Search

The estimation of postmortem interval (PMI) has long been a focal point in the field of forensic science. Following the death of an organism, microorganisms exhibit a clock-like proliferation pattern during the course of cadaver decomposition, forming the foundation for utilizing microbiology in PMI estimation. The establishment of PMI estimation models based on datasets from different seasons is of great practical significance. In this experiment, we conducted microbiota sequencing and analysis on gravesoil and mouse intestinal contents collected during both the winter and summer seasons and constructed a PMI estimation model using the Random Forest algorithm. The results showed that the MAE of the gut microbiota model in summer was 0.47 ± 0.26 d, R2 = 0.991, and the MAE of the gravesoil model in winter was 1.04 ± 0.22 d, R2 = 0.998. We propose that, in practical applications, it is advantageous to selectively build PMI estimation models based on seasonal variations. Additionally, through a combination of morphological observations, gravesoil microbiota sequencing results, and soil physicochemical data, we identified the time of cadaveric rupture for mouse cadavers, occurring at around days 24–27 in winter and days 6–9 in summer. This study not only confirms previous research findings but also introduces novel insights, contributing to the foundational knowledge necessary to advance the utilization of microbiota for PMI estimation.

Microbial communities can undergo significant successional changes during decay and decomposition, potentially providing valuable insights for determining the postmortem interval (PMI). The microbiota produce various gases that cause cadaver bloating, and rupture releases nutrient-rich bodily fluids into the environment, altering the soil microbiota around the carcasses. In this study, we aimed to investigate the underlying principles governing the succession of microbial communities during the decomposition of pig carcasses and the soil beneath the carcasses. At early decay, the phylum Firmicutes and Bacteroidota were the most abundant in both the winter and summer pig rectum. However, Proteobacteria became the most abundant in the winter pig rectum in late decay. Using genus as a biomarker to estimate the PMI could get the MAE from 1.375 days to 2.478 days based on the RF model. The abundance of bacterial communities showed a decreasing trend with prolonged decomposition time. There were statistically significant differences in microbial diversity in the two periods (pre-rupture and post-rupture) of the four groups (WPG 0–8Dvs. WPG 16–40D, p < 0.0001; WPS 0–16Dvs. WPS 24–40D, p = 0.003; SPG 0D vs. SPG 8–40D, p = 0.0005; and SPS 0D vs. SPS 8–40D, p = 0.0208). Most of the biomarkers in the pre-rupture period belong to obligate anaerobes. In contrast, the biomarkers in the post-rupture period belong to aerobic bacteria. Furthermore, the genus Vagococcus shows a similar increase trend, whether in winter or summer. Together, these results suggest that microbial succession was predictable and can be developed into a forensic tool for estimating the PMI.

Considering the limitation of COVID-19 so that undergraduates can't return to the campus to continue classroom learning, on-line teaching based on Zoom Meetings mixed with rain classroom is being adopted to teach medical immunology and microbiology in students majoring in Chinese Medicine. At the same time, flipped classroom is also applied to this on-line teaching. In this article, it is described in detail how this blending teaching is constructed. What's more, personal teaching reflection and summary is also analyzed.

Taking two kinds of typical involute splines applied in the power split system as the research object，the theoretical analysis models of the load distribution factor in the tooth direction are established respectively，and the effects of load position，torque distribution and torsional rigidity of splines on the load distribution in the tooth direction are studied. The results show that，for the first kind of involute spline pair，with the increase of load distribution position and load distribution ratio，the maximum load distribution factor first decreases and then increases. There are optimal load position and optimal load distribution ratio to minimize the maximum load distribution factor，with the reduction ratio of 56.67% and 51.83% respectively. When the left torsional stiffness is 450×106 N/rad and the right torsional stiffness is 522×106 N/rad，the maximum load distribution factor reaches the minimum value of 2.08，and the maximum value increases by 31.8% compared with the minimum value. For the second involute spline pair，the maximum load distribution factor increases gradually with the increase of the left torque position，and the increase proportion is 14.36%，the influence of load distribution on the maximum load distribution factor is very small. With the increase of torsional rigidity，the maximum load distribution factor increases by 16.86%.

Our brains constantly generate predictions of sensory input that are compared with actual inputs, propagate the prediction-errors through a hierarchy of brain regions, and subsequently update the internal predictions of the world. However, the essential feature of predictive coding, the notion of hierarchical depth and its neural mechanisms, remains largely unexplored. Here, we investigated the hierarchical depth of predictive auditory processing by combining functional magnetic resonance imaging (fMRI) and high-density whole-brain electrocorticography (ECoG) in marmoset monkeys during an auditory local-global paradigm in which the temporal regularities of the stimuli were designed at two hierarchical levels. The prediction-errors and prediction updates were examined as neural responses to auditory mismatches and omissions. Using fMRI, we identified a hierarchical gradient along the auditory pathway: midbrain and sensory regions represented local, shorter-time-scale predictive processing followed by associative auditory regions, whereas anterior temporal and prefrontal areas represented global, longer-time-scale sequence processing. The complementary ECoG recordings confirmed the activations at cortical surface areas and further differentiated the signals of prediction-error and update, which were transmitted via putative bottom-up γ and top-down β oscillations, respectively. Furthermore, omission responses caused by absence of input, reflecting solely the two levels of prediction signals that are unique to the hierarchical predictive coding framework, demonstrated the hierarchical top-down process of predictions in the auditory, temporal, and prefrontal areas. Thus, our findings support the hierarchical predictive coding framework, and outline how neural networks and spatiotemporal dynamics are used to represent and arrange a hierarchical structure of auditory sequences in the marmoset brain.

