Concepts and Revised Models for Phanerozoic Orogenic Gold Deposits

Existing published models for orogenic gold deposits (OGDs) do not adequately describe or explain most deposits of Phanerozoic age, and there are numerous reasons why Phanerozoic OGDs might differ significantly from older deposits. We subdivide Phanerozoic OGDs into four main subtypes, based on a number of descriptive criteria, including tectonic setting, lithological siting, and characteristics of the mineralization in each subtype. The four subtypes are: 1) crustal scale fault associated (CSF) subtype, 2) sediment-hosted orogenic gold (SHOG) subtype, 3) forearc (FA) subtype, and 4) syn- and late tectonic dispersed (SLTD) subtype. Lead isotopic studies suggest that Pb and other metals in all but the FA subtype were likely derived from relatively small source reservoirs in the middle or upper crust. OGDs formed in large, lithologically and structurally homogeneous regions will tend to be of the same subtype; however, in geologically complex orogenic belts it is common to find two or more subtypes that formed at approximately the same time. Based on the synthesis of global OGDs of Phanerozoic age districts containing CSF or SHOG subtype deposits appear to have the best potential for hosting multiple large deposits. FA subtype deposits form in a relatively uncommon tectonic setting (accretionary forearc, possibly overlying a subducting spreading ridge) and are likely to be rare. SLTD subtype OGDs are the most common, but most are small and uneconomic, although they commonly generate substantial alluvial gold deposits.

Screening of Swiss Pig Herds for Hepatitis E Virus: A Pilot Study

Hepatitis E virus (HEV) is an important cause of acute hepatitis in humans worldwide. In industrialised countries, most infections are caused by the zoonotic genotype 3. The main reservoir was found in pigs, with fattening pigs as the main shedders. The aim of this study was to establish a screening tool to detect HEV in pig farms. HEV-positive samples were sequenced using Sanger sequencing. First, different sample materials, including floor swabs, slurry, dust swabs and faeces were tested for HEV. Floor swabs turned out to give the best results and, in the form of sock swabs, were used for the screening of Swiss pig herds. A total of 138 pig farms were tested, with a focus on fattening pigs. Overall, 81 farms (58.8%) were HEV positive. Most sequences belonged to subtype 3h, in which they formed a specific cluster (Swiss cluster). In addition, subtype 3 l and two unassigned sequences were detected. As a conclusion, sock swabs were found to be a helpful tool to screen pig herds for HEV and establish a sequence collection that may enable molecular epidemiology and support outbreak investigation and prevention.

Filtration, concentration and detection of salmonid alphavirus in seawater during a post-smolt salmon (Salmo salar) cohabitant challenge

Currently, the prevalence of salmonid alphavirus (SAV) in Norwegian Atlantic salmon farms is largely surveyed via sacrificing fish and sampling of organ tissue on a monthly basis. However, a more cost-efficient, straightforward, rapid, reliable, reproducible and animal welfare friendly method based on the detection of SAV in water could be considered as an alternative method. In the present study, such a method was developed and optimized through a 6 wk cohabitant challenge trial, using post-smolt Atlantic salmon Salmo salar L challenged with high or low doses of SAV subtype 3 (SAV3). Tank water and tissue samples from cohabitant fish were collected at 16 time points. SAV3 was concentrated from the water by filtration, using either electronegative or electropositive membrane filters, which were subsequently rinsed with one of 4 different buffer solutions. SAV3 was detected first in tank water (7 d post-challenge, DPC), and later in cohabitant fish organ tissue samples (12 DPC). The electronegative filter (MF-Millipore™) and rinsing with NucliSENS® easyMAG® Lysis Buffer presented the best SAV3 recovery. A significant positive correlation was found between SAV3 in the tank water concentrates and the mid-kidney samples. Based on these results, detection of SAV3 in filtrated seawater is believed to have the potential to serve as an alternative method for surveillance of SAV in Atlantic salmon farms.

