Set Of Markers Research Articles

PMSeeker: A Scheme Based on the Greedy Algorithm and the Exhaustive Algorithm to Screen Low-Redundancy Marker Sets for Large-Scale Parentage Assignment with Full Parental Genotyping.

Parentage assignment is a genetic test that utilizes genetic characteristics, such as molecular markers, to identify the parental relationships within populations, which, in commercial fish farming, are almost always large and where full information on potential parents is known. To accurately find the true parents, the genotypes of all loci in the parentage marker set (PMS) are required for each individual being tested. With the same accuracy, a PMS containing a smaller number of markers will undoubtedly save experimental costs. Thus, this study established a scheme to screen low-redundancy PMSs using the exhaustive algorithm and greedy algorithm. When screening PMSs, the greedy algorithm selects markers based on the parental dispersity index (PDI), a uniquely defined metric that outperforms the probability of exclusion (PE). With the conjunctive use of the two algorithms, non-redundant PMSs were found for more than 99.7% of solvable cases in three groups of random sample experiments in this study. Then, a low-redundancy PMS can be composed using two or more of these non-redundant PMSs. This scheme effectively reduces the number of markers in PMSs, thus conserving human and experimental resources and laying the groundwork for the widespread implementation of parentage assignment technology in economic species breeding.

A Set of Possible Markers for Monitoring Heart Failure and Cognitive Impairment Associated: A Review of Literature from the Past 5 Years.

Heart failure is an epidemiologically relevant disease because of the aging population and widespread lifestyles that promote it. In addition to the acute event, it is possible for the disease to become chronic with periodic flare-ups. It is essential to study pathology from a diagnostic and prognostic point of view and to identify parameters for effective monitoring. In addition, heart failure is associated with multiple comorbidities, including cognitive impairment, which is monitored clinically but not through specific biomarkers in these patients. The purpose of this review is to gather the most recent scientific evidence on a few possible biomarkers previously identified for monitoring heart failure and associated cognitive impairment. We surveyed studies inherent to a set of previously identified markers, evaluating English-language articles from the past five years conducted in adult heart failure patient populations. We used the databases PubMed, Web of Sciences, and Cochrane Library for search studies, and we considered articles published in journals with an impact factor greater than five in the publication year. Among the biomarkers evaluated, a concordant indication for serial measurements for heart failure monitoring emerged only for interleukin-6. For the other markers, there is still little evidence available, which is interesting but sometimes conflicting. Interesting studies have also emerged for biomarkers of cognitive decline assessed in patients with heart failure, confirming the hypotheses of the increasingly studied heart-brain correlation. Certainly, further studies in large populations are needed to identify effective biomarkers for monitoring heart failure and associated cognitive impairment.

Development of Gene-Based InDel Markers on Putative Drought Stress-Responsive Genes and Genetic Diversity of Durian (Durio zibethinus).

Insertion-deletion (InDel) markers are co-dominant, relatively abundant and practical for agarose gel genotyping. InDel polymorphism usually affects gene functions. Nucleotide sequences of durian (Durio zibethinus) are available, but InDel makers have not been well established. This study aimed to develop drought-related gene-based InDel markers for durian through bioinformatic analysis of RNA-Seq datasets. The polymorphism of the markers was verified in 24 durian genotypes local to Thailand. Bioinformatic analysis indicated 496 InDel loci having lengths more than 9bp. To evaluate these InDel markers, 15 InDel loci were selected. Nine markers were successfully amplified a clear polymorphic band pattern on 2% agarose gel. The polymorphic information content (PIC) of these nine markers ranged from 0.1103 to 0.5808. The genetic distance between the 24 genotypes ranged from 0.222 to 0.889. The phylogeny based on the nine InDel loci distinguished the 24 genotypes and divided samples into four groups. This set of gene-based InDel markers on putative drought-responsive genes will be useful for genetic studies.

Construction of SNP fingerprints and genetic diversity analysis of radish (Raphanus sativus L.).

