Spatial transcriptomics at the brain-electrode interface in rat motor cortex and the relationship to recording quality.

Study of the foreign body reaction to implanted electrodes in the brain is an important area of research for the future development of neuroprostheses and experimental electrophysiology. After electrode implantation in the brain, microglial activation, reactive astrogliosis, and neuronal cell death create an environment immediately surrounding the electrode that is significantly altered from its homeostatic state.Objective.To uncover physiological changes potentially affecting device function and longevity, spatial transcriptomics (ST) was implemented to identify changes in gene expression driven by electrode implantation and compare this differential gene expression to traditional metrics of glial reactivity, neuronal loss, and electrophysiological recording quality.Approach.For these experiments, rats were chronically implanted with functional Michigan-style microelectrode arrays, from which electrophysiological recordings (multi-unit activity, local field potential) were taken over a six-week time course. Brain tissue cryosections surrounding each electrode were then mounted for ST processing. The tissue was immunolabeled for neurons and astrocytes, which provided both a spatial reference for ST and a quantitative measure of glial fibrillary acidic protein and neuronal nuclei immunolabeling surrounding each implant.Main results. Results from rat motor cortex within 300µm of the implanted electrodes at 24 h, 1 week, and 6 weeks post-implantation showed up to 553 significantly differentially expressed (DE) genes between implanted and non-implanted tissue sections. Regression on the significant DE genes identified the 6-7 genes that had the strongest relationship to histological and electrophysiological metrics, revealing potential candidate biomarkers of recording quality and the tissue response to implanted electrodes.Significance. Our analysis has shed new light onto the potential mechanisms involved in the tissue response to implanted electrodes while generating hypotheses regarding potential biomarkers related to recorded signal quality. A new approach has been developed to understand the tissue response to electrodes implanted in the brain using genes identified through transcriptomics, and to screen those results for potential relationships with functional outcomes.

Structural, Functional, and Genetic Changes Surrounding Electrodes Implanted in the Brain.

Implantable neurotechnology enables monitoring and stimulating of the brain signals responsible for performing cognitive, motor, and sensory tasks. Electrode arrays implanted in the brain are increasingly used in the clinic to treat a variety of sources of neurological diseases and injuries. However, the implantation of a foreign body typically initiates a tissue response characterized by physical disruption of vasculature and the neuropil as well as the initiation of inflammation and the induction of reactive glial states. Likewise, electrical stimulation can induce damage to the surrounding tissue depending on the intensity and waveform parameters of the applied stimulus. These phenomena, in turn, are likely influenced by the surface chemistry and characteristics of the materials employed, but further information is needed to effectively link the biological responses observed to specific aspects of device design. In order to inform improved design of implantable neurotechnology, we are investigating the basic science principles governing device-tissue integration. We are employing multiple techniques to characterize the structural, functional, and genetic changes that occur in the cells surrounding implanted electrodes. First, we have developed a new "device-in-slice" technique to capture chronically implanted electrodes within thick slices of live rat brain tissue for interrogation with single-cell electrophysiology and two-photon imaging techniques. Our data revealed several new observations of tissue remodeling surrounding devices: (a) there was significant disruption of dendritic arbors in neurons near implants, where losses were driven asymmetrically on the implant-facing side. (b) There was a significant loss of dendritic spine densities in neurons near implants, with a shift toward more immature (nonfunctional) morphologies. (c) There was a reduction in excitatory neurotransmission surrounding implants, as evidenced by a reduction in the frequency of excitatory postsynaptic currents (EPSCs). Lastly, (d) there were changes in the electrophysiological underpinnings of neuronal spiking regularity. In parallel, we initiated new studies to explore changes in gene expression surrounding devices through spatial transcriptomics, which we applied to both recording and stimulating arrays. We found that (a) device implantation is associated with the induction of hundreds of genes associated with neuroinflammation, glial reactivity, oligodendrocyte function, and cellular metabolism and (b) electrical stimulation induces gene expression associated with damage or plasticity in a manner dependent upon the intensity of the applied stimulus. We are currently developing computational analysis tools to distill biomarkers of device-tissue interactions from large transcriptomics data sets. These results improve the current understanding of the biological response to electrodes implanted in the brain while producing new biomarkers for benchmarking the effects of novel electrode designs on responses. As the next generation of neurotechnology is developed, it will be increasingly important to understand the influence of novel materials, surface chemistries, and implant architectures on device performance as well as the relationship with the induction of specific cellular signaling pathways.

