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The authors use Ca 2+ imaging in freely behaving mice to look at the long-term dynamics of CA1 hippocampal place codes. They find that, in a familiar environment, there is substantial change in the population of place-coding cells over time, but the ensembles of these cells are sufficiently stable to preserve an accurate spatial representation across weeks. Using Ca 2+ imaging in freely behaving mice that repeatedly explored a familiar environment, we tracked thousands of CA1 pyramidal cells' place fields over weeks. Place coding was dynamic, as each day the ensemble representation of this environment involved a unique subset of cells. However, cells in the ∼15–25% overlap between any two of these subsets retained the same place fields, which sufficed to preserve an accurate spatial representation across weeks.

An integrated, miniature (1.9 g) fluorescence microscope containing light source, optics and sensor allows high-speed, wide field of view imaging of calcium spiking in hundreds of neurons in freely moving mice. The mass-producible portable microscope is also useful for a variety of fluorescence assays for which size, cost and portability can be concerns. The light microscope is traditionally an instrument of substantial size and expense. Its miniaturized integration would enable many new applications based on mass-producible, tiny microscopes. Key prospective usages include brain imaging in behaving animals for relating cellular dynamics to animal behavior. Here we introduce a miniature (1.9 g) integrated fluorescence microscope made from mass-producible parts, including a semiconductor light source and sensor. This device enables high-speed cellular imaging across ∼0.5 mm 2 areas in active mice. This capability allowed concurrent tracking of Ca 2+ spiking in >200 Purkinje neurons across nine cerebellar microzones. During mouse locomotion, individual microzones exhibited large-scale, synchronized Ca 2+ spiking. This is a mesoscopic neural dynamic missed by prior techniques for studying the brain at other length scales. Overall, the integrated microscope is a potentially transformative technology that permits distribution to many animals and enables diverse usages, such as portable diagnostics or microscope arrays for large-scale screens.

Deep learning methods for the prediction of molecular excitation spectra are presented. For the example of the electronic density of states of 132k organic molecules, three different neural network architectures: multilayer perceptron (MLP), convolutional neural network (CNN), and deep tensor neural network (DTNN) are trained and assessed. The inputs for the neural networks are the coordinates and charges of the constituent atoms of each molecule. Already, the MLP is able to learn spectra, but the root mean square error (RMSE) is still as high as 0.3 eV. The learning quality improves significantly for the CNN (RMSE = 0.23 eV) and reaches its best performance for the DTNN (RMSE = 0.19 eV). Both CNN and DTNN capture even small nuances in the spectral shape. In a showcase application of this method, the structures of 10k previously unseen organic molecules are scanned and instant spectra predictions are obtained to identify molecules for potential applications. Deep neural networks are set up to learn excitation spectra of molecules from their atomic structure alone. Once trained, the neural networks predict spectra instantly and at no further cost to the user. Deep learning spectroscopy complements conventional theoretical and experimental spectroscopy to accelerate the spectroscopic analysis of materials and to make predictions for novel and hitherto uncharacterized materials.

With the advancement of technology, inexpensive and highly scalable deposition techniques are extremely desirable for low-cost device fabrication. Aerosol-assisted chemical vapor deposition (AACVD) is a less complex and scalable deposition technique that works at atmospheric pressure. In this article, we demonstrate the growth of chromium oxide (Cr 2 O 3 ) films onto n-type silicon substrates utilizing the AACVD method, leading to the first-ever p-Cr 2 O 3 /n-silicon heterojunction device by this method. In this paper, we have analyzed the effect of temperature and concentration of precursor solution on the morphology of the deposited films. The structural analysis of the Cr 2 O 3 films shows a closely packed uniform structure with the root mean square (RMS) value of surface roughness varying from 1.6 nm to 5.99 nm. The maximum growth rate of 45.20 nm/min on silicon substrate was observed at 500 °C with 0.05 M precursor solution. X-ray photoelectron spectroscopy (XPS) showed a mixed phase layer, with the ratio of Cr 3+ to Cr 6+ for the film deposited at 450 °C, with 0.05 M precursor solution being 1.51 and increasing with the deposition temperature. The current–voltage characteristics showed a diode-like behavior with a high cut-in voltage of about 2 V. A high cut-in voltage exists due to a significant band offset at both the conduction band and valence band exists between Cr 2 O 3 and silicon. The utilization of the AACVD technique to grow Cr 2 O 3 on silicon substrate using chromium acetylacetonate as a precursor combined with the illustration of diode characteristics provide the novelty of this work.

