Stigmastane-type steroid saponins from the leaves of Vernonia amygdalina and their α-glucosidase and xanthine oxidase inhibitory activities.

Two new vernonioside K (1) and vernonioside L (2) and four known Δ7,9(11) stigmastane-type steroidal saponins-vernonioside B2 (3), vernoniacum B (4), vernonioside B1 (5), and vernoamyoside A (6)-were isolated from the leaves of Vernonia amygdalina. Their structures were determined by comprehensive spectroscopic analysis with one-dimensional nuclear magnetic resonance, two-dimensional nuclear magnetic resonance, and high-resolution mass spectrometry. All isolated compounds (1-6) were evaluated to determine their inhibitory effects on α-glucosidase and xanthine oxidase. Among them, two new compounds 1 and 2 showed significant inhibition of α-glucosidase with IC50 values of 78.56 ± 7.28 and 14.74 ± 1.57 (µM), respectively, comparable with acarbose as a positive control (127.53 ± 1.73 µM); none of these compounds inhibited xanthine oxidase activity. Compounds 1 and 2 are promising candidates for the development of antidiabetic agents from natural sources.

Sigma-1 Receptor Activation Is Protective against TGFß2-Induced Extracellular Matrix Changes in Human Trabecular Meshwork Cells.

The trabecular meshwork (TM) route is the principal outflow egress of the aqueous humor. Actin cytoskeletal remodeling in the TM and extracellular matrix (ECM) deposition increase TM stiffness, outflow resistance, and elevate intraocular pressure (IOP). These alterations are strongly linked to transforming growth factor-ß2 (TGFß2), a known profibrotic cytokine that is markedly elevated in the aqueous humor of glaucomatous eyes. Sigma-1 receptor (S1R) has been shown to have neuroprotective effects in the retina, but data are lacking about its role in the TM. In this study, we identified the presence of S1R in mouse TM tissue and investigated the effect of an S1R agonist fluvoxamine (FLU) on TGFß2-induced human TM cells regarding cell proliferation; ECM-related functions, including F-actin reorganization; and the accumulation of ECM elements. TGFß2 increased the proliferation, cytoskeletal remodeling, and protein levels of fibronectin, collagen type IV, and connective tissue growth factor, and decreased the level of matrix metalloproteinase-2. Most importantly, FLU reversed all these effects of TGFß2, suggesting that S1R agonists could be potential candidates for preserving TM function and thus maintaining normal IOP.

Sigma-1 Receptor Agonist Fluvoxamine Ameliorates Fibrotic Response of Trabecular Meshwork Cells.

Primary open-angle glaucoma remains a global issue, lacking a definitive treatment. Increased intraocular pressure (IOP) is considered the primary risk factor of the disease and it can be caused by fibrotic-like changes in the trabecular meshwork (TM) such as increased tissue stiffness and outflow resistance. Previously, we demonstrated that the sigma-1 receptor (S1R) agonist fluvoxamine (FLU) has anti-fibrotic properties in the kidney and lung. In this study, the localization of the S1R in TM cells was determined, and the anti-fibrotic efficacy of FLU was examined in both mouse and human TM cells. Treatment with FLU reduced the F-actin rearrangement, inhibited cell proliferation and migration induced by the platelet-derived growth factor and decreased the levels of fibrotic proteins. The protective role of the S1R in fibrosis was confirmed by a more pronounced increase in alpha smooth muscle actin and F-actin bundle and clump formation in primary mouse S1R knockout TM cells. Furthermore, FLU demonstrated its protective effects by increasing the production of nitric oxide and facilitating the degradation of the extracellular matrix through the elevation of cathepsin K. These findings suggest that the S1R could be a novel target for the development of anti-fibrotic drugs and offer a new therapeutic approach for glaucoma.

Time-evolving psychological processes over repeated decisions.

Many psychological experiments have subjects repeat a task to gain the statistical precision required to test quantitative theories of psychological performance. In such experiments, time-on-task can have sizable effects on performance, changing the psychological processes under investigation. Most research has either ignored these changes, treating the underlying process as static, or sacrificed some psychological content of the models for statistical simplicity. We use particle Markov chain Monte-Carlo methods to study psychologically plausible time-varying changes in model parameters. Using data from three highly cited experiments, we find strong evidence in favor of a hidden Markov switching process as an explanation of time-varying effects. This embodies the psychological assumption of "regime switching," with subjects alternating between different cognitive states representing different modes of decision-making. The switching model explains key long- and short-term dynamic effects in the data. The central idea of our approach can be applied quite generally to quantitative psychological theories, beyond the models and datasets that we investigate. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Efficient selection between hierarchical cognitive models: Cross-validation with variational Bayes.

