Multi-dimensional Fusion and Consistency for Semi-supervised Medical Image Segmentation

In this paper, we introduce a novel semi-supervised learning framework tailored for medical image segmentation. Central to our approach is the innovative Multi-scale Text-aware ViT-CNN Fusion scheme. This scheme adeptly combines the strengths of both ViTs and CNNs, capitalizing on the unique advantages of both architectures as well as the complementary information in vision-language modalities. Further enriching our framework, we propose the Multi-Axis Consistency framework for generating robust pseudo labels, thereby enhancing the semi-supervised learning process. Our extensive experiments on several widely-used datasets unequivocally demonstrate the efficacy of our approach

Fast Charging of Lithium-Ion Batteries Using Deep Bayesian Optimization with Recurrent Neural Network

Fast charging has attracted increasing attention from the battery community for electrical vehicles (EVs) to alleviate range anxiety and reduce charging time for EVs. However, inappropriate charging strategies would cause severe degradation of batteries or even hazardous accidents. To optimize fast-charging strategies under various constraints, particularly safety limits, we propose a novel deep Bayesian optimization (BO) approach that utilizes Bayesian recurrent neural network (BRNN) as the surrogate model, given its capability in handling sequential data. In addition, a combined acquisition function of expected improvement (EI) and upper confidence bound (UCB) is developed to better balance the exploitation and exploration. The effectiveness of the proposed approach is demonstrated on the PETLION, a porous electrode theory-based battery simulator. Our method is also compared with the state-of-the-art BO methods that use Gaussian process (GP) and non-recurrent network as surrogate models. The results verify the superior performance of the proposed fast charging approaches, which mainly results from that: (i) the BRNN-based surrogate model provides a more precise prediction of battery lifetime than that based on GP or non-recurrent network; and (ii) the combined acquisition function outperforms traditional EI or UCB criteria in exploring the optimal charging protocol that maintains the longest battery lifetime

Antibacterial activity of isopropoxy benzene guanidine against Riemerella anatipestifer

Introduction:Riemerella anatipestifer (R. anatipestifer) is an important pathogen in waterfowl, leading to substantial economic losses. In recent years, there has been a notable escalation in the drug resistance rate of R. anatipestifer. Consequently, there is an imperative need to expedite the development of novel antibacterial medications to effectively manage the infection caused by R. anatipestifer.Methods: This study investigated the in vitro and in vivo antibacterial activities of a novel substituted benzene guanidine analog, namely, isopropoxy benzene guanidine (IBG), against R. anatipestifer by using the microdilution method, time-killing curve, and a pericarditis model. The possible mechanisms of these activities were explored.Results and Discussion: The minimal inhibitory concentration (MIC) range of IBG for R. anatipestifer was 0.5–2 μg/mL. Time-killing curves showed a concentration-dependent antibacterial effect. IBG alone or in combination with gentamicin significantly reduced the bacterial load of R. anatipestifer in the pericarditis model. Serial-passage mutagenicity assays showed a low probability for developing IBG resistance. Mechanistic studies suggested that IBG induced membrane damage by binding to phosphatidylglycerol and cardiolipin, leading to an imbalance in membrane potential and the transmembrane proton gradient, as well as the decreased of intracellular adenosine triphosphate. In summary, IBG is a potential antibacterial for controlling R. anatipestifer infections

Drug-coated balloons: A better revascularization strategy in patients with multivessel coronary artery disease undergoing one-stop hybrid coronary revascularization surgery

Background: The optimal revascularization strategy for non-left anterior descending coronary artery (LAD) lesions during one-stop hybrid coronary revascularization (HCR) surgery lacks current evidence.Aims: This study aimed to compare the outcomes of the drug-coated balloon (DCB) and drug-eluting stent (DES) strategies in patients with non-small non-LAD lesions undergoing one-stop HCR.Methods: A total of 141 consecutive patients with multivessel coronary artery disease (MVCAD) undergoing one-stop HCR between June 1, 2018 and March 1, 2022 were retrospectively included in this study. In-hospital outcomes and mid-term major adverse cardiovascular and cerebrovascular events (MACCE) were observed. Kaplan-Meier curve analysis was used to evaluate the MACCE-free survival rate. The Cox proportional hazard model was used to identify risk factors of mid-term MACCE.Results: Thirty-eight and 103 patients received only DCB or DES therapy, respectively, in this study. There were no significant differences in demographic characteristics and laboratory parameters between the two groups. The in-hospital MACCE rate in the DES group was numerically higher than that in the DCB group (9.7% vs. 5.3%, respectively), but the difference was not statistically significant (P = 0.4). The incidence of MACCE after patients’ discharge was significantly higher in the DES group (22% vs. 5.3%, respectively, P = 0.02) during a median follow-up of 20 months. After multivariable Cox proportional hazard analysis, DCB therapy was independently associated with reduced risk of mid-term MACCE (hazard ratio, 0.21; 95% confidence interval, 0.06–0.91; P = 0.04).Conclusion: For patients with MVCAD undergoing one-stop HCR, DCB therapy may be the optimal revascularization strategy for non-small non-LAD coronary artery lesions with a significantly lower rate of mid-term MACCE

