Semi-Supervised Medical Image Segmentation with Co-Distribution Alignment

Medical image segmentation has made significant progress when a large amount of labeled data are available. However, annotating medical image segmentation datasets is expensive due to the requirement of professional skills. Additionally, classes are often unevenly distributed in medical images, which severely affects the classification performance on minority classes. To address these problems, this paper proposes Co-Distribution Alignment (Co-DA) for semi-supervised medical image segmentation. Specifically, Co-DA aligns marginal predictions on unlabeled data to marginal predictions on labeled data in a class-wise manner with two differently initialized models before using the pseudo-labels generated by one model to supervise the other. Besides, we design an over-expectation cross-entropy loss for filtering the unlabeled pixels to reduce noise in their pseudo-labels. Quantitative and qualitative experiments on three public datasets demonstrate that the proposed approach outperforms existing state-of-the-art semi-supervised medical image segmentation methods on both the 2D CaDIS dataset and the 3D LGE-MRI and ACDC datasets, achieving an mIoU of 0.8515 with only 24% labeled data on CaDIS, and a Dice score of 0.8824 and 0.8773 with only 20% data on LGE-MRI and ACDC, respectively.Comment: Paper appears in Bioengineering 2023, 10(7), 86

Povećanje stabilnosti i antioksidacijske aktivnosti antocijana iz ploda duda aciliranjem s jantarnom kiselinom

Research background. Anthocyanins possess valuable health-promoting activities with significant health benefits for humans. However, their instability is a limiting factor for their usage in functional foods and beverages. Experimental approach. In this work, a new method to enhance the stability of anthocyanins from mulberry fruit through acylation by using succinic acid as a selected acyl donor was explored. The Box-Behnken design of response surface methodology was applied to determine the optimized conditions for the acylation process. Results and conclusions. The highest acylation conversion rate was 79.04% at anthocyanins to succinic acid mass ratio 1:8.96, acylation duration 3 h and temperature 50 °C. Structural analysis of acylated anthocyanins revealed that succinic acid introduces a C-O-C bond and a hydroxyl group. The thermostability and light stability of mulberry anthocyanins were significantly improved after acylation, and the antioxidant activity expressed as total reducing power and Fe2+-chelating capacity of the acylated anthocyanins was also enhanced. Novelty and scientific contribution. Succinic acid acylation provides a novel method for stabilizing mulberry anthocyanins, as evidenced by the increased stability and antioxidant ability of anthocyanins, and thus facilitates its use in the food and nutraceutical industries.Pozadina istraživanja. Antocijani imaju pozitivni učinak na ljudsko zdravlje, no njihova im nestabilnost ograničava uporabu u proizvodnji funkcionalne hrane i pića. Eksperimentalni pristup. U ovom je radu ispitana nova metoda povećanja stabilnosti antocijana iz ploda duda aciliranjem s jantarnom kiselinom kao donorom acilne skupine. Optimalni uvjeti reakcije određeni su pomoću Box-Behnkenovog statističkog plana i metodom odzivnih površina. Rezultati i zaključci. Najveći postotak konverzije od 79,04 % postignut je pri masenom omjeru antocijana i jantarne kiseline od 1:8,96; trajanju acilacije od 3 h i temperaturi od 50 °C. Analizom strukture aciliranih antocijana utvrđeno je da sadržavaju C-O-C vezu i hidroksilnu skupinu iz jantarne kiseline. Aciliranje je bitno povećalo stabilnost antocijana pri izlaganju povišenim temperaturama i svjetlosti, te njihovu antioksidacijsku aktivnost, izraženu kroz ukupnu reducirajuću snagu i sposobnost keliranja Fe2+ iona. Novina i znanstveni doprinos. Aciliranje jantarnom kiselinom predstavlja novu metodu stabilizacije antocijana iz duda, što potvrđuje njihova povećana stabilnost i antioksidacijska sposobnost, čime je olakšana njihova primjena u proizvodnji hrane i nutraceutika

