Multiple Mechanisms Research Articles

Multiplicative versus additive modelling of causal effects using instrumental variables for survival outcomes - a comparison.

Instrumental variables (IVs) methods have recently gained popularity since, under certain assumptions, they may yield consistent causal effect estimators in the presence of unmeasured confounding. Existing simulation studies that evaluate the performance of IV approaches for time-to-event outcomes tend to consider either an additive or a multiplicative data-generating mechanism (DGM) and have been limited to an exponential constant baseline hazard model. In particular, the relative merits of additive versus multiplicative IV models have not been fully explored. All IV methods produce less biased estimators than naïve estimators that ignore unmeasured confounding, unless the IV is very weak and there is very little unmeasured confounding. However, the mean squared error of IV estimators may be higher than that of the naïve, biased but more stable estimators, especially when the IV is weak, the sample size is small to moderate, and the unmeasured confounding is strong. In addition, the sensitivity of IV methods to departures from their assumed DGMs differ substantially. Additive IV methods yield clearly biased effect estimators under a multiplicative DGM whereas multiplicative approaches appear less sensitive. All can be extremely variable. We would recommend that survival probabilities should always be reported alongside the relevant hazard contrasts as these can be more reliable and circumvent some of the known issues with causal interpretation of hazard contrasts. In summary, both additive IV and Cox IV methods can perform well in some circumstances but an awareness of their limitations is required in analyses of real data where the true underlying DGM is unknown.

Anti-Tumor Activity of Indole: A Review

: Generally, heterocyclic compounds are included in a large class of pharmacologically active compounds. The indole scaffold in this category is widely distributed in nature and present in many active compounds, especially anti-cancer agents. Due to its unique physicochemical and biological properties, the indole platform has been considered a favorable scaffold in anti-cancer drug design and development. Various indole compounds (synthetic, semisynthetic, and natural) show remarkable anti-proliferative activity. According to the recent literature, this review describes the role of indole scaffolds as anti-cancer agents. Indole was reported to induce anti-tumor activity through multiple mechanisms, for example, Epidermal Growth Factor Receptors (EGFR), histone deacetylase (HDAC), kinase, DNA-topoisomerases, and tubulin inhibition. The current review focuses on some indole compounds with amazing effects against different types of cancers as there are too many FDA-approved drugs, for example, osimertinib, alectinib, and anlotinib in NSCLC treatment, panobinostat in multiple myeloma, midostaurin in acute myeloid leukemia treatment, etc. Moreover, several compounds are still in clinical trials to treat different cancer types. Additionally, there are some oxindole derivatives with potent inhibition against different types of tumors, such as ovarian cancer, colorectal cancer, and prostate cancer. Different series of oxindoles are promising and recommended for further studies due to their remarkable inhibition of tumor cells. Accordingly, the collection of data on a pharmacologically significant motif might aid researchers in further employing indoles in developing novel anti-cancer drugs with potentially fewer side effects and higher potency against this rapidly spreading disease.

Mechanism of novel defect multiplication impacting high power 4H-SiC devices

Basal plane dislocations and stacking faults are critical defects influencing silicon carbide (SiC) based high power devices that are rapidly emerging to enable the future needs of electric vehicles, locomotives, renewables, and grid-scale applications. Microstructural properties of three novel interactions between basal plane dislocations and threading mixed dislocations (TMDs) are described. This leads to multiplication of Shockley stacking faults (SSFs) in SiC epitaxial layers. First is a mechanism of double interaction of two SSFs with TMDs that causes the SSFs to glide on multiple basal planes, and creation of locked partial dislocation dipoles (PDD) due to the attractive force between the opposite sign partial dislocations. Second type of interaction occurs between SSFs and a tilted TMD, that results in formation of another SSF. The third type of interaction causes further SSF multiplication by unlocking previously created PDDs. This occurs when the newly formed SSF intersects with the previously locked PDD, and unlocks it, leaving behind a freely gliding partial dislocation and formation of another SSF. Multiplication of SSFs can severely degrade reliability and performance of high power SiC devices by increasing reverse leakage current and on-state resistance, and could eventually lead to device failure.