Fungal Cell-based Microcarriers and Their 3D Assembly as Encapsulation and Oral Delivery Systems for Bioactive Compounds

Plant-derived bioactive compounds are minor constituents of natural plant foods that exist in trace amounts but have validated benefits to human health. Despite the promising evidence on their therapeutic or health-maintaining effects, it cannot be directly translated into dietary recommendations. One of the many challenges is that the fraction of bioactive compounds that can be released from food matrices, defined as bioaccessibility, will be different depending on the compositions and structures of the food matrices and their physical and chemical interactions with the bioactive compound. Majority of the current studies investigated such a food matrix effect on bioaccessibility of plant-derived bioactive compounds by evaluating the effects of different processing techniques and conditions on the bioaccessibility. Considered as a top-down approach, this method often fails to decouple the effects from various contributing mechanisms, such as the chemical and structural factors, since the observed effects are lumped and collective.This research adopted a bottom-up approach to investigate the multi-scale food matrix effect on bioaccessibility of plant-derived bioactive compounds during in vitro digestion. Inspired by natural foods, tissue-like encapsulation systems for bioactive compounds were constructed with different levels of compositional and structural complexities, using yeast-based microcarriers as the building blocks, and techniques such as 3D printing for guided cell assembly. A combined experimental and modeling approach was taken to investigate the bioactive compound – food matrix interactions in the encapsulation systems. The central hypothesis tested in the current research was that the encapsulation and release of bioactive compounds could be affected by the chemical and physical interactions among the bioactive compounds, the encapsulation matrices, and the digestion fluids’ components at different scale levels, i.e., the sub-cellular, cellular, and tissue levels. The encapsulation of bioactive compounds and their release profiles during in vitro digestion were evaluated for all the cell-based and tissue-like matrices. This research found that at a cellular level, it was mainly the interactions among the bioactive compounds, the cellular components, and the digestion fluids components that controlled the release kinetics. Such interactions included complex formation and dissociation between compounds and cellular proteins, partition among the extracellular environment and intracellular lipid-rich phase and protein-rich aqueous phase, as well as the changes in cellular composition and intracellular structures during digestion. At a tissue level, besides the abovementioned interactions, physical barriers to compounds’ release become more significant, such as diffusion impediment and physical entrapment due to the presence of extracellular matrix or the assembly of single cells. Moreover, this research incorporated the element of in silico evaluation by constructing predictive models for the encapsulation efficiency and release patterns of compounds in cell-based carriers, and printability of food inks. In the effort to construct the in silico evaluation pipelines, we exploited multimodal data to characterize the encapsulation systems and utilized statistical tools and machine learning/ deep learning algorithms for proper feature selection and model training. Overall, the findings of this research resulted in a better understanding of the bioactive compound – food matrix interaction both during encapsulation and in vitro digestion, demonstrating the possibility to modulate the in vitro release kinetics of bioactive compounds by modifying the chemical or structural features of encapsulation matrices at the cell level or tissue level. Further development of the in silico pipelines for formulation evaluation and optimization could facilitate the design and development of novel nutraceuticals and functional foods

The Effect of Perceived Error Stability, Brand Perception, and Relationship Norms on Consumer Reaction to Data Breaches

The issue of data breaches has received increasing attention in the hospitality industry. Companies’ efforts to fix such errors affect consumers’ evaluations and behavioral intentions toward those companies. This study investigates the impact of perceived error stability on hotel guests’ intentions to spread positive word-of-mouth (WOM) about a hotel. The findings reveal that when a data breach occurs, consumers are likely to spread positive WOM about a company that is typically considered competent if the consumers perceive the error stability to be low rather than high. Consumers have similar reactions to companies with which they have communal relationships. This research suggests that hotels should strategically allocate their resources on the basis of brand perception in the minds of their target consumers as well as their relationships with their target markets