Alternative Approach Research Articles

The dual-feature approach-avoidance task: validity, training efficacy, and the role of contingency awareness in changing food preference

ABSTRACT The approach-avoidance task (AAT) probes tendencies contributing to unwanted behaviours, like excessive snacking, by measuring RT differences between approach and avoidance responses to different stimuli. It retrains such tendencies using repeated avoidance of appetitive stimuli and approach of healthy alternatives. The most common paradigm, the irrelevant-feature AAT, conceals these stimulus-response contingencies by requiring approach or avoidance based on features irrelevant to the tendencies (e.g. frame colour). Unfortunately, it is an unreliable measure and not always successful as a training, likely because the stimuli can be ignored. In the novel dual-feature AAT, the combination of stimulus and irrelevant feature determines responses: participants approach foods and avoid objects surrounded by frame A, and vice versa given frame B. We trained 219 online participants to approach fruit and avoid chocolate using active (7:1 stimulus-to-frame contingency) and sham (1:1 stimulus-to-frame contingency) versions of these two trainings. Compared to sham, active irrelevant-feature training was associated with more selection and desire to eat fruit, and active dual-feature training increased approach bias for fruit. Participants’ perceived rate of approaching fruit versus chocolate correlated with many outcome measures, suggesting contingency awareness plays a major role in AAT training effectiveness, challenging implicit accounts. While the dual-feature paradigm shows potential, its high error rates, RTs, and difficulty mandate improvement.

A Systematic Review on the Potential Applications of Theranostic Nanoparticles in Diabetes and its Associated Complication Diabetic Neuropathy

Background: Diabetes neuropathy is a frequent ailment that has a substantial impact on patients by increasing the risk of falls and causing discomfort. The lower extremities are where diabetic neuropathy patients first feel pain. This discomfort could seem like a pinprick, an electric shock, or something else. Objective: Here, we give a comprehensive overview of this quickly developing theranostic appli-cation that includes all relevant imaging, diagnostic, therapeutic, and monitoring elements for the management of diabetes and diabetes neuropathy. Methods: The data for the current study was gathered by searching PubMed and Google Scholar. Several research and review publications from various publishers, including Springer Nature, Bentham Science, PLOS one, MDPI, and ACS Publishing Centre, were evaluated to compile the data. Result: Recent developments in theranostics have shown promise as alternate management ap-proaches for diabetes and ailments linked to diabetes. Numerous nanotechnology-built biosen-sors, including multiwalled carbon nanotubes, copper nanowires, zinc oxide tetrapods, and nano-particle-embedded contact lenses, offer benefits in monitoring diabetic neuropathy. Conclusion: The potency, usability, and dependability of insulin substitutes have been demon-strated by a variety of innovative methods for the management of diabetes, which includes nano-technology approaches using Gene-Based Nanoparticles (siRNA), Liposomes, Exo-somes/Extracellular Vesicles, Neuromodulation, and Inhalable Nanoparticles. Over the past few years, the development of various theranostic nanoparticles for Diabetic neuropathy has experi-enced an unprecedented expansion. Even though much work needs to be done to precisely evalu-ate the genuine benefits provided by these particles, such as issues with nanotoxicity, theranostic nanoparticles will have a significant impact on the field of nanomedicine.

Potential Neuroprotective Role of Neurotrophin in Traumatic Brain Injury.

Traumatic brain injury (TBI) is a major global health issue that affects millions of people every year. It is caused by any form of external force, resulting in temporary or permanent impairments in the brain. The pathophysiological process following TBI usually involves excitotoxicity, mitochondrial dysfunction, oxidative stress, inflammation, ischemia, and apoptotic cell death. It is challenging to find treatment for TBI due to its heterogeneous nature, and no therapeutic interventions have been approved thus far. Neurotrophins may represent an alternative approach for TBI treatment because they influence various functional activities in the brain. The present review highlights recent studies on neurotrophins shown to possess neuroprotective roles in TBI. Neurotrophins, specifically brain-derived neurotrophic factor (BDNF) and nerve growth factor (NGF) have demonstrated reduced neuronal death, alleviated neuroinflammatory responses and improved neurological functions following TBI via their immunomodulatory, anti-inflammatory and antioxidant properties. Further studies are required to ensure the efficacy and safety of neurotrophins to be used as TBI treatment in clinical settings.

