Distinct Signal Patterns Research Articles

Correspondence between multiple signaling and developmental cellular patterns: a computational perspective.

The spatial arrangement of variant phenotypes during stem cell division plays a crucial role in the self-organization of cell tissues. The patterns observed in these cellular assemblies, where multiple phenotypes vie for space and resources, are largely influenced by a mixture of different diffusible chemical signals. This complex process is carried out within a chronological framework of interplaying intracellular and intercellular events. This includes receiving external stimulants, whether secreted by other individuals or provided by the environment, interpreting these environmental signals, and incorporating the information to designate cell fate. Here, given two distinct signaling patterns generated by Turing systems, we investigated the spatial distribution of differentiating cells that use these signals as external cues for modifying the production rates. By proposing a computational map, we show that there is a correspondence between the multiple signaling and developmental cellular patterns. In other words, the model provides an appropriate prediction for the final structure of the differentiated cells in a multi-signal, multi-cell environment. Conversely, when a final snapshot of cellular patterns is given, our algorithm can partially identify the signaling patterns that influenced the formation of the cellular structure, provided that the governing dynamic of the signaling patterns is already known.

Compressive sensing imaging with periodic perturbation induced caustic lens masks in a ripple tank

Terahertz imaging presents immense potential across many fields but the affordability of multiple-pixel imaging equipment remains a challenge for many researchers. To address this, the adoption of single-pixel imaging emerges as a lower-cost option, however, the data acquisition process necessary for reconstructing images is time-intensive. Compressive Sensing, which allows for generation of images using a reduced number of measurements than Nyquist's theorem demands, presents a promising solution but long processing times are still issue particularly large-sized images. Our proposed solution to this issue involves using caustic lens effect induced by perturbations in a ripple tank as a sampling mask. The dynamic nature of the ripple tank introduces randomness into the sampling process and this reduces measurement time by exploiting the inherent sparsity of THz band signals. This work employed Convolutional Neural Network to perform target classification based on the distinct signal patterns acquired through the caustic lens mask. The proposed classifier achieved 99.22% accuracy rate in distinguishing targets shaped like Latin letters. The controlled randomness introduced by the caustic lens mask is believed to play a crucial role in achieving this high accuracy by mitigating overfitting, a common challenge in machine learning.

Mechanobiological insight into brain diseases based on mechanosensitive channels: Common mechanisms and clinical potential.

As physical signals, mechanical cues regulate the neural cells in the brain. The mechanosensitive channels (MSCs) perceive the mechanical cues and transduce them by permeating specific ions or molecules across the plasma membrane, and finally trigger a series of intracellular bioelectrical and biochemical signals. Emerging evidence supports that wide-distributed, high-expressed MSCs like Piezo1 play important roles in several neurophysiological processes and neurological disorders. To systematically conclude the functions of MSCs in the brain and provide a novel mechanobiological perspective for brain diseases. We summarized the mechanical cues and MSCs detected in the brain and the research progress on the functional roles of MSCs in physiological conditions. We then concluded the pathological activation and downstream pathways triggered by MSCs in two categories of brain diseases, neurodegenerative diseases and place-occupying damages. Finally, we outlined the methods for manipulating MSCs and discussed their medical potential with some crucial outstanding issues. The MSCs present underlying common mechanisms in different brain diseases by acting as the "transportation hubs" to transduce the distinct signal patterns: the upstream mechanical cues and the downstream intracellular pathways. Manipulating the MSCs is feasible to alter the complicated downstream processes, providing them promising targets for clinical treatment. Recent research on MSCs provides a novel insight into brain diseases. The common mechanisms mediated by MSCs inspire a wide range of therapeutic potentials targeted on MSCs in different brain diseases.

