Application of Random Forests in ToF-SIMS Data

Surface analysis techniques are particularly important in the field of materials science, which help researchers to understand the mechanism behind complex chemical reactions and study the properties of different materials. Time-of-flight secondary ion mass spectrometry (ToF-SIMS), a highly sensitive surface analysis technique, allows the reliable determination of various materials. ToF-SIMS spectra of materials are usually enormously complex since typical raw data may include many peaks over large mass-to-charge ratio (m/z) ranges. Hence, the use of data-mining methods in processing ToF-SIMS data is becoming more popular and important. In this study we show that random forests model can be used to automatically classify several different lithium-containing materials and to extract representative peaks from ToF-SIMS spectra of these materials. Our study shows good performance in analyzing spectra of materials with similar and dissimilar compositions, which can provide researchers with the possibility of quick and automatic analysis of ToF-SIMS data

Characterization of porous membranes using artificial neural networks

Porous membranes have been utilized intensively in a wide range of fields due to their special characteristics and a rigorous characterization of their microstructures is crucial for understanding their properties and improving the performance for target applications. A promising method for the quantitative analysis of porous structures leverages the physics-based generation of porous structures at the pore scale, which can be validated against real experimental microstructures, followed by building the process–structure–property relationships with data-driven algorithms such as artificial neural networks. In this study, a Variational AutoEncoder (VAE) neural network model is used to characterize the 3D structural information of porous materials and to represent them with low-dimensional latent variables, which further model the structure–property relationship and solve the inverse problem of process–structure linkage combined with the Bayesian optimization method. Our methods provide a quantitative way to learn structural descriptors in an unsupervised manner which can characterize porous microstructures robustly

Kadi4Mat : A Research Data Infrastructure for Materials Science

The concepts and current developments of a research data infrastructure for materials science are presented, extending and combining the features of an electronic lab notebook and a repository. The objective of this infrastructure is to incorporate the possibility of structured data storage and data exchange with documented and reproducible data analysis and visualization, which finally leads to the publication of the data. This way, researchers can be supported throughout the entire research process. The software is being developed as a web-based and desktop-based system, offering both a graphical user interface and a programmatic interface. The focus of the development is on the integration of technologies and systems based on both established as well as new concepts. Due to the heterogeneous nature of materials science data, the current features are kept mostly generic, and the structuring of the data is largely left to the users. As a result, an extension of the research data infrastructure to other disciplines is possible in the future. The source code of the project is publicly available under a permissive Apache 2.0 license

Identification of Lithium Compounds on Surfaces of Lithium Metal Anode with Machine-Learning-Assisted Analysis of ToF-SIMS Spectra

Detailed knowledge about contamination and passivation compounds on the surface of lithium metal anodes (LMAs) is essential to enable their use in all-solid-state batteries (ASSBs). Time-of-flight secondary ion mass spectrometry (ToF-SIMS), a highly surface-sensitive technique, can be used to reliably characterize the surface status of LMAs. However, as ToF-SIMS data are usually highly complex, manual data analysis can be difficult and time-consuming. In this study, machine learning techniques, especially logistic regression (LR), are used to identify the characteristic secondary ions of 5 different pure lithium compounds. Furthermore, these models are applied to the mixture and LMA samples to enable identification of their compositions based on the measured ToF-SIMS spectra. This machine-learning-based analysis approach shows good performance in identifying characteristic ions of the analyzed compounds that fit well with their chemical nature. Moreover, satisfying accuracy in identifying the compositions of unseen new samples is achieved. In addition, the scope and limitations of such a strategy in practical applications are discussed. This work presents a robust analytical method that can assist researchers in simplifying the analysis of the studied lithium compound samples, offering the potential for broader applications in other material systems

Apoptosis signal-regulating kinase 1 (Ask1) deficiency alleviates MPP+-induced impairment of evoked dopamine release in the mouse hippocampus