An alternative approach for characterization of the linear viscoelastic behavior of asphalt binders

This study aimed to develop an alternative approach to characterise the linear viscoelastic behaviour of asphalt binders. A rheogram model was firstly proposed based on the Carreau–Yasuda model; its accuracy was substantiated by high R 2 values. The dynamic shear modulus master curve model was then obtained using the relationship between the magnitude of complex viscosity and dynamic shear modulus and the two definitions of the time-temperature shift factor. Afterwards, the phase angle master curve model was derived according to the approximate Kramers–Kronig relation. The proposed approach was demonstrated to be capable of accurately characterising the linear viscoelastic behaviour of selected asphalt binders. In addition, the predicted zero shear viscosity (ZSV) could be potentially employed to evaluate the asphalt binder rutting resistance. Compared with the widely-used Christensen-Anderson-Marasteanu (CAM) model, the developed master curves models were better at constructing the phase angle master curves of selected asphalt binders.

Recent advancements in novel formulations of anti-psoriatic agents for effective delivery: clinical importance and patent survey.

Objectives An autoimmune-mediated dermatological ailment featuring recurrent episodes is acknowledged as psoriasis. Around the world, 2-3% of people suffer from this autoimmune skin condition. The primary goal of the current review is to analyse and determine the effectiveness of conventional and emerging nano technological strategies to alleviate psoriasis and discuss future perspectives. Evidence acquisition A thorough search of numerous electronic databases, including Science Direct, Scopus, Google Scholar, Clinical Trials, Google Patents, Research Gate, and PubMed, yielded all the data used in this review paper about the management of psoriasis via various anti-psoriatic agent and nanotechnology approaches. Keywords such as topical, liposomes, niosomes, micro needles, clinical trials, patents, pathogenesis, biosimilars, cytokines, and other pertinent words were investigated. Results Nano technological approaches are gaining prominence since they enable targeted delivery, rapid onset of action with limited systemic exposure. Researchers have investigated innovative, alternative therapeutic approaches that are both secure and efficient for treating psoriatic conditions. Further, the potential role of numerous psoriatic conventional therapies has been explored. The patents granted or in process to address psoriasis via topical route have been well explored. Modern nanotechnology has made it possible for pharmaceuticals to be delivered with improved physical, chemical, pharmacokinetic, and pharmacodynamic qualities. Despite extensive research complete cure for psoriasis is hampered. Conclusion Relying on the extensive literature review, it can be inferred that nanoparticles based novel delivery strategies have the possibility of enhancing the pharmacological activity and eliminating or resolving problems associated with this ailment. The different drug delivery systems available for the treatment of psoriasis along with the clinical trials in different stages, patents in process and granted, the commercialized status of therapeutic molecules, and the future of research in this area have been thoroughly reviewed.

Synthesis and Characterization of Baicalein-loaded Aquasomes: An In vitro and In silico Perspective for Diabetes Mellitus.

Millions of individuals worldwide suffer from metabolic abnormalities induced by diabetes. Baicalein, a flavonoid, has shown several properties in various treatments with potential properties, including anti-inflammatory, antioxidant, and anti-diabetic properties. Practically, its application is hindered due to low solubility in aqueous media. Overcoming this challenge, aquasomes can offer an effective approach for delivering drugs and bioactive molecules to target various diseases. The study aimed to develop and evaluate baicalein-loaded aquasomes for improving solubility and comparing their antidiabetic properties to acarbose through in silico docking. Baicalein-loaded aquasomes were prepared through a three-step process: core preparation, lactose coating, and drug loading. The evaluation included assessing particle size, drug-excipient interactions, drug entrapment efficiency, loading capacity, in vitro drug release, and the kinetics of drug release. In silico docking and in vitro α-amylase inhibition activity was evaluated to assess the anti-diabetic potential of baicalein. The baicalein-loaded aquasomes were spherical with sizes ranging from 300-400 nm. FTIR analysis indicated no interaction between the components. The formulation exhibited drug entrapment efficiency of 94.04±0 4.01% and drug loading of 17.60 ± 01.03%. Drug release study showed sustained and complete (97.30 ± 02.06%) release, following first-order kinetics. Docking analysis revealed comparable binding affinity to acarbose, while the α-amylase inhibition assay showed greater inhibition potential of the aquasomes compared to the baicalein solution. Aquasomes offer an alternative approach to conventional delivery methods. The selfassembling characteristics of aquasomes greatly simplify their preparation process, adding to their appeal as a drug delivery system.