Radish (Raphanus sativus L.) is a vegetable crop with economic value and ecological significance in the genus Radish, family Brassicaceae. In recent years, developed countries have attached great importance to the collection and conservation of radish germplasm resources and their research and utilization, but the lack of population genetic information and molecular markers has hindered the development of the genetic breeding of radish. In this study, we integrated the radish genomic data published in databases for the development of single-nucleotide polymorphism (SNP) markers, and obtained a dataset of 308 high-quality SNPs under strict selection criteria. With the support of Kompetitive Allele-Specific PCR (KASP) technology, we screened a set of 32 candidate core SNP marker sets to analyse the genetic diversity of the collected 356 radish varieties. The results showed that the mean values of polymorphism information content (PIC), minor allele frequency (MAF), gene diversity and heterozygosity of the 32 candidate core SNP markers were 0.32, 0.30, 0.40 and 0.25, respectively. Population structural analysis, principal component analysis and genetic evolutionary tree analysis indicated that the 356 radish materials were best classified into two taxa, and that the two taxa of the material were closely genetically exchanged. Finally, on the basis of 32 candidate core SNP markers we calculated 15 core markers using a computer algorithm to construct a fingerprint map of 356 radish varieties. Furthermore, we constructed a core germplasm population consisting of 71 radish materials using 32 candidate core markers. In this study, we developed SNP markers for radish cultivar identification and genetic diversity analysis, and constructed DNA fingerprints, providing a basis for the identification of radish germplasm resources and molecular marker-assisted breeding as well as genetic research.

Expanding the coverage of spatial proteomics: a machine learning approach.

Multiplexed protein imaging methods use a chosen set of markers and provide valuable information about complex tissue structure and cellular heterogeneity. However, the number of markers that can be measured in the same tissue sample is inherently limited. In this paper, we present an efficient method to choose a minimal predictive subset of markers that for the first time allows the prediction of full images for a much larger set of markers. We demonstrate that our approach also outperforms previous methods for predicting cell-level protein composition. Most importantly, we demonstrate that our approach can be used to select a marker set that enables prediction of a much larger set than could be measured concurrently. All code and intermediate results are available in a Reproducible Research Archive at https://github.com/murphygroup/CODEXPanelOptimization.

Mechanistically based blood proteomic markers in the TGF-β pathway stratify risk of hepatocellular cancer in patients with cirrhosis.

Hepatocellular carcinoma (HCC) is the third leading cause of death from cancer worldwide but is often diagnosed at an advanced incurable stage. Yet, despite the urgent need for blood-based biomarkers for early detection, few studies capture ongoing biology to identify risk-stratifying biomarkers. We address this gap using the TGF-β pathway because of its biological role in liver disease and cancer, established through rigorous animal models and human studies. Using machine learning methods with blood levels of 108 proteomic markers in the TGF-β family, we found a pattern that differentiates HCC from non-HCC in a cohort of 216 patients with cirrhosis, which we refer to as TGF-β based Protein Markers for Early Detection of HCC (TPEARLE) comprising 31 markers. Notably, 20 of the patients with cirrhosis alone presented an HCC-like pattern, suggesting that they may be a group with as yet undetected HCC or at high risk for developing HCC. In addition, we found two other biologically relevant markers, Myostatin and Pyruvate Kinase M2 (PKM2), which were significantly associated with HCC. We tested these for risk stratification of HCC in multivariable models adjusted for demographic and clinical variables, as well as batch and site. These markers reflect ongoing biology in the liver. They potentially indicate the presence of HCC early in its evolution and before it is manifest as a detectable lesion, thereby providing a set of markers that may be able to stratify risk for HCC.

Development of a novel five-dye panel for human identification insertion/deletion (INDEL) polymorphisms.

DNA analysis of forensic case samples relies on short tandem repeats (STRs), a key component of the combined DNA index system (CODIS) used to identify individuals. However, limitations arise when dealing with challenging samples, prompting the exploration of alternative markers such as single nucleotide polymorphisms (SNPs) and insertion/deletion (INDELs) polymorphisms. Unlike SNPs, INDELs can be differentiated easily by size, making them compatible with electrophoresis methods. It is possible to design small INDEL amplicons (<200 bp) to enhance recovery from degraded samples. To this end, a set of INDEL Human Identification Markers (HID) was curated from the 1000 Genomes Project, employing criteria including a fixation index (FST) ≤ 0.06, minor allele frequency (MAF) >0.2, and high allele frequency divergence. A panel of 33 INDEL-HIDs was optimized and validated following the Scientific Working Group on DNA Analysis Methods (SWGDAM) guidelines, utilizing a five-dye multiplex electrophoresis system. A small sample set (n = 79 unrelated individuals) was genotyped to assess the assay's performance. The validation studies exhibited reproducibility, inhibition tolerance, ability to detect a two-person mixture from a 4:1 to 1:6 ratio, robustness with challenging samples, and sensitivity down to 125 pg of DNA. In summary, the 33-loci INDEL-HID panel exhibited robust recovery with low-template and degraded samples and proved effective for individualization within a small sample set.

