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Melamine-cored glucosides for membrane protein solubilization and stabilization: importance of water-mediated intermolecular hydrogen bonding in detergent performance
No full textA melamine-based glucoside, MG-C11, has the ability to form a dynamic hydrogen-bonding network between detergent molecules, responsible for the markedly enhanced efficacy for GPCR stabilization compared to LMNG and previously developed TTG-C11.</jats:p
Solution composition dependent Soret coefficient using commercial MicroScale Thermophoresis instrument
No full textThe developed method quantifies the thermophoretic migration of colloidal particles as the Soret coefficient, indicating a dependence on interfacial properties and ionic composition of the dispersing medium.</jats:p
Stress, coping, and quality of life in the United States during the COVID-19 pandemic
No full textWhile research has widely explored stress, coping, and quality of life (QOL) individually and the potential links between them, a critical dearth exists in the literature regarding these constructs in the context of the COVID-19 pandemic. Our study aims to identify the salient stressors experienced, describe the coping strategies used, and examine the relationships between stressors, coping, and QOL among individuals during the pandemic. Data are from a sample of 1,004 respondents who completed an online survey. Key measures included stressful life events (SLEs), coping strategies, and the physical and psychological health domains of QOL. Staged multivariate linear regression analyses examined the relationships between SLEs and the two QOL domains, controlling for sociodemographic and pre-existing health conditions and testing for the effects of coping strategies on these relationships. The most common SLEs experienced during the pandemic were a decrease in financial status, personal injury or illness, and change in living conditions. Problem-focused coping (β = 0.42, σ = 0.13, p < 0.001 for physical QOL; β = 0.57, σ = 0.12, p < 0.001 for psychological QOL) and emotion-focused coping (β = 0.86, σ = 0.13, p < 0.001 for psychological QOL) were significantly related to higher levels of QOL, whereas avoidant coping (β = –0.93, σ = 0.13, p < 0.001 for physical QOL; β = -1.33, σ = 0.12, p < 0.001 for psychological QOL) was associated with lower QOL. Avoidant coping partially mediated the relationships between experiencing SLEs and lower physical and psychological QOL. Our study informs clinical interventions to help individuals adopt healthy behaviors to effectively manage stressors, especially large-scale, stressful events like the pandemic. Our findings also call for public health and clinical interventions to address the long-term impacts of the most prevalent stressors experienced during the pandemic among vulnerable groups.</jats:p
Development and Application of a Comprehensive Measure of Access to Health Services to Examine COVID-19 Health Disparities
No full textResearch on access to health services during the COVID-19 pandemic is limited, and the conceptualization of access has not typically included access to community resources. We developed and tested an access-to-health-services measure and examined disparities in access among individuals in the U.S. during the pandemic. Data are from a U.S. sample of 1491 respondents who completed an online survey in August 2021. Linear regression models assessed the relationships between the access-to-health-services-measure components, including impact on access to medicine and medical equipment, impact on access to healthcare visits, and confidence in accessing community resources, and predictor variables, including sociodemographic- and health-related factors. Disparities in access to healthcare during the pandemic were associated with sociodemographic characteristics (i.e., race, gender, and age) and health-related characteristics (i.e., chronic illness, mental health condition, and disability). Factors such as race, gender, income, and age were associated with individuals’ degree of confidence in accessing community services. Our study presents a new access-to-health-services measure, sheds light on which populations may be most vulnerable to experiencing reduced access to health services, and informs the development of programmatic interventions to address the salient needs of these populations.</jats:p
Random field calibration with data on irregular grid for regional analyses: A case study on the bare carrying capacity of bats in Africa