Role of Flow Inertia in Aggregate Restructuring and Breakage at Finite Reynolds Numbers.

Forces acting on aggregates depend on their properties, such as size and structure. Breakage rate, stable size, and structure of fractal aggregates in multiphase flows are strongly related to the imposed hydrodynamic forces. While these forces are prevalently viscous for finite Reynolds number conditions, flow inertia cannot be ignored, thereby requiring one to fully resolve the Navier-Stokes equations. To highlight the effect of flow inertia on aggregate evolution, numerical investigation of aggregate evolution in simple shear flow at the finite Reynolds number is conducted. The evolution of aggregates exposed to shear flow is tracked over time. Particle coupling with the flow is resolved with an immersed boundary method, and flow dynamics are solved using a lattice Boltzmann method. Particle dynamics are tracked by a discrete element method, accounting for interactions between primary particles composing the aggregates. Over the range of aggregate-scale Reynolds numbers tested, the breakage rate appears to be governed by the combined effect of momentum diffusion and the ratio of particle interaction forces to the hydrodynamic forces. For higher shear stresses, even when no stable size exists, breakage is not instantaneous because of momentum diffusion kinetics. Simulations with particle interaction forces scaled with the viscous drag, to isolate the effect of finite Reynolds hydrodynamics on aggregate evolution, show that flow inertia at such moderate aggregate Reynolds numbers has no impact on the morphology of nonbreaking aggregates but significantly favors breakage probability. This is a first-of-its-kind study that establishes the role of flow inertia in aggregate evolution. The findings present a novel perspective into breakage kinetics for systems in low but finite Reynolds number conditions.

Gene Expression Changes in Cultured Reactive Rat Astrocyte Models and Comparison to Device-Associated Effects in the Brain.

Implanted microelectrode arrays hold immense therapeutic potential for many neurodegenerative diseases. However, a foreign body response limits long-term device performance. Recent literature supports the role of astrocytes in the response to damage to the central nervous system (CNS) and suggests that reactive astrocytes exist on a spectrum of phenotypes, from beneficial to neurotoxic. The goal of our study was to gain insight into the subtypes of reactive astrocytes responding to electrodes implanted in the brain. In this study, we tested the transcriptomic profile of two reactive astrocyte culture models (cytokine cocktail or lipopolysaccharide, LPS) utilizing RNA sequencing, which we then compared to differential gene expression surrounding devices inserted into rat motor cortex via spatial transcriptomics. We interpreted changes in the genetic expression of the culture models to that of 24 hour, 1 week and 6 week rat tissue samples at multiple distances radiating from the injury site. We found overlapping expression of up to ∼250 genes between in vitro models and in vivo effects, depending on duration of implantation. Cytokine-induced cells shared more genes in common with chronically implanted tissue (≥1 week) in comparison to LPS-exposed cells. We revealed localized expression of a subset of these intersecting genes (e.g., Serping1, Chi3l1, and Cyp7b1) in regions of device-encapsulating, glial fibrillary acidic protein (GFAP)-expressing astrocytes identified with immunohistochemistry. We applied a factorization approach to assess the strength of the relationship between reactivity markers and the spatial distribution of GFAP-expressing astrocytes in vivo . We also provide lists of hundreds of differentially expressed genes between reactive culture models and untreated controls, and we observed 311 shared genes between the cytokine induced model and the LPS-reaction induced control model. Our results show that comparisons of reactive astrocyte culture models with spatial transcriptomics data can reveal new biomarkers of the foreign body response to implantable neurotechnology. These comparisons also provide a strategy to assess the development of in vitro models of the tissue response to implanted electrodes.

