Study the hydrotropic behaviour of butyl stearate using ANN tools.

This study investigates the prediction of the thermophysical properties of butyl stearate in solutions with citric acid, urea, and nicotinamide using Artificial Neural Networks (ANNs). The ANN model uses hydrotropic concentration and temperature to predict these properties. The study focuses on binary mixtures at various temperatures (303, 313, 323, and 333 K). To achieve accurate predictions, researchers trained a committee of ANNs using experimental data. This iterative process optimizes the network architecture and avoids overfitting, a common problem in machine learning. The trained ANN can then predict thermophysical properties for intermediate hydrotropic concentrations without additional experiments. Besides, the study demonstrates the versatility of ANNs by implementing a successful model for multi-pass turning operations in MATLAB, showing superior accuracy compared to other methods. Visualizations like magnitude response curves, FFT spectrums, contour plots, and 3D surface plots helped to identify the optimal hydrotropic concentration with a remarkable 2% margin of error.

Lightning Current Measurement Method Using Rogowski Coil Based on Integral Circuit with Low-Frequency Attenuation Feedback.

A lightning current measurement method using a Rogowski coil based on an integral circuit with low-frequency attenuation feedback was proposed to address the issue of low-frequency distortion in the measurement of lightning currents on transmission lines using Rogowski coils. Firstly, the causes of low-frequency distortion in lightning current measurements using Rogowski coils were analyzed from the perspective of frequency domains. On this basis, an integration correction optimization circuit with a low-frequency attenuation feedback network was designed to correct the low-frequency distortion. The optimized integration circuit can also reduce the impact of low-frequency noise and the DC bias of the operational amplifier (op-amp) on the integration circuit due to the high low-frequency gain. Additionally, a high-pass filtering and voltage-divided sampling circuit has been added to ensure the normal operation of the integrator and improve the measurement range of the measurement system. Then, according to the relationship between the amplitude-frequency characteristics of the measurement system and the parameters of each component, the appropriate types of components and op-amp were selected to expand the measurement bandwidth. Finally, a simulation verification was conducted, and the simulation results show that this measurement method can effectively expand the lower measurement frequency limit to 20 Hz, correct the low-frequency distortion caused by Rogowski coils measuring lightning currents on transmission lines, and accurately restore the measured lightning current waveform.

Combinatorial Cooperativity in miR200-Zeb Feedback Network can Control Epithelial-Mesenchymal Transition.

Carcinomas often utilize epithelial-mesenchymal transition (EMT) programs for cancer progression and metastasis. Numerous studies report SNAIL-induced miR200/Zeb feedback circuit as crucial in regulating EMT by placing cancer cells in at least three phenotypic states, viz. epithelial (E), hybrid (h-E/M), mesenchymal (M), along the E-M phenotypic spectrum. However, a coherent molecular-level understanding of how such a tiny circuit controls carcinoma cell entrance into and residence in various states is lacking. Here, we use molecular binding data and mathematical modeling to report that the miR200/Zeb circuit can essentially utilize combinatorial cooperativity to control E-M phenotypic plasticity. We identify minimal combinatorial cooperativities that give rise to E, h-E/M, and M phenotypes. We show that disrupting a specific number of miR200 binding sites on Zeb as well as Zeb binding sites on miR200 can have phenotypic consequences-the circuit can dynamically switch between two (E, M) and three (E, h-E/M, M) phenotypes. Further, we report that in both SNAIL-induced and SNAIL knock-out miR200/Zeb circuits, cooperative transcriptional feedback on Zeb as well as Zeb translation inhibition due to miR200 are essential for the occurrence of intermediate h-E/M phenotype. Finally, we demonstrate that SNAIL can be dispensable for EMT, and in the absence of SNAIL, the transcriptional feedback can control cell state transition from E to h-E/M, to M state. Our results thus highlight molecular-level regulation of EMT in miR200/Zeb circuit and we expect these findings to be crucial to future efforts aiming to prevent EMT-facilitated dissemination of carcinomas.

Bioinformatics Research and qRT-PCR Verify Hub Genes and a Transcription Factor-MicroRNA Feedback Network in Intervertebral Disc Degeneration.

