Trace Metal Distribution in Sulfide Minerals from Ultramafic-Hosted Hydrothermal Systems: Examples from the Kairei Vent Field, Central Indian Ridge

The ultramafic-hosted Kairei vent field is located at 25°19′ S, 70°02′ E, towards the Northern end of segment 1 of the Central Indian Ridge (CIR-S1) at a water depth of ~2450 m. This study aims to investigate the distribution of trace elements among sulfide minerals of differing textures and to examine the possible factors controlling the trace element distribution in those minerals using LA-ICP-MS spot and line scan analyses. Our results show that there are distinct systematic differences in trace element distributions throughout the different minerals, as follows: (1) pyrite is divided into three types at Kairei, including early-stage euhedral pyrite (py-I), sub-euhedral pyrite (py-II), and colloform pyrite (py-III). Pyrite is generally enriched with Mo, Au, As, Tl, Mn, and U. Pyrite-I has high contents of Se, Te, Bi, and Ni when compared to the other types; py-II is enriched in Au relative to py-I and py-III, but poor in Ni; py-III is enriched in Mo, Pb, and U but is poor in Se, Te, Bi, and Au relative to py-I and py-II. Variations in the concentrations of Se, Te, and Bi in pyrite are most likely governed by the strong temperature gradient. There is generally a lower concentration of nickel than Co in pyrite, indicating that our samples precipitated at high temperatures, whereas the extreme Co enrichment is likely from a magmatic heat source combined with an influence of serpentinization reactions. (2) Chalcopyrite is characterized by high concentrations of Co, Se, and Te. The abundance of Se and Te in chalcopyrite over the other minerals is interpreted to have been caused by the high solubilities of Se and Te in the chalcopyrite lattice at high temperatures. The concentrations of Sb, As, and Au are relatively low in chalcopyrite from the Kairei vent field. (3) Sphalerite from Zn-rich chimneys is characterized by high concentrations of Sn, Co, Ga, Ge, Ag, Pb, Sb, As, and Cd, but is depleted in Se, Te, Bi, Mo, Au, Ni, Tl, Mn, Ba, V, and U in comparison with the other minerals. The high concentrations of Cd and Co are likely caused by the substitution of Cd2+ and Co2+ for Zn2+ in sphalerite. A high concentration of Pb accompanied by a high Ag concentration in sphalerite indicates that Ag occurs as Pb–Ag sulfosalts. Gold is generally low in sphalerite and strongly correlates with Pb, suggesting its presence in microinclusions of galena. The strong correlation of As with Ge in sphalerite from Kairei suggests that they might precipitate at medium temperatures and under moderately reduced conditions. (4) Bornite–digenite has very low concentrations of most trace elements, except for Co, Se, and Bi. Serpentinization in ultramafic-hosted hydrothermal systems might play an important role in Au enrichment in pyrite with low As contents. Compared to felsic-hosted seafloor massive sulfide deposits, sulfide minerals from ultramafic-hosted deposits show higher concentrations of Se and Te, but lower As, Sb, and Au concentrations, the latter often attributed to the contribution of magmatic volatiles. As with typical ultramafic-hosted seafloor massive sulfide deposits, Se enrichment in chalcopyrite from Kairei indicates that the primary factor that controls the Se enrichment is temperature-controlled mobility in vent fluids

Identification of necroptosis-related genes in Parkinson’s disease by integrated bioinformatics analysis and experimental validation

