Roughness Factors of Electrodeposited Nanostructured Copper Foams.

Copper-based electrocatalytic materials play a critical role in various electrocatalytic processes, including the electroreduction of carbon dioxide and nitrate. Three-dimensional nanostructured electrodes are particularly advantageous for electrocatalytic applications due to their large surface area, which facilitates charge transfer and mass transport. However, the real surface area (RSA) of electrocatalysts is a crucial parameter that is often overlooked in experimental studies of high-surface-area copper electrodes. In this study, we investigate the roughness factors of electrodeposited copper foams with varying thicknesses and morphologies, obtained using the hydrogen bubble dynamic template technique. Underpotential deposition (UPD) of metal adatoms is one of the most reliable methods for estimating the RSA of highly dispersed catalysts. We aim to illustrate the applicability of UPD of lead for the determination of the RSA of copper deposits with hierarchical porosity. To find the appropriate experimental conditions that allow for efficient minimization of the limitations related to the slow diffusion of lead ions in the pores of the material and background currents of the reduction of traces of oxygen, we explore the effect of lead ion concentration, stirring rate, scan rate, monolayer deposition time and solution pH on the accuracy of RSA estimates. Under the optimized measurement conditions, Pb UPD allowed to estimate roughness factors as high as 400 for 100 µm thick foams, which translates into a specific surface area of ~6 m2·g-1. The proposed measurement protocol may be further applied to estimate the RSA of copper deposits with similar or higher roughness.

Maximizing Roughness Factors in Oxide-Derived Copper Coatings through Electrodeposition Parameters for Enhanced Electrocatalytic Performance.

The pursuit of novel techniques for obtaining dispersed copper-based catalysts is crucial in addressing environmental issues like decarbonization. One method for producing nanostructured metals involves the reduction of their oxides, a technique that has found widespread use in CO2 electroreduction. Currently, the intrinsic activities of oxide-derived copper electrocatalysts produced via different routes cannot be compared effectively due to the lack of information on electrochemically active surface area values, despite the availability of electrochemical methods that enable estimation of surface roughness for highly dispersed copper coatings. In this study, we aim to explore the potential of oxide-derived copper to achieve a high electrochemically active surface area by examining samples obtained from acetic and lactic acid deposition solutions. Our results revealed that Cu2O oxides had distinct morphologies depending on the electrodeposition solution used; acetate series samples were dense films with a columnar structure, while electrodeposition from lactic acid yielded a fine-grained, porous coating. The roughness factors of the electroreduced films followed linear relationships with the deposition charge, with significantly different slopes between the two solutions. Notably, a high roughness factor of 650 was achieved for samples deposited from lactic acid solution, which represents one of the highest estimates of electrochemically active surface area for oxide-derived copper catalysts. Our results highlight the importance of controlling the microstructure of the electrodeposited oxide electrocatalysts to maximize surface roughness.

Topological network analysis of patient similarity for precision management of acute blood pressure in spinal cord injury.

Background: Predicting neurological recovery after spinal cord injury (SCI) is challenging. Using topological data analysis, we have previously shown that mean arterial pressure (MAP) during SCI surgery predicts long-term functional recovery in rodent models, motivating the present multicenter study in patients. Methods: Intra-operative monitoring records and neurological outcome data were extracted (n = 118 patients). We built a similarity network of patients from a low-dimensional space embedded using a non-linear algorithm, Isomap, and ensured topological extraction using persistent homology metrics. Confirmatory analysis was conducted through regression methods. Results: Network analysis suggested that time outside of an optimum MAP range (hypotension or hypertension) during surgery was associated with lower likelihood of neurological recovery at hospital discharge. Logistic and LASSO (least absolute shrinkage and selection operator) regression confirmed these findings, revealing an optimal MAP range of 76-[104-117] mmHg associated with neurological recovery. Conclusions: We show that deviation from this optimal MAP range during SCI surgery predicts lower probability of neurological recovery and suggest new targets for therapeutic intervention. Funding: NIH/NINDS: R01NS088475 (ARF); R01NS122888 (ARF); UH3NS106899 (ARF); Department of Veterans Affairs: 1I01RX002245 (ARF), I01RX002787 (ARF); Wings for Life Foundation (ATE, ARF); Craig H. Neilsen Foundation (ARF); and DOD: SC150198 (MSB); SC190233 (MSB).