Heart Rate Variability Biofeedback as a Treatment for Military PTSD: A Meta-Analysis.

Emerging research has provided tentative support for the use of heart rate variability biofeedback (HRVB) as a treatment for several psychological disorders, with meta-analyses providing compelling evidence for HRVB as a promising treatment for anxiety, depression, and PTSD. Given the prevalence of PTSD in military veterans and the comparatively lower benefit and higher attrition rate of traditional psychological treatment for PTSD relative to civilian counterparts, it is important to examine complementary and alternative treatment approaches such as HRVB in this population. Although studies of HRVB for PTSD have been conducted with military veterans, they have involved relatively small sample sizes, limiting interpretation. To address this, the current article presents a comprehensive meta-analysis, consolidating existing literature to more accurately evaluate the efficacy of HRVB in reducing PTSD symptoms within military populations. This meta-analysis was conducted according to Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines, and our protocol was registered with PROSPERO to increase review transparency. A literature search of HRVB interventions was conducted using PubMed, PsycINFO, Military Database, PTSDPubs, and EBSCO's Psychological and Behavioral Sciences Collection. Five studies met eligibility criteria, providing a combined sample size of 95 military services members. For all studies, effect sizes were negative, indicating a reduction in PTSD symptoms. Effect sizes ranged from -1.614 to -0.414, resulting in an overall moderate to large mean effect for HRVB (Hedges's g = -0.557; 95% confidence interval = -0.818 to -0.296; P < .001). Additionally, cumulative attrition was 5.8%, significantly lower than commonly reported rates for evidence-based treatments (16%-36%). The present study is the first meta-analysis to examine HRVB as a treatment for military service members with PTSD. Results indicate that HRVB may be a viable treatment approach to reduce PTSD symptomatology. Low attrition rates, ease of accessibility, and favorable participant outlook serve as additional benefits for the use of HRVB.

Curbing Key Digestive Enzymes by Three Plant Extracts for Sustainable Management of Postprandial Hyperglycemia

Background: Diabetes mellitus is a chronic metabolic condition marked by persistently elevated blood sugar levels. Key digestive enzymes viz. α-amylase and α-glucosidase, hydrolyze consumed carbohydrates into glucose which raises the postprandial blood glucose level in a diabetic patient. So, the development of α-amylase and α-glucosidase inhibitors procured from medicinal plants to retard starch digestion is an alternative approach for controlling type 2 diabetes mellitus. Objective: The current study aimed to evaluate the inhibitory potentials of the key digestive enzymes viz. α-amylase and α-glucosidase by the extracts of three medicinal plants; red dragon fruit (Hylocereus polyrhizus) pulp and peel, bamboo (Bambusa vulgaris) shoot, turnip (Brassica rapa L.) shoot and leaf by performing α-amylase and α-glucosidase inhibition assays in vitro. Methods: Inhibition of α-amylase activity was conducted using 3,5-dinitrosalicylic acid method, and 4- Nitrophenyl-α-D-glucopyranoside was used as a substrate to perform α-glucosidase inhibition assay in vitro. Results: Among all the selected sample extracts, red dragon fruit pulp expressed the highest percentage of α-amylase inhibition (59.73 ± 4.33%) at the concentration of 1000 μg/mL which is comparable to standard antidiabetic drug Acarbose (70.59 ± 2.64%), whereas the lowest inhibition was observed in turnip shoot extract (42.48 ± 2.10%) at the same concentration. In terms of α-glucosidase inhibition activity, again, red dragon fruit pulp extract demonstrated the maximum inhibition rate (56.42 ± 2.38%) at 1000 μg/mL concentration. This is respectable in comparison to the reference Acarbose (66.45 ± 1.78%). In contrast, turnip shoot extracts displayed the lowest α-glucosidase inhibition activity (38.27 ± 2.21%) at the same concentration. Conclusion: The current study demonstrated that the red dragon fruit pulp extract possesses substantial antihyperglycemic activity (α-amylase inhibition: 59.73 ± 4.33%, α-glucosidase inhibition: 56.42 ± 2.38%) in vitro, which could be a putative nutraceutical to manage postprandial hyperglycemia.

