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.