Changes in the gut microbiota of forest musk deer (Moschus berezovskii) during ex situ conservation.

Ex situ conservation is an important technique for protecting rare and endangered wildlife, and maintaining stable individual health is crucial to its success. Gut microbiota composition is a critical indicator of animal health and should therefore be closely monitored during ex situ conservation to track impacts on animal health. Forest musk deer (Moschus berezovskii) were historically distributed in Hebei Province, China, however, they are now extinct in the region. Thus, ex situ conservation efforts were conducted in 2016 whereby approximately 50 individuals were artificially migrated from Weinan, Shaanxi to Huailai, Hebei. To monitor gut health of these migrated individuals, we used 16S rRNA high-throughput sequencing technology to examine the microbiota differences between Huailai juvenile and Weinan juvenile groups, and between Huailai adult and Weinan adult groups. Alpha diversity analysis indicated that the richness of microbiota significantly decreased after migration to the Huailai area, and the beta diversity results also showed significant dissimilarity in gut microbial communities, demonstrating the distinct microbial structure differences in the forest musk deer population from the two areas, for both juvenile and adult groups, respectively. In addition, PICRUSt functional profile prediction indicated that the functions of gut digestion and absorption, and degradation of toxic substances were significantly weakened after ex situ conservation. Differences in diet composition between the individuals of the two sites were also observed and the impact of food on gut microbiota compositions within forest musk deer during ex situ conservation was investigated. This study provides a theoretical basis for developing ex situ conservation measures, especially for the protection of forest musk deer.

Androgen plays an important role in regulating the synthesis of pheromone in the scent gland of muskrat.

The scent (musk) gland is an organ unique to muskrats and other scent-secreting animals, and the pheromones (musk) synthesized and secreted by the scent gland play a role in chemical communication among scent-secreting animals. The musk gland is synchronized with testicular developmental changes; however, little is known regarding androgen secretion from the testis and how this regulates pheromone synthesis and the secretion of scent. To investigate the effect of androgens on the synthesis of pheromones in the musk gland, we established a muskrat castration model by surgical removal of the testis, and analyzed the histomorphology, hormone concentration, gene expression, and changes in the chemical composition of the musk gland in castration and control groups by histomorphological analysis, Enzyme-Linked ImmunoSorbent Assay (ELISA), RNA sequencing (RNA-seq), and gas chromatography-mass spectrometry (GCMS). Histomorphological analysis results showed that after castration, muskrat gland cells underwent significant atrophy (P < 0.05). Hormone measurement results showed that there was a significant decrease in serum testosterone and muskrat musk testosterone (P < 0.05) after muskrat castration. Transcriptome sequencing results showed that 510 differentially expressed transcripts (DETs) were mainly enriched in fatty acid metabolism, terpenoid backbone biosynthesis, fatty acid degradation, PPAR signaling pathway, and fatty acid biosynthesis. GCMS results showed that macrocyclic ketones, steroids, fatty acids, alcohols, and esters in musk were significantly changed (P < 0.05). In conclusion, androgens were found to play an important function in the chemical communication exchange between muskrats through regulating pheromone synthesis in musk cells. This study provides a basis for understanding the mechanism of animal communication influenced by androgens.

Correlation Analysis of EEG Brain Network With Modulated Acoustic Stimulation for Chronic Tinnitus Patients.

The acoustic stimulation influences of the brain is still unveiled, especially from the brain network point, which can reveal how interaction is propagated and integrated between different brain zones for chronic tinnitus patients. We specifically designed a paradigm to record the electroencephalograms (EEGs) for tinnitus patients when they were treated with consecutive acoustic stimulation neuromodulation therapy for up to 75 days, using the tinnitus handicap inventory (THI) to evaluate the tinnitus severity or the acoustic stimulation treatment efficacy, and the EEG to record the brain activities every 2 weeks. Then, we used an EEG-based coherence analysis to investigate if the changes in brain network consistent with the clinical outcomes can be observed during 75-days acoustic treatment. Finally, correlation analysis was conducted to study potential relationships between network properties and tinnitus handicap inventory score change. The EEG network became significantly weaker after long-term periodic acoustic stimulation treatment, and tinnitus handicap inventory score changes or the acoustic stimulation treatment efficacy are strongly correlated with the varying brain network properties. Long-term acoustic stimulation neuromodulation intervention can improve the rehabilitation of chronic tinnitus patients, and the EEG network provides a relatively reliable and quantitative analysis approach for objective evaluation of tinnitus clinical diagnosis and treatment.

PCG Classification Using Multidomain Features and SVM Classifier.

This paper proposes a method using multidomain features and support vector machine (SVM) for classifying normal and abnormal heart sound recordings. The database was provided by the PhysioNet/CinC Challenge 2016. A total of 515 features are extracted from nine feature domains, i.e., time interval, frequency spectrum of states, state amplitude, energy, frequency spectrum of records, cepstrum, cyclostationarity, high-order statistics, and entropy. Correlation analysis is conducted to quantify the feature discrimination abilities, and the results show that "frequency spectrum of state", "energy", and "entropy" are top domains to contribute effective features. A SVM with radial basis kernel function was trained for signal quality estimation and classification. The SVM classifier is independently trained and tested by many groups of top features. It shows the average of sensitivity, specificity, and overall score are high up to 0.88, 0.87, and 0.88, respectively, when top 400 features are used. This score is competitive to the best previous scores. The classifier has very good performance with even small number of top features for training and it has stable output regardless of randomly selected features for training. These simulations demonstrate that the proposed features and SVM classifier are jointly powerful for classifying heart sound recordings.

Identification of Pulmonary Hypertension Using Entropy Measure Analysis of Heart Sound Signal.

This study introduced entropy measures to analyze the heart sound signals of people with and without pulmonary hypertension (PH). The lead II Electrocardiography (ECG) signal and heart sound signal were simultaneously collected from 104 subjects aged between 22 and 89. Fifty of them were PH patients and 54 were healthy. Eleven heart sound features were extracted and three entropy measures, namely sample entropy (SampEn), fuzzy entropy (FuzzyEn) and fuzzy measure entropy (FuzzyMEn) of the feature sequences were calculated. The Mann-Whitney U test was used to study the feature significance between the patient and health group. To reduce the age confounding factor, nine entropy measures were selected based on correlation analysis. Further, the probability density function (pdf) of a single selected entropy measure of both groups was constructed by kernel density estimation, as well as the joint pdf of any two and multiple selected entropy measures. Therefore, a patient or a healthy subject can be classified using his/her entropy measure probability based on Bayes' decision rule. The results showed that the best identification performance by a single selected measure had sensitivity of 0.720 and specificity of 0.648. The identification performance was improved to 0.680, 0.796 by the joint pdf of two measures and 0.740, 0.870 by the joint pdf of multiple measures. This study showed that entropy measures could be a powerful tool for early screening of PH patients.