Refining the relationship between gut microbiota and common hematologic malignancies: insights from a bidirectional Mendelian randomization study

BackgroundThe relationship between gut microbiota and hematologic malignancies has attracted considerable attention. As research progresses, it has become increasingly clear that the composition of gut microbiota may influence the onset and progression of hematologic malignancies. However, our understanding of this association remains limited.MethodsIn our study, we classified gut microbiota into five groups based on information at the phylum, class, order, family, and genus levels. Subsequently, we obtained data related to common hematologic malignancies from the IEU Open GWAS project. We then employed a bidirectional Mendelian Randomization (MR) approach to determine whether there is a causal relationship between gut microbiota and hematologic malignancies. Additionally, we conducted bidirectional MR analyses to ascertain the directionality of this causal relationship.ResultsThrough forward and reverse MR analyses, we found the risk of lymphoid leukemia was significantly associated with the abundance of phylum Cyanobacteria, order Methanobacteriales, class Methanobacteria, family Peptococcaceae, family Methanobacteriaceae, and genera Lachnospiraceae UCG010, Methanobrevibacter, Eubacterium brachy group, and Butyrivibrio. The risk of myeloid leukemia was significantly associated with the abundance of phylum Actinobacteria, phylum Firmicutes, order Bifidobacteriales, order Clostridiales, class Actinobacteria, class Gammaproteobacteria, class Clostridia, family Bifidobacteriaceae, and genera Fusicatenibacter, Eubacterium hallii group, Blautia, Collinsella, Ruminococcus gauvreauii group, and Bifidobacterium. The risk of Hodgkin lymphoma was significantly associated with the abundance of family Clostridiales vadinBB60 group, genus Peptococcus, and genus Ruminococcaceae UCG010. The risk of malignant plasma cell tumor was significantly associated with the abundance of genera Romboutsia and Eubacterium rectale group. The risk of diffuse large B-cell lymphoma was significantly associated with the abundance of genera Erysipelatoclostridium and Eubacterium coprostanoligenes group. The risk of mature T/NK cell lymphomas was significantly associated with the abundance of phylum Verrucomicrobia, genus Ruminococcaceae UCG013, genus Lachnoclostridium, and genus Eubacterium rectale group. Lastly, the risk of myeloproliferative neoplasms was significantly associated with the abundance of genus Coprococcus 3 and Eubacterium hallii group.ConclusionOur study provided new evidence for the causal relationship between gut microbiota and hematologic malignancies, offering novel insights and approaches for the prevention and treatment of these tumors

Experimental evidence on the Altshuler-Aronov-Spivak interference of the topological surface states in the exfoliated Bi2Te3 nanoflakes

Here we demonstrate the Altshuler-Aronov-Spivak (AAS) interference of the topological surface states on the exfoliated Bi2Te3 microflakes by a flux period of h/2e in their magnetoresistance oscillations and its weak field character. Both the osillations with the period of h/e and h/2e are observed. The h/2e-period AAS oscillation gradually dominates with increasing the sample widths and the temperatures. This reveals the transition of the Dirac Fermions' transport to the diffusive regime.Comment: version 3;Applied Physics Letters in pres

Land Cover Classification with GF-3 Polarimetric Synthetic Aperture Radar Data by Random Forest Classifier and Fast Super-Pixel Segmentation

Chinese Gaofen-3 (GF-3), a vital satellite for high-resolution earth observation, was the first C-band polarimetric synthetic aperture radar (SAR) launched in China with a resolution of up to one meter. Polarimetric SAR can obtain the complete physical scattering mechanisms of targets, thereby having the potential to differentiate objects. In this paper, several classification methods are briefly summarized and the types of features that should be chosen during classification are discussed. A pre-classification step is introduced to reduce the workload of precise labeling. The Random Forest classifier, which performs well for many other classification tasks, is used for the initial land cover classification. Then, based on a polarimetric constant false-alarm rate (CFAR) edge detector, a fast super-pixel generation method for polarimetric SAR image is proposed, which does not require the adjustment of parameters in advance. Following that, majority vote is conducted on the initial classification result based on the super-pixels, so that the classification result can be optimized to better meet the mapping requirements. The experimental results based on GF-3 polarimetric SAR data verify the effectiveness of proposed procedure and demonstrate that GF-3 data has excellent performance in land cover classification

Label-free detection of rare circulating tumor cells by image analysis and machine learning.

Detection and characterization of rare circulating tumor cells (CTCs) in patients\u27 blood is important for the diagnosis and monitoring of cancer. The traditional way of counting CTCs via fluorescent images requires a series of tedious experimental procedures and often impacts the viability of cells. Here we present a method for label-free detection of CTCs from patient blood samples, by taking advantage of data analysis of bright field microscopy images. The approach uses the convolutional neural network, a powerful image classification and machine learning algorithm to perform label-free classification of cells detected in microscopic images of patient blood samples containing white blood cells and CTCs. It requires minimal data pre-processing and has an easy experimental setup. Through our experiments, we show that our method can achieve high accuracy on the identification of rare CTCs without the need for advanced devices or expert users, thus providing a faster and simpler way for counting and identifying CTCs. With more data becoming available in the future, the machine learning model can be further improved and can serve as an accurate and easy-to-use tool for CTC analysis

Atom electronics in single-molecule transistors: single-atom access and manipulation

ABSTRACTThe aim of atom electronics, i.e. the final scale of electronics, is to make use of specific individual atoms as active electronic components. Here, we review recent researches on atom electronics in single-molecule transistors (SMTs) through single-atom access and manipulation. We begin by describing the basic concepts and characteristics of atom electronics in SMTs, before discussing some of the most recent examples, including atomic transistors and atomic storage. In our concluding remarks, we discuss some perspectives on fabrication, integration, and other potential atomic devices in which high precision access to, and manipulation of single atoms could be of great significance. This will affect integrated circuits, quantum computing, and other devices that will drive the electronics of the future