A hybrid summation and multiplication displacement amplification mechanism for piezoelectric actuators

Abstract This paper reports a novel amplified piezoelectric actuator featuring compliant displacement amplification mechanism of hybrid summation and multiplication. The hybrid amplification mechanism uses the concept of nesting a pair of Scott–Russell linkages into a toggle linkage to realize the hybrid amplification functions of summation and multiplication. Also, compliant amplification mechanisms with double output ports are designed, enhancing the flexibility of designs and applications. The hybrid displacement amplification principle is mathematically explained in detail. It is demonstrated based on finite element simulations that the displacement amplification ratio, output stiffness and resonance frequency of the proposed hybrid amplification mechanism of summation and multiplication outperform those of the classic rhombus-type and bridge-type compliant mechanisms. The experimental testing results of a prototype show that the amplified piezoelectric actuator is capable of providing 315 μm strokes with the displacement amplification ratio of 16.2. The fundamental resonance frequency with piezoelectric stacks mounted is 1218 Hz. Comparison to typical designs in literature shows well comprehensive performances of statics and dynamics, verifying the advantages of such a hybrid amplification mechanism of summation and multiplication.

A Unified Model for Chinese Cyber Threat Intelligence Flat Entity and Nested Entity Recognition

In recent years, as cybersecurity threats have become increasingly severe and cyberattacks have occurred frequently, higher requirements have been put forward for cybersecurity protection. Therefore, the Named Entity Recognition (NER) technique, which is the cornerstone of Cyber Threat Intelligence (CTI) analysis, is particularly important. However, most existing NER studies are limited to recognizing single-layer flat entities, ignoring the possible nested entities in CTI. On the other hand, most of the existing studies focus on English CTIs, and the existing models performed poorly in a limited number of Chinese CTI studies. Given the above challenges, we propose in this paper a novel unified model, RBTG, which aims to identify flat and nested entities in Chinese CTI effectively. To overcome the difficult boundary recognition problem and the direction-dependent and distance-dependent properties in Chinese CTI NER, we use Global Pointer as the decoder and TENER as the encoder layer, respectively. Specifically, the Global Pointer layer solves the problem of the insensitivity of general NER methods to entity boundaries by utilizing the relative position information and the multiplicative attention mechanism. The TENER layer adapts to the Chinese CTI NER task by introducing an attention mechanism with direction awareness and distance awareness. Meanwhile, to cope with the complex feature capture of hierarchical structure and dependencies among Chinese CTI nested entities, the TENER layer solves the problem by following the structure of multiple self-attention layers and feed-forward network layers superimposed on each other in the Transformer. In addition, to fill the gap in the Chinese CTI nested entity dataset, we further apply the Large Language Modeling (LLM) technique and domain knowledge to construct a high-quality Chinese CTI nested entity dataset, CDTinee, which consists of six entity types selected from STIX, including nearly 4000 entity types extracted from more than 3000 threatening sentences. In the experimental session, we conduct extensive experiments on multiple datasets, and the results show that the proposed model RBTG outperforms the baseline model in both flat NER and nested NER.

Berberine: A Multi-Target Natural PCSK9 Inhibitor with the Potential to Treat Diabetes, Alzheimer's, Cancer and Cardiovascular Disease.

Berberine is a natural product with a wide range of pharmacological effects. It has antimicrobial, anti-cancer, anti-inflammatory, anti-hyperlipidemic, neuroprotective, and cholesterollowering properties, among others. It has been used in traditional Chinese and Ayurvedic medicine for 3000 years and is generally well-tolerated with few side effects. Its main drawback is low oral bioavailability, which has hindered widespread clinical use. However, recent interest has surged with the emergence of evidence that berberine is effective in treating cancer, diabetes, Alzheimer's disease, and cardiovascular disease via multiple mechanisms. It enhances insulin sensitivity and secretion by pancreatic β-cells in Type 2 Diabetes Mellitus in addition to reducing pro-inflammatory cytokines such as IL-6, IL-1β, TLR4 and TNF-α. These cytokines are elevated in Alzheimer's disease, cardiovascular disease, and diabetes. Reductions in pro-inflammatory cytokine levels are associated with positive outcomes such as improved cognition, reduced cardiovascular events, and improved glucose metabolism and insulin sensitivity. Berberine is a natural PCSK9 inhibitor, which contributes to its hypolipidemic effects. It also increases low-density lipoprotein receptor expression, reduces intestinal cholesterol absorption, and promotes cholesterol excretion from the liver to the bile. This translates into a notable decrease in LDL cholesterol levels. High LDL cholesterol levels are associated with increased cardiovascular disease risk. Novel synthetic berberine derivatives are currently being developed that optimize LDL reduction, bioavailability, and other pharmacokinetic properties.