Spinal cord injury is a devastating condition that involves damage to the nerve fibers connecting the brain with the spinal cord, often leading to permanent changes in strength, sensation and body functions, and in severe cases paralysis. Scientists around the world work hard to find ways to treat or even repair spinal cord injuries but few patients with complete immediate paralysis recover fully. Immediate paralysis is caused by direct damage to neurons and their extension in the spinal cord. Previous research has shown that blood pressure regulation may be key in saving these damaged neurons, as spinal cord injuries can break the communication between nerves that is involved in controlling blood pressure. This can lead to a vicious cycle of dysregulation of blood pressure and limit the supply of blood and oxygen to the damaged spinal cord tissue, exacerbating the death of spinal neurons. Management of blood pressure is therefore a key target for spinal cord injury care, but so far, the precise thresholds to enable neurons to recover are poorly understood. To find out more, Torres-Espin, Haefeli et al. used machine learning software to analyze previously recorded blood pressure and heart rate data obtained from 118 patients that underwent spinal cord surgery after acute spinal cord injury. The analyses revealed that patients who suffered from either low or high blood pressure during surgery had poorer prospects of recovery. Statistical models confirming these findings showed that the optimal blood pressure range to ensure recovery lies between 76 to 104-117 mmHg. Any deviation from this narrow window would dramatically worsen the ability to recover. These findings suggests that dysregulated blood pressure during surgery affects to odds of recovery in patients with a spinal cord injury. Torres-Espin, Haefeli et al. provide specific information that could improve current clinical practice in trauma centers. In the future, such machine learning tools and models could help develop real-time models that could predict the likelihood of a patient's recovery following spinal cord injury and related neurological conditions.

Evaluating State Space Discovery by Persistent Cohomology in the Spatial Representation System.

Persistent cohomology is a powerful technique for discovering topological structure in data. Strategies for its use in neuroscience are still undergoing development. We comprehensively and rigorously assess its performance in simulated neural recordings of the brain's spatial representation system. Grid, head direction, and conjunctive cell populations each span low-dimensional topological structures embedded in high-dimensional neural activity space. We evaluate the ability for persistent cohomology to discover these structures for different dataset dimensions, variations in spatial tuning, and forms of noise. We quantify its ability to decode simulated animal trajectories contained within these topological structures. We also identify regimes under which mixtures of populations form product topologies that can be detected. Our results reveal how dataset parameters affect the success of topological discovery and suggest principles for applying persistent cohomology, as well as persistent homology, to experimental neural recordings.

Machine learning with persistent homology and chemical word embeddings improves prediction accuracy and interpretability in metal-organic frameworks.

Machine learning has emerged as a powerful approach in materials discovery. Its major challenge is selecting features that create interpretable representations of materials, useful across multiple prediction tasks. We introduce an end-to-end machine learning model that automatically generates descriptors that capture a complex representation of a material's structure and chemistry. This approach builds on computational topology techniques (namely, persistent homology) and word embeddings from natural language processing. It automatically encapsulates geometric and chemical information directly from the material system. We demonstrate our approach on multiple nanoporous metal-organic framework datasets by predicting methane and carbon dioxide adsorption across different conditions. Our results show considerable improvement in both accuracy and transferability across targets compared to models constructed from the commonly-used, manually-curated features, consistently achieving an average 25-30% decrease in root-mean-squared-deviation and an average increase of 40-50% in R2 scores. A key advantage of our approach is interpretability: Our model identifies the pores that correlate best to adsorption at different pressures, which contributes to understanding atomic-level structure-property relationships for materials design.

Potential regulatory SNPs in the ATXN7L3B and KRT15 genes are associated with gender-specific colorectal cancer risk.

Aim: According to the current data, a major factor for phenotypic variation of complex traits and disease susceptibility is the cis-acting effects of noncoding variants on gene expression. Our purpose was to evaluate the association between colorectal cancer (CRC) and six single nucleotide polymorphisms identified using our original bioinformatics approach as regulatory and putatively related to CRC. Materials: One hundred and sixty CRC patients and 185 healthy controls have been genotyped for rs590352, rs2072580, rs78317230, rs3829202, rs11542583 and rs4796672. Results: Genotypes and alleles distributions of rs590352 of ATXN7L3B gene were significantly different between the male CRC subjects and controls. Significant correlation of genotype with CRC is observable for women only for the rs4796672 of KRT15 gene. Analysis of haplotypes reveals that rs2072580 of the ISCU and SART3 genes can be also associated with CRC. Conclusion: We have identified three SNPs associated with CRC risk and demonstrated a gender specificity of rs590352 and rs4796672.

Replays of spatial memories suppress topological fluctuations in cognitive map.