Inhibitory effect of truncated isoforms on GPCR dimerization predicted by combinatorial computational strategy

G protein-coupled receptors (GPCRs) play a pivotal role in fundamental biological processes and disease development. GPCR isoforms, derived from alternative splicing, can exhibit distinct signaling patterns. Some highly-truncated isoforms can impact functional performance of full-length receptors, suggesting their intriguing regulatory roles. However, how these truncated isoforms interact with full-length counterparts remains largely unexplored. Here, we computationally investigated the interaction patterns of three human GPCRs from three different classes, ADORA1 (Class A), mGlu2 (Class C) and SMO (Class F) with their respective truncated isoforms because their homodimer structures have been experimentally determined, and they have truncated isoforms deposited and identified at protein level in Uniprot database. Combining the neural network-based AlphaFold2 and two physics-based protein-protein docking tools, we generated multiple complex structures and assessed the binding affinity in the context of atomistic molecular dynamics simulations. Our computational results suggested all the four studied truncated isoforms showed potent binding to their counterparts and overlapping interfaces with homodimers, indicating their strong potential to block homodimerization of their counterparts. Our study offers insights into functional significance of GPCR truncated isoforms and supports the ubiquity of their regulatory roles.

The persistence and antitumor efficacy of CAR-T cells are modulated by tonic signaling within the CDR

Chimeric antigen receptor (CAR) T cell therapy has demonstrated remarkable clinical efficacy, but challenges related to relapse and CAR-T cell exhaustion persist. One contributing factor to this exhaustion is CAR tonic signaling, where CAR-T cells self-activate without antigen stimulation, leading to reduced persistence and impaired antitumor activity. To address this issue, we conducted a preclinical study evaluating tonic signaling using nanobody-derived CAR-T cells. Our investigation revealed that specific characteristics of the complementary determining regions (CDRs), including low solubility, polarity, positive charge, energy, and area of ionic and positive CDR patches of amino acids, were associated with low antigen-independent tonic signaling. Significantly, we observed that stronger tonic signaling directly impacted CAR-T cell proliferation in vitro, consequently leading to CAR-T cell exhaustion and diminished persistence and effectiveness in vivo. Our findings provide compelling preclinical evidence and lay the foundation for the clinical assessment of CAR-T cells with distinct tonic signaling patterns. Understanding the role of CDRs in modulating tonic signaling holds promise for advancing the development of more efficient and durable CAR-T cell therapies, thereby enhancing the treatment of cancer and addressing the challenges of relapse in CAR-T cell therapy.

A low complexity binary-weighted energy disaggregation framework for residential electricity consumption

The discipline of Non-Intrusive Load Monitoring (NILM) has witnessed a surge in the application of machine learning and pattern recognition approaches, enabling researchers to investigate NILM problems. This paper introduces a novel energy disaggregation system that employs a binary-weight matrix to separate the power consumption into distinct signal patterns. The framework includes filtering techniques, followed by event detection and energy disaggregation. To address the challenges of event detection, a regional threshold-based algorithm is developed, eliminating the need for predefined thresholds. A comprehensive complexity analysis of the developed algorithms reveals a reduced computational complexity, making the framework suitable for real-time deployment. For performance assessment, the Reference Energy Disaggregation dataset (REDD) and Energy Monitoring via Building Electricity Disaggregation dataset (EMBED) are utilized. A frequency of 1 Hz is maintained to ensure accurate evaluation. The proposed event detection algorithm achieves a precision of 92.5% and an f1-score of 73.6% on EMBED data, improving average precision by 23.5% and a substantial reduction in false positives compared to an existing method. The energy disaggregation algorithm separates the power consumption of four devices in 15.6 s and the entire framework (filtering, event detection, and energy disaggregation) takes 15.9 s for execution, all achieved using a semi-supervised training-less approach.

Exploration of cell state heterogeneity using single-cell proteomics through sensitivity-tailored data-independent acquisition

Single-cell resolution analysis of complex biological tissues is fundamental to capture cell-state heterogeneity and distinct cellular signaling patterns that remain obscured with population-based techniques. The limited amount of material encapsulated in a single cell however, raises significant technical challenges to molecular profiling. Due to extensive optimization efforts, single-cell proteomics by Mass Spectrometry (scp-MS) has emerged as a powerful tool to facilitate proteome profiling from ultra-low amounts of input, although further development is needed to realize its full potential. To this end, we carry out comprehensive analysis of orbitrap-based data-independent acquisition (DIA) for limited material proteomics. Notably, we find a fundamental difference between optimal DIA methods for high- and low-load samples. We further improve our low-input DIA method by relying on high-resolution MS1 quantification, thus enhancing sensitivity by more efficiently utilizing available mass analyzer time. With our ultra-low input tailored DIA method, we are able to accommodate long injection times and high resolution, while keeping the scan cycle time low enough to ensure robust quantification. Finally, we demonstrate the capability of our approach by profiling mouse embryonic stem cell culture conditions, showcasing heterogeneity in global proteomes and highlighting distinct differences in key metabolic enzyme expression in distinct cell subclusters.

