Predicting postmortem interval based on microbial community sequences and machine learning algorithms

Microbes play an essential role in the decomposition process but were poorly understood in their succession and behaviour. Previous researches have shown that microbes show predictable behaviour that starts at death and changes during the decomposition process. Research of such behaviour enhances the understanding of decomposition and benefits estimating the postmortem interval (PMI) in forensic investigations, which is critical but faces multiple challenges. In this study, we combined microbial community characterization, microbiome sequencing from different organs (i.e. brain, heart and cecum) and machine learning algorithms [random forest (RF), support vector machine (SVM) and artificial neural network (ANN)] to investigate microbial succession pattern during corpse decomposition and estimate PMI in a mouse corpse system. Microbial communities exhibited significant differences between the death point and advanced decay stages. Enterococcus faecalis, Anaerosalibacter bizertensis, Lactobacillus reuteri, and so forth were identified as the most informative species in the decomposition process. Furthermore, the ANN model combined with the postmortem microbial data set from the cecum, which was the best combination among all candidates, yielded a mean absolute error of 1.5 ± 0.8 h within 24-h decomposition and 14.5 ± 4.4 h within 15-day decomposition. This integrated model can serve as a reliable and accurate technology in PMI estimation.     

Performance of Social Network Sensors during Hurricane Sandy

Information flow during catastrophic events is a critical aspect of disaster management. Modern communication platforms, in particular online social networks, provide an opportunity to study such flow and derive early-warning sensors, thus improving emergency preparedness and response. Performance of the social networks sensor method, based on topological and behavioral properties derived from the “friendship paradox”, is studied here for over 50 million Twitter messages posted before, during, and after Hurricane Sandy. We find that differences in users’ network centrality effectively translate into moderate awareness advantage (up to 26 hours); and that geo-location of users within or outside of the hurricane-affected area plays a significant role in determining the scale of such an advantage. Emotional response appears to be universal regardless of the position in the network topology, and displays characteristic, easily detectable patterns, opening a possibility to implement a simple “sentiment sensing” technique that can detect and locate disasters.     

Characterization of small genomic regions of the hepatitis B virus should be performed with more caution

Background

Hepatitis B virus is a hepatotropic DNA virus that reproduces via an RNA intermediate. It can lead to an increased risk of serious liver diseases such as hepatocellular carcinoma and is a serious threat to public health. Currently, the HBV are designated based on greater than 8% nucleotide variation along the whole genome. The recombination of HBV is very common, a large majority of which are recombinants between 2 genotypes. The current work aims to characterize a suspected recombinant involving 3 genotypes.

Methods

Fifty-seven HBV full-genome sequences were obtained from 57 patients co-infected with HBV and HIV-1 by amplification coupled with sequencing. JpHMM and RDP4 were used to perform recombination analysis respectively. The recombination results of a suspected 3-genotypic recombinant were further confirmed by both maximum likelihood phylogenetic tree and Mrbayes tree.

Results

JpHMM recombination analysis clearly indicated one 3-genotypic HBV recombinant composing of B/C/D. The genotype assignments are supported by significant posterior probabilities. The subsequent phylogenetic analysis of sub-regions derived from inferred breakpoints led to a disagreement on the assignment of D segment. Investigating the conflict, further exploration by RDP4 and phylogenies revealed that the jpHMM-derived 3-genotypic recombinant is actually a B/C genotypic recombinant with C fragment spanning 1899 to 2295 (jpHMM) or 1821 to 2199 (RDP4).

Conclusions

The whole analysis indicated that (i) determination of small genomic regions should be performed with more caution, (ii) combinations of various recombination detection approaches conduce to obtain impartial results, and (iii) a unified system of nomenclature of HBV genotypes is necessary.

     

The RNF26/CBX7 axis modulates the TNF pathway to promote cell proliferation and regulate sensitivity to TKIs in ccRCC

Clear cell renal cell carcinoma (ccRCC) accounts for 85% of all malignant renal tumors. Currently, the pathogenesis of ccRCC is not fully understood. Chromobox (CBX) family proteins are the major subunits of PcG complexes and are implicated in regulating mammalian development. The CBX family consists of eight members, namely, CBX1-8. Numerous studies have highlighted that each CBX protein exhibits distinct functions and prognostic roles in specific cancer types. In this study, in silico analysis indicated that CBX7 was downregulated in ccRCC and correlated with favorable prognosis in a ccRCC cohort. Subsequent studies showed that CBX7 inhibited cancer cell proliferation and invasion. Then, we showed that CBX7 downregulated ETS1 to inactivate the tumor necrosis factor (TNF) signaling pathway, which inhibited tumor proliferation and enhanced the sensitivity of ccRCC cells to tyrosine kinase inhibitors (TKIs). Moreover, we found that CBX7 was a bona fide substrate of RNF26. RNF26 promoted the degradation of CBX7 and enhanced ccRCC tumor growth. Therefore, our results revealed a novel RNF26/CBX7 axis that modulates the TNF signaling pathway in ccRCC.     

The environmental footprint of electric vehicle battery packs during the production and use phases with different functional units

Purpose

Battery electric vehicles (BEVs) have been widely publicized. Their driving performances depend mainly on lithium-ion batteries (LIBs). Research on this topic has been concerned with the battery pack’s integrative environmental burden based on battery components, functional unit settings during the production phase, and different electricity grids during the use phase. We adopt a synthetic index to evaluate the sustainability of battery packs.

Methods

A life cycle assessment (LCA) is used to reveal the aspects of global warming potential (GWP), water consumption, and ecological impact during the two phases. An integrative indicator, the footprint-friendly negative index (FFNI), is combined with footprint family indicators of battery packs and electricity sources. We investigate two cases of 1 kg battery production and 1 kWh battery production to assess nickel–cobalt–manganese (NMC) and lithium–iron phosphate (LFP) battery packs and compare their degrees of environmental friendliness. Then, we break down the battery pack to identify the key factors influencing the environmental burden and use sensitivity analysis to analyze the causes. Moreover, we evaluate the environmental impact of battery packs during the use phase among different regions.

Results and discussion

Regardless of the functional unit (FU), the weights of the carbon footprint (CF), water footprint (WF), and ecological footprint (EF) are approximately the same. The results of the integrative environmental indicator, the FFNI, illustrate that the LFP is approximately 0.014, which is lower than that of the NMC battery pack in the mass production case. When using energy units as the FU, the FFNI of the NMC is 0.015, which reflects a lower environmental burden than that of other battery packs. In the use phase, 1kWh electricity consumption in China and Europe has the highest and lowest FFNI, respectively. When breaking down the battery-pack components, the simplified model advocates the cathode as the major contributor that determines the total environmental performance. In the following sensitivity analysis, the battery management system (BMS) is found to be the most intensive part of the footprint of most battery packs.

Conclusion

FU can influence the evaluation results. Developing proper renewable energy sources can reduce the footprints of battery packs during the use phase. The positive electrode pastes in the battery cell, BMS, and packaging in the battery pack can influence the environmental burden. Adopting green materials in sections like the BMS may be a specific measure to enhance the environmental friendliness of a battery pack during the production phase.