Effect of oral metformin on gut microbiota characteristics and metabolite fractions in normal-weight type 2 diabetic mellitus patients.

Background and aims: To analyze the effect of oral metformin on changes in gut microbiota characteristics and metabolite composition in normal weight type 2 diabetic patients. Methods: T2DM patients in the cross-sectional study were given metformin for 12 weeks. Patients with unmedicated T2DM were used as a control group to observe the metrics of T2DM patients treated with metformin regimen. 16S rDNA high-throughput gene sequencing of fecal gut microbiota of the study subjects was performed by llumina NovaSeq6000 platform. Targeted macro-metabolomics was performed on 14 cases of each of the gut microbiota metabolites of the study subjects using UPLC-MS/MS technology. Correlations between the characteristics of the gut microbiota and its metabolites, basic human parameters, glycolipid metabolism indicators, and inflammatory factors were analyzed using spearman analysis. Results: Glycolipid metabolism indexes and inflammatory factors were higher in normal-weight T2DM patients than in the healthy population (P<0.05), but body weight, BMI, waist circumference, and inflammatory factor concentrations were lower in normal-weight T2DM patients than in obese T2DM patients (P<0.05). Treatment with metformin in T2DM patients improved glycolipid metabolism, but the recovery of glycolipid metabolism was more pronounced in obese T2DM patients. None of the differences in α-diversity indexes were statistically significant (P>0.05), and the differences in ß-diversity were statistically significant (P <0.05). Community diversity and species richness recovered after metformin intervention compared to before, and were closer to the healthy population. We found that Anaerostipes/Xylose/Ribulose/Xylulose may play an important role in the treatment of normal-weight T2DM with metformin by improving glycemic lipids and reducing inflammation. And Metformin may play a role in obese T2DM through Romboutsia, medium-chain fatty acids (octanoic acid, decanoic acid, and dodecanoic acid). Conclusion: Gut microbial dysbiosis and metabolic disorders were closely related to glucose-lipid metabolism and systemic inflammatory response in normal-weight T2DM patients. Metformin treatment improved glucose metabolism levels, systemic inflammation levels in T2DM patients, closer to the state of healthy population. This effect may be mediated by influencing the gut microbiota and microbial host co-metabolites, mainly associated with Anaerostipes and xylose/Ribulose/Xylulose. Metformin may exert its effects through different pathways in normal-weight versus obese T2DM patients.

Role of Source, Mineralogy, and Organic Complexation on Lability and Fe Isotopic Composition of Terrestrial Fe sources to the Gulf of Alaska.

Iron (Fe) is a key trace nutrient supporting marine primary production, and its deposition in the surface ocean can impact multiple biogeochemical cycles. Understanding Fe cycling in the subarctic is key for tracking the fate of particulate-bound sources of oceans in a changing climate. Recently, Fe isotope ratios have been proposed as a potential tool to trace sources of Fe to the marine environment. Here, we investigate the Fe isotopic composition of terrestrial sources of Fe including glacial sediment, loess, volcanic ash, and wildfire aerosols, all from Alaska. Results show that the δ56Fe values of glaciofluvial silt, glacial dissolved load, volcanic ash, and wildfire aerosols fall in a restricted range of δ56Fe values from -0.02 to +0.12‰, in contrast to the broader range of Fe isotopic compositions observed in loess, -0.50 to +0.13‰. The Fe isotopic composition of the dissolved load of glacial meltwater was consistently lighter compared to its particulate counterpart. The 'aging' (exposure to environmental conditions) of volcanic ash did not significantly fractionate the Fe isotopic composition. The Fe isotopic composition of wildfire aerosols collected during an active fire season in Alaska in the summer of 2019 was not significantly fractionated from those of the average upper continental crust composition. We find that the δ56Fe values of loess (<5 µm fraction) were more negative (-0.32 to +0.05‰) with respect to all samples measured here, had the highest proportion of easily reducible Fe (5.9-59.6%), and were correlated with the degree of chemical weathering and organic matter content. Transmission electron spectroscopy measurements indicate an accumulation of amorphous Fe phases in the loess. Our results indicate that Fe isotopes can be related to Fe lability when in the presence of organic matter and that higher organic matter content is associated with a distinctly more negative Fe isotope signature likely due to Fe-organic complexation.