DiamondNet: A Neural-Network-Based Heterogeneous Sensor Attentive Fusion for Human Activity Recognition.

With the proliferation of intelligent sensors integrated into mobile devices, fine-grained human activity recognition (HAR) based on lightweight sensors has emerged as a useful tool for personalized applications. Although shallow and deep learning algorithms have been proposed for HAR problems in the past decades, these methods have limited capability to exploit semantic features from multiple sensor types. To address this limitation, we propose a novel HAR framework, DiamondNet, which can create heterogeneous multisensor modalities, denoise, extract, and fuse features from a fresh perspective. In DiamondNet, we leverage multiple 1-D convolutional denoising autoencoders (1-D-CDAEs) to extract robust encoder features. We further introduce an attention-based graph convolutional network to construct new heterogeneous multisensor modalities, which adaptively exploit the potential relationship between different sensors. Moreover, the proposed attentive fusion subnet, which jointly employs a global-attention mechanism and shallow features, effectively calibrates different-level features of multiple sensor modalities. This approach amplifies informative features and provides a comprehensive and robust perception for HAR. The efficacy of the DiamondNet framework is validated on three public datasets. The experimental results demonstrate that our proposed DiamondNet outperforms other state-of-the-art baselines, achieving remarkable and consistent accuracy improvements. Overall, our work introduces a new perspective on HAR, leveraging the power of multiple sensor modalities and attention mechanisms to significantly improve the performance.

A Mechanistic Review on Protective Effects of Mangosteen and its Xanthones Against Hazardous Materials and Toxins.

Due to its pharmacological properties, α-Mangostin, mainly found in Garcinia mangostana (G. mangostana) L. (Mangosteen, queen of fruits), treats wounds, skin infections, and many other disorders. In fact, α-Mangostin and other xanthonoid, including β-Mangostin and γ-Mangostin, are found in G. mangostana, which have various advantages, namely neuroprotective, anti-proliferative, antinociceptive, antioxidant, pro-apoptotic, anti-obesity, anti-inflammatory, and hypoglycemic through multiple signaling mechanisms, for instance, extracellular signal-regulated kinase1/2 (ERK 1/2), mitogenactivated Protein kinase (MAPK), nuclear factor-kappa B (NF-kB), transforming growth factor beta1 (TGF-β1) and AMP-activated protein kinase (AMPK). This review presents comprehensive information on Mangosteen's pharmacological and antitoxic aspects and its xanthones against various natural and chemical toxins. Because of the insufficient clinical study, we hope the current research can benefit from performing clinical and preclinical studies against different toxic agents.

Comparative study of multiple statistical damage mechanics models for rock behaviors under high temperature

Under the influence of external factors such as high temperature, chemical corrosion, and loading-unloading cycles, micro-cracks and pores may develop within rock structures. Continuum damage mechanics (CDM) characterizes these micro-defects arising within rocks, investigating the physico-mechanical behaviors of rocks in damaged states. This study, grounded in the theoretical framework of CDM and utilizing the Mohr-Coulomb failure criterion, integrates different probability distributions and damage coupling methods to develop three statistical damage models. The investigation primarily explores the effects of strength criteria, probability functions, and damage coupling mechanisms on the statistical damage models. In addressing the complex interplay between rock micro-defects and external stressors such as high temperatures and chemical corrosion, this study leverages the principles of CDM within the Mohr-Coulomb criterion framework. It advances the field by developing three innovative statistical damage models, enriched by diverse probability distributions and damage coupling methods. The research delineates the substantial impact of strength criteria, probability functions, and damage coupling mechanisms on the behavior of these models. Notably, it achieves a synthesis of theoretical constructs and experimental validation, demonstrating the models' capacity to reflect the nuanced behaviors of damaged rock accurately. Furthermore, this investigation contributes a methodological blueprint for statistical damage model construction, underscoring the critical selection of strength criteria and probability distributions. These efforts fortify the theoretical underpinnings necessary for the enhanced engineering application of statistics-driven damage analysis.