Ecological Speciation without Morphological Differentiation? A New Cryptic Species of Diodontus Curtis (Hymenoptera, Pemphredonidae) from the Centre of Europe.

Upon exploring the mitotype diversity of the aphid-hunting wasp, Diodontus tristis, we revealed specimens with highly divergent mitotypes from two localities in Lithuania and nesting in clayey substrate, while the specimens with typical mitotypes were found nesting in sandy sites. The comparison of inter- and intra-specific distances and application of delimitation algorithms supported the species status of the clay-nesting populations. Using a set of DNA markers that included complete or partial sequences of six mitochondrial genes, three markers of ribosomal operon, two homeobox genes, and four other nuclear genes, we clarified the phylogenetic relationships of the new cryptic species. The endosymbiotic bacteria infestation was checked, considering the option that the divergent populations may represent clades isolated by Wolbachia infection; however, it did not demonstrate any specificity. We found only subtle morphological differences in the new clay-nesting species, D. argillicola sp. nov.; the discriminant analysis of morphometric measurements did not reliably segregate it as well. Thus, we provide the molecular characters of the cryptic species, which allow confident identification, its phylogenetic position within the genus, and an updated identification key for the D. tristis species group.

A comparison of minimum segment models for the estimation of centre of mass position and velocity for slip recovery during a bathtub transfer task

BackgroundExploring the use of minimum marker sets is important for balancing the technical quality of motion capture with challenging data collection environments and protocols. While minimum marker sets have been demonstrated to be appropriate for evaluation of some motion patterns, there is limited evidence to support model choices for abrupt, asymmetrical, non-cyclic motion such as balance disturbance during a bathtub exit task. Research questionHow effective are six models of reduced complexity for the estimation of centre of mass (COM) displacement and velocity, relative to a full-body model. MethodsEight participants completed a bathtub exit task. Participants received a balance perturbation as they crossed the bathtub rim, stepping from a soapy wet bathtub to a dry floor. Six reduced models were developed from the full, 72-marker, 12 segment 3D kinematic data set. Peak displacement and velocity of the body COM, and RMSE (relative to the full-body model) for displacement and velocity of the body COM were determined for each model. ResultsMain effects were observed for peak right, left, anterior, posterior, upwards and downwards motion, and peak left, anterior, posterior, upwards and downwards velocity. Time-varying (RMSE) was smaller for models including the thighs than models not containing the thighs. In contrast, inclusion of upper arm, forearm, and hand segments did not improve model performance. The model containing the sacrum marker only consistently performed the worst across peak and RMSE metrics. SignificanceFindings suggest a simplified centre of mass model may adequately capture abrupt, asymmetrical, non-cyclic tasks, such as balance disturbance recovery during obstacle crossing. A reduced kinematic model should include the thighs, trunk and pelvis segments, although models that are more complex are recommended, depending on the metrics of interest.

Genomic selection for growth characteristics in Korean red pine (Pinus densiflora Seibold & Zucc.).