Integrative Analysis Reveals Potentially Functional N6-Methylandenosine-Related Long Noncoding RNAs in Colon Adenocarcinoma

N6-methyladenosine (m6A) is one of the most prevalent RNA modifications in mRNA and non-coding RNA. In this study, we identified 10 upregulated m6A regulators at both mRNA and protein levels, and 2,479 m6A-related lncRNAs. Moreover, the m6A-related long noncoding RNAs (lncRNAs) could clearly stratify the colon adenocarcinoma (COAD) samples into three subtypes. The subtype 2 had nearly 40% of samples with microsatellite instability (MSI), significantly higher than the two other subtypes. In accordance with this finding, the inflammatory response-related pathways were highly activated in this subtype. The subtype-3 had a shorter overall survival and a higher proportion of patients with advanced stage than subtypes 1 and 2 (p-value < 0.05). Pathway analysis suggested that the energy metabolism-related pathways might be aberrantly activated in subtype 3. In addition, we observed that most of the m6A readers and m6A-related lncRNAs were upregulated in subtype 3, suggesting that the m6A readers and the m6A-related lncRNAs might be associated with metabolic reprogramming and unfavorable outcome in COAD. Among the m6A-related lncRNAs in subtype 3, four were predicted as prognostically relevant. Functional inference suggested that CTD-3184A7.4, RP11-458F8.4, and RP11-108L7.15 were positively correlated with the energy metabolism-related pathways, further suggesting that these lncRNAs might be involved in energy metabolism-related pathways. In summary, we conducted a systematic data analysis to identify the key m6A regulators and m6A-related lncRNAs, and evaluated their clinical and functional importance in COAD, which may provide important evidences for further m6A-related researches.

Large Evolutionary Rate Heterogeneity among and within HIV-1 Subtypes and CRFs

HIV-1 is a fast-evolving, genetically diverse virus presently classified into several groups and subtypes. The virus evolves rapidly because of an error-prone polymerase, high rates of recombination, and selection in response to the host immune system and clinical management of the infection. The rate of evolution is also influenced by the rate of virus spread in a population and nature of the outbreak, among other factors. HIV-1 evolution is thus driven by a range of complex genetic, social, and epidemiological factors that complicates disease management and prevention. Here, we quantify the evolutionary (substitution) rate heterogeneity among major HIV-1 subtypes and recombinants by analyzing the largest collection of HIV-1 genetic data spanning the widest possible geographical (100 countries) and temporal (1981–2019) spread. We show that HIV-1 substitution rates vary substantially, sometimes by several folds, both across the virus genome and between major subtypes and recombinants, but also within a subtype. Across subtypes, rates ranged 3.5-fold from 1.34 × 10−3 to 4.72 × 10−3 in env and 2.3-fold from 0.95 × 10−3 to 2.18 × 10−3 substitutions site−1 year−1 in pol. Within the subtype, 3-fold rate variation was observed in env in different human populations. It is possible that HIV-1 lineages in different parts of the world are operating under different selection pressures leading to substantial rate heterogeneity within and between subtypes. We further highlight how such rate heterogeneity can complicate HIV-1 phylodynamic studies, specifically, inferences on epidemiological linkage of transmission clusters based on genetic distance or phylogenetic data, and can mislead estimates about the timing of HIV-1 lineages.