Recent Advances in Self-healing Polymer Materials: Routes and Strategies

: The most intriguing area of scientific study and engineering applications is to synthetize materials that can autonomously heal damage similar to biological tissues. Since the concept of self-healing materials was established, several variations of self-healing polymer materials have been developed based on distinct healing techniques. This review outlines the most recent breakthroughs in self-healing polymers and utilizes the opportunity to categories the descriptions of self-healing technologies in the literatures according to extrinsic and intrinsic routes (noncovalent bonds, physical interaction, and covalent bonds). The major focus is on the routes and strategies used to generate self-healing polymer materials. In addition, the strengths and weaknesses of polymer materials with different self-healing routes and strategies in terms of mechanical and self-healing properties were summarized. Furthermore, we also reviewed the progress of the development from a single self-healing mechanism to multiple self-healing mechanisms to achieve balanced comprehensive performance. Finally, the future trajectory of self-healing polymers is envisioned.

Postbiotic as Novel Alternative Agent or Adjuvant for the Common Antibiotic Utilized in the Food Industry.

Antibiotic resistance is a serious public health problem as it causes previously manageable diseases to become deadly infections that can cause serious disability or even death. Scientists are creating novel approaches and procedures that are essential for the treatment of infections and limiting the improper use of antibiotics in an effort to counter this rising risk. With a focus on the numerous postbiotic metabolites formed from the beneficial gut microorganisms, their potential antimicrobial actions, and recent associated advancements in the food and medical areas, this review presents an overview of the emerging ways to prevent antibiotic resistance. Presently, scientific literature confirms that plant-derived antimicrobials, RNA therapy, fecal microbiota transplantation, vaccines, nanoantibiotics, haemofiltration, predatory bacteria, immunotherapeutics, quorum-sensing inhibitors, phage therapies, and probiotics can be considered natural and efficient antibiotic alternative candidates. The investigations on appropriate probiotic strains have led to the characterization of specific metabolic byproducts of probiotics named postbiotics. Based on preclinical and clinical studies, postbiotics with their unique characteristics in terms of clinical (safe origin, without the potential spread of antibiotic resistance genes, unique and multiple antimicrobial action mechanisms), technological (stability and feasibility of largescale production), and economic (low production costs) aspects can be used as a novel alternative agent or adjuvant for the common antibiotics utilized in the production of animal-based foods. Postbiotic constituents may be a new approach for utilization in the pharmaceutical and food sectors for developing therapeutic treatments. Further metabolomics investigations are required to describe novel postbiotics and clinical trials are also required to define the sufficient dose and optimum administration frequency of postbiotics.

Effectiveness of Multiple Representation-Based Mechanics Lectures Using YouTube Video Content on Concept Mastery

Effectiveness of Multiple Representation-Based Mechanics Lectures Using YouTube Video Content on Concept Mastery

Progressive transfer learning for advancing machine learning-based reduced-order modeling