BackgroundParkinson’s disease (PD) is the second most common neurodegeneration disease worldwide. Necroptosis, which is a new form of programmed cell death with high relationship with inflammation, plays a vital role in the progression of PD. However, the key necroptosis related genes in PD are not fully elucidated.PurposeIdentification of key necroptosis-related genes in PD.MethodThe PD associated datasets and necroptosis related genes were downloaded from the GEO Database and GeneCards platform, respectively. The DEGs associated with necroptosis in PD were obtained by gap analysis, and followed by cluster analysis, enrichment analysis and WGCNA analysis. Moreover, the key necroptosis related genes were generated by PPI network analysis and their relationship by spearman correlation analysis. Immune infiltration analysis was used for explore the immune state of PD brain accompanied with the expression levels of these genes in various types of immune cells. Finally, the gene expression levels of these key necroptosis related genes were validated by an external dataset, blood samples from PD patients and toxin-induced PD cell model using real-time PCR analysis.ResultTwelve key necroptosis-related genes including ASGR2, CCNA1, FGF10, FGF19, HJURP, NTF3, OIP5, RRM2, SLC22A1, SLC28A3, WNT1 and WNT10B were identified by integrated bioinformatics analysis of PD related dataset GSE7621. According to the correlation analysis of these genes, RRM2 and WNT1 were positively and negatively correlated with SLC22A1 respectively, while WNT10B was positively correlated with both OIF5 and FGF19. As the results from immune infiltration analysis, M2 macrophage was the highest population of immune cell in analyzed PD brain samples. Moreover, we found that 3 genes (CCNA1, OIP5 and WNT10B) and 9 genes (ASGR2, FGF10, FGF19, HJURP, NTF3, RRM2, SLC22A1, SLC28A3 and WNT1) were down- and up- regulated in an external dataset GSE20141, respectively. All the mRNA expression levels of these 12 genes were obviously upregulated in 6-OHDA-induced SH-SY5Y cell PD model while CCNA1 and OIP5 were up- and down- regulated, respectively, in peripheral blood lymphocytes of PD patients.ConclusionNecroptosis and its associated inflammation play fundamental roles in the progression of PD and these identified 12 key genes might be served as new diagnostic markers and therapeutic targets for PD

Genome-wide CRISPR/Cas9 screening for drug resistance in tumors

Genome-wide clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR associated nuclease 9 (Cas9) screening is a simple screening method for locating loci under specific conditions, and it has been utilized in tumor drug resistance research for finding potential drug resistance-associated genes. This screening strategy has significant implications for further treatment of malignancies with acquired drug resistance. In recent years, studies involving genome-wide CRISPR/Cas9 screening have gradually increased. Here we review the recent application of genome-wide CRISPR/Cas9 screening for drug resistance, involving mitogen-activated protein kinase (MAPK) pathway inhibitors, poly (ADP-ribose) polymerase inhibitors (PARPi), alkylating agents, mitotic inhibitors, antimetabolites, immune checkpoint inhibitors (ICIs), and cyclin-dependent kinase inhibitors (CDKI). We summarize drug resistance pathways such as the KEAP1/Nrf2 pathway MAPK pathway, and NF-κB pathway. Also, we analyze the limitations and conditions for the application of genome-wide CRISPR/Cas9 screening techniques

Glutamic acid decarboxylase autoantibodies are dominant but insufficient to identify most Chinese with adult-onset non-insulin requiring autoimmune diabetes: LADA China study 5.

AIMS: Adult-onset autoimmune diabetes is prevalent in China, in contrast to childhood-onset type 1 diabetes mellitus. Islet autoantibodies are the most important immune biomarkers to diagnose autoimmune diabetes. We assayed four different islet autoantibodies in recently diagnosed adult non-insulin-requiring diabetes Chinese subjects to investigate the best antibody assay strategy for the correct diagnosis of these subjects. METHODS: LADA China study is a nation-wide multicenter study conducted in diabetes patients from 46 university-affiliated hospitals in China. Non-insulin-treated newly diagnosed adult diabetes patients (n = 2388) were centrally assayed for glutamic acid decarboxylase autoantibody (GADA), protein tyrosine phosphatase-2 autoantibody (IA-2A), and zinc transporter 8 autoantibody (ZnT8A) by radioligand assay and insulin autoantibody (IAA) by microtiter plate radioimmunoassay. Clinical data were determined locally. RESULTS: Two hundred and six (8.63 %) subjects were autoantibody positive, of which GADA identified 5.78 % (138/2388) of the total, but only 67 % (138/206) of the autoimmune cases. IA-2A, ZnT8A, and IAA were found in 1.51, 1.84, and 1.26 % of the total study subjects, respectively. When assaying three islet autoantibodies, the most effective strategy was the combination of GADA, ZnT8A, and IAA, which could identify 92.2 % (190/206) autoimmune diabetes patients. The clinical data showed that those subjects with positive GADA had lower random C-peptide than autoantibody negative subjects (P < 0.05). CONCLUSIONS: As with Europeans, GADA is the dominant autoantibody in this form of autoimmune diabetes in China, but in contrast to Europeans, screening should include other diabetes-associated autoantibodies

Prospective cohort study evaluating efficacy and safety of efgartigimod in Chinese generalized myasthenia gravis patients