No full textAbstractMany applications in science and engineering involve data defined at specific geospatial locations, which are often modeled as random fields. The modeling of a proper correlation function is essential for the probabilistic calibration of the random fields, but traditional methods were developed with the assumption to have observations with evenly spaced data. Available methods dealing with irregularly spaced data generally require either interpolation or computationally expensive solutions. Instead, we propose a simple approach based on least square regression to estimate the autocorrelation function. We first tested our methodology on an artificially produced dataset to assess the performance of our method. The accuracy of the method and its robustness to the level of noise in the data indicate that it is suitable for use in realistic problems. In addition, the methodology was used on a major application, the modeling of animal species connected with zoonotic diseases. Understanding the population dynamics of reservoirs of zoonotic diseases, such as bats, is a crucial first step to predict and prevent potential spillover of deadly viruses like Ebola. Due to the limited data on bats across Africa, their density and migrations can only be studied with probabilistic numerical models based on samples of the ecological bare carrying capacity (). For this purpose, the bare carrying capacity was modeled as a random field and its statistics calibrated with the available data. The bare carrying capacity of bats was found to be denser in central Africa. This is because climatic and environmental conditions are more suitable for the survival of bats. The proposed methodology for random field calibration was shown to be a promising approach, which can cope with large gaps in data and with complex applications involving large geographical areas and high resolution.</jats:p
A deep neural network approach for parameterized PDEs and Bayesian inverse problems
No full textAbstract
We consider the simulation of Bayesian statistical inverse problems governed by large-scale linear and nonlinear partial differential equations (PDEs). Markov chain Monte Carlo (MCMC) algorithms are standard techniques to solve such problems. However, MCMC techniques are computationally challenging as they require a prohibitive number of forward PDE solves. The goal of this paper is to introduce a fractional deep neural network (fDNN) based approach for the forward solves within an MCMC routine. Moreover, we discuss some approximation error estimates. We illustrate the efficiency of fDNN on inverse problems governed by nonlinear elliptic PDEs and the unsteady Navier–Stokes equations. In the former case, two examples are discussed, respectively depending on two and 100 parameters, with significant observed savings. The unsteady Navier–Stokes example illustrates that fDNN can outperform existing DNNs, doing a better job of capturing essential features such as vortex shedding.</jats:p
Integrating cognition in the laboratory with cognition in the real world: the time cognition takes, task fidelity, and finding tasks when they are mixed together
No full textIt is now possible for real-life activities, unfolding over their natural range of temporal and spatial scales, to become the primary targets of cognitive studies. Movement toward this type of research will require an integrated methodological approach currently uncommon in the field. When executed hand in hand with thorough and ecologically valid empirical description, properly developed laboratory tasks can serve as model systems to capture the essentials of a targeted real-life activity. When integrated together, data from these two kinds of studies can facilitate causal analysis and modeling of the mental and neural processes that govern that activity, enabling a fuller account than either method can provide on its own. The resulting account, situated in the activity’s natural environmental, social, and motivational context, can then enable effective and efficient development of interventions to support and improve the activity as it actually unfolds in real time. We believe that such an integrated multi-level research program should be common rather than rare and is necessary to achieve scientifically and societally important goals. The time is right to finally abandon the boundaries that separate the laboratory from the outside world.</jats:p
Controlling neocortical epileptic seizures using forced temporal spike-time stimulation: an in silico computational study
No full textEpileptic seizure is typically characterized by highly synchronized episodes of neural activity. Existing stimulation therapies focus purely on suppressing the pathologically synchronized neuronal firing patterns during the ictal (seizure) period. While these strategies are effective in suppressing seizures when they occur, they fail to prevent the re-emergence of seizures once the stimulation is turned off. Previously, we developed a novel neurostimulation motif, which we refer to as “Forced Temporal Spike-Time Stimulation” (FTSTS) that has shown remarkable promise in long-lasting desynchronization of excessively synchronized neuronal firing patterns by harnessing synaptic plasticity. In this paper, we build upon this prior work by optimizing the parameters of the FTSTS protocol in order to efficiently desynchronize the pathologically synchronous neuronal firing patterns that occur during epileptic seizures using a recently published computational model of neocortical-onset seizures. We show that the FTSTS protocol applied during the ictal period can modify the excitatory-to-inhibitory synaptic weight in order to effectively desynchronize the pathological neuronal firing patterns even after the ictal period. Our investigation opens the door to a possible new neurostimulation therapy for epilepsy.</jats:p