To maximize knowledge transfer and improve the data requirement for data-driven machine learning (ML) modeling, a progressive transfer learning for reduced-order modeling (p-ROM) framework is proposed. A key concept of p-ROM is to selectively transfer knowledge from previously trained ML models and effectively develop a new ML model(s) for unseen tasks by optimizing information gates in hidden layers. The p-ROM framework is designed to work with any type of data-driven ROMs. For demonstration purposes, we evaluate the p-ROM with specific Barlow Twins ROMs (p-BT-ROMs) to highlight how progress learning can apply to multiple topological and physical problems with an emphasis on a small training set regime. The proposed p-BT-ROM framework has been tested using multiple examples, including transport, flow, and solid mechanics, to illustrate the importance of progressive knowledge transfer and its impact on model accuracy with reduced training samples. In both similar and different topologies, p-BT-ROM achieves improved model accuracy with much less training data. For instance, p-BT-ROM with four-parent (i.e., pre-trained models) outperforms the no-parent counterpart trained on data nine times larger. The p-ROM framework is poised to significantly enhance the capabilities of ML-based ROM approaches for scientific and engineering applications by mitigating data scarcity through progressively transferring knowledge.

Antioxidants, Hormetic Nutrition, and Autism.

Autism spectrum disorder (ASD) includes a heterogeneous group of complex neurodevelopmental disorders characterized by atypical behaviors with two core pathological manifestations: deficits in social interaction/communication and repetitive behaviors, which are associated with disturbed redox homeostasis. Modulation of cellular resilience mechanisms induced by low levels of stressors represents a novel approach for the development of therapeutic strategies, and in this context, neuroprotective effects of a wide range of polyphenol compounds have been demonstrated in several in vitro and in vivo studies and thoroughly reviewed. Mushrooms have been used in traditional medicine for many years and have been associated with a long list of therapeutic properties, including antitumor, immunomodulatory, antioxidant, antiviral, antibacterial, and hepatoprotective effects. Our recent studies have strikingly indicated the presence of polyphenols in nutritional mushrooms and demonstrated their protective effects in different models of neurodegenerative disorders in humans and rats. Although their therapeutic effects are exerted through multiple mechanisms, increasing attention is focusing on their capacity to induce endogenous defense systems by modulating cellular signaling processes such as nuclear factor erythroid 2 related factor 2 (Nrf2) and nuclear factor-kappa B (NF-κB) pathways. Here we discuss the protective role of hormesis and its modulation by hormetic nutrients in ASD.

The execution of saccadic eye movements suppresses visual processing of both color and luminance in the early visual cortex of humans.

Our eyes execute rapid, directional movements known as saccades, occurring several times per second, to focus on objects of interest in our environment. During these movements, visual sensitivity is temporarily reduced. Despite numerous studies on this topic, the underlying mechanism remains elusive, including a lingering debate on whether saccadic suppression affects the parvocellular visual pathway. To address this issue, we conducted a study employing steady-state visual evoked potentials (SSVEPs) elicited by chromatic and luminance stimuli while observers performed saccadic eye movements. We also employed an innovative analysis pipeline to enhance the signal-to-noise ratio, yielding superior results compared to the previous method. Our findings revealed a clear suppression effect on SSVEP signals during saccades compared to fixation periods. Notably, this suppression effect was comparable for both chromatic and luminance stimuli. We went further to measure the suppression effect across various contrast levels, which enabled us to model SSVEP responses with contrast response functions. The results suggest that saccades primarily reduce response gain without significantly affecting contrast gain and that this reduction applies uniformly to both chromatic and luminance pathways. In summary, our study provides robust evidence that saccades similarly suppress visual processing in both the parvocellular and magnocellular pathways within the human early visual cortex, as indicated by SSVEP responses. The observation that saccadic eye movements impact response gain rather than contrast gain implies that they influence visual processing through a multiplicative mechanism.NEW & NOTEWORTHY The present study demonstrates that saccadic eye movements reduce the processing of both luminance and chromatic stimuli in the early visual cortex of humans. By modeling the contrast response function, the study further shows that saccades affect visual processing by reducing the response gain rather than altering the contrast gain, suggesting that a multiplicative mechanism of visual attenuation affects both parvocellular and magnocellular pathways.

Diabetic Neuropathy: An Overview of Molecular Pathways and Protective Mechanisms of Phytobioactives.