The dopaminergic system is susceptible to dysfunction in numerous neurological diseases, including Parkinson’s disease (PD). In addition to motor symptoms, some PD patients may experience non-motor symptoms, including cognitive and memory deficits. A possible explanation for their manifestation is a disturbed pattern of dopamine release in brain regions involved in learning and memory, such as the hippocampus. Therefore, investigating neuropathological alterations in dopamine release prior to neurodegeneration is imperative. This study aimed to characterize evoked hippocampal dopamine release and assess the impact of the neurotoxin MPP+ using a genetically encoded dopamine sensor and gene expression analysis. Additionally, considering the potential neuroprotective attributes demonstrated by apoptosis signal-regulating kinase 1 (Ask1) in various animal-disease-like models, the study also aimed to determine whether Ask1 knockdown restores MPP+-altered dopamine release in acute hippocampal slices. We applied variations of low- and high-frequency stimulation to evoke dopamine release within different hippocampal regions and discovered that acute application of MPP+ reduced the amount of dopamine released and hindered the recovery of dopamine release after repeated stimulation. In addition, we observed that Ask1 deficiency attenuated the detrimental effects of MPP+ on the recovery of dopamine release after repeated stimulation. RNA sequencing analysis indicated that genes associated with the synaptic pathways are involved in response to MPP+ exposure. Notably, Ask1 deficiency was found to downregulate the expression of Slc5a7, a gene encoding a sodium-dependent high-affinity choline transporter that regulates acetylcholine levels. Respective follow-up experiments indicated that Slc5a7 plays a role in Ask1 deficiency-mediated protection against MPP+ neurotoxicity. In addition, increasing acetylcholine levels using an acetylcholinesterase inhibitor could exacerbate the toxicity of MPP+. In conclusion, our data imply that the modulation of the dopamine-acetylcholine balance may be a crucial mechanism of action underlying the neuroprotective effects of Ask1 deficiency in PD

Distribution Adaptive INT8 Quantization for Training CNNs

Researches have demonstrated that low bit-width (e.g., INT8) quantization can be employed to accelerate the inference process. It makes the gradient quantization very promising since the backward propagation requires approximately twice more computation than forward one. Due to the variability and uncertainty of gradient distribution, a lot of methods have been proposed to attain training stability. However, most of them ignore the channel-wise gradient distributions and the impact of gradients with different magnitudes, resulting in the degradation of final accuracy. In this paper, we propose a novel INT8 quantization training framework for convolutional neural network to address the above issues. Specifically, we adopt Gradient Vectorized Quantization to quantize the gradient, based on the observation that layer-wise gradients contain multiple distributions along the channel dimension. Then, Magnitude-aware Clipping Strategy is introduced by taking the magnitudes of gradients into consideration when minimizing the quantization error, and we present a theoretical derivation to solve the quantization parameters of different distributions. Experimental results on broad range of computer vision tasks, such as image classification, object detection and video classification, demonstrate that the proposed Distribution Adaptive INT8 Quantization training method has achieved almost lossless training accuracy for different backbones, including ResNet, MobileNetV2, InceptionV3, VGG and AlexNet, which is superior to the state-of-the-art techniques. Moreover, we further implement the INT8 kernel that can accelerate the training iteration more than 200% under the latest Turing architecture, i.e., our method excels on both training accuracy and speed

High-Quality GaSe Single Crystal Grown by the Bridgman Method

A high-quality GaSe single crystal was grown by the Bridgman method. The X-ray rocking curve for the studied GaSe sample is symmetric and the Full Width at Half Maximum (FWHM) is only 46 arcs, which is the smallest value ever reported for GaSe crystals. The IR-transmittance is about 66% in the range from 500 to 4000 cm−1. The photoluminescence spectrum at 9.2 K shows a symmetric and sharp excition peak in 2.1046 eV. The results indicate that the as-grown GaSe crystal is of high crystalline quality. The as-grown ε -GaSe crystal has a p-type conductance with the resistivity of 103 Ω/cm, and the Hall mobility is ~25 cm2 V−1 s−1. Few-layer GaSe crystals were prepared through mechanical exfoliation from this high-quality crystal sample. Few-layer GaSe-based photodetectors were fabricated, which exhibit an on/off ratio of 104, a field-effect differential mobility of 0.4 cm2 V−1 s−1, and have a fast response time less than 60 ms under light illumination

Carbon Dioxide Conversion with High-Performance Photocatalysis into Methanol on NiSe2/WSe2

