The obtained ZnTiO3/TiO2 composites were characterized by scanning electron microscopy (SEM) and X-ray diffraction techniques (XRD). Photocatalytic activities of ZnTiO3/TiO2 composites were evaluated by using Rhodamine B (RhB) as a model pollutant under visible light irradiation. The experimental results showed that the as-prepared ZnTiO3 (2%)/TiO2 composite exhibited better photocatalytic activity than that of pure TiO2

Species authentication and geographical origin discrimination of herbal medicines by near infrared spectroscopy: A review

Near infrared (NIR) spectroscopy as a rapid and nondestructive analytical technique, integrated with chemometrics, is a powerful process analytical tool for the pharmaceutical industry and is becoming an attractive complementary technique for herbal medicine analysis. This review mainly focuses on the recent applications of NIR spectroscopy in species authentication of herbal medicines and their geographical origin discrimination. Keywords: Near infrared spectroscopy, Herbal medicine, Species authentication, Geographical origin discrimination, Quality contro

Reinforcement Learning based Latency Minimization in Secure NOMA-MEC Systems with Hybrid SIC

—In this paper, physical layer security (PLS) in a non-orthogonal multiple access (NOMA)-based mobile edge computing (MEC) system is investigated, where hybrid successive interference cancellation (SIC) decoding is considered. Specifically , users intend to complete confidential tasks with the help of the MEC server, while an eavesdropper attempts to intercept the offloaded tasks. By jointly designing computational resource allocation, task assignment, and power allocation, a latency minimization problem is formulated. Based on the interactions between local computing time and MEC processing time, the closed-from solutions of computational resource allocation and task assignment are derived. After that, a strategy selection mechanism is established to select offloading strategies based on the corresponding conditions. Moreover, according to the analysis of hybrid SIC decoding, the conditions of different decoding orders in secure NOMA networks are derived. Furthermore, a reinforcement learning based algorithm is proposed to solve the power allocation problems for NOMA and OMA offloading strategies. This work is extended to a multiuser scenario, in which a matching-based algorithm is proposed to solve the formulated sub-channel assignment problem. Simulation results indicate that: i) the proposed solution can significantly reduce the latency and provide dynamic strategy selection for various scenarios; ii) the NOMA offloading strategy with hybrid SIC decoding can outperform other strategies in the considered system. Index Terms—Mobile edge computing (MEC), Non-orthogonal multiple access (NOMA), physical layer security (PLS), reinforcement learning, sub-channel assignment