Diabetic neuropathy (DN) is a common and debilitating complication of diabetes mellitus that affects the peripheral nerves and causes pain, numbness, and impaired function. The pathogenesis of DN involves multiple molecular mechanisms, such as oxidative stress, inflammation, and pathways of advanced glycation end products, polyol, hexosamine, and protein kinase C. Phytochemicals are natural compounds derived from plants that have various biological activities and therapeutic potential. Flavonoids, terpenes, alkaloids, stilbenes, and tannins are some of the phytochemicals that have been identified as having protective potential for diabetic neuropathy. These compounds can modulate various cellular pathways involved in the development and progression of neuropathy, including reducing oxidative stress and inflammation and promoting nerve growth and repair. In this review, the current evidence on the effects of phytochemicals on DN by focusing on five major classes, flavonoids, terpenes, alkaloids, stilbenes, and tannins, are summarized. This compilation also discusses the possible molecular targets of numerous pathways of DN that these phytochemicals modulate. These phytochemicals may offer a promising alternative or complementary approach to conventional drugs for DN management by modulating multiple pathological pathways and restoring nerve function.

Learning-Based Platooning Control of Automated Vehicles via Multiple Description Encoding Mechanisms

Learning-Based Platooning Control of Automated Vehicles via Multiple Description Encoding Mechanisms

Multi-mechanism neural machine translation framework for automatic program repair

Automatic program repair (APR) is crucial to improve software quality. Recently, neural machine translation (NMT) based modeling for bug fixes has demonstrated great potential. However, these approaches still have two major challenges. One is that their search space is limited due to the out-of-vocabulary (OOV) problem. The other is that the NMT-based APR models tend to ignore past translation information, which often leads to over-translation and under-translation. To address the above challenges, we propose MNRepair, a new NMT-based APR approach that combines multiple mechanisms to fix bugs in source code. Specifically, we devise an encoder-decoder NMT framework with the attention mechanism. Our framework combines the copy mechanism to overcome the OOV problem that occurs with source code. To deal with the over-translation and under-translation, we utilize a coverage mechanism to record past translation information. MNRepair is able to capture a wide range of repair operators and fix 26 bugs in Defects4J. Our evaluation shows the effectiveness of multiple mechanisms in the repair process.

The Natural Product Oridonin as an Anticancer Agent: Current Achievements and Problems.

Oridonin, an active diterpenoid isolated from traditional Chinese herbal medicine, has received a rising attention for its remarkable roles in cancer therapy. In recent years, increasing evidences have revealed that oridonin inhibits the occurrence and development of tumor cells through multiple mechanisms, including induction of apoptosis and autophagy, cell cycle arrest, and inhibition of angiogenesis as well as migration and invasion. In addition, several molecular signal targets have been identified, including ROS, EGFR, NF-κB, PI3K/Akt, and MAPK. In this paper, we review considerable knowledge about the molecular mechanisms and signal targets of oridonin, which has been studied in recent years. It is expected that oridonin may be developed as a novel anti-tumor herbal medicine in human cancer treatment.

MoReVis: A Visual Summary for Spatiotemporal Moving Regions.

Spatial and temporal interactions are central and fundamental in many activities in our world. A common problem faced when visualizing this type of data is how to provide an overview that helps users navigate efficiently. Traditional approaches use coordinated views or 3D metaphors like the Space-time cube to tackle this problem. However, they suffer from overplotting and often lack spatial context, hindering data exploration. More recent techniques, such as MotionRugs, propose compact temporal summaries based on 1D projection. While powerful, these techniques do not support the situation for which the spatial extent of the objects and their intersections is relevant, such as the analysis of surveillance videos or tracking weather storms. In this article, we propose MoReVis, a visual overview of spatiotemporal data that considers the objects' spatial extent and strives to show spatial interactions among these objects by displaying spatial intersections. Like previous techniques, our method involves projecting the spatial coordinates to 1D to produce compact summaries. However, our solution's core consists of performing a layout optimization step that sets the size and positions of the visual marks on the summary to resemble the actual values on the original space. We also provide multiple interactive mechanisms to make interpreting the results more straightforward for the user. We perform an extensive experimental evaluation and usage scenarios. Moreover, we evaluated the usefulness of MoReVis in a study with 9 participants. The results point out the effectiveness and suitability of our method in representing different datasets compared to traditional techniques.