The present study explores the potentials of bioinformatics analysis to identify hub genes linked to intervertebral disc degeneration (IDD) and explored the potential molecular mechanism of transcription factor-microRNA regulatory network. Furthermore, the hub genes were identified through quantitative reverse transcriptase PCR (qRT-PCR). GEO database expression profile datasets for candidate genes (GSE124272) were downloaded. Genes that were differentially expressed (DEGs) were detected utilizing limma technique in the R programming language. Search Tool for the Retrieval of Interacting Genes/Proteins and NetworkAnalyst software identified hub genes. The Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis as well as Gene Ontology annotation of the DEGs were performed using Metascape. Using Bioinformatics data from the TRRUST, StarBase, and TransmiR databases, a TF-miRNA-hub genes network was constructed. qRT-PCR was utilized to confirm the result. As compared to healthy persons, 521 DEGs, comprising 203 down-regulated and 318 up-regulated genes, as well as 7 core genes, were found in people with IDD. Analysis revealed that all seven essential genes were under-expressed. qRT-PCR further confirmed the low expression of these seven important genes. Based on the TRRUST database, 16 TFs that could target five junction genes were then predicted. According to the StarBase database, four miRNAs were linked to crucial genes, while the TransmiR database predicted regulatory connections between four miRNAs and five TFs. The expression of the TP53-(hsa-miR-183-5p)-CCNB1 TF-miRNA-mRNA interaction network was discovered to be correlated with IDD. Throughout this investigation, a network of TF-miRNA-mRNA connections was built for investigation of the probable molecular mechanisms responsible for IDD. The identification of hub genes associated with IDD may reveal promising IDD treatment strategies.

FNSAM: Image super-resolution using a feedback network with self-attention mechanism.

BACKGROUND: High-resolution (HR) magnetic resonance imaging (MRI) provides rich pathological information which is of great significance in diagnosis and treatment of brain lesions. However, obtaining HR brain MRI images comes at the cost of extending scan time and using sophisticated expensive instruments. OBJECTIVE: This study aims to reconstruct HR MRI images from low-resolution (LR) images by developing a deep learning based super-resolution (SR) method. METHODS: We propose a feedback network with self-attention mechanism (FNSAM) for SR reconstruction of brain MRI images. Specifically, a feedback network is built to correct shallow features by using a recurrent neural network (RNN) and the self-attention mechanism (SAM) is integrated into the feedback network for extraction of important information as the feedback signal, which promotes image hierarchy. RESULTS: Experimental results show that the proposed FNSAM obtains more reasonable SR reconstruction of brain MRI images both in peak signal to noise ratio (PSNR) and structural similarity index measure (SSIM) than some state-of-the-arts. CONCLUSION: Our proposed method is suitable for SR reconstruction of MRI images.

A 53-µA-Quiescent 400-mA Load Demultiplexer Based CMOS Multi-Voltage Domain Low Dropout Regulator for RF Energy Harvester.

A low-power capacitorless demultiplexer-based multi-voltage domain low-dropout regulator (MVD-LDO) with 180 nm CMOS technology is proposed in this work. The MVD-LDO has a 1.5 V supply voltage headroom and regulates an output from four voltage domains ranging from 0.8 V to 1.4 V, with a high current efficiency of 99.98% with quiescent current of 53 µA with the aid of an integrated low-power demultiplexer controller which consumes only 68.85 pW. The fabricated chip has an area of 0.149 mm2 and can deliver up to 400 mA of current. The MVD-LDO's line and load regulations are 1.85 mV/V and 0.0003 mV/mA for the low-output voltage domain and 3.53 mV/V and 0.079 mV/mA for the high-output voltage domain. The LDO consumes only 174.5 µW in standby mode, making it suitable for integrating with an RF energy harvester chip to power sensor nodes.

A Local and Non-Local Features Based Feedback Network on Super-Resolution.

Recent advances in Single Image Super-Resolution (SISR) achieved a powerful reconstruction performance. The CNN-based network (both sequential-based and feedback-based) performs well in local features, while the self-attention-based network performs well in non-local features. However, single block cannot always perform well due to the realistic images always with multiple kinds of features. In order to take full advantage of different blocks on different features. We have chosen three different blocks cooperating to extract different kinds of features. Addressing this problem, in this paper, we propose a new Local and non-local features-based feedback network for SR (LNFSR): (1) The traditional deep convolutional network block is used to extract the local non-feedbackable information directly and non-local non-feedbackable information (needs to cooperate with other blocks). (2) The dense skip-based feedback block is use to extract local feedbackable information. (3) The non-local self-attention block is used to extract non-local feedbackable information and the based LR feature information. We also introduced the feature up-fusion-delivery blocks to help the features be delivered to the right block at the end of each iteration. Experiments show our proposed LNFSR can extract different kinds of feature maps by different blocks and outperform other state-of-the-art algorithms.