Integration of Multikinds Imputation With Covariance Adaptation Based on Evidence Theory.

For incomplete data classification, missing attribute values are often estimated by imputation methods before building classifiers. The estimated attribute values are not actual attribute values. Thus, the distributions of data will be changed after imputing, and this phenomenon often results in degradation of classification performance. Here, we propose a new framework called integration of multikinds imputation with covariance adaptation (MICA) based on evidence theory (ET) to effectively deal with the classification problem with incomplete training data and complete test data. In MICA, we first employ different kinds of imputation methods to obtain multiple imputed training datasets. In general, the distributions of each imputed training dataset and test dataset will be different. A covariance adaptation module (CAM) is then developed to reduce the distribution difference of each imputed training dataset and test dataset. Then, multiple classifiers can be learned on the multiple imputed training datasets, and they are complementary to each other. For a test pattern, we can combine the multiple pieces of soft classification results yielded by these classifiers based on ET to obtain better classification performance. However, the reliabilities/weights of different imputed training datasets are usually different, so the soft classification results cannot be treated equally during fusion. We propose to use covariance difference across datasets and accuracy of imputed training data to estimate the weights. Finally, the soft classification results discounted by the estimated weights are combined by ET to make the final class decision. MICA was compared with a variety of related methods on several datasets, and the experimental results demonstrate that this new method can significantly improve the classification performance.

Overexpressed KCNK1 regulates potassium channels affecting molecular mechanisms and biological pathways in bladder cancer.

BACKGROUND: This study aimed to explore the expression, molecular mechanism and its biological function of potassium two pore domain channel subfamily K member 1 (KCNK1) in bladder cancer (BC). METHODS: We integrated large numbers of external samples (n = 1486) to assess KCNK1 mRNA expression levels and collected in-house samples (n = 245) for immunohistochemistry (IHC) experiments to validate at the KCNK1 protein level. Single-cell RNA sequencing (scRNA-seq) analysis was performed to further assess KCNK1 expression and cellular communication. The transcriptional regulatory mechanisms of KCNK1 expression were explored by ChIP-seq, ATAC-seq and ChIA-PET data. Highly expressed co-expressed genes (HECEGs) of KCNK1 were used to explore potential signalling pathways. Furthermore, the immunoassay, clinical significance and molecular docking of KCNK1 were calculated. RESULTS: KCNK1 mRNA was significantly overexpressed in BC (SMD = 0.58, 95% CI [0.05; 1.11]), validated at the protein level (p < 0.0001). Upregulated KCNK1 mRNA exhibited highly distinguishing ability between BC and control samples (AUC = 0.82 [0.78-0.85]). Further, scRNA-seq analysis revealed that KCNK1 expression was predominantly clustered in BC epithelial cells and tended to increase with cellular differentiation. BC epithelial cells were involved in cellular communication mainly through the MK signalling pathway. Secondly, the KCNK1 transcription start site (TSS) showed promoter-enhancer interactions in three-dimensional space, while being transcriptionally regulated by GRHL2 and FOXA1. Most of the KCNK1 HECEGs were enriched in cell cycle-related signalling pathways. KCNK1 was mainly involved in cellular metabolism-related pathways and regulated cell membrane potassium channel activity. KCNK1 expression was associated with the level of infiltration of various immune cells. Immunotherapy and chemotherapy (docetaxel, paclitaxel and vinblastine) were more effective in BC patients in the high KCNK1 expression group. KCNK1 expression correlated with age, pathology grade and pathologic_M in BC patients. CONCLUSIONS: KCNK1 was significantly overexpressed in BC. A complex and sophisticated three-dimensional spatial transcriptional regulatory network existed in the KCNK1 TSS and promoted the upregulated of KCNK1 expression. The high expression of KCNK1 might be involved in the cell cycle, cellular metabolism, and tumour microenvironment through the regulation of potassium channels, and ultimately contributed to the deterioration of BC.