Numerical investigation of the respective roles of cohesive and hydrodynamic forces in aggregate restructuring under shear flow.

HYPOTHESIS: Aggregate structure is conditioned by a balance of cohesive forces between primary particles and hydrodynamic forces induced by the surrounding flow. Numerical simulations for different ratios between radial and tangential components of cohesive forces to hydrodynamic forces should highlight the role of the each force in aggregate restructuring under shear flow. EXPERIMENTS: Aggregates sharing similar morphological characteristics were algorithmically created. The forces between primary particles were accounted for using models taken from the literature. Aggregates with different cohesive forces were then submitted to shear by imposing a shear stress in the liquid phase. Hydrodynamic forces were calculated following two approaches: first, with a free draining approximation to extract general trends, then with immersed boundaries in a lattice Boltzmann flow solver to fully resolve the flow and particle dynamics. FINDINGS: Aggregate structural changes were tracked over time and their stable final size, or eventual breakage, was recorded. Their final structure was found to depend little on normal cohesive forces but is strongly impacted by tangential forces. Normal forces, however, strongly affect breakage probability. Furthermore, resistance to deformation at the aggregate scale induces a flow disturbance that reduces drag forces compared to the free-draining approximation, significantly impacting aggregate restructuring.

Grey forecasting models based on internal optimization for Novel Corona virus (COVID-19).

Pandemic forecasting has become an uphill task for the researchers on account of the paucity of sufficient data in the present times. The world is fighting with the Novel Coronavirus to save human life. In a bid to extend help to the concerned authorities, forecasting engines are invaluable assets. Considering this fact, the presented work is a proposal of two Internally Optimized Grey Prediction Models (IOGMs). These models are based on the modification of the conventional Grey Forecasting model (GM(1,1)). The IOGMs are formed by stacking infected case data with diverse overlap periods for forecasting pandemic spread at different locations in India. First, IOGM is tested using time series data. Its two models are then employed for forecasting the pandemic spread in three large Indian states namely, Rajasthan, Gujarat, Maharashtra and union territory Delhi. Several test runs are carried out to evaluate the performance of proposed grey models and conventional grey models GM(1,1) and NGM(1,1,k). It is observed that the prediction accuracies of the proposed models are satisfactory and the forecasted results align with the mean infected cases. Investigations based on the evaluation of error indices indicate that the model with a higher overlap period provides better results.

Spatiotemporal patterns of gene expression around implanted silicon electrode arrays.

Objective.Intracortical brain interfaces are an ever evolving technology with growing potential for clinical and research applications. The chronic tissue response to these devices traditionally has been characterized by glial scarring, inflammation, oxidative stress, neuronal loss, and blood-brain barrier disruptions. The full complexity of the tissue response to implanted devices is still under investigation.Approach.In this study, we have utilized RNA-sequencing to identify the spatiotemporal gene expression patterns in interfacial (within 100µm) and distal (500µm from implant) brain tissue around implanted silicon microelectrode arrays. Naïve, unimplanted tissue served as a control.Main results.The data revealed significant overall differential expression (DE) in contrasts comparing interfacial tissue vs naïve (157 DE genes), interfacial vs distal (94 DE genes), and distal vs naïve tissues (21 DE genes). Our results captured previously characterized mechanisms of the foreign body response, such as astroglial encapsulation, as well as novel mechanisms which have not yet been characterized in the context of indwelling neurotechnologies. In particular, we have observed perturbations in multiple neuron-associated genes which potentially impact the intrinsic function and structure of neurons at the device interface. In addition to neuron-associated genes, the results presented in this study identified significant DE in genes which are associated with oligodendrocyte, microglia, and astrocyte involvement in the chronic tissue response.Significance. The results of this study increase the fundamental understanding of the complexity of tissue response in the brain and provide an expanded toolkit for future investigation into the bio-integration of implanted electronics with tissues in the central nervous system.

Next-Generation Diamond Electrodes for Neurochemical Sensing: Challenges and Opportunities.