The estimation of a postmortem interval (PMI) is particularly important for forensic investigations. The aim of this study was to assess the succession of bacterial communities associated with the decomposition of mouse cadavers and determine the most important biomarker taxa for estimating PMIs. High-throughput sequencing was used to investigate the bacterial communities of gravesoil samples with different PMIs, and a random forest model was used to identify biomarker taxa. Redundancy analysis was used to determine the significance of environmental factors that were related to bacterial communities. Our data showed that the relative abundance of Proteobacteria, Bacteroidetes and Firmicutes showed an increasing trend during decomposition, but that of Acidobacteria, Actinobacteria and Chloroflexi decreased. At the genus level, Pseudomonas was the most abundant bacterial group, showing a trend similar to that of Proteobacteria. Soil temperature, total nitrogen, NH4+-N and NO3−-N levels were significantly related to the relative abundance of bacterial communities. Random forest models could predict PMIs with a mean absolute error of 1.27 days within 36 days of decomposition and identified 18 important biomarker taxa, such as Sphingobacterium, Solirubrobacter and Pseudomonas. Our results highlighted that microbiome data combined with machine learning algorithms could provide accurate models for predicting PMIs in forensic science and provide a better understanding of decomposition processes.

The complete chloroplast genome sequence of Centipeda minima (L.) A. Braun et Aschers (Compositae) has been characterized by Illumina pair-end sequencing. The chloroplast genome is 152,351 bp in length, including a pair of inverted repeats (IRs) of 25,004 bp (12.1%) and a large single copy (LSC) and a small single copy (SSC) region of 83,972 bp (55.1%) and 18,371 bp (32.8%), respectively. The chloroplast genome includes 127 genes, which contain 83 protein-coding genes, 36 transfer RNA genes, and 8 ribosomal RNA genes. The nucleotide composition is asymmetric (31.1% A, 18.5% C, 19.0% G, 31.4% T). The overall GC content of C. minima chloroplast genome is 37.5%. Phylogenetic analysis illustrates that C. minima is closely related to other Asteraceae species, including Helianthus annuus subsp. texanus, Tithonia diversifolia and Xanthium sibiricum with a strong bootstrap value of 100.

Previous studies have examined the neural correlates for crossmodal paired-associate (PA) memory and the temporal dynamics of its formation. However, the neural dynamics for feedback processing of crossmodal PA learning remain unclear. To examine this process, we recorded event-related scalp electrical potentials for PA learning of unimodal visual-visual pairs and crossmodal visual-tactile pairs when participants performed unimodal and crossmodal tasks. We examined event-related potentials (ERPs) after the onset of feedback in the tasks for three effects: feedback type (positive feedback vs. negative feedback), learning (as the learning progressed) and the task modality (crossmodal vs. unimodal). The results were as follows: (1) feedback type: the amplitude of P300 decreased with incorrect trials and the P400/N400 complex was only present in incorrect trials; (2) learning: progressive positive voltage shifts in frontal recording sites and negative voltage shifts in central and posterior recording sites were identified as learning proceeded; and (3) task modality: compared with the unimodal PA learning task, positive voltage shifts in frontal sites and negative voltage shifts in posterior sites were found in the crossmodal PA learning task. To sum up, these results shed light on cortical excitability related to feedback processing of crossmodal PA learning.

Visual stimuli are known to activate the auditory cortex of deaf people, presenting evidence of cross-modal plasticity. However, the mechanisms underlying such plasticity are poorly understood. In this functional MRI study, we presented two types of visual stimuli, language stimuli (words, sign language, and lip-reading) and a general stimulus (checkerboard) to investigate neural reorganization in the superior temporal cortex (STC) of deaf subjects and hearing controls. We found that only in the deaf subjects, all visual stimuli activated the STC. The cross-modal activation induced by the checkerboard was mainly due to a sensory component via a feed-forward pathway from the thalamus and primary visual cortex, positively correlated with duration of deafness, indicating a consequence of pure sensory deprivation. In contrast, the STC activity evoked by language stimuli was functionally connected to both the visual cortex and the frontotemporal areas, which were highly correlated with the learning of sign language, suggesting a strong language component via a possible feedback modulation. While the sensory component exhibited specificity to features of a visual stimulus (e.g., selective to the form of words, bodies, or faces) and the language (semantic) component appeared to recruit a common frontotemporal neural network, the two components converged to the STC and caused plasticity with different multivoxel activity patterns. In summary, the present study showed plausible neural pathways for auditory reorganization and correlations of activations of the reorganized cortical areas with developmental factors and provided unique evidence towards the understanding of neural circuits involved in cross-modal plasticity.