The effect of metformin combined with liraglutide on gut microbiota of Chinese patients with type 2 diabetes / El efecto de la metformina combinada con liraglutida sobre la microbiota intestinal de pacientes chinos con diabetes tipo 2

Background: Metformin (MET) is a first-line therapy for type-2 diabetes mellitus (T2DM). Liraglutide (LRG) is a glucagon-like peptide-1 receptor agonist used as a second-line therapy in combination with MET. Methods: We performed a longitudinal analysis comparing the gut microbiota of overweight and/or pre-diabetic participants (NCP group) with that of each following their progression to T2DM diagnosis (UNT group) using 16S ribosomal RNA gene sequencing of fecal bacteria samples. We also examined the effects of MET (MET group) and MET plus LRG (MET+LRG group) on the gut microbiota of these participants following 60 days of anti-diabetic drug therapy in two parallel treatment arms. Results: In the UNT group, the relative abundances of Paraprevotella (P = 0.002) and Megamonas (P = 0.029) were greater, and that of Lachnospira (P = 0.003) was lower, compared with the NCP group. In the MET group, the relative abundance of Bacteroides (P = 0.039) was greater, and those of Paraprevotella (P = 0.018), Blautia (P = 0.001), and Faecalibacterium (P = 0.005) were lower, compared with the UNT group. In the MET+LRG group, the relative abundances of Blautia (P = 0.005) and Dialister (P = 0.045) were significantly lower than in the UNT group. The relative abundance of Megasphaera in the MET group was significantly greater than in the MET+LRG group (P = 0.041). Conclusions: Treatment with MET and MET+LRG results in significant alterations in gut microbiota, compared with the profiles of patients at the time of T2DM diagnosis. These alterations differed significantly between the MET and MET+LRG groups, which suggests that LRG exerted an additive effect on the composition of gut microbiota.(AU)

KMT-PLL: K-Means Cross-Attention Transformer for Partial Label Learning.

Partial label learning (PLL) studies the problem of learning instance classification with a set of candidate labels and only one is correct. While recent works have demonstrated that the Vision Transformer (ViT) has achieved good results when training from clean data, its applications to PLL remain limited and challenging. To address this issue, we rethink the relationship between instances and object queries to propose K-means cross-attention transformer for PLL (KMT-PLL), which can continuously learn cluster centers and be used for downstream disambiguation tasks. More specifically, K-means cross-attention as a clustering process can effectively learn the cluster centers to represent label classes. The purpose of this operation is to make the similarity between instances and labels measurable, which can effectively detect noise labels. Furthermore, we propose a new corrected cross entropy formulation, which can assign weights to candidate labels according to the instance-to-label relevance to guide the training of the instance classifier. As the training goes on, the ground-truth label is progressively identified, and the refined labels and cluster centers in turn help to improve the classifier. Simulation results demonstrate the advantage of the KMT-PLL and its suitability for PLL.

The effect of metformin combined with liraglutide on gut microbiota of Chinese patients with type 2 diabetes.

BACKGROUND: Metformin (MET) is a first-line therapy for type-2 diabetes mellitus (T2DM). Liraglutide (LRG) is a glucagon-like peptide-1 receptor agonist used as a second-line therapy in combination with MET. METHODS: We performed a longitudinal analysis comparing the gut microbiota of overweight and/or pre-diabetic participants (NCP group) with that of each following their progression to T2DM diagnosis (UNT group) using 16S ribosomal RNA gene sequencing of fecal bacteria samples. We also examined the effects of MET (MET group) and MET plus LRG (MET+LRG group) on the gut microbiota of these participants following 60 days of anti-diabetic drug therapy in two parallel treatment arms. RESULTS: In the UNT group, the relative abundances of Paraprevotella (P = 0.002) and Megamonas (P = 0.029) were greater, and that of Lachnospira (P = 0.003) was lower, compared with the NCP group. In the MET group, the relative abundance of Bacteroides (P = 0.039) was greater, and those of Paraprevotella (P = 0.018), Blautia (P = 0.001), and Faecalibacterium (P = 0.005) were lower, compared with the UNT group. In the MET+LRG group, the relative abundances of Blautia (P = 0.005) and Dialister (P = 0.045) were significantly lower than in the UNT group. The relative abundance of Megasphaera in the MET group was significantly greater than in the MET+LRG group (P = 0.041). CONCLUSIONS: Treatment with MET and MET+LRG results in significant alterations in gut microbiota, compared with the profiles of patients at the time of T2DM diagnosis. These alterations differed significantly between the MET and MET+LRG groups, which suggests that LRG exerted an additive effect on the composition of gut microbiota.