BackgroundDespite the efficacy of efgartigimod demonstrated in ADAPT phase 3 trial, data specifically derived from Chinese participants are not available. Therefore, we aimed to evaluate the efficacy and safety of efgartigimod in Chinese patients with generalized myasthenia gravis (gMG).MethodsThis is a prospective cohort study conducted in 8 hospitals across China. gMG patients received weekly intravenous infusions of efgartigimod (10 mg/kg) under a named patient program (NPP). The present study is an 8-week study, consisting of 4 consecutive doses of efgartigimod administered over 3 weeks (one cycle), followed by a 5-week follow-up period to assess the tolerability of efgartigimod’s therapeutic effects. The primary outcome was the mean change in MG activities of daily living (MG-ADL) total score from baseline to 4 weeks. MG-ADL responder was defined as a ≥ 2-point improvement that persisted for 4 weeks, starting by week 4. Safety evaluations encompassed the monitoring of adverse events (AE) and serious AE (SAE) throughout the study.ResultsBetween 5 July 2022 and 25 August 2023, a total of 14 gMG patients were included. The mean age was 57.7 years, with a mean MG-ADL score of 10.86 ± 3.32. At week 4, MG-ADL scores showed a mean reduction of 6 points, reaching a maximum decline of 13 points. Among the patients, 85.7% (12/14) achieved MG-ADL responder status after one cycle of treatment. The most significant reduction in quantitative MG (QMG) scores also occurred at week 4, with a mean decrease of 7 points. Notably, the improvements in MG-ADL and QMG scores persisted until week 8. During treatment and follow-up period, only two mild neck rashes occurred and resolved promptly. No infections or SAE were reported.DiscussionA single cycle of efgartigimod treatment demonstrates effectiveness and the tolerability through week 8, with no new safety signals observed in Chinese gMG patients

Two new species of the Phaonia boleticola-group (Diptera, Muscidae, Phaoniinae) from China

Two new species of Phaonia are described: Phaonia agitata Zhou & Wei, sp. nov. and Phaonia nujiangensis Zhou & Wei, sp. nov., which were collected from Guizhou and Yunnan provinces of southwestern China and are assigned to the boleticola-group. A key to the species of this group is provided. The type specimens are deposited in the Wei Lianmeng Model Worker Innovation Studio, Anshun, Guizhou, China (WLMWISAGC)

Learning Spatial-context-aware Global Visual Feature Representation for Instance Image Retrieval

In instance image retrieval, considering local spatial information within an image has proven effective to boost retrieval performance, as demonstrated by local visual descriptor based geometric verification. Nevertheless, it will be highly valuable to make ordinary global image representations spatial-context-aware because global representation based image retrieval is appealing thanks to its algorithmic simplicity, low memory cost, and being friendly to sophisticated data structures. To this end, we propose a novel feature learning framework for instance image retrieval, which embeds local spatial context information into the learned global feature representations. Specifically, in parallel to the visual feature branch in a CNN backbone, we design a spatial context branch that consists of two modules called online token learning and distance encoding. For each local descriptor learned in CNN, the former module is used to indicate the types of its surrounding descriptors, while their spatial distribution information is captured by the latter module. After that, the visual feature branch and the spatial context branch are fused to produce a single global feature representation per image. As experimentally demonstrated, with the spatial-context-aware characteristic, we can well improve the performance of global representation based image retrieval while maintaining all of its appealing properties. Our code is available at https://github.com/Zy-Zhang/SpCa

Instance image retrieval by aggregating sample-based discriminative characteristics

Identifying the discriminative characteristic of a query is important for image retrieval. For retrieval without human interaction, such characteristic is usually obtained by average query expansion (AQE) or its discriminative variant (DQE) learned from pseudo-examples online, among others. In this paper, we propose a new query expansion method to further improve the above ones. The key idea is to learn a unique\u27\u27 discriminative characteristic for each database image, in an offline manner. During retrieval, the characteristic of a query is obtained by aggregating the unique characteristics of the query-relevant images collected from an initial retrieval result. Compared with AQE which works in the original feature space, our method works in the space of the unique characteristics of database images, significantly enhancing the discriminative power of the characteristic identified for a query. Compared with DQE, our method needs neither pseudo-labeled negatives nor the online learning process, leading to more efficient retrieval and even better performance. The experimental study conducted on seven benchmark datasets verifies the considerable improvement achieved by the proposed method, and also demonstrates its application to the state-of-the-art diffusion-based image retrieval