Micromorphological Traits of Balcanic Micromeria and Closely Related Clinopodium Species (Lamiaceae)

A study of the trichomes types and distribution and pollen morphology was carried out in nine Micromeria taxa (M. cristata ssp. cristata, M. cristata ssp. kosaninii, M. croatica, M. graeca ssp. graeca, M. graeca ssp. fruticulosa, M. juliana, M. kerneri, M. longipedunculata and M. microphylla) and five closely related Clinopodium species (C. dalmaticum, C. frivaldszkyanum, C. pulegium, C. serpyllifolium and C. thymifolium) from the Lamiaceae family of the Balkan Peninsula. By scanning electron microscope, non-glandular trichomes, peltate and capitate trichomes were observed on the calyx, leaves and stem of the studied species. Two subtypes of capitate trichomes were observed in Micromeria species: subtype 1 (consisting of a basal epidermal cell and an elliptically shaped head cell) and subtype 2 (consisting of a basal epidermal cell, two to three stalk cells and a round head cell). In Clinopodium species, three types of capitate trichomes were observed: subtype 1, subtype 3 (consisting of a basal epidermal cell, a short peduncle cell, and a single round head cell), and subtype 4 (consisting of a basal epidermal cell, a stalk cell, and an elongated head cell). These results support the recent transfer of Micromeria species from the section Pseudomelissa to the genus Clinopodium.

Tumour-associated Angiogenesis and Intermediate Blood Vessels in Renal Cell Carcinoma

Background/Aim: Renal cell carcinoma is strongly vascularized, and formation of new blood vessels is a complex and multi-step process. In this study, we analysed the subtypes of intermediate blood vessels, as shown by double immunohistochemistry. Materials and Methods: Tumour-associated blood vessels were identified by double immunostaining based on CD34 and smooth muscle cell actin. Blood vessels were classified both quantitatively and qualitatively based on the expression of the aforementioned two markers. The main criteria to sub-classify intermediate blood vessels was the presence, distribution, and arrangement of perivascular cells. Results: We described three subtypes of intermediate blood vessels found particularly in the tumour area: Subtype 1 lacked perivascular cells, subtype 2 showed scattered pericytes attached to the vascular wall, and subtype 3 showed a continuous layer of perivascular cells on one side. Conclusion: We describe for the first time three subtypes of renal cell carcinoma-associated intermediate blood vessels, which could be important in prognosis and as potential targets for anti-vascular therapy.

The Immune Subtypes and Landscape of Gastric Cancer and to Predict Based on the Whole-Slide Images Using Deep Learning

BackgroundGastric cancer (GC) is a highly heterogeneous tumor with different responses to immunotherapy. Identifying immune subtypes and landscape of GC could improve immunotherapeutic strategies.MethodsBased on the abundance of tumor-infiltrating immune cells in GC patients from The Cancer Genome Atlas, we used unsupervised consensus clustering algorithm to identify robust clusters of patients, and assessed their reproducibility in an independent cohort from Gene Expression Omnibus. We further confirmed the feasibility of our immune subtypes in five independent pan-cancer cohorts. Finally, functional enrichment analyses were provided, and a deep learning model studying the pathological images was constructed to identify the immune subtypes.ResultsWe identified and validated three reproducible immune subtypes presented with diverse components of tumor-infiltrating immune cells, molecular features, and clinical characteristics. An immune-inflamed subtype 3, with better prognosis and the highest immune score, had the highest abundance of CD8+ T cells, CD4+ T–activated cells, follicular helper T cells, M1 macrophages, and NK cells among three subtypes. By contrast, an immune-excluded subtype 1, with the worst prognosis and the highest stromal score, demonstrated the highest infiltration of CD4+ T resting cells, regulatory T cells, B cells, and dendritic cells, while an immune-desert subtype 2, with an intermediate prognosis and the lowest immune score, demonstrated the highest infiltration of M2 macrophages and mast cells, and the lowest infiltration of M1 macrophages. Besides, higher proportion of EVB and MSI of TCGA molecular subtyping, over expression of CTLA4, PD1, PDL1, and TP53, and low expression of JAK1 were observed in immune subtype 3, which consisted with the results from Gene Set Enrichment Analysis. These subtypes may suggest different immunotherapy strategies. Finally, deep learning can predict the immune subtypes well.ConclusionThis study offers a conceptual frame to better understand the tumor immune microenvironment of GC. Future work is required to estimate its reference value for the design of immune-related studies and immunotherapy selection.