Explore the vast range of titles available.

Forensic imaging is a non-invasive examination process during the forensic investigation. It is mainly used in forensic pathology as an adjunct to the traditional autopsy. In the past two decades, forensic imaging has been vigorously developed by forensic experts from computed tomography (CT) to multiple augmented techniques through CT and magnetic resonance imaging (MRI). The application field of forensic imaging has also been broadened as its advantages are recognised by more forensic practitioners. In addition to the forensic pathology, this technique has been used in other forensic disciplines, including forensic anthropology, forensic odontology, forensic ballistics and wildlife forensics, etc. This article reviews the development of forensic imaging as the practice and research development in different forensic disciplines based on the relevant literature analysis.

Whereas modern digital cameras use a pixelated detector array to capture images, single-pixel imaging reconstructs images by sampling a scene with a series of masks and associating the knowledge of these masks with the corresponding intensity measured with a single-pixel detector. Though not performing as well as digital cameras in conventional visible imaging, single-pixel imaging has been demonstrated to be advantageous in unconventional applications, such as multi-wavelength imaging, terahertz imaging, X-ray imaging, and three-dimensional imaging. The developments and working principles of single-pixel imaging are reviewed, a mathematical interpretation is given, and the key elements are analyzed. The research works of three-dimensional single-pixel imaging and their potential applications are further reviewed and discussed.

To escape replicative senescence, cancer cells have to overcome telomere attrition during DNA replication. Most of cancers rely on telomerase to extend and maintain telomeres, but 4–11% of cancers use a homologous recombination-based pathway called alternative lengthening of telomeres (ALT). ALT is prevalent in cancers from the mesenchymal origin and usually associates with poor clinical outcome. Given its critical role in protecting telomeres and genomic integrity in tumor cells, ALT is an Achilles heel of tumors and an attractive target for cancer therapy. Here, we review the recent progress in the mechanistic studies of ALT, and discuss the emerging therapeutic strategies to target ALT-positive cancers.

Background The plant microbiome is an integral part of the host and increasingly recognized as playing fundamental roles in plant growth and health. Increasing evidence indicates that plant rhizosphere recruits beneficial microbes to the plant to suppress soil-borne pathogens. However, the ecological processes that govern plant microbiome assembly and functions in the below- and aboveground compartments under pathogen invasion are not fully understood. Here, we studied the bacterial and fungal communities associated with 12 compartments (e.g., soils, roots, stems, and fruits) of chili pepper ( Capsicum annuum L.) using amplicons (16S and ITS) and metagenomics approaches at the main pepper production sites in China and investigated how Fusarium wilt disease (FWD) affects the assembly, co-occurrence patterns, and ecological functions of plant-associated microbiomes. Results The amplicon data analyses revealed that FWD affected less on the microbiome of pepper reproductive organs (fruit) than vegetative organs (root and stem), with the strongest impact on the upper stem epidermis. Fungal intra-kingdom networks were less stable and their communities were more sensitive to FWD than the bacterial communities. The analysis of microbial interkingdom network further indicated that FWD destabilized the network and induced the ecological importance of fungal taxa. Although the diseased plants were more susceptible to colonization by other pathogenic fungi, their below- and aboveground compartments can also recruit potential beneficial bacteria. Some of the beneficial bacterial taxa enriched in the diseased plants were also identified as core taxa for plant microbiomes and hub taxa in networks. On the other hand, metagenomic analysis revealed significant enrichment of several functional genes involved in detoxification, biofilm formation, and plant-microbiome signaling pathways (i.e., chemotaxis) in the diseased plants. Conclusions Together, we demonstrate that a diseased plant could recruit beneficial bacteria and mitigate the changes in reproductive organ microbiome to facilitate host or its offspring survival. The host plants may attract the beneficial microbes through the modulation of plant-microbiome signaling pathways. These findings significantly advance our understanding on plant-microbiome interactions and could provide fundamental and important data for harnessing the plant microbiome in sustainable agriculture. DwWQb6Dg7ZT1-tarvfq632 Video abstract

Integration of digital technologies and public health (or digital healthcare) helps us to fight the Coronavirus Disease 2019 (COVID-19) pandemic, which is the biggest public health crisis humanity has faced since the 1918 Influenza Pandemic. In order to better understand the digital healthcare, this work conducted a systematic and comprehensive review of digital healthcare, with the purpose of helping us combat the COVID-19 pandemic. This paper covers the background information and research overview of digital healthcare, summarizes its applications and challenges in the COVID-19 pandemic, and finally puts forward the prospects of digital healthcare. First, main concepts, key development processes, and common application scenarios of integrating digital technologies and digital healthcare were offered in the part of background information. Second, the bibliometric techniques were used to analyze the research output, geographic distribution, discipline distribution, collaboration network, and hot topics of digital healthcare before and after COVID-19 pandemic. We found that the COVID-19 pandemic has greatly accelerated research on the integration of digital technologies and healthcare. Third, application cases of China, EU and U.S using digital technologies to fight the COVID-19 pandemic were collected and analyzed. Among these digital technologies, big data, artificial intelligence, cloud computing, 5G are most effective weapons to combat the COVID-19 pandemic. Applications cases show that these technologies play an irreplaceable role in controlling the spread of the COVID-19. By comparing the application cases in these three regions, we contend that the key to China’s success in avoiding the second wave of COVID-19 pandemic is to integrate digital technologies and public health on a large scale without hesitation. Fourth, the application challenges of digital technologies in the public health field are summarized. These challenges mainly come from four aspects: data delays, data fragmentation, privacy security, and data security vulnerabilities. Finally, this study provides the future application prospects of digital healthcare. In addition, we also provide policy recommendations for other countries that use digital technology to combat COVID-19.