We employ machine learning to derive tight-binding parametrizations for the electronic structure of defects. We test several machine learning methods that map the atomic and electronic structure of a defect onto a sparse tight-binding parameterization. Since Multi-layer perceptrons (i.e., feed-forward neural networks) perform best we adopt them for our further investigations. We demonstrate the accuracy of our parameterizations for a range of important electronic structure properties such as band structure, local density of states, transport and level spacing simulations for two common defects in single layer graphene. Our machine learning approach achieves results comparable to maximally localized Wannier functions (i.e., DFT accuracy) without prior knowledge about the electronic structure of the defects while also allowing for a reduced interaction range which substantially reduces calculation time. It is general and can be applied to a wide range of other materials, enabling accurate large-scale simulations of material properties in the presence of different defects.

Therapeutic drugs for cognitive and psychiatric disorders are often characterized by their molecular mechanism of action. Here we demonstrate a new approach to elucidate drug action on large-scale neuronal activity by tracking somatic calcium dynamics in hundreds of CA1 hippocampal neurons of pharmacologically manipulated behaving mice. We used an adeno-associated viral vector to express the calcium sensor GCaMP3 in CA1 pyramidal cells under control of the CaMKII promoter and a miniaturized microscope to observe cellular dynamics. We visualized these dynamics with and without a systemic administration of Zolpidem, a GABAA agonist that is the most commonly prescribed drug for the treatment of insomnia in the United States. Despite growing concerns about the potential adverse effects of Zolpidem on memory and cognition, it remained unclear whether Zolpidem alters neuronal activity in the hippocampus, a brain area critical for cognition and memory. Zolpidem, when delivered at a dose known to induce and prolong sleep, strongly suppressed CA1 calcium signaling. The rate of calcium transients after Zolpidem administration was significantly lower compared to vehicle treatment. To factor out the contribution of changes in locomotor or physiological conditions following Zolpidem treatment, we compared the cellular activity across comparable epochs matched by locomotor and physiological assessments. This analysis revealed significantly depressive effects of Zolpidem regardless of the animal's state. Individual hippocampal CA1 pyramidal cells differed in their responses to Zolpidem with the majority (∼ 65%) significantly decreasing the rate of calcium transients, and a small subset (3%) showing an unexpected and significant increase. By linking molecular mechanisms with the dynamics of neural circuitry and behavioral states, this approach has the potential to contribute substantially to the development of new therapeutics for the treatment of CNS disorders.

The surface composition of aerosol droplets is central to predicting cloud droplet number concentrations, understanding atmospheric pollutant transformation, and interpreting observations of accelerated droplet chemistry. Due to the large surface-area-to-volume ratios of aerosol droplets, adsorption of surfactant at the air–liquid interface can deplete the droplet’s bulk concentration, leading to droplet surface compositions that do not match those of the solutions that produced them. Through direct measurements of individual surfactant-containing, micrometer-sized droplet surface tensions, and fully independent predictive thermodynamic modeling of droplet surface tension, we demonstrate that, for strong surfactants, the droplet’s surface-area-to-volume ratio becomes the key factor in determining droplet surface tension rather than differences in surfactant properties. For the same total surfactant concentration, the surface tension of a droplet can be >40 mN/m higher than that of the macroscopic solution that produced it. These observations indicate that an explicit consideration of surface-area-to-volume ratios is required when investigating heterogeneous chemical reactivity at the surface of aerosol droplets or estimating aerosol activation to cloud droplets.