Traditionally, selective breeding has been used to improve tree growth. However, traditional selection methods are time-consuming and limit annual genetic gain. Genomic selection (GS) offers an alternative to progeny testing by estimating the genotype-based breeding values of individuals based on genomic information using molecular markers. In the present study, we introduced GS to an open-pollinated breeding population of Korean red pine (Pinus densiflora), which is in high demand in South Korea, to shorten the breeding cycle. We compared the prediction accuracies of GS for growth characteristics (diameter at breast height [DBH], height, straightness, and volume) in Korean red pines under various conditions (marker set, model, and training set) and evaluated the selection efficiency of GS compared to traditional selection methods. Training the GS model to include individuals from various environments using genomic best linear unbiased prediction (GBLUP) and markers with a minor allele frequency larger than 0.05 was effective. The optimized model had an accuracy of 0.164-0.498 and a predictive ability of 0.018-0.441. The predictive ability of GBLUP against that of additive best linear unbiased prediction (ABLUP) was 0.86-5.10, and against the square root of heritability was 0.19-0.76, indicating that GS for Korean red pine was as efficient as in previous studies on forest trees. Moreover, the response to GS was higher than that to traditional selection regarding the annual genetic gain. Therefore, we conclude that the trained GS model is more effective than the traditional breeding methods for Korean red pines. We anticipate that the next generation of trees selected by GS will lay the foundation for the accelerated breeding of Korean red pine.

Untargeted metabolomics for lifestyle biomarker discovery in human hair

There is a risk of crimes remaining unsolved when no matching DNA profiles or fingermarks are found. If this is the case, forensic investigations are faced with a significant shortage of evidence and information regarding the unknown perpetrator and/or victim as well as any missing persons. However, a rather commonly found biological trace encountered at crime scenes is human hair. As hair acts as a biochemical reservoir, it may contain valuable information regarding one’s characteristics and habits. This study aimed to build an analytical method capable of determining a marker set of relevant metabolites in hair, eventually building up a profile of its donor. To find potential markers, an untargeted metabolomics approach was developed to select and identify statistically significant features. For that purpose, a total of 68 hair samples were collected at several hairdresser shops in varying neighbourhoods. Compound extraction was achieved via methanolic incubation overnight and analysis performed using a high-resolution mass spectrometry (HRMS) Orbitrap Q Exactive Focus. The acquired data was uploaded and statistically evaluated using two free online software/libraries, where a total of eight compounds have given a match on both tools. Their presumptive identity was confirmed using reference standards and consequently added to a dynamic target donor profiling list. These results show the potential of using untargeted metabolomics for the search for lifestyle biomarkers capable of differentiating individuals. Such tools are of paramount importance in a forensic setting with little or no evidence available and no clear tactical leads.

An integrated precision medicine approach in major depressive disorder: a study protocol to create a new algorithm for the prediction of treatment response.

Major depressive disorder (MDD) is the most common psychiatric disease worldwide with a huge socio-economic impact. Pharmacotherapy represents the most common option among the first-line treatment choice; however, only about one third of patients respond to the first trial and about 30% are classified as treatment-resistant depression (TRD). TRD is associated with specific clinical features and genetic/gene expression signatures. To date, single sets of markers have shown limited power in response prediction. Here we describe the methodology of the PROMPT project that aims at the development of a precision medicine algorithm that would help early detection of non-responder patients, who might be more prone to later develop TRD. To address this, the project will be organized in 2 phases. Phase 1 will involve 300 patients with MDD already recruited, comprising 150 TRD and 150 responders, considered as extremes phenotypes of response. A deep clinical stratification will be performed for all patients; moreover, a genomic, transcriptomic and miRNomic profiling will be conducted. The data generated will be exploited to develop an innovative algorithm integrating clinical, omics and sex-related data, in order to predict treatment response and TRD development. In phase 2, a new naturalistic cohort of 300 MDD patients will be recruited to assess, under real-world conditions, the capability of the algorithm to correctly predict the treatment outcomes. Moreover, in this phase we will investigate shared decision making (SDM) in the context of pharmacogenetic testing and evaluate various needs and perspectives of different stakeholders toward the use of predictive tools for MDD treatment to foster active participation and patients' empowerment. This project represents a proof-of-concept study. The obtained results will provide information about the feasibility and usefulness of the proposed approach, with the perspective of designing future clinical trials in which algorithms could be tested as a predictive tool to drive decision making by clinicians, enabling a better prevention and management of MDD resistance.