Harnessing highly efficient triboelectric sensors and machine learning for self-powered intelligent security applications

In the contemporary epoch, distinguished by a transition from the internet-of-things (IoT) to the artificial intelligence of things (AIoT), individual electronic appliances necessitate inherent power-generation, independence from internet connectivity, and an imbued degree of intellect. Devices governed by pressure or strain sensors particularly demand such attributes. Responding to this technological imperative, our study endeavored to conceive an intelligent door security apparatus grounded on the universally adopted numerical input system. Despite the commercialization of identification systems such as fingerprint, iris, or facial recognition, these mechanisms suffer from susceptibility to a variety of functional aberrations. Consequently, our investigation concentrated on a security system predicated on numerical input. This necessitated the formulation of a swift, self-powered pressure sensor characterized by sensitivity to minute pressure changes. As such, we engineered a triboelectric pressure sensor incorporating a composite of Ti3C2-based MXene and polydimethylsiloxane (PDMS) as the electronegative stratum, and Nylon functioning as the electropositive layer. Addressing the sensor's intrinsic deficiency in sensitivity to pressure, we augmented the MXene-PDMS composite's surface with an out-of-plane wavy structure, and utilized a Nylon stratum composed of nanofibers, thereby amplifying the contact area under pressurized conditions. This meticulously developed sensor displayed a sensitivity metric of 0.604 kPa−1 at 15 kPa, and notably, the swiftest response times recorded amongst triboelectric pressure sensors to date. Post attachment of the sensor to a numeric keypad (ranging from 0 to 9), we meticulously measured the signal alterations contingent on each key press, resulting in a comprehensive dataset. Employing a multitude of machine learning algorithms, we realized an exemplary degree of precision in both training and testing phases. The pragmatic implications of this work are noteworthy. Not only does our technology facilitate the unlocking of a door by entering the correct numerical code, but it is capable of recognizing distinct triboelectric signal patterns, corresponding to the specific manner of key entry by an authorized user, offering an additional dimension of security.

Algorithm for biological second messenger analysis with dynamic regions of interest.

Physiological function is regulated through cellular communication that is facilitated by multiple signaling molecules such as second messengers. Analysis of signal dynamics obtained from cell and tissue imaging is difficult because of intricate spatially and temporally distinct signals. Signal analysis tools based on static region of interest analysis may under- or overestimate signals in relation to region of interest size and location. Therefore, we developed an algorithm for biological signal detection and analysis based on dynamic regions of interest, where time-dependent polygonal regions of interest are automatically assigned to the changing perimeter of detected and segmented signals. This approach allows signal profiles to be rigorously and precisely tracked over time, eliminating the signal distortion observed with static methods. Integration of our approach with state-of-the-art image processing and particle tracking pipelines enabled the isolation of dynamic cellular signaling events and characterization of biological signaling patterns with distinct combinations of parameters including amplitude, duration, and spatial spread. Our algorithm was validated using synthetically generated datasets and compared with other available methods. Application of the algorithm to volumetric time-lapse hyperspectral images of cyclic adenosine monophosphate measurements in rat microvascular endothelial cells revealed distinct signal heterogeneity with respect to cell depth, confirming the utility of our approach for analysis of 5-dimensional data. In human tibial arteries, our approach allowed the identification of distinct calcium signal patterns associated with atherosclerosis. Our algorithm for automated detection and analysis of second messenger signals enables the decoding of signaling patterns in diverse tissues and identification of pathologic cellular responses.

Monitoring Cell Adhesion on Polycaprolactone-Chitosan Films with Varying Blend Ratios by Quartz Crystal Microbalance with Dissipation.