Mechanism-guided strategies for combating antibiotic resistance.

Bacterial antibiotic resistance has been recognized as a global threat to public health. It challenges the antibiotics currently used in clinical practice and causes severe and often fatal infectious diseases. Fighting against antibiotic-resistant bacteria (ARB) is growing more urgent. While understanding the molecular mechanisms that underlie resistance is a prerequisite, several major mechanisms have been previously proposed including bacterial efflux systems, reduced cell membrane permeability, antibiotic inactivation by enzymes, target modification, and target protection. In this context, this review presents a panel of promising and potential strategies to combat antibiotic resistance/resistant bacteria. Different types of direct-acting and indirect resistance breakers, such as efflux pump inhibitors, antibiotic adjuvants, and oxidative treatments are discussed. In addition, the emerging multi-omics approaches for rapid resistance identification and promising alternatives to existing antibiotics are highlighted. Overall, this review suggests that continued effort and investment in research are required to develop new antibiotics and alternatives to existing antibiotics and translate them into environmental and clinical applications.

ATM-R: An Adaptive Tradeoff Model With Reference Points for Constrained Multiobjective Evolutionary Optimization.

The goal of constrained multiobjective evolutionary optimization is to obtain a set of well-converged and well-distributed feasible solutions. To achieve this goal, a delicate tradeoff must be struck among feasibility, diversity, and convergence. However, balancing these three elements simultaneously through a single tradeoff model is nontrivial, mainly because the significance of each element varies in different evolutionary phases. As an alternative approach, we adapt distinct tradeoff models in various phases and introduce a novel algorithm named adaptive tradeoff model with reference points (ATM-R). In the infeasible phase, ATM-R takes the tradeoff between diversity and feasibility into account, aiming to move the population toward feasible regions from diverse search directions. In the semi-feasible phase, ATM-R promotes the transition from "the tradeoff between feasibility and diversity" to "the tradeoff between diversity and convergence." This transition is instrumental in discovering an adequate number of feasible regions and accelerating the search for feasible Pareto optima in succession. In the feasible phase, ATM-R places an emphasis on balancing diversity and convergence to obtain a set of feasible solutions that are both well-converged and well-distributed. It is worth noting that the merits of reference points are leveraged in ATM-R to accomplish these tradeoff models. Also, in ATM-R, a multiphase mating selection strategy is developed to generate promising solutions beneficial to different evolutionary phases. Systemic experiments on a diverse set of benchmark test functions and real-world problems demonstrate that ATM-R is effective. When compared to eight state-of-the-art constrained multiobjective optimization evolutionary algorithms, ATM-R consistently demonstrates its competitive performance.

MPMNet: A Data-Driven MPM Framework for Dynamic Fluid-Solid Interaction.

High-accuracy, high-efficiency physics-based fluid-solid interaction is essential for reality modeling and computer animation in online games or real-time Virtual Reality (VR) systems. However, the large-scale simulation of incompressible fluid and its interaction with the surrounding solid environment is either time-consuming or suffering from the reduced time/space resolution due to the complicated iterative nature pertinent to numerical computations of involved Partial Differential Equations (PDEs). In recent years, we have witnessed significant growth in exploring a different, alternative data-driven approach to addressing some of the existing technical challenges in conventional model-centric graphics and animation methods. This article showcases some of our exploratory efforts in this direction. One technical concern of our research is to address the central key challenge of how to best construct the numerical solver effectively and how to best integrate spatiotemporal/dimensional neural networks with the available MPM's pressure solvers. In particular, we devise the MPMNet, a hybrid data-driven framework supporting the popular and powerful MPM, to combine the comprehensive properties of MPM in numerically handling physical behaviors ranging from fluid to deformable solids and the high efficiency of data-driven models. At the architectural level, our MPMNet comprises three primary components: A data processing module to describe the physical properties by way of the input fields; A deep neural network group to learn the spatiotemporal features; And an iterative refinement process to continue to reduce possible numerical errors. The goal of these special technical developments is to aim at involved numerical acceleration while preserving physical accuracy, realizing efficient and accurate fluid-solid interactions in a data-driven fashion. The extensive experimental results verify that our MPMNet can tremendously speed up the computation compared with the popular numerical methods as the complexity of interaction scenes increases while better retaining the numerical accuracy.