Virulence diversity of the yellow rust pathogen population in Dagestan

Background. Yellow rust incidence on Triticum L. has been observed to surge up in Dagestan in recent years, facilitating selection of highly resistant genotypes. Information on the effectiveness of Yr genes and the pathogen’s virulence and racial composition is needed to interpret field assessment data correctly. The objective of this study was to describe the virulence of the Dagestan population of Puccinia striiformis West. at the seedling stage, evaluate the resistance of differentiators to yellow rust in the field, and identify invasive PstS1 and PstS2 races.Materials and methods. Leaves with urediniopustules were collected from bread wheat accessions at Dagestan Experiment Station of VIR (DЕS VIR) in 2020–2022. Virulence was analyzed in the laboratory using 14 AvYr lines (Avocet NIL) and 15 differential wheat cultivars. Age-specific resistance of virulence testers under high natural infection pressure was studied in the field of DES VIR. A set of SCAR markers (SCP19M24a1, SCP19M24a2, SCP19M26a1, and SCP19M26a2) were used to identify invasive races.Results and discussion. Avocet lines with Yr5, Yr10, Yr15, Yr24, and Yr26, and cv. ‘Moro’ (Yr10, YrMor) manifested resistance to all isolates. Virulence to Yr17 was detected only in the isolates from cvs. ‘Graf’ and ‘Svarog’ carrying this gene, and ‘Siete Ceros’. The Dagestan collection demonstrated high genetic diversity. Common phenotypes were identified on cv. ‘Graf’ in 2021 and 2020. There was no temporal differentiation between pathogen collections in the years of testing. High field resistance to yellow rust was recorded in lines with Yr5, Yr8, Yr10, Yr15 and Yr24, and in cvs. ‘Moro’, ‘Compair’, ‘Carstens’ and ‘Spaldings Prolific’. Cvs. ‘Reichersberg 42’ and ‘Heines Peko Vilmorin 23’ were resistant in 2020 and 2022, and moderately affected (up to 10%) in 2021. Molecular analysis identified all three isolates belonging to the invasive PstS2 group on cv. ‘Siete Cerros’ and L 650 in 2021, and cv. ‘Vassa’ in 2022.

Effects of Ball Size and Weight on Throwing Kinematics and Kinetics in Youth Baseball Athletes

Background: In baseball, youth athletes play on smaller fields with shorter distances between bases, shorter pitching distances, and smaller mounds. Despite this, youth athletes use baseballs weighing the same amount as those used at the professional level, possibly predisposing youth baseball players to injuries. Purpose: (1) To determine the effects of throwing a smaller, lighter, and both smaller and lighter baseball on throwing arm stress in youth athletes and (2) to also investigate how changing the ball size and weight would affect elbow varus torque, shoulder distraction force, and throwing arm internal rotation velocity during the throwing motion. Study Design: Cross-sectional study; Level of evidence, 3. Methods: This cross-sectional cohort study analyzed the kinematics and kinetics of 38 youth baseball players (mean age, 8.3 ± 0.8 years) throwing a baseball modified in size and weight. Three-dimensional motion data were collected using a retroreflective marker set and a 12-camera motion analysis system. Full-body kinematics and kinetics were calculated using commercial software. Participants threw 5 different types of baseballs 3 times each, in random order, with full effort from a pitching mound to a target 14 m away. The balls used were a 5-oz regular baseball, 5-oz (0.142-kg) baseball with a 5% reduced circumference, 4-oz (0.113-kg) baseball, 4-oz baseball with a 5% reduced circumference, and 3-oz (0.085-kg) baseball. Analysis of variance was used to determine statistical differences in elbow varus torque, shoulder distraction force, and throwing arm internal rotation velocity among baseball types. The Tukey post hoc test was used to further investigate differences between the ball groups, considering P < .05 to be significant. Results: Analysis of variance detected a significant difference in elbow varus torque among ball groups (P = .024). The Tukey post hoc test revealed a moderate difference in elbow varus torque between the 5-oz baseball (4.73 ± 1.06 percentage body weight × height [%BW × H]) and 3-oz baseball (4.06 ± 0.83 %BW × H) (P = .017; d = 0.677 [95% CI, 0.08-1.27]). No significant differences were found in shoulder distraction force or throwing arm internal rotation velocity among ball groups. Conclusion: Compared with a 5-oz baseball, throwing a 3-oz baseball resulted in decreased elbow varus torque with a moderate effect size.

Genetic analysis of purple pigmentation in rice seed and vegetative parts - implications on developing high-yielding purple rice (Oryza sativa L.).

