Spatial profiles of the bacterial microbiota throughout the gastrointestinal tract of dairy goats.

The gastrointestinal tract (GIT) is stationed by a dynamic and complex microbial community with functions in digestion, metabolism, immunomodulation, and reproduction. However, there is relatively little research on the composition and function of microorganisms in different GIT segments in dairy goats. Herein, 80 chyme samples were taken from ten GIT sites of eight Xinong Saanen dairy goats and then analyzed and identified the microbial composition via 16S rRNA V1-V9 amplicon sequencing. A total of 6669 different operational taxonomic units (OTUs) were clustered, and 187 OTUs were shared by ten GIT segments. We observed 264 species belonging to 23 different phyla scattered across ten GITs, with Firmicutes (52.42%) and Bacteroidetes (22.88%) predominating. The results revealed obvious location differences in the composition, diversity, and function of the GIT microbiota. In LEfSe analysis, unidentified_Lachnospiraceae and unidentified_Succinniclassicum were significantly enriched in the four chambers of stomach, with functions in carbohydrate fermentation to compose short-chain fatty acids. Aeriscardovia, Candidatus_Saccharimonas, and Romboutsia were significantly higher in the foregut, playing an important role in synthesizing enzymes, amino acids, and vitamins and immunomodulation. Akkermansia, Bacteroides, and Alistipes were significantly abundant in the hindgut to degrade polysaccharides and oligosaccharides, etc. From rumen to rectum, α-diversity decreased first and then increased, while ß-diversity showed the opposite trend. Metabolism was the major function of the GIT microbiome predicted by PICRUSt2, but with variation in target substrates along the regions. In summary, GIT segments play a decisive role in the composition and functions of microorganisms. KEY POINTS: • The jejunum and ileum were harsh for microorganisms to colonize due to the presence of bile acids, enzymes, faster chyme circulation, etc., exhibiting the lowest α-diversity and the highest ß-diversity. • Variability in microbial profiles between the three foregut segments was greater than four chambers of stomach and hindgut, with a higher abundance of Firmicutes dominating than others. • Dairy goats dominated a higher abundance of Kiritimatiellaeota than cows, which was reported to be associated with fatty acid synthesis.

Vision-based activity recognition in children with autism-related behaviors.

Advances in machine learning and contactless sensors have enabled the understanding complex human behaviors in a healthcare setting. In particular, several deep learning systems have been introduced to enable comprehensive analysis of neuro-developmental conditions such as Autism Spectrum Disorder (ASD). This condition affects children from their early developmental stages onwards, and diagnosis relies entirely on observing the child's behavior and detecting behavioral cues. However, the diagnosis process is time-consuming as it requires long-term behavior observation, and the scarce availability of specialists. We demonstrate the effect of a region-based computer vision system to help clinicians and parents analyze a child's behavior. For this purpose, we adopt and enhance a dataset for analyzing autism-related actions using videos of children captured in uncontrolled environments (e.g. videos collected with consumer-grade cameras, in varied environments). The data is pre-processed by detecting the target child in the video to reduce the impact of background noise. Motivated by the effectiveness of temporal convolutional models, we propose both light-weight and conventional models capable of extracting action features from video frames and classifying autism-related behaviors by analyzing the relationships between frames in a video. By extensively evaluating feature extraction and learning strategies, we demonstrate that the highest performance is attained through the use of an Inflated 3D Convnet and Multi-Stage Temporal Convolutional Network. Our model achieved a Weighted F1-score of 0.83 for the classification of the three autism-related actions. We also propose a light-weight solution by employing the ESNet backbone with the same action recognition model, achieving a competitive 0.71 Weighted F1-score, and enabling potential deployment on embedded systems. Experimental results demonstrate the ability of our proposed models to recognize autism-related actions from videos captured in an uncontrolled environment, and thus can assist clinicians in analyzing ASD.

Cancellous bone-like porous Fe@Zn scaffolds with core-shell-structured skeletons for biodegradable bone implants.

Three-dimensional (3D) porous zinc (Zn) with a moderate degradation rate is a promising candidate for biodegradable bone scaffolds. However, fabrication of such scaffolds with adequate mechanical properties remains a challenge. Moreover, the composition, crystallography and microstructure of the in vivo degradation products formed at or near the implant-bone interface are still not precisely known. Here, we have fabricated porous Fe@Zn scaffolds with skeletons consisting of an inner core layer of Fe and an outer shell layer of Zn using template-assisted electrodeposition technique, and systematically evaluated their porous structure, mechanical properties, degradation mechanism, antibacterial ability and in vitro and in vivo biocompatibility. In situ site-specific focused ion beam micromilling and transmission electron microscopy were used to identify the in vivo degradation products at the nanometer scale. The 3D porous Fe@Zn scaffolds show similar structure and comparable mechanical properties to human cancellous bone. The degradation rates can be adjusted by varying the layer thickness of Zn and Fe. The antibacterial rates reach over 95% against S. aureus and almost 100% against E. coli. A threshold of released Zn ion concentration (~ 0.3 mM) was found to determine the in vitro biocompatibility. Intense new bone formation and ingrowth were observed despite with a slight inflammatory response. The in vivo degradation products were identified to be equiaxed nanocrystalline zinc oxide with dispersed zinc carbonate. This study not only demonstrates the feasibility of porous Fe@Zn for biodegradable bone implants, but also provides significant insight into the degradation mechanism of porous Zn in physiological environment.

Improved FBAM and GO/PO Method for EM Scattering Analyses of Ship Targets in a Marine Environment.

The combination of the fact-based asymptotic method (FBAM) and the geometrical optics and physical optics (GO/PO) hybrid method is an effective way to analyze the electromagnetic (EM) scattering from electrically large ship targets in a marine environment because it takes the multiple scattering of the ship targets into consideration as well as the coupling scattering field between the targets and the sea surface. However, regarding an electrically large marine scene that contains a large target, the occlusion judgement process for calculating the multiple scattering field and the coupling field makes it inefficient. To solve this problem, this paper proposes a physical mechanism-based improved method to reduce the invalid occlusion judgment between different patches on the composite ship-ocean scene, and this operation enhances the computational efficiency significantly. With the proposed method, radar cross section (RCS) results of different targets and composite ship-ocean scenes were calculated and compared with the original FBAM and GO/PO method. Numerical results showed that the proposed method had higher efficiency compared with the original method with the same good accuracy. In addition, synthetic aperture radar (SAR) images of a composite ship-ocean scene with different radar parameters and sea conditions were simulated with the proposed method for detection purpose. Finally, the proposed method was used to analyze the EM scattering characteristic of a marine environment with multiple ships.

[Design of ECG Monitoring Analyzer Based on MicrocontrollerCC2530].

OBJECTIVE: A remote wireless electrocardiogram (ECG) monitoring system is designed by using the CC2530 micro-controller as the device core. METHODS: Acquisition, conversion and data processing for ECG signals are realized on CC2530 micro-controllers. And the ECG data is transmitted to the coordinator by using ZigBee. It realizes the real-time monitoring of ECG signals and heart rate variability (HRV) data. RESULTS: The test results show that the maximum error of the designed ECG monitoring analyzer is 3 beats per minute and the average error is 1.6 beats per minute, which can meet the requirement of pharmaceutical industry standards of the People's Republic of China. CONCLUSIONS: The ECG monitoring analyzer has good portability, high measurement precision and good practical application values.