A Review on Probiotics and Their Role in the Management of Cancer

: Probiotics are advantageous bacteria that provide numerous health benefits to individuals when ingested and help to keep the gut microbiota balance. These beneficial bacteria can be found in food, supplements, and medicines. Foods that contain probiotics include yogurt, tempeh, and miso, while bacteria of lactic acid and bifido are the predominant microbial strains commonly employed in various applications. Due to their potential health benefits and minimal side effects, probiotics are widely accepted globally. Maintaining a balanced gut microbiome is crucial for overall health, and any disturbance in the gut's microbiota can trigger inflammatory immune responses and initiate various diseases such as cancer. Cancer is a grave and frequently fatal illness that impacts a significant number of individuals. It arises due to uncontrolled cell growth and is usually diagnosed at an advanced stage. The disease originates from a single cell and can potentially metastasize, spreading through the body. As of now, cancer has no known cure, and the safety and efficacy of conventional chemotherapies and synthetic drugs employed in its treatment are subject to scrutiny. These treatments can negatively impact a patient's well-being, affect drug resistance, and are often too expensive for many patients to afford. Therefore, researchers are exploring more effective and safe ways to manage cancer. Certain probiotic strains have the potential to be beneficial in preventing cancer or as a complement to traditional chemotherapy by altering gut microbiota and improving the reactions of the body’s defense system. Hence, the review examines the likelihood of probiotics as simply an alternative approach to preventing and treating cancer.

The time for ambitious action is now: Science-based recommendations for plastic chemicals to inform an effective global plastic treaty

The ubiquitous and global ecological footprint arising from the rapidly increasing rates of plastic production, use, and release into the environment is an important modern environmental issue. Of increasing concern are the risks associated with at least 16,000 chemicals present in plastics, some of which are known to be toxic, and which may leach out both during use and once exposed to environmental conditions, leading to environmental and human exposure. In response, the United Nations member states agreed to establish an international legally binding instrument on plastic pollution, the global plastics treaty. The resolution acknowledges that the treaty should prevent plastic pollution and its related impacts, that effective prevention requires consideration of the transboundary nature of plastic production, use and pollution, and that the full life cycle of plastics must be addressed. As a group of scientific experts and members of the Scientists' Coalition for an Effective Plastics Treaty, we concur that there are six essential “pillars” necessary to truly reduce plastic pollution and allow for chemical detoxification across the full life cycle of plastics. These include a plastic chemical reduction and simplification, safe and sustainable design of plastic chemicals, incentives for change, holistic approaches for alternatives, just transition and equitable interventions, and centering human rights. There is a critical need for scientifically informed and globally harmonized information, transparency, and traceability criteria to protect the environment and public health. The right to a clean, healthy, and sustainable environment must be upheld, and thus it is crucial that scientists, industry, and policy makers work in concert to create a future free from hazardous plastic contamination.