Pigmentation in rice grains is an important quality parameter. Purple-coloured rice (Oryza sativa L.) indicates the presence of high anthocyanin with benefits of antioxidant properties. However, the genetic mechanism of grain colour is not fully understood. Therefore, the study focused on understanding pigmentation in grain pericarp and vegetative parts, and its relationship with blast resistance and enhanced grain yield. Three local cultivars from the northeastern region (NER) of India - Chakhao Poireiton (purple), Mang Meikri (light brown), and Kala Joha (white) - along with high-yielding varieties (HYVs) Shasharang (light brown) and Sahbhagi dhan (white) were used to develop biparental populations. The findings suggested that pigmentation in vegetative tissue was governed by the inter-allelic interaction of several genes. Haplotype analysis revealed that Kala3 complemented Kala4 in enhancing purple pigmentation and that Kala4 is not the only gene responsible for purple colour as evident by the presence of a desired allele for markers RID3 and RID4 (Kala4 locus) in Chakhao Poireiton and Kala Joha irrespective of their pericarp colour, implying the involvement of some other additional, unidentified genes/loci. RID3 and RID4 together with RM15191 (Kala3 locus) could be employed as a reliable marker set for marker-assisted selection (MAS). Pericarp colour was strongly correlated with colour in different vegetative parts, but showed a negative correlation with grain yield. Pb1, reported to be associated with panicle blast resistance, contributed to leaf blast resistance. Transgressive segregants for improved pigmentation and high yield were identified. The selection of lines exhibiting coloured pericarp, high anthocyanin content, aroma, blast resistance, and increased yield compared to their respective HYV parents will be valuable resources in the rice breeding programme.

Polygenic risk score comparator (PRScomp): Test population vs. worldwide populations

BackgroundPolygenic risk scores (PRS) are a powerful tool for predicting an individual's genetic risk for complex diseases. MethodsWe have developed a web service (PRScomp) as a user-friendly tool to evaluate PRS of the user own population and compare it with worldwide populations. ResultsA disease/trait database has been constructed from GWAS Catalog summary statistics. Genotype data of test population is uploaded and merged with the reference dataset (1000 Genome Project and Human Genome Diversity Project) to obtain a file including the common SNPs. The user can select a disease/trait from the database and a curated set of risk markers is used to calculate summatory PRS. Distribution of z-scored PRS values is presented in publication-ready plots and text files that can be downloaded. DiscussionPRScomp can be useful for public health decision-making by identifying population-specific genetic risk factors and informing the development of targeted interventions for at-risk populations.

Far from home: tracing the origin of non-native water frogs (genus Pelophylax) in Malta by molecular markers

AbstractOne of the most frequently translocated species outside their native range in Europe are water frogs of the genus Pelophylax. Recently, water frogs belonging to the same genus have also been recorded on the island of Gozo in Malta. To trace their origin, we genetically examined 17 individuals from three Gozitan localities where water frogs have been recorded recently. We analysed one mitochondrial (NADH dehydrogenase 2, ND2) and one nuclear (serum albumin intron 1, SAI-1) fragment to identify the geographic origin of the frogs and a set of microsatellite markers to determine their population-genetic structure and the predicted number of source populations. Based on the ND2 and SAI-1 markers, the water frogs on the island of Gozo originate from southern Anatolia, Turkey. According to sequence variation in ND2, they were assigned to a caralitanus mtDNA clade, which is endemic to southern Anatolia and taxonomically represents either an evolutionary lineage within P. cf. bedriagae or a separate species P. caralitanus. All Gozo water frogs had only one haplotype in the ND2 and one allele in the SAI-1 fragment, indicating a recent and single introduction event. These results are supported by microsatellite analysis, which revealed low genetic variability and the absence of any population-genetic structure, suggesting that Gozo water frogs originate from only one source population.

A comprehensive battery of flow cytometric immunoassays for the in vitro testing of chemical effects in human blood cells.