Carbon-based electrodes combined with fast-scan cyclic voltammetry (FSCV) enable neurochemical sensing with high spatiotemporal resolution and sensitivity. While their attractive electrochemical and conductive properties have established a long history of use in the detection of neurotransmitters both in vitro and in vivo, carbon fiber microelectrodes (CFMEs) also have limitations in their fabrication, flexibility, and chronic stability. Diamond is a form of carbon with a more rigid bonding structure (sp3-hybridized) which can become conductive when boron-doped. Boron-doped diamond (BDD) is characterized by an extremely wide potential window, low background current, and good biocompatibility. Additionally, methods for processing and patterning diamond allow for high-throughput batch fabrication and customization of electrode arrays with unique architectures. While tradeoffs in sensitivity can undermine the advantages of BDD as a neurochemical sensor, there are numerous untapped opportunities to further improve performance, including anodic pretreatment, or optimization of the FSCV waveform, instrumentation, sp2/sp3 character, doping, surface characteristics, and signal processing. Here, we review the state-of-the-art in diamond electrodes for neurochemical sensing and discuss potential opportunities for future advancements of the technology. We highlight our team's progress with the development of an all-diamond fiber ultramicroelectrode as a novel approach to advance the performance and applications of diamond-based neurochemical sensors.

Development and applications of an intelligent crow search algorithm based on opposition based learning.

Metaheuristics are proven beneficial tools for solving complex, hard optimization problems. Recently, a plethora of work has been reported on bio inspired optimization algorithms. These algorithms are mimicry of behavior of animals, plants and processes into mathematical paradigms. With these developments, a new entrant in this group is Crow Search Algorithm (CSA). CSA is based on the strategic behavior of crows while searching food, thievery and chasing behavior. This algorithm sometimes suffers with local minima stagnation and unbalance exploration and exploitation phases. To overcome this problem, a cosine function is proposed first, to accelerate the exploration and retard the exploitation process with due course of the iterative process. Secondly the opposition based learning concept is incorporated for enhancing the exploration virtue of CSA. The evolved variant with the inculcation of these two concepts is named as Intelligent Crow Search Algorithm (ICSA). The algorithm is benchmarked on two benchmark function sets, one is the set of 23 standard test functions and another is set of latest benchmark function CEC-2017. Further, the applicability of this variant is tested over structural design problem, frequency wave synthesis problem and Model Order Reduction (MOR). Results reveal that ICSA exhibits competitive performance on benchmarks and real applications when compared with some contemporary optimizers.

Identification of generator criticality and transient instability by supervising real-time rotor angle trajectories employing RBFNN.

Identification of transient stability state in real-time and maintaining stability through preventive control technology are challenging tasks for a large power system while integrating deregulation constraints. Widely employment of the phasor measurement units (PMUs) in a power system and development of wide area management systems (WAMS) give relaxation to monitoring, measurement and control hurdles. This paper focuses on two research objectives; the first is transient stability assessment (TSA) and second is selection of the appropriate member for the control operation in unstable operating scenario. A model based on the artificial machine learning and PMU data is constructed for achieving both the objectives. This model works through prompt TSA status with radial basis function neural network (RBFNN) and validates it with PMU data to determine the criticality level of the generators. To reduce the complexity of the model a transient stability index (TSI) is proposed in this paper. A RBFNN is used to determine the transient stability aspects like stability status of system, coherent group and criticality rank of generator and preventive control action, following a large perturbation. PMUs measure post-fault rotor angle values and these are used as input for training RBFNN. The proposed approach is demonstrated on the IEEE 10-generator 39-bus, 16-generator 68-bus and 50-generator 145-bus test power systems successfully and the effectiveness of the approaches is discussed.

Toward Better Control of Inclusion Cleanliness in a Gas Stirred Ladle Using Multiscale Numerical Modeling.