Evaluating and classifying market alternatives using the CAPMA technique to assess potential export volume: an application for Turkey’s fig exports

PurposeAppropriate target market selection and classification are complex, time-consuming, and strategically important management decisions. The current study proposes and implements a new systematic approach that evaluates and ranks alternative markets’ potential to increase the export volume of Turkey’s fig exports.Design/methodology/approachThe classification approach of potential market alternatives (CAPMA) method, which was developed to evaluate, compare, and classify alternative markets according to their potential, was used in this study. CAPMA, an objective decision-making technique, is based on calculating the classification coefficient and classifying the alternatives into four dimensions, taking this coefficient into consideration. In the current study, 25 alternative markets were analysed on the basis of four evaluation criteria and classified into four dimensions according to their potential to increase export volume.FindingsAccording to the application results, alternative markets with the greatest potential to increase export volume were identified as the United States of America (USA), Netherlands, China, and Spain. The USA has the greatest potential for expansion of export volume. By implementing correct and effective marketing strategies, fig export volume has the potential to reach very high levels.Originality/valueThe study’s results on alternative markets for fig exports were categorized, countries with greater potential to increase export volume were identified, and findings that could contribute to the development of the sector were obtained. The approach proposed in the current research can be used to determine the target market of many other products, especially agricultural products, whose production is concentrated within a specific geography.

Preservice Physical Education Teachers’ Perceptions of Health-Related Fitness Self-Testing in a Teaching Methods Course

Purpose: This study explored how preservice physical education teachers (PPETs) perceive health-related fitness testing (HRFT) administered in a self-testing format with the aim to enhance their HRFT learning and better prepare them for future HRFT implementation. Methods: The study utilized a constructive phenomenological research design. A total of 11 PPETs participated in the study. Data were collected through a focus group interview, individual interviews, observation and field notes, and written assignments. Two researchers analyzed the data in NVivo 12. Results: Three findings were concluded from the data. First, the administration of self-testing engaged PPETs in active learning of HRFT. Second, PPETs reported positive attitudes toward self-testing when their needs for competence, autonomy, and relatedness were satisfied. Lastly, PPETs identified challenges and proposed suggestions for completing and administrating HRFT. Conclusions: Self-testing may be used as an alternative approach to preparing PPETs for HRFT. More research is needed to examine the effectiveness and reliability of self-testing in educational settings.

Deep-Learning-Based Simulation and Inversion of Transient Electromagnetic Sounding Signals in Permafrost Monitoring Problem

Abstract —This article presents a novel approach to addressing the challenges in permafrost monitoring through the integration of deep-learning techniques with conventional electromagnetic sounding methods. Our methodology comprises a forward finite element method (FEM) solver, augmented with the Sumudu transform, and an artificial neural network (ANN) solver trained on FEM-generated data. Remarkably, the ANN solver demonstrates similar accuracy to the FEM solver but operates at a superior speed that is nearly 10,000 times faster. Furthermore, we introduce an inverse problem solution drawing on the PARS algorithm. In addition, we present an ANN-based inverse solver, where the input and output roles are inverted. The ANN inverse solver is trained on the same data, thereby offering an alternative approach to solving the inverse problem. In a computational experiment, we compare the numerical inversion results using the PARS algorithm with those obtained from the ANN forward solver, ANN inversion, and a linear combination of these solutions. This comprehensive analysis sheds light on the effectiveness of our deep-learning-based approach in permafrost monitoring, providing insights for future applications in geophysics and environmental science.

Developmental exposure to the Fox River PCB mixture modulates behavior in juvenile mice