Conformational Toggling of Yeast Iso-1-Cytochrome c in the Oxidized and Reduced States

To convert cyt c into a peroxidase-like metalloenzyme, the P71H mutant was designed to introduce a distal histidine. Unexpectedly, its peroxidase activity was found even lower than that of the native, and that the axial ligation of heme iron was changed to His71/His18 in the oxidized state, while to Met80/His18 in the reduced state, characterized by UV-visible, circular dichroism, and resonance Raman spectroscopy. To further probe the functional importance of Pro71 in oxidation state dependent conformational changes occurred in cyt c, the solution structures of P71H mutant in both oxidation states were determined. The structures indicate that the half molecule of cyt c (aa 50–102) presents a kind of “zigzag riveting ruler” structure, residues at certain positions of this region such as Pro71, Lys73 can move a big distance by altering the tertiary structure while maintaining the secondary structures. This finding provides a molecular insight into conformational toggling in different oxidation states of cyt c that is principle significance to its biological functions in electron transfer and apoptosis. Structural analysis also reveals that Pro71 functions as a key hydrophobic patch in the folding of the polypeptide of the region (aa 50–102), to prevent heme pocket from the solvent

Extended-State-Observer-Based Super Twisting Control for Pneumatic Muscle Actuators

This paper presents a tracking control method for pneumatic muscle actuators (PMAs). Considering that the PMA platform only feedbacks position, and the velocity and disturbances cannot be observed directly, we use the extended-state-observer (ESO) for simultaneously estimating the system states and disturbances by using measurable variables. Integrated with the ESO, a super twisting controller (STC) is design based on estimated states to realize the high-precision tracking. According to the Lyapunov theorem, the stability of the closed-loop system is ensured. Simulation and experimental studies are conducted, and the results show the convergence of the ESO and the effectiveness of the proposed method

Relationship between Parkinson’s disease and cardio-cerebrovascular diseases: a Mendelian randomized study

Abstract Parkinson’s disease (PD) and cardio-cerebrovascular diseases are related, according to earlier studies, but these studies have some controversy. Our aim was to assess the impact of PD on cardiocerebrovascular diseases using a Mendelian randomization (MR) method. The data for PD were single nucleotide polymorphisms (SNPs) from a publicly available genome-wide association study (GWAS) dataset containing data on 482,730 individuals. And the outcome SNPs data is were derived from five different GWAS datasets. The basic method for MR analysis was the inverse variance weighted (IVW) approach. We use the weighted median method and the MR-Egger method to supplement the MR analysis conclusion. Finally, We used Cochran’s Q test to test heterogeneity, MR-PRESSO method and leave-one-out analysis method to perform sensitivity analysis. We used ratio ratios (OR) to assess the strength of the association between exposure and outcome, and 95% confidence intervals (CI) to show the reliability of the results. Our findings imply that PD is linked to a higher occurrence of coronary artery disease (CAD) (OR = 1.055, 95% CI 1.020–1.091, P = 0.001), stroke (OR = 1.039, 95% CI 1.007–1.072, P = 0.014). IVW analyses for stroke’s subgroups of ischemic stroke (IS) and 95% CI 1.007–1.072, P = 0.014). IVW analyses for stroke’s subgroups of ischemic stroke (IS) and cardioembolic stroke (CES) also yielded positive results, respectively (OR = 1.043, 95% CI 1.008–1.079, P = 0.013), (OR = 1.076, 95% CI 1.008–1.149, P = 0.026). There is no evidence of a relationship between PD and other cardio-cerebrovascular diseases. Additionally, sensitivity analysis revealed reliable outcomes. Our MR study analysis that PD is related with an elevated risk of CAD, stroke, IS, and CES