Model comparison is the cornerstone of theoretical progress in psychological research. Common practice overwhelmingly relies on tools that evaluate competing models by balancing in-sample descriptive adequacy against model flexibility, with modern approaches advocating the use of marginal likelihood for hierarchical cognitive models. Cross-validation is another popular approach but its implementation remains out of reach for cognitive models evaluated in a Bayesian hierarchical framework, with the major hurdle being its prohibitive computational cost. To address this issue, we develop novel algorithms that make variational Bayes (VB) inference for hierarchical models feasible and computationally efficient for complex cognitive models of substantive theoretical interest. It is well known that VB produces good estimates of the first moments of the parameters, which gives good predictive densities estimates. We thus develop a novel VB algorithm with Bayesian prediction as a tool to perform model comparison by cross-validation, which we refer to as CVVB. In particular, CVVB can be used as a model screening device that quickly identifies bad models. We demonstrate the utility of CVVB by revisiting a classic question in decision making research: what latent components of processing drive the ubiquitous speed-accuracy tradeoff? We demonstrate that CVVB strongly agrees with model comparison via marginal likelihood, yet achieves the outcome in much less time. Our approach brings cross-validation within reach of theoretically important psychological models, making it feasible to compare much larger families of hierarchically specified cognitive models than has previously been possible. To enhance the applicability of the algorithm, we provide Matlab code together with a user manual so users can easily implement VB and/or CVVB for the models considered in this article and their variants. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

QoS-Based Service-Time Scheduling in the IoT-Edge Cloud.

In edge computing, scheduling heterogeneous workloads with diverse resource requirements is challenging. Besides limited resources, the servers may be overwhelmed with computational tasks, resulting in lengthy task queues and congestion occasioned by unusual network traffic patterns. Additionally, Internet of Things (IoT)/Edge applications have different characteristics coupled with performance requirements, which become determinants if most edge applications can both satisfy deadlines and each user's QoS requirements. This study aims to address these restrictions by proposing a mechanism that improves the cluster resource utilization and Quality of Service (QoS) in an edge cloud cluster in terms of service time. Containerization can provide a way to improve the performance of the IoT-Edge cloud by factoring in task dependencies and heterogeneous application resource demands. In this paper, we propose STaSA, a service time aware scheduler for the edge environment. The algorithm automatically assigns requests onto different processing nodes and then schedules their execution under real-time constraints, thus minimizing the number of QoS violations. The effectiveness of our scheduling model is demonstrated through implementation on KubeEdge, a container orchestration platform based on Kubernetes. Experimental results show significantly fewer violations in QoS during scheduling and improved performance compared to the state of the art.

Identifying relationships between cognitive processes across tasks, contexts, and time.

It is commonly assumed that a specific testing occasion (task, design, procedure, etc.) provides insights that generalize beyond that occasion. This assumption is infrequently carefully tested in data. We develop a statistically principled method to directly estimate the correlation between latent components of cognitive processing across tasks, contexts, and time. This method simultaneously estimates individual-participant parameters of a cognitive model at each testing occasion, group-level parameters representing across-participant parameter averages and variances, and across-task correlations. The approach provides a natural way to "borrow" strength across testing occasions, which can increase the precision of parameter estimates across all testing occasions. Two example applications demonstrate that the method is practical in standard designs. The examples, and a simulation study, also provide evidence about the reliability and validity of parameter estimates from the linear ballistic accumulator model. We conclude by highlighting the potential of the parameter-correlation method to provide an "assumption-light" tool for estimating the relatedness of cognitive processes across tasks, contexts, and time.

Didymocarpus phuquocensis, a new species of Gesneriaceae from Phu Quoc Island, South-western Vietnam.

A new species of Gesneriaceae, Didymocarpus phuquocensis, is described and illustrated from Phu Quoc National Park, Kien Giang Province, South-western Vietnam. It is most similar to D. pulcher, D. hookeri and D. punduanus in having 3-verticillate petiolate leaves, morphologically similar calyx, corolla, stamens, pistil and fruit, but differs from all in the glandular-pubescent stems, petioles and leaf blades, 1(-2)-flowered cymes, longer corolla and fruit and longer and densely glandular-puberulent ovary. Data on distribution, ecology, phenology and provisional conservation assessment of the new species are given along with an illustration and a colour plate.

Uterine Tumor Resembling Ovarian Sex Cord Tumor: Clinicopathological Characteristics of a Rare Case.

Uterine tumor resembling ovarian sex cord tumor (UTROSCT) is a rare indolent stromal neoplasm of unclear histogenesis with a distinct histopathological entity. Immunophenotypes of sex cord positivity are the most significant information to confirm the diagnosis. We present the case of a 61-year-old female with a polypoid mass in the uterus which was successfully removed by surgical resection as hysterectomy. The pathological diagnosis was UTROSCT, which was characterized microscopically by sex cord images and immunohistochemical features of calretinin, CD99, and WT1 positivity.

Discrimination of cinnamon bark and cinnamon twig samples sourced from various countries using HPLC-based fingerprint analysis.

A simple and efficient HPLC method was developed to evaluate the quality of traditional herbal medicines made from cinnamon bark (CB) and cinnamon twig (CT). Seven major bioactive ingredients in 56 samples (24 CB and 32 CT) collected from China, Vietnam, and Indonesia were separated and quantified. The method was validated following the International Conference on Harmonisation (ICH) guidelines. A fingerprint analysis method to discriminate between CB and CT using major component content levels was developed. The discrimination process included the use of similarity indices and partial least-squares discriminant analysis (PLS-DA). Classification accuracy by the PLS-DA method was about 98%. The pattern analysis method was specific and could be readily used for the comprehensive evaluation of cinnamon samples. Therefore, an HPLC fingerprint in combination with pattern analysis provides a very flexible and reliable method for quality assessment of herbal drugs.