There is a growing need for immunological assays to test toxic and modulatory effects of chemicals. The assays should be easy to use, reproducible and superior to cell line-based assays. We have therefore developed a comprehensive portfolio of assays based on primary human blood cells that are suitable for testing chemical effects. The flow cytometry-based assays were designed to target a wide range of human peripheral blood mononuclear cells and whole blood, including T cells, NK cells, B cells, basophils and innate-like T cells such as γδT, MAIT and NKT cells. We have selected a set of activation markers for each immune cell, e.g: CD154 (T cells), CD137, CD107a (NK cells), CD63 (basophils), CD69, CD83 (B cells), CD69, IFN-γ (MAIT cells) and we selected cell specific stimuli: aCD3 antibodies (T cells); E. coli and cytokines IL-12/15/18 (MAIT cells); CpG ODN2006, R848 or aCD40 antibodies (B cells), fMLP or aFcϵR1 (basophils) or K562 cells (NK cells). By selecting immune cell-specific markers and cell-specific stimuli, we were able to induce particular immune responses from the targeted immune cells. For example, the response to stimulation with anti-CD3 antibodies was in 36.8% of CD107a+CD8+ cells. Cytokine stimulation induced the production of IFN-γ in 30% of MAIT cells. After stimulation with E. coli, around 50% of MAIT cells produced TNF. About 40% of basophils responded to aFcƐR1 stimulation. Similar activation ranges were achieved in K562-stimulated NK cells. Our test portfolio covers the most relevant immune cells present in human blood, providing a solid basis for in vitro toxicity and immunomodulatory testing of chemicals. By using human blood, the natural composition of cells found in the blood can be determined and the effects of chemicals can be detected at the cellular level.

A Geometric Flow Approach for Segmentation of Images with Inhomongeneous Intensity and Missing Boundaries

Image segmentation is a complex mathematical problem, especially for images that contain intensity inhomogeneity and tightly packed objects with missing boundaries in between. For instance, Magnetic Resonance (MR) muscle images often contain both issues, making muscle segmentation especially difficult. In this paper we propose a novel intensity correction and a semi-automatic active contour-based segmentation approach. The approach uses a geometric flow that incorporates a Reproducing Kernel Hilbert Space (RKHS) edge detector and a geodesic distance penalty term from a set of markers and anti-markers. We test the proposed scheme on MR muscle segmentation and compare with some state-of-the-art methods. To help deal with the intensity inhomogeneity in this kind of image, a new approach to estimate the bias field using a fat fraction image, called Prior Bias-Corrected Fuzzy C-means (PBCFCM), is introduced. Numerical experiments show that the proposed scheme leads to significantly better results than compared ones. The average dice values of the proposed method are 92.5%, 85.3%, 85.3% for quadriceps, hamstrings and other muscle groups while other approaches are at least 10% worse.

Source profile of PM2.5 emissions from different primary sources in the coal capital city Dhanbad, India

The rise of fine particulate matter (PM2.5) levels in urban areas, driven by traffic, construction, and combustion emissions, has prompted urgent air quality concerns. Understanding source-specific PM2.5 chemical characteristics and developing associated source markers is essential for knowing their accurate contribution to atmospheric PM2.5. This study focuses on developing PM2.5 chemical source profiles from nine different emissions, primarily categorized under traffic and combustion sources. The chemical characterization included the carbonaceous thermal fractions, inorganic ions, and elemental composition. Heterogeneity in chemical composition across emission sources was examined using the coefficient of divergence and diagnostic ratio, and finally, source-specific chemical fingerprints were developed using the ratio normalization approach. The finding revealed significant inter and intra-variation in the chemical composition among traffic and combustion emission sources. Organic matter is observed significantly higher in combustion sources (84%–92%) than in traffic sources (22%–45%). Both OC/EC and char-EC/soot-EC values showed much higher values for combustion sources than traffic emissions, with cow dung cake burning emissions displaying the largest values. Also, char-EC/soot-EC values showed a similar trend with OC/EC values and thus can be used as an additional marker for deciphering emission sources. The ion-balance ratio revealed particle emissions from coal, cow dung, and garbage burning to be highly acidic, while traffic and construction sources were alkaline. Source marker results provide new insights into differences in the chemical fingerprint of specific emission sources. A new set of source markers was seen for garbage burning while coal-burning emissions showed varying chemical fingerprints and were found to be dependent on coal processing. Among elements, bromine and chlorine are found to be the unique markers for cow dung cake-burning emissions. Receptor models can use the database developed from the current work to demarcate the emission sources accurately and benefit the regulatory bodies in developing efficient control measures.