The Solar System Notification Alert Processing System (SNAPS): Design, Architecture, and First Data Release (SNAPShot1)
No full textAbstract
We present here the design, architecture, and first data release for the Solar System Notification Alert Processing System (SNAPS). SNAPS is a solar system broker that ingests alert data from all-sky surveys. At present, we ingest data from the Zwicky Transient Facility (ZTF) public survey, and we will ingest data from the forthcoming Legacy Survey of Space and Time (LSST) when it comes online. SNAPS is an official LSST downstream broker. In this paper we present the SNAPS design goals and requirements. We describe the details of our automatic pipeline processing in which the physical properties of asteroids are derived. We present SNAPShot1, our first data release, which contains 5,458,459 observations of 31,693 asteroids observed by ZTF from 2018 July to 2020 May. By comparing a number of derived properties for this ensemble to previously published results for overlapping objects we show that our automatic processing is highly reliable. We present a short list of science results, among many that will be enabled by our SNAPS catalog: (1) we demonstrate that there are no known asteroids with very short periods and high amplitudes, which clearly indicates that in general asteroids in the size range 0.3–20 km are strengthless; (2) we find no difference in the period distributions of Jupiter Trojan asteroids, implying that the L4 and L5 clouds have different shape distributions; and (3) we highlight several individual asteroids of interest. Finally, we describe future work for SNAPS and our ability to operate at LSST scale.</jats:p
Hierarchical Network-on-Chip Design for Interposer-Based Systems and DNN Accelerators
No full textNetwork-on-Chip (NoC) is a crucial chip multiprocessor component to communicate between many nodes. Continued increases toward multicore and manycore scalability have led to performance challenges of NoCs because of the increasing network diameter. Also, up to 30% of the chip\u27s overall power budget is contributed by NoCs in modern chips, and on-chip power consumption exceeds the total power budget by increasing cores in the general-purpose chip multiprocessors. The hierarchical design approach is a promising solution to offer straightforward paths to improve performance and minimize power consumption. Hierarchical on-chip interconnection design is suitable for large systems by providing routes with shorter hop counts in the network. The hierarchical design approaches generally require inter-chip communication; however, as the number of small chips increases, the chip-to-chip communication becomes a performance bottleneck. Therefore, the interconnection network should be carefully designed to provide the shortest paths for as many source-destination pairs and avoid network congestion and minimum area and power consumption overhead. Furthermore, many widespread applications like modern AI systems require a large amount of data to support the computation, creating considerable data movement for on-chip and off-chip communications. Therefore, general-purpose on-chip network designs could not be appropriate for providing power efficiency in large-scale AI systems. Application-specific on-chip networks are proposed to leverage in the embedded systems to address the mentioned challenges in the general purpose. Therefore, hierarchical interconnect approaches such as tile-based architectures are well studied and applied frequently in deep-learning accelerator designs. In this dissertation, three projects have been proposed. The first work proposes a new hierarchical topology design, ClusCross, to improve multicore interconnection networks on silicon interposer-based systems. The key idea is to treat each small chip as a cluster and use cross-cluster long links to increase bisection width and decrease average hop count without increasing the number of ports in the routers. The second work proposes a HW/SW co-design architecture to compute SpGEMM efficiently without requiring complex interconnection networks. A novel fast-packing algorithm, SorPack, is proposed to convert a sparse matrix into a dense matrix that increases PE utilization. Additionally, The HIRAC, a novel hierarchical accelerator, is proposed for executing Sparse GEMM and provides a scalable system that maximizes the parallelism of the PEs. The last chapter presents the heterogeneous design approach that can be used in applications requiring both sampled SpGEMM and Highly SpGEMMs and efficiently covering the higher sparsity ranges. The proposed heterogeneous design achieves 24% faster runtime estimation over HIRAC for a dynamic sparse attention matrix extracted from a state-of-the-art sparse attention model layer