Plasma-water-based nitrogen fixation : Status, mechanisms, and opportunities

Nitrogen-based crop fertilizers are the most important industrial chemicals supporting the global food supply. Plasma-water-based nitrogen fixation (PWBNF) provides a clean, sustainable, and flexible alternative, which is amenable for decentralized, small-to-medium-scale production systems. This process is based on the targeted activation of N2 or air molecules by plasmas. Plasma can interact with water molecules, water droplets, and water layers through the plasma physical and chemical mechanisms. This review summarizes the current state of the art of PWBNF and provides insights into the effective mechanisms for the synthesis of NH3, NO2− and NO3− in highly reactive plasma environments. The opportunities and challenges for this plasma-enabled approach are identified to guide the development of sustainable nitrogen fixation technology.</p

Overcoming vascular barriers to improve the theranostic outcomes of nanomedicines

Nanotheranostics aims to utilize nanomaterials to prevent, diagnose, and treat diseases to improve the quality of patients' lives. Blood vessels are responsible to deliver nutrients and oxygen to the whole body, eliminate waste, and provide access for patrolling immune cells for healthy tissues. Meanwhile, they can also nourish disease tissues, spread disease factors or cells into other healthy tissues, and deliver nanotheranostic agents to cover all the regions of a disease tissue. Thus, blood vessels are the first and the most important barrier for highly efficient nanotheranostics. Here, the structure and function of blood vessels are explored and how these characteristics affect nanotheranostics is discussed. Moreover, new mechanisms and related strategies about overcoming vascular obstacles for improved nanotheranostic outcomes are critically summarized, and their merits and demerits of each strategy are analyzed. Moreover, the present challenges to completely exhibit the potential of overcoming vascular barriers to improve the theranostic outcomes of nanomedicines in life science are also discussed. Finally, the future perspective is further discussed.Published versionThis work was financially supported by the National Natural Science Foundation of China (No. 61905111), and the China Postdoctoral Science Foundation (Nos. 2019M651816 and 2020T130293)

Personalized Human Activity Recognition Based on Integrated Wearable Sensor and Transfer Learning

Human activity recognition (HAR) based on the wearable device has attracted more attention from researchers with sensor technology development in recent years. However, personalized HAR requires high accuracy of recognition, while maintaining the model&rsquo;s generalization capability is a major challenge in this field. This paper designed a compact wireless wearable sensor node, which combines an air pressure sensor and inertial measurement unit (IMU) to provide multi-modal information for HAR model training. To solve personalized recognition of user activities, we propose a new transfer learning algorithm, which is a joint probability domain adaptive method with improved pseudo-labels (IPL-JPDA). This method adds the improved pseudo-label strategy to the JPDA algorithm to avoid cumulative errors due to inaccurate initial pseudo-labels. In order to verify our equipment and method, we use the newly designed sensor node to collect seven daily activities of 7 subjects. Nine different HAR models are trained by traditional machine learning and transfer learning methods. The experimental results show that the multi-modal data improve the accuracy of the HAR system. The IPL-JPDA algorithm proposed in this paper has the best performance among five HAR models, and the average recognition accuracy of different subjects is 93.2%

Personalized Human Activity Recognition Based on Integrated Wearable Sensor and Transfer Learning

Human activity recognition (HAR) based on the wearable device has attracted more attention from researchers with sensor technology development in recent years. However, personalized HAR requires high accuracy of recognition, while maintaining the model’s generalization capability is a major challenge in this field. This paper designed a compact wireless wearable sensor node, which combines an air pressure sensor and inertial measurement unit (IMU) to provide multi-modal information for HAR model training. To solve personalized recognition of user activities, we propose a new transfer learning algorithm, which is a joint probability domain adaptive method with improved pseudo-labels (IPL-JPDA). This method adds the improved pseudo-label strategy to the JPDA algorithm to avoid cumulative errors due to inaccurate initial pseudo-labels. In order to verify our equipment and method, we use the newly designed sensor node to collect seven daily activities of 7 subjects. Nine different HAR models are trained by traditional machine learning and transfer learning methods. The experimental results show that the multi-modal data improve the accuracy of the HAR system. The IPL-JPDA algorithm proposed in this paper has the best performance among five HAR models, and the average recognition accuracy of different subjects is 93.2%