The NOAA/NESDIS/NCEI Daily Optimum Interpolation Sea Surface Temperature (SST), version 2.0, dataset (DOISST v2.0) is a blend of in situ ship and buoy SSTs with satellite SSTs derived from the Advanced Very High Resolution Radiometer (AVHRR). DOISST v2.0 exhibited a cold bias in the Indian, South Pacific, and South Atlantic Oceans that is due to a lack of ingested drifting-buoy SSTs in the system, which resulted from a gradual data format change from the traditional alphanumeric codes (TAC) to the binary universal form for the representation of meteorological data (BUFR). The cold bias against Argo was about −0.14°C on global average and −0.28°C in the Indian Ocean from January 2016 to August 2019. We explored the reasons for these cold biases through six progressive experiments. These experiments showed that the cold biases can be effectively reduced by adjusting ship SSTs with available buoy SSTs, using the latest available ICOADS R3.0.2 derived from merging BUFR and TAC, as well as by including Argo observations above 5-m depth. The impact of using the satellite MetOp-B instead of NOAA-19 was notable for high-latitude oceans but small on global average, since their biases are adjusted using in situ SSTs. In addition, the warm SSTs in the Arctic were improved by applying a freezing point instead of regressed ice-SST proxy. This paper describes an upgraded version, DOISST v2.1, which addresses biases in v2.0. Overall, by updating v2.0 to v2.1, the biases are reduced to −0.07° and −0.14°C in the global ocean and Indian Ocean, respectively, when compared with independent Argo observations and are reduced to −0.04° and −0.08°C in the global ocean and Indian Ocean, respectively, when compared with dependent Argo observations. The difference against the Group for High Resolution SST (GHRSST) Multiproduct Ensemble (GMPE) product is reduced from −0.09° to −0.01°C in the global oceans and from −0.20° to −0.04°C in the Indian Ocean.

Rockburst is one of the main disasters in railway tunnel construction. In order to accurately predict the rockburst intensity level of the railway tunnel, the rock stress coefficient σθ/σc, rock brittleness coefficient σc/σt, and elastic energy index Wet are used as evaluation indexes of rockburst intensity, and a BP neural network rockburst prediction model based on hybrid particle swarm optimization algorithm is proposed. First, 90 groups of existing rockburst examples are selected as the basic data of the mode based on the research results at home and abroad. Then, the BP neural network is improved by using particle swarm optimization (PSO) combined with the simulated annealing algorithm. The results are obtained from the training data. Based on hybrid PSO-BP neural network, the prediction model of rockburst intensity is obtained. Finally, the model is applied to the actual railway tunnel project to verify. The results show that the model takes into account individual optimization and global optimization and can correctly and effectively predict the rockburst grade of the railway tunnel, which provides a new method for rockburst prediction of the railway tunnel.

Summary Cepharanthine (CEP) is an active alkaloid isolated from Stephania Cepharantha Hayata. It is reported that the anti‐inflammatory properties of CEP could be employed to treat a variety of diseases. In this study, we first found that CEP ameliorates ulcerative colitis (UC) induced by DSS. The effect of CEP on gut microbiota was further evaluated by 16S rRNA gene sequencing, antibiotic pretreatment and faecal microbiota transplantation (FMT). Results showed that the abundances of gut microbiota, such as Romboutsia, Turicibacter and Escherichia‐Shigella (especially Romboutsia), were significantly reduced after CEP treatment. Additionally, we explored the mechanisms of CEP by a strategy integrating transcriptomics with network pharmacology. The transcriptome data confirmed that CEP functioned through cytokine and cytokine receptor pathways. The expression levels of 10 pro‐inflammatory hub genes (such as CXCL1, CXCL9, CCL7) were positively correlated with the abundance of Romboutsia. Our data identified Romboutsia as a potential pathobiont in UC. Collectively, we confirmed that CEP relieved colon inflammation by modulating gut microbiota and pro‐inflammatory cytokine expression. CEP can be adopted to design novel effective therapeutic strategies for UC. We confirmed that Cepharanthine relieved colon inflammation by modulating gut microbiota and pro‐inflammatory cytokine expression. Cepharanthine can be adopted to design novel effective therapeutic strategies for UC.

Grain size is an important component trait of grain yield, which is frequently threatened by abiotic stress. However, little is known about how grain yield and abiotic stress tolerance are regulated. Here, we characterize GSA1 , a quantitative trait locus (QTL) regulating grain size and abiotic stress tolerance associated with metabolic flux redirection. GSA1 encodes a UDP-glucosyltransferase, which exhibits glucosyltransferase activity toward flavonoids and monolignols. GSA1 regulates grain size by modulating cell proliferation and expansion, which are regulated by flavonoid-mediated auxin levels and related gene expression. GSA1 is required for the redirection of metabolic flux from lignin biosynthesis to flavonoid biosynthesis under abiotic stress and the accumulation of flavonoid glycosides, which protect rice against abiotic stress. GSA1 overexpression results in larger grains and enhanced abiotic stress tolerance. Our findings provide insights into the regulation of grain size and abiotic stress tolerance associated with metabolic flux redirection and a potential means to improve crops. Increasing grain yield needs to be put in the context of environmental stress. Here, the authors reveal that a UDP-glucosyltransferase is associated with regulation of rice grain size, abiotic stress tolerance, flavonoid-mediated auxin signaling, and redirection of carbon flux to flavonoid glycosides synthesis.