An Evidential Multi-Target Domain Adaptation Method Based on Weighted Fusion for Cross-Domain Pattern Classification.

For cross-domain pattern classification, the supervised information (i.e., labeled patterns) in the source domain is often employed to help classify the unlabeled target domain patterns. In practice, multiple target domains are usually available. The unlabeled patterns (in different target domains) which have high-confidence predictions, can also provide some pseudo-supervised information for the downstream classification task. The performance in each target domain would be further improved if the pseudo-supervised information in different target domains can be effectively used. To this end, we propose an evidential multi-target domain adaptation (EMDA) method to take full advantage of the useful information in the single-source and multiple target domains. In EMDA, we first align distributions of the source and target domains by reducing maximum mean discrepancy (MMD) and covariance difference across domains. After that, we use the classifier learned by the labeled source domain data to classify query patterns in the target domains. The query patterns with high-confidence predictions are then selected to train a new classifier for yielding an extra piece of soft classification results of query patterns. The two pieces of soft classification results are then combined by evidence theory. In practice, their reliabilities/weights are usually diverse, and an equal treatment of them often yields the unreliable combination result. Thus, we propose to use the distribution discrepancy across domains to estimate their weighting factors, and discount them before fusing. The evidential combination of the two pieces of discounted soft classification results is employed to make the final class decision. The effectiveness of EMDA was verified by comparing with many advanced domain adaptation methods on several cross-domain pattern classification benchmark datasets.

Cross-Domain Pattern Classification With Distribution Adaptation Based on Evidence Theory.

In pattern classification, there may not exist labeled patterns in the target domain to train a classifier. Domain adaptation (DA) techniques can transfer the knowledge from the source domain with massive labeled patterns to the target domain for learning a classification model. In practice, some objects in the target domain are easily classified by this classification model, and these objects usually can provide more or less useful information for classifying the other objects in the target domain. So a new method called distribution adaptation based on evidence theory (DAET) is proposed to improve the classification accuracy by combining the complementary information derived from both the source and target domains. In DAET, the objects that are easy to classify are first selected as easy-target objects, and the other objects are regarded as hard-target objects. For each hard-target object, we can obtain one classification result with the assistance of massive labeled patterns in the source domain, and another classification result can be acquired based on the easy-target objects with confidently predicted (pseudo) labels. However, the weights of these classification results may vary because the reliabilities of the used information sources are different. The weights are estimated by mean difference reflecting the information source quality. Then, we discount the classification results with the corresponding weights under the framework of the evidence theory, which is expert at dealing with uncertain information. These discounted classification results are combined by an evidential combination rule for making the final class decision. The effectiveness of DAET for cross-domain pattern classification is evaluated with respect to some advanced DA methods, and the experiment results show DAET can significantly improve the classification accuracy.

A Novel and Fast Encryption System Based on Improved Josephus Scrambling and Chaotic Mapping.

To address the shortcomings of weak confusion and high time complexity of the existing permutation algorithms, including the traditional Josephus ring permutation (TJRP), an improved Josephus ring-based permutation (IJRBP) algorithm is developed. The proposed IJRBP replaces the remove operation used in TJRP with the position exchange operation and employs random permutation steps instead of fixed steps, which can offer a better scrambling effect and a higher permutation efficiency, compared with various scrambling methods. Then, a new encryption algorithm based on the IJRBP and chaotic system is developed. In our scheme, the plaintext feature parameter, which is related to the plaintext and a random sequence generated by a chaotic system, is used as the shift step of the circular shift operation to generate the diffusion matrix, which means that a minor change in the source image will generate a totally different encrypted image. Such a strategy strikes a balance between plaintext sensitivity and ciphertext sensitivity to obtain the ability to resist chosen-plaintext attacks (CPAs) and the high robustness of resisting noise attacks and data loss. Simulation results demonstrate that the proposed image cryptosystem has the advantages of great encryption efficiency and the ability to resist various common attacks.

