A stirring solution hydrothermal approach is widely used to rationally grow elongated oxide nanostructures with controllable aspect ratios. Depending on the synthesis conditions, the following are observed (i) no nanostructure formation (the system exists as a pure liquid), (ii) formation of nanostructure starting from a critical powder/initial volume of the liquid solution, and (iii) monotonic increase in the nanostructure’s aspect ratio (towards asymptotic value) with stirring rate. Despite these experimental observations, the theoretical understanding of the process is limited. Herein, using an athermal ballistic atomic jump model, we develop a phenomenological theory of nanostructure growth under different stirring rates, demonstrating the conditions necessary for breaking the equilibrium Wulff shape, the formation of elongated one-dimensional structures, and explaining regimes (i-iii) reported experimentally. Moreover, the comparison of the phenomenological models without and with the account of ripening effects in the open ensemble of nanowires under stirring provides the theoretical guidance for the controllable growth of elongated nanostructures by the stirring solution hydrothermal approach.A urinary tract infection (UTI) is a recurrent infection that requires timely diagnosis and appropriate treatment. Conventional urinalysis methods are laborious and time-consuming, and lack sensitivity and specificity. In this context, photoluminescence (PL)-based biosensors have gained more attention due to their fast response time, and enhanced sensitivity and specificity. In relation to this, a PL-based biosensor was developed using ZnO nanoparticles obtained via a microwave-assisted process functionalized with cysteamine (ZnO-Cys) to detect the quorum sensing signalling molecules of Gram-negative bacteria, N-acyl-homoserine lactones (AHLs). These AHLs are involved in bacterial communication and are responsible for activating virulence and pathogenicity. Biosensing measurements based on PL intensity variations corresponding to the O2 acceptor defect level of ZnO with reference to ZnO-Cys were considered. A maximum sensitivity of 97% was achieved in the detection of AHL. The linear detection range of the developed biosensor was 10-120 nM in artificial urine media (AUM). The effect of pH on the sensitivity of the biosensor in AUM was also investigated and reported. The developed sensor was validated using the AHLs produced by Pseudomonas aeruginosa (MCC3101) in real-time analysis, which further confirmed the overall specificity and sensitivity.In December 2019, several patients with pneumonia of an unknown cause were detected in Wuhan, China. On 7 January 2020, the causal organism was identified as a new coronavirus, later named as the 2019 novel coronavirus (2019-nCoV). Genome sequencing found the genetic sequence of 2019-nCoV homologous to that of severe acute respiratory syndrome-associated coronavirus. As of 29 January 2020, the virus had been diagnosed in more than 7000 patients in China and 77 patients in other countries. It is reported that both symptomatic and asymptomatic patients with 2019-nCoV can play a role in disease transmission via airborne and contact. This finding has caused a great concern about the prevention of illness spread. The clinical features of the infection are not specific and are often indistinguishable from those of other respiratory infections, making it difficult to diagnose. Given that the virus has a strong ability to spread between individuals, it is of top priority to identify potential or suspected patients as soon as possible-or the virus may cause a serious pandemic. Therefore, a precision medicine approach to managing this disease is urgently needed for detecting and controlling the spread of the virus. In this article, we present such an approach to managing 2019-nCoV-related pneumonia based on the unique traits of the virus recently revealed and on our experience with coronaviruses at West China Hospital in Chengdu, China. © The Author(s) 2020. Published by Oxford University Press on behalf of West China School of Medicine and West China Hospital of Sichuan University.The 2019 novel coronavirus (renamed SARS-CoV-2, and generally referred to as the COVID-19 virus) has spread to 184 countries with over 1.5 million confirmed cases. Such major viral outbreaks demand early elucidation of taxonomic classification and origin of the virus genomic sequence, for strategic planning, containment, and treatment. This paper identifies an intrinsic COVID-19 virus genomic signature and uses it together with a machine learning-based alignment-free approach for an ultra-fast, scalable, and highly accurate classification of whole COVID-19 virus genomes. CH7233163 chemical structure The proposed method combines supervised machine learning with digital signal processing (MLDSP) for genome analyses, augmented by a decision tree approach to the machine learning component, and a Spearman’s rank correlation coefficient analysis for result validation. These tools are used to analyze a large dataset of over 5000 unique viral genomic sequences, totalling 61.8 million bp, including the 29 COVID-19 virus sequences available on January 27, 2020. Our results support a hypothesis of a bat origin and classify the COVID-19 virus as Sarbecovirus, within Betacoronavirus. Our method achieves 100% accurate classification of the COVID-19 virus sequences, and discovers the most relevant relationships among over 5000 viral genomes within a few minutes, ab initio, using raw DNA sequence data alone, and without any specialized biological knowledge, training, gene or genome annotations. This suggests that, for novel viral and pathogen genome sequences, this alignment-free whole-genome machine-learning approach can provide a reliable real-time option for taxonomic classification.Recent trends in renewable energy development in the United States (U.S.) show that new installed capacity of utility-scale solar energy has exceeded 30% of total installed capacity of all sources per year since 2013. Photovoltaic solar energy provides benefits in that no emissions are produced; however, there are potential impacts from photovoltaic solar development on birds that include habitat loss and potential for collision mortality. Only 2 papers in the peer-reviewed literature present fatality information from fatality monitoring studies at a photovoltaic utility-scale solar energy facility; however, more data exists in unpublished reports. To provide a more comprehensive overview of bird mortality patterns, we synthesized results from fatality monitoring studies at 10 photovoltaic solar facilities across 13 site-years in California and Nevada. We found variability in the distribution of avian orders and species among and within Bird Conservation Regions, and found that water-obligate birds, which rely on water for take-off and landing, occurred at 90% (9/10) of site-years in the Sonoran and Mojave Deserts Bird Conservation Region.