Climate change has been recognized as a threatening environmental problem around the world. CO2 is considered to be the main component of greenhouse gas. By using solar energy (light energy) as the energy source, photocatalytic conversion is one of the most effective technologies to reveal the clean utilization of CO2. Herein, using sodium tungstate, nickel nitrate, and selenium powder as the main raw materials, the high absorption and utilization of WSe2 for light energy and the high intrinsic conductivity of NiSe2 were combined by a hydrothermal method to prepare NiSe2/WSe2 and hydrazine hydrate as the reductant. Then, high-performance NiSe2/WSe2 photocatalytic material was prepared. The characterization results of XRD, XPS, SEM, specific surface area, and UV-visible spectroscopy show that the main diffraction peak of synthesized NiSe2/WSe2 is sharp, which basically coincides with the standard card. After doping NiSe2, the morphology of WSe2 was changed from a flake shape to smaller and more trivial crystal flakes, which demonstrates richer exposed edges and more active sites; the specific surface area increased from 3.01 m2 g−1 to 8.52 m2 g−1, and the band gap becomes wider, increasing from 1.66 eV to 1.68 eV. The results of a photocatalytic experiment show that when the prepared NiSe2/WSe2 catalyst is used to conduct photocatalytic reduction of CO2, the yield of CH3OH is significantly increased. After reaction for 10 h, the maximum yield could reach 3.80 mmol g−1, which presents great photocatalytic activity

Presentation_1_Apoptosis signal-regulating kinase 1 (Ask1) deficiency alleviates MPP+-induced impairment of evoked dopamine release in the mouse hippocampus.pdf

The dopaminergic system is susceptible to dysfunction in numerous neurological diseases, including Parkinson’s disease (PD). In addition to motor symptoms, some PD patients may experience non-motor symptoms, including cognitive and memory deficits. A possible explanation for their manifestation is a disturbed pattern of dopamine release in brain regions involved in learning and memory, such as the hippocampus. Therefore, investigating neuropathological alterations in dopamine release prior to neurodegeneration is imperative. This study aimed to characterize evoked hippocampal dopamine release and assess the impact of the neurotoxin MPP+ using a genetically encoded dopamine sensor and gene expression analysis. Additionally, considering the potential neuroprotective attributes demonstrated by apoptosis signal-regulating kinase 1 (Ask1) in various animal-disease-like models, the study also aimed to determine whether Ask1 knockdown restores MPP+-altered dopamine release in acute hippocampal slices. We applied variations of low- and high-frequency stimulation to evoke dopamine release within different hippocampal regions and discovered that acute application of MPP+ reduced the amount of dopamine released and hindered the recovery of dopamine release after repeated stimulation. In addition, we observed that Ask1 deficiency attenuated the detrimental effects of MPP+ on the recovery of dopamine release after repeated stimulation. RNA sequencing analysis indicated that genes associated with the synaptic pathways are involved in response to MPP+ exposure. Notably, Ask1 deficiency was found to downregulate the expression of Slc5a7, a gene encoding a sodium-dependent high-affinity choline transporter that regulates acetylcholine levels. Respective follow-up experiments indicated that Slc5a7 plays a role in Ask1 deficiency-mediated protection against MPP+ neurotoxicity. In addition, increasing acetylcholine levels using an acetylcholinesterase inhibitor could exacerbate the toxicity of MPP+. In conclusion, our data imply that the modulation of the dopamine-acetylcholine balance may be a crucial mechanism of action underlying the neuroprotective effects of Ask1 deficiency in PD.</p

Discovery of Novel ncRNA Sequences in Multiple Genome Alignments on the Basis of Conserved and Stable Secondary Structures

<div><p>Recently, non-coding RNAs (ncRNAs) have been discovered with novel functions, and it has been appreciated that there is pervasive transcription of genomes. Moreover, many novel ncRNAs are not conserved on the primary sequence level. Therefore, de novo computational ncRNA detection that is accurate and efficient is desirable. The purpose of this study is to develop a ncRNA detection method based on conservation of structure in more than two genomes. A new method called Multifind, using Multilign, was developed. Multilign predicts the common secondary structure for multiple input sequences. Multifind then uses measures of structure conservation to estimate the probability that the input sequences are a conserved ncRNA using a classification support vector machine. Multilign is based on Dynalign, which folds and aligns two sequences simultaneously using a scoring scheme that does not include sequence identity; its structure prediction quality is therefore not affected by input sequence diversity. Additionally, ensemble defect was introduced to Multifind as an additional discriminating feature that quantifies the compactness of the folding space for a sequence. Benchmarks showed Multifind performs better than RNAz and LocARNATE+RNAz, a method that uses RNAz on structure alignments generated by LocARNATE, on testing sequences extracted from the Rfam database. For de novo ncRNA discovery in three genomes, Multifind and LocARNATE+RNAz had an advantage over RNAz in low similarity regions of genome alignments. Additionally, Multifind and LocARNATE+RNAz found different subsets of known ncRNA sequences, suggesting the two approaches are complementary.</p></div