Combination of Transferable Classification With Multisource Domain Adaptation Based on Evidential Reasoning.

In applications of domain adaptation, there may exist multiple source domains, which can provide more or less complementary knowledge for pattern classification in the target domain. In order to improve the classification accuracy, a decision-level combination method is proposed for the multisource domain adaptation based on evidential reasoning. The classification results obtained from different source domains usually have different reliabilities/weights, which are calculated according to domain consistency. Therefore, the multiple classification results are discounted by the corresponding weights under belief functions framework, and then, Dempster's rule is employed to combine these discounted results. In order to reduce errors, a neighborhood-based cautious decision-making rule is developed to make the class decision depending on the combination result. The object is assigned to a singleton class if its neighborhoods can be (almost) correctly classified. Otherwise, it is cautiously committed to the disjunction of several possible classes. By doing this, we can well characterize the partial imprecision of classification and reduce the error risk as well. A unified utility value is defined here to reflect the benefit of such classification. This cautious decision-making rule can achieve the maximum unified utility value because partial imprecision is considered better than an error. Several real data sets are used to test the performance of the proposed method, and the experimental results show that our new method can efficiently improve the classification accuracy with respect to other related combination methods.

A Symmetric Plaintext-Related Color Image Encryption System Based on Bit Permutation.

Recently, a variety of chaos-based image encryption algorithms adopting the traditional permutation-diffusion structure have been suggested. Most of these algorithms cannot resist the powerful chosen-plaintext attack and chosen-ciphertext attack efficiently for less sensitivity to plain-image. This paper presents a symmetric color image encryption system based on plaintext-related random access bit-permutation mechanism (PRRABPM). In the proposed scheme, a new random access bit-permutation mechanism is used to shuffle 3D bit matrix transformed from an original color image, making the RGB components of the color image interact with each other. Furthermore, the key streams used in random access bit-permutation mechanism operation are extremely dependent on plain image in an ingenious way. Therefore, the encryption system is sensitive to tiny differences in key and original images, which means that it can efficiently resist chosen-plaintext attack and chosen-ciphertext attack. In the diffusion stage, the previous encrypted pixel is used to encrypt the current pixel. The simulation results show that even though the permutation-diffusion operation in our encryption scheme is performed only one time, the proposed algorithm has favorable security performance. Considering real-time applications, the encryption speed can be further improved.

A Simple Chaotic Map-Based Image Encryption System Using Both Plaintext Related Permutation and Diffusion.

Recently, to conquer most non-plain related chaos-based image cryptosystems' security flaws that cannot resist the powerful chosen/knownn plain-text attacks or differential attacks efficiently for less plaintext sensitivity, many plain related chaos-based image cryptosystems have been developed. Most cryptosystems that have adopted the traditional permutation-diffusion structure still have some drawbacks and security flaws: (1) most plaintext related image encryption schemes using only plaintext related confusion operation or only plaintext related diffusion operation relate to plaintext inadequately that cannot achieve high plaintext sensitivity; (2) in some algorithms, the generation of security key that needs to be sent to the receiver is determined by the original image, so these algorithms may not applicable to real-time image encryption; (3) most plaintext related image encryption schemes have less efficiency because more than one round permutation-diffusion operation is required to achieve high security. To obtain high security and efficiency, a simple chaotic based color image encryption system by using both plaintext related permutation and diffusion is presented in this paper. In our cryptosystem, the values of the parameters of cat map used in permutation stage are related to plain image and the parameters of cat map are also influenced by the diffusion operation. Thus, both the permutation stage and diffusion stage are related to plain images, which can obtain high key sensitivity and plaintext sensitivity to resist chosen/known plaintext attacks or differential attacks efficiently. Furthermore, only one round of plaintext related permutation and diffusion operation is performed to process the original image to obtain cipher image. Thus, the proposed scheme has high efficiency. Complete simulations are given and the simulation results prove the excellent security and efficiency of the proposed scheme.