Network Modeling of Complex Time-Dependent Changes in Patient Adherence to Adjuvant Endocrine Treatment in ER+ Breast Cancer.

Early patient discontinuation from adjuvant endocrine treatment (ET) is multifactorial and complex: Patients must adapt to various challenges and make the best decisions they can within changing contexts over time. Predictive models are needed that can account for the changing influence of multiple factors over time as well as decisional uncertainty due to incomplete data. AtlasTi8 analyses of longitudinal interview data from 82 estrogen receptor-positive (ER+) breast cancer patients generated a model conceptualizing patient-, patient-provider relationship, and treatment-related influences on early discontinuation. Prospective self-report data from validated psychometric measures were discretized and constrained into a decisional logic network to refine and validate the conceptual model. Minimal intervention set (MIS) optimization identified parsimonious intervention strategies that reversed discontinuation paths back to adherence. Logic network simulation produced 96 candidate decisional models which accounted for 75% of the coordinated changes in the 16 network nodes over time. Collectively the models supported 15 persistent end-states, all discontinued. The 15 end-states were characterized by median levels of general anxiety and low levels of perceived recurrence risk, quality of life (QoL) and ET side effects. MIS optimization identified 3 effective interventions: reducing general anxiety, reinforcing pill-taking routines, and increasing trust in healthcare providers. Increasing health literacy also improved adherence for patients without a college degree. Given complex regulatory networks' intractability to end-state identification, the predictive models performed reasonably well in identifying specific discontinuation profiles and potentially effective interventions.

SRAS-net: Low-resolution chromosome image classification based on deep learning.

Prenatal karyotype diagnosis is important to determine if the foetus has genetic diseases and some congenital diseases. Chromosome classification is an important part of karyotype analysis, and the task is tedious and lengthy. Chromosome classification methods based on deep learning have achieved good results, but if the quality of the chromosome image is not high, these methods cannot learn image features well, resulting in unsatisfactory classification results. Moreover, the existing methods generally have a poor effect on sex chromosome classification. Therefore, in this work, the authors propose to use a super-resolution network, Self-Attention Negative Feedback Network, and combine it with traditional neural networks to obtain an efficient chromosome classification method called SRAS-net. The method first inputs the low-resolution chromosome images into the super-resolution network to generate high-resolution chromosome images and then uses the traditional deep learning model to classify the chromosomes. To solve the problem of inaccurate sex chromosome classification, the authors also propose to use the SMOTE algorithm to generate a small number of sex chromosome samples to ensure a balanced number of samples while allowing the model to learn more sex chromosome features. Experimental results show that our method achieves 97.55% accuracy and is better than state-of-the-art methods.

Short-term depression and transient memory in sensory cortex.

Persistent neuronal activity is usually studied in the context of short-term memory localized in central cortical areas. Recent studies show that early sensory areas also can have persistent representations of stimuli which emerge quickly (over tens of milliseconds) and decay slowly (over seconds). Traditional positive feedback models cannot explain sensory persistence for at least two reasons: (i) They show attractor dynamics, with transient perturbations resulting in a quasi-permanent change of system state, whereas sensory systems return to the original state after a transient. (ii) As we show, those positive feedback models which decay to baseline lose their persistence when their recurrent connections are subject to short-term depression, a common property of excitatory connections in early sensory areas. Dual time constant network behavior has also been implemented by nonlinear afferents producing a large transient input followed by much smaller steady state input. We show that such networks require unphysiologically large onset transients to produce the rise and decay observed in sensory areas. Our study explores how memory and persistence can be implemented in another model class, derivative feedback networks. We show that these networks can operate with two vastly different time courses, changing their state quickly when new information is coming in but retaining it for a long time, and that these capabilities are robust to short-term depression. Specifically, derivative feedback networks with short-term depression that acts differentially on positive and negative feedback projections are capable of dynamically changing their time constant, thus allowing fast onset and slow decay of responses without requiring unrealistically large input transients.

Pli Selon Pli: Mechanochemical Feedback and the Morphogenetic Role of Contractility at Cadherin Cell-Cell Junctions.

Cellular contractility, driven by actomyosin networks coupled to cadherin cell-cell adhesion junctions, is a major determinant of cellular rearrangement during morphogenesis. It now emerges that contractility arises as the emergent property of a mechanochemical feedback system that encompasses the signals that regulate contractility and the elements of the actomyosin network itself.