Unveiling facial kinship: The BioKinVis dataset for facial kinship verification and genetic association studies.

Facial image-based kinship verification represents a burgeoning frontier within the realms of computer vision and biomedicine. Recent genome-wide association studies have underscored the heritability of human facial morphology, revealing its predictability based on genetic information. These revelations form a robust foundation for advancing facial image-based kinship verification. Despite strides in computer vision, there remains a discernible gap between the biomedical and computer vision domains. Notably, the absence of family photo datasets established through biological paternity testing methods poses a significant challenge. This study addresses this gap by introducing the biological kinship visualization dataset, encompassing 5773 individuals from 2412 families with biologically confirmed kinship. Our analysis delves into the distribution and influencing factors of facial similarity among parent-child pairs, probing the potential association between forensic short tandem repeat polymorphisms and facial similarity. Additionally, we have developed a machine learning model for facial image-based kinship verification, achieving an accuracy of 0.80 in the dataset. To facilitate further exploration, we have established an online tool and database, accessible at http://120.55.161.230:88/.

The relative abundance of alkane-degrading bacteria oscillated similarly to a sinusoidal curve in an artificial ecosystem model from oil-well products.

Microbial phylogenetic diversity and species interactions in natural ecosystems have been investigated extensively, but our knowledge about their ecological roles, community dynamics and succession patterns is far from complete. This knowledge is essential to understand the complicated interactions of microorganisms in natural ecosystems. Here, an artificial ecosystem model of microorganisms was constructed from oil-well products and cultivated in a chemostat to investigate the succession pattern of alkane-degrading bacteria, a functional population in oil reservoirs. Their abundance was quantified by an improved qPCR technique. Our results showed that the phylogenetic structure of this artificial ecosystem model is stable during most of the chemostat cultivation process, while the genotype structure of alkane-degrading bacteria containing alkB genes shifted and their relative abundance oscillated similarly to a sinusoidal curve, like the succession pattern of producers in the Lotka-Volterra model. These results suggest that some theoretical frameworks of macroecology may work well in microbial ecosystems and be an efficient tool to understand them.

Characterization of microbial diversity and community in water flooding oil reservoirs in China.

The diversity and distribution of bacterial and archaeal communities in four different water flooding oil reservoirs with different geological properties were investigated using 16S rDNA clone library construction method. Canonical correspondence analysis was used to analyze microbial community clustering and the correlation with environmental factors. The results indicated that the diversity and abundance in the bacterial communities were significantly higher than the archaeal communities, while both of them had high similarity within the communities respectively. Phylogenetic analysis showed that of compositions of bacterial communities were distinctly different both at phylum and genus level. Proteobacteria dominated in each bacterial community, ranging from 61.35 to 75.83 %, in which α-proteobacteria and γ-proteobacteria were the main groups. In comparison to bacterial communities, the compositions of archaeal communities were similar at phylum level, while varied at genus level, and the dominant population was Methanomicrobia, ranging from 65.91 to 92.74 % in the single oil reservoir. The factor that most significantly influenced the microbial communities in these reservoirs was found to be temperature. Other environmental factors also influenced the microbial communities but not significantly. It is therefore assumed that microbial communities are formed by an accumulated effect of several factors. These results are essential for understanding ecological environment of the water flooding oil reservoirs and providing scientific guidance to the performance of MEOR technology.

[Research on population structure and distribution characteristic of indigenous microorganism in post-polymer-flooding oil reservoir].

Denaturing gradient gel electrophoresis (DGGE) method and principal component analysis (PCA) method were used to analyze the structures of microorganism population in injection wells and production wells of a post-polymer-flooding oil reservoir in Daqing oil field. The results showed that the dominant species in injection wellhead were aerobic bacteria Pseudomonas and Acinenobacter. Facultative anaerobic bacteria Enterbacter was the dominant bacteria in near area of injection wells. Bacteria detected in production wells included Thauera, Clostridia, Pseudomonas, Petrobacter and some uncultured bacteria. Methanosaeta turned out to be the only archaea detected in injection wells, which was an aceticlastic methane-producing archaeon. Archaea detected in production wells consisted of Methanomicrobium, Methanospirillum and Methanobacterium. In general, aerobic bacteria, facultative anaerobe, and strictly anaerobic bacteria distributed successively from injection wells to production wells in this block. The dominant populations of archaea were different between injection wells and production wells, while were influenced by different environments and microbial metabolism products.