In manufacturing industries, digitization of design and manufacturing processes adds competitiveness to business in terms of automation, interconnectedness, better user experience, easier process analysis, and machine intelligence. In this paper, we have delineated our experience of estimating efforts required for digitizing design and manufacturing processes of large complex products prevalent in industries where myriad of such processes exist along with their individual complexities. We have analyzed process complexities and reconstructed use case points method of estimation. Prediction error analysis has been performed based on various established methods while validating estimation model. Historical data has been used for model training and validation. A sustained productivity factor of 28.5 consultant-hour/use case point exhibits acceptable average estimation error. We also delve into the replication of digitization effort estimation of homologous components. Analysis of an automotive sheet metal component realization process and its digitization effort estimation has been presented as a proof of concept. The method can be adopted for process digitization in both design and manufacturing realms.

BackgroundA randomized trial in glioblastoma patients with methylated-MGMT (m-MGMT) found an improvement in median survival of 16.7 months for combination therapy with temozolomide (TMZ) and lomustine, however the approach remains controversial and relatively under-utilized. Therefore, we sought to determine whether comprehensive genomic analysis can predict which patients would derive large, intermediate, or negligible benefits from the combination compared to single agent chemotherapy.MethodsComprehensive genomic information from 274 newly diagnosed patients with methylated-MGMT glioblastoma (GBM) was downloaded from TCGA. Mutation and copy number changes were input into a computational biologic model to create an avatar of disease behavior and the malignant phenotypes representing hallmark behavior of cancers. In silico responses to TMZ, lomustine, and combination treatment were biosimulated. Efficacy scores representing the effect of treatment for each treatment strategy were generated and compared to each other to ascertain the differential benefit in drug response.ResultsDifferential benefits for each drug were identified, including strong, modest-intermediate, negligible, and deleterious (harmful) effects for subgroups of patients. Similarly, the benefits of combination therapy ranged from synergy, little or negligible benefit, and deleterious effects compared to single agent approaches.ConclusionsThe benefit of combination chemotherapy is predicted to vary widely in the population. Biosimulation appears to be a useful tool to address the disease heterogeneity, drug response, and the relevance of particular clinical trials observations to individual patients. Biosimulation has potential to spare some patients the experience of over-treatment while identifying patients uniquely situated to benefit from combination treatment. Validation of this new artificial intelligence tool is needed.

Prolonged exposure to abnormally high calcium concentrations is thought to be a core mechanism underlying hippocampal damage in epileptic patients; however, no prior study has characterized calcium activity during seizures in the live, intact hippocampus. We have directly investigated this possibility by combining whole-brain electroencephalographic (EEG) measurements with microendoscopic calcium imaging of pyramidal cells in the CA1 hippocampal region of freely behaving mice treated with the pro-convulsant kainic acid (KA). We observed that KA administration led to systematic patterns of epileptiform calcium activity: a series of large-scale, intensifying flashes of increased calcium fluorescence concurrent with a cluster of low-amplitude EEG waveforms. This was accompanied by a steady increase in cellular calcium levels (>5 fold increase relative to the baseline), followed by an intense spreading calcium wave characterized by a 218% increase in global mean intensity of calcium fluorescence (n = 8, range [114-349%], p < 10(-4); t-test). The wave had no consistent EEG phenotype and occurred before the onset of motor convulsions. Similar changes in calcium activity were also observed in animals treated with 2 different proconvulsant agents, N-methyl-D-aspartate (NMDA) and pentylenetetrazol (PTZ), suggesting the measured changes in calcium dynamics are a signature of seizure activity rather than a KA-specific pathology. Additionally, despite reducing the behavioral severity of KA-induced seizures, the anticonvulsant drug valproate (VA, 300 mg/kg) did not modify the observed abnormalities in calcium dynamics. These results confirm the presence of pathological calcium activity preceding convulsive motor seizures and support calcium as a candidate signaling molecule in a pathway connecting seizures to subsequent cellular damage. Integrating in vivo calcium imaging with traditional assessment of seizures could potentially increase translatability of pharmacological intervention, leading to novel drug screening paradigms and therapeutics designed to target and abolish abnormal patterns of both electrical and calcium excitation.