The industrial objective of lowering the mass of mechanical structures requires continuous improvement in controlling the mechanical properties of metallic materials. Steel cleanliness and especially control of inclusion size distribution have, therefore, become major challenges. Inclusions have a detrimental effect on fatigue that strongly depends both on inclusion content and on the size of the largest inclusions. Ladle treatment of liquid steel has long been recognized as the processing stage responsible for the inclusion of cleanliness. A multiscale modeling has been proposed to investigate the inclusion behavior. The evolution of the inclusion size distribution is simulated at the process scale due to coupling a computational fluid dynamics calculation with a population balance method integrating all mechanisms, i.e., flotation, aggregation, settling, and capture at the top layer. Particular attention has been paid to the aggregation mechanism and the simulations at an inclusion scale with fully resolved inclusions that represent hydrodynamic conditions of the ladle, which have been specifically developed. Simulations of an industrial-type ladle highlight that inclusion cleanliness is mainly ruled by aggregation. Quantitative knowledge of aggregation kinetics has been extracted and captured from mesoscale simulations. Aggregation efficiency has been observed to drop drastically when increasing the particle size ratio.

Ambient Air Quality Classification by Grey Wolf Optimizer Based Support Vector Machine.

With the development of society along with an escalating population, the concerns regarding public health have cropped up. The quality of air becomes primary concern regarding constant increase in the number of vehicles and industrial development. With this concern, several indices have been proposed to indicate the pollutant concentrations. In this paper, we present a mathematical framework to formulate a Cumulative Index (CI) on the basis of an individual concentration of four major pollutants (SO2, NO2, PM2.5, and PM10). Further, a supervised learning algorithm based classifier is proposed. This classifier employs support vector machine (SVM) to classify air quality into two types, that is, good or harmful. The potential inputs for this classifier are the calculated values of CIs. The efficacy of the classifier is tested on the real data of three locations: Kolkata, Delhi, and Bhopal. It is observed that the classifier performs well to classify the quality of air.

Sleep and motor performance in on-call internal medicine residents.

STUDY OBJECTIVES: To compare vigilance and performance among internal medicine residents doing in-house call versus residents not doing in-house call. DESIGN: Prospective study of resident cohorts with repeated testing. SETTING: University Teaching Hospital. PARTICIPANTS: Internal medicine residents doing in-house call and residents not doing in-house call (pathology, endocrinology) (controls). MEASUREMENTS AND RESULTS: Subjective sleepiness scores (daily Stanford Sleepiness Scale and Epworth Sleepiness Scale at start and end of the test period), actigraphy, and daily sleep logs as well as regular psychomotor vigilance testing using a Palm version (Walter Reed Army Institute of Research) of the Psychomotor Vigilance Test (PVT). Subjects were enrolled for a period of 28 to 32 days, which included 4 to 6 on-call nights for the internal medicine residents. Controls took call from home. Participants were compensated for their time. RESULTS: Twenty residents were evaluated, 13 internal medicine and 7 controls. Overall median reaction time was slower in the internal medicine residents (264.7 +/- 102.9 vs 239.2 +/- 26.1 milliseconds; P < .001). Internal medicine residents showed no difference in reaction time postcall versus other periods (269.9 +/- 131.2 vs 263.6 +/- 95.6; P = .65). Actigraphic sleep time was shorter during on-call than noncall nights and in internal medicine residents as compared with controls (287.48 +/- 143.8 vs 453.49 +/- 178.5 and 476.08 +/- 71.9 minutes; P < .001). Internal medicine residents had significantly greater major and minor reaction-time lapses compared with controls (1.26 +/- 3.4 vs 0.53 +/- 1.1 & 2.4 +/- 7.4 vs 0.45 +/- 1.0; P < .001). They reported increased sleepiness on postcall days compared with the start of their call (Stanford Sleepiness Scale: 3.26 +/- 1.2 vs 2.22 +/- 0.8; P < .001) but had scores similar to those of controls by their next call (2.22 +/- 0.8 vs 2.07 +/- 0.8; P = .13). CONCLUSIONS: Internal medicine residents have impaired reaction time and reduced vigilance compared with controls. Despite subjective improvements in sleepiness postcall, there was no change in their objective performance across the study period, suggesting no recovery. Internal medicine residents did not get extra sleep on postcall nights in an attempt to recover their lost sleep time. Implications for residents' well-being and patient care remain unclear.