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Staphylococcus aureus-induced infective endocarditis (IE) is a life-threatening disease. Differences in virulence between distinct S. aureus strains, which are partly based on the molecular mechanisms during bacterial adhesion, are not fully understood. Yet, distinct molecular or elemental patterns, occurring during specific steps in the adhesion process, may help to identify novel targets for accelerated diagnosis or improved treatment. Here, we use laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) of post-mortem tissue slices of an established mouse model of IE to obtain fingerprints of element distributions in infected aortic valve tissue. Three S. aureus strains with different virulence due to deficiency in distinct adhesion molecules (fibronectin-binding protein A and staphylococcal protein A) were used to assess strain-specific patterns. Data analysis was performed by t-distributed stochastic neighbor embedding (t-SNE) of mass spectrometry imaging data, using manual reference tissue classification in histological specimens. This procedure allowed for obtaining distinct element patterns in infected tissue for all three bacterial strains and for comparing those to patterns observed in healthy mice or after sterile inflammation of the valve. In tissue from infected mice, increased concentrations of calcium, zinc, and magnesium were observed compared to noninfected mice. Between S. aureus strains, pronounced variations were observed for manganese. The presented approach is sensitive for detection of S. aureus infection. For strain-specific tissue characterization, however, further improvements such as establishing a database with elemental fingerprints may be required.

Guangdong province is situated in the south of China with a population size of 113.46 million. Hakka is officially recognized as a branch of Han Chinese, and She is the official minority group in mainland China. There are approximately 25 million Hakka people who mainly live in the East and North regions of China, while there are only 0.7 million She people. The genetic characterization and forensic parameters of these two groups are poorly defined (She) or still need to be explored (Hakka). In this study, we have genotyped 475 unrelated Guangdong males (260 Hakka and 215 She) with Promega PowerPlex® Y23 System. A total of 176 and 155 different alleles were observed across all 23 Y-STRs for Guangdong Hakka (with a range of allele frequencies from 0.0038 to 0.7423) and Guangdong She (0.0047–0.8605), respectively. The gene diversity ranged from 0.4877 to 0.9671 (Guangdong Hakka) and 0.3277–0.9526 (Guangdong She), while the haplotype diversities were 0.9994 and 0.9939 for Guangdong Hakka and Guangdong She, with discrimination capacity values of 0.8885 and 0.5674, respectively. With reference to geographical and linguistic scales, the phylogenetic analyses showed us that Guangdong Hakka has a close relationship with Southern Han, and the genetic pool of Guangdong Hakka was influenced by surrounding Han populations. The predominant haplogroups of the Guangdong She group were O2-M122 and O2a2a1a2-M7, while Guangdong She clustered with other Tibeto-Burman language-speaking populations (Guizhou Tujia and Hunan Tujia), which shows us that the Guangdong She group is one of the branches of Tibeto-Burman populations and the Huonie dialect of She languages may be a branch of Tibeto-Burman language families.

Mass spectrometry imaging (MSI) is an imaging technique used in analytical chemistry to study the molecular distribution of various compounds at a micro-scale level. For each pixel, MSI stores a mass spectrum obtained by measuring signal intensities of thousands of mass-to-charge ratios (m/z-ratios), each linked to an individual molecular ion species. Traditional analysis tools focus on few individual m/z-ratios, which neglects most of the data. Recently, clustering methods of the spectral information have emerged, but faithful detection of all relevant image regions is not always possible. We propose an interactive visual analysis approach that considers all available information in coordinated views of image and spectral space visualizations, where the spectral space is treated as a multi-dimensional space. We use non-linear embeddings of the spectral information to interactively define clusters and respective image regions. Of particular interest is, then, which of the molecular ion species cause the formation of the clusters. We propose to use linear embeddings of the clustered data, as they allow for relating the projected views to the given dimensions. We document the effectiveness of our approach in analyzing matrix-assisted laser desorption/ionization (MALDI-2) imaging data with ground truth obtained from histological images.

Mitochondrial DNA is inherited maternally and is thought to be evolved stepwise from one population to another population in the history of mankind. Haplogroup for any mtDNA provides us a solution for the logical classification of the mitochondrial DNA based on established phylogenetic principles. There is a huge amount of scattered mtDNA sequence data from different global and regional populations. It demands a professional platform for representation of data to draw meaningful and simple-to-understand information about mtDNA distribution. Here, mtDNAmap provides geographical representation of mtDNA haplogroups' frequencies in various populations all over the world according to their present day reported locations. It is a haplogroup frequency database of different populations calculated from the published data using their reported valid mtDNA sequences. Publicly available MtDNA sequences, processed through mtDNAprofiler for SNP determinations based on revised Cambridge Reference Sequence and followed by Haplogrep 2.0 for the determination of the haplogroups on the basis of most updated Phylotree version-17, are graphically represented on the dynamic map in the form of frequencies. mtDNAmap provides the open access to the whole or part of published high-quality curated data. The tool is not only useful for researchers from forensic and anthropology backgrounds but also in general public.

Researchers proposed various visual based methods for estimating the fruit quantity and performing qualitative analysis, they used ariel and ground vehicles to capture the fruit images in orchards. Fruit yield estimation is a challenging task with environmental noise such as illumination changes, color variation, overlapped fruits, cluttered environment, and branches or leaves shading. In this paper, we proposed a learning free fast visual based method to correctly count the apple fruits tightly overlapped in a complex outdoor orchard environment. We first carefully build the color based HS model to perform the color based segmentation. This step extracts the apple fruits from the complex orchard background and produces the blobs representing apples along with the additional noisy regions. We used the fine tuned morphological operators to refine the blobs received from the previous step and remove the noisy regions followed by the Gaussian smoothing. Finally we treated the circular shaped blobs with Hough Transform algorithm to calculate the center coordinates of each apple edge and the method correctly locates the apples in the images. The results ensures the proposed algorithm successfully detects and count apple fruits in the images captured from apple orchard and outperforms the standard state of the art contoured based method.

Forensic science has been helping law enforcement agencies in better understanding and presenting the evidence in the court of law. In certain situations, when conventional forensic methods of investigations cannot make better conclusions with more specific accuracy, then molecular techniques do help in reaching the acquired accuracy in the results regarding the identification of evidence. Advanced molecular techniques, which are using Deoxyribose Nucleic acid (DNA), Ribose Nucleic Acid (RNA), and protein molecules to produce forensically important information from the samples recovered at the crime scene. DNA can only distinguish among individuals but is unable to discriminate the type of samples originated from the same sample. For this RNA has become a molecule of interest for its different levels of expression in different cells/tissues of an individual. RNA molecules of different types are being used to build up models for several purposes (injury-age, new-born age, molecular cause of death, etc.). Modern techniques like Real-time Polymerase Chain Reaction (PCR) and Microarray are being used for the detection of RNA molecules of interest both in the form of its abundance and as a unique molecular detection. This script will help in understanding the importance of RNA application in forensic sciences by providing an overview of the research done to date and the techniques being used for this purpose.

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