Developmental exposures to PCBs are implicated in the etiology of neurodevelopmental disorders (NDDs). This observation is concerning given the continued presence of PCBs in the human environment and the increasing incidence of NDDs. Previous studies reported that developmental exposure to legacy commercial PCB mixtures (Aroclors) or single PCB congeners found in Aroclors caused NDD-relevant behavioral phenotypes in animal models. However, the PCB congener profile in contemporary human samples is dissimilar to that of the legacy Aroclors, raising the question of whether human-relevant PCB mixtures similarly interfere with normal brain development. To address this question, we assessed the developmental neurotoxicity of the Fox River Mixture (FRM), which was designed to mimic the congener profile identified in fish from the PCB-contaminated Fox River that constitute a primary protein source in the diet of surrounding communities. Adult female C57BL/6 J mouse dams (8–10 weeks old) were exposed to vehicle (peanut oil) or FRM at 0.1, 1.0, or 6.0 mg/kg/d in their diet throughout gestation and lactation, and neurodevelopmental outcomes were assessed in their pups. Ultrasonic vocalizations (USVs) and measures of general development were quantified at postnatal day (P) 7, while performance in the spontaneous alternation task and the 3-chambered social approach/social novelty task was assessed on P35. Triiodothyronine (T3) and thyroxine (T4) were quantified in serum collected from the dams when pups were weaned and from pups on P28 and P35. Developmental exposure to FRM did not alter pup weight or body temperature on P7, but USVs were significantly decreased in litters exposed to FRM at 0.1 or 6.0 mg/kg/d in the maternal diet. FRM also impaired male and female pups’ performance in the social novelty task. Compared to sex-matched vehicles, significantly decreased social novelty was observed in male and female pups in the 0.1 and 6.0 mg/kg/d dose groups. FRM did not alter performance in the spontaneous alternation or social approach tasks. FRM increased serum T3 levels but decreased serum T4 levels in P28 male pups in the 1.0 and 6.0 mg/kg/d dose groups. In P35 female pups and dams, serum T3 levels decreased in the 6.0 mg/kg/d dose group while T4 levels were not altered. Collectively, these findings suggest that FRM interferes with the development of social communication and social novelty, but not memory, supporting the hypothesis that contemporary PCB exposures pose a risk to the developing brain. FRM had sex, age, and dose-dependent effects on serum thyroid hormone levels that overlapped but did not perfectly align with the FRM effects on behavioral outcomes. These observations suggest that changes in thyroid hormone levels are not likely the major factor underlying the behavioral deficits observed in FRM-exposed animals.

SARI: Shared Autonomy across Repeated Interaction

Assistive robot arms try to help their users perform everyday tasks. One way robots can provide this assistance is shared autonomy . Within shared autonomy, both the human and robot maintain control over the robot’s motion: as the robot becomes confident it understands what the human wants, it intervenes to automate the task. But how does the robot know these tasks in the first place? State-of-the-art approaches to shared autonomy often rely on prior knowledge. For instance, the robot may need to know the human’s potential goals beforehand. During long-term interaction these methods will inevitably break down—sooner or later the human will attempt to perform a task that the robot does not expect. Accordingly, in this article we formulate an alternate approach to shared autonomy that learns assistance from scratch. Our insight is that operators repeat important tasks on a daily basis (e.g., opening the fridge, making coffee). Instead of relying on prior knowledge, we therefore take advantage of these repeated interactions to learn assistive policies. We introduce SARI, an algorithm that recognizes the human’s task, replicates similar demonstrations, and returns control when unsure. We then combine learning with control to demonstrate that the error of our approach is uniformly ultimately bounded. We perform simulations to support this error bound, compare our approach to imitation learning baselines, and explore its capacity to assist for an increasing number of tasks. Finally, we conduct three user studies with industry-standard methods and shared autonomy baselines, including a pilot test with a disabled user. Our results indicate that learning shared autonomy across repeated interactions matches existing approaches for known tasks and outperforms baselines on new tasks. See videos of our user studies here: https://youtu.be/3vE4omSvLvc .

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.

Signal Propagation: The Framework for Learning and Inference in a Forward Pass.

We propose a new learning framework, signal propagation (sigprop), for propagating a learning signal and updating neural network parameters via a forward pass, as an alternative to backpropagation (BP). In sigprop, there is only the forward path for inference and learning. So, there are no structural or computational constraints necessary for learning to take place, beyond the inference model itself, such as feedback connectivity, weight transport, or a backward pass, which exist under BP-based approaches. That is, sigprop enables global supervised learning with only a forward path. This is ideal for parallel training of layers or modules. In biology, this explains how neurons without feedback connections can still receive a global learning signal. In hardware, this provides an approach for global supervised learning without backward connectivity. Sigprop by construction has compatibility with models of learning in the brain and in hardware than BP, including alternative approaches relaxing learning constraints. We also demonstrate that sigprop is more efficient in time and memory than they are. To further explain the behavior of sigprop, we provide evidence that sigprop provides useful learning signals in context to BP. To further support relevance to biological and hardware learning, we use sigprop to train continuous time neural networks with the Hebbian updates and train spiking neural networks (SNNs) with only the voltage or with biologically and hardware-compatible surrogate functions.