The spiking activity of the hippocampal place cells plays a key role in producing and sustaining an internalized representation of the ambient space-a cognitive map. These cells do not only exhibit location-specific spiking during navigation, but also may rapidly replay the navigated routs through endogenous dynamics of the hippocampal network. Physiologically, such reactivations are viewed as manifestations of "memory replays" that help to learn new information and to consolidate previously acquired memories by reinforcing synapses in the parahippocampal networks. Below we propose a computational model of these processes that allows assessing the effect of replays on acquiring a robust topological map of the environment and demonstrate that replays may play a key role in stabilizing the hippocampal representation of space.

Robust spatial memory maps encoded by networks with transient connections.

The spiking activity of principal cells in mammalian hippocampus encodes an internalized neuronal representation of the ambient space-a cognitive map. Once learned, such a map enables the animal to navigate a given environment for a long period. However, the neuronal substrate that produces this map is transient: the synaptic connections in the hippocampus and in the downstream neuronal networks never cease to form and to deteriorate at a rapid rate. How can the brain maintain a robust, reliable representation of space using a network that constantly changes its architecture? We address this question using a computational framework that allows evaluating the effect produced by the decaying connections between simulated hippocampal neurons on the properties of the cognitive map. Using novel Algebraic Topology techniques, we demonstrate that emergence of stable cognitive maps produced by networks with transient architectures is a generic phenomenon. The model also points out that deterioration of the cognitive map caused by weakening or lost connections between neurons may be compensated by simulating the neuronal activity. Lastly, the model explicates the importance of the complementary learning systems for processing spatial information at different levels of spatiotemporal granularity.

In situ and in-transit analysis of cosmological simulations.

Modern cosmological simulations have reached the trillion-element scale, rendering data storage and subsequent analysis formidable tasks. To address this circumstance, we present a new MPI-parallel approach for analysis of simulation data while the simulation runs, as an alternative to the traditional workflow consisting of periodically saving large data sets to disk for subsequent 'offline' analysis. We demonstrate this approach in the compressible gasdynamics/N-body code Nyx, a hybrid MPI + OpenMP code based on the BoxLib framework, used for large-scale cosmological simulations. We have enabled on-the-fly workflows in two different ways: one is a straightforward approach consisting of all MPI processes periodically halting the main simulation and analyzing each component of data that they own ('in situ'). The other consists of partitioning processes into disjoint MPI groups, with one performing the simulation and periodically sending data to the other 'sidecar' group, which post-processes it while the simulation continues ('in-transit'). The two groups execute their tasks asynchronously, stopping only to synchronize when a new set of simulation data needs to be analyzed. For both the in situ and in-transit approaches, we experiment with two different analysis suites with distinct performance behavior: one which finds dark matter halos in the simulation using merge trees to calculate the mass contained within iso-density contours, and another which calculates probability distribution functions and power spectra of various fields in the simulation. Both are common analysis tasks for cosmology, and both result in summary statistics significantly smaller than the original data set. We study the behavior of each type of analysis in each workflow in order to determine the optimal configuration for the different data analysis algorithms.

A Pilot Study of Confocal Laser Endomicroscopy to Predict Barrier Dysfunction and Relapse in Pediatric Inflammatory Bowel Disease.

OBJECTIVES: Probe-based confocal laser endomicroscopy (pCLE) is a novel imaging modality that enables virtual optical biopsy in vivo. Loss of barrier function of the small bowel observed via pCLE as increased density of epithelial gaps (extrusion zones left in the intestinal lining after cells are shed) is predictive of relapse in adult patients with inflammatory bowel disease (IBD). This study aims to determine whether such observations on pCLE are similarly predictive of disease relapse in pediatric patients with IBD. METHODS: Pediatric patients with biopsy-proven IBD underwent pCLE during colonoscopy and subsequent clinical follow-up every 6 months. Relapse was defined as moderate to severe flare with endoscopic evidence of inflammation during the follow-up period. The relations between epithelial gap density, disease relapse, and imaging parameters were determined using Cox models. RESULTS: Twenty-four patients with IBD (13 with Crohn disease, 11 with ulcerative colitis) with a median age of 14 years (range 10-21) were studied for a median of 13 (4-33) months. The median duration of disease was 2.9 years (range 0-9). Increased epithelial gap density in the terminal ileum on pCLE of normal endoscopic appearing terminal ileum mucosa (N = 19) was predictive of disease relapse when 3 or more areas were imaged (N = 6, log-rank P = 0.02, C-statistic = 0.94). CONCLUSIONS: In pediatric patients with IBD, barrier dysfunction observed on pCLE imaging of the small bowel was predictive of disease relapse.