A detailed understanding of the cell adhesion on polymeric surfaces is required to improve the performance of biomaterials. Quartz crystal microbalance with dissipation (QCM-D) as a surface-sensitive technique has the advantage of label-free and real-time monitoring of the cell-polymer interface, providing distinct signal patterns for cell-polymer interactions. In this study, QCM-D was used to monitor human fetal osteoblastic (hFOB) cell adhesion onto polycaprolactone (PCL) and chitosan (CH) homopolymer films as well as their blend films (75:25 and 25:75). Complementary cell culture assays were performed to verify the findings of QCM-D. The thin polymer films were successfully prepared by spin-coating, and relevant properties, i.e., surface morphology, ζ-potential, wettability, film swelling, and fibrinogen adsorption, were characterized. The adsorbed amount of fibrinogen decreased with an increasing percentage of chitosan in the films, which predominantly showed an inverse correlation with surface hydrophilicity. Similarly, the initial cell sedimentation after 1 h resulted in lesser cell deposition as the chitosan ratio increased in the film. Furthermore, the QCM-D signal patterns, which were measured on the homopolymer and blend films during the first 18 h of cell adhesion, also showed an influence of the different interfacial properties. Cells fully spread on pure PCL films and had elongated morphologies as monitored by fluorescence microscopy and scanning electron microscopy (SEM). Corresponding QCM-D signals showed the highest frequency drop and the highest dissipation. Blend films supported cell adhesion but with lower dissipation values than for the PCL film. This could be the result of a higher rigidity of the cell-blend interface because the cells do not pass to the next stages of spreading after secretion of their extracellular matrix (ECM) proteins. Variations in the QCM-D data, which were obtained at the blend films, could be attributed to differences in the morphology of the films. Pure chitosan films showed limited cell adhesion accompanied by low frequency drop and low dissipation.

Distinct Transcriptional Responses of Skeletal Muscle to Short-Term Cold Exposure in Tibetan Pigs and Bama Pigs.

Piglets are susceptible to cold, and piglet death caused by cold stress leads to economic losses in the pig industry in cold areas. Skeletal muscle plays a key role in adaptive thermogenesis in mammals, but the related mechanism in pigs is unclear. In this study, cold-tolerant Tibetan pigs and cold-sensitive Bama pigs were subjected to either a cold environment (4 °C) or a room temperature environment (25 °C) for 3 days. The biceps femoris (BF) and longissimus dorsi muscle (LDM) were collected for phenotypic analysis, and the BF was used for genome-wide transcriptional profiling. Our results showed that Tibetan pigs had a higher body temperature than Bama pigs upon cold stimulation. RNA-seq data indicated a stronger transcriptional response in the skeletal muscle of Tibetan pigs upon cold stimulation, as more differentially expressed genes (DEGs) were identified with the same criteria (p < 0.05 and fold change > 2). In addition, distinct pathway signaling patterns in skeletal muscle upon cold exposure were found between the breeds of pigs. Mitochondrial beta-oxidation-related genes and pathways were significantly upregulated in Tibetan pigs, indicating that Tibetan pigs may use fatty acids as the primary fuel source to protect against cold. However, the significant upregulation of inflammatory response- and glycolysis-related genes and pathways in the skeletal muscle of Bama pigs suggested that these pigs may use glucose as the primary fuel source in cold environments. Together, our study revealed the distinct transcriptional responses of skeletal muscle to cold stimulation in Tibetan pigs and Bama pigs and provided novel insights for future investigation of the cold adaptation mechanism in pigs.

Single-cell resolved ploidy and chromosomal aberrations in nonalcoholic steatohepatitis-(NASH) induced hepatocellular carcinoma and its precursor lesions

Nonalcoholic steatohepatitis (NASH)-induced hepatocellular carcinoma (HCC) and its precursor, nonalcoholic fatty liver disease (NAFLD) are an unmet health issue due to widespread obesity. We assessed copy number changes of genes associated with hepatocarcinogenesis and oxidative pathways at a single-cell level. Eleven patients with NASH-HCC and 11 patients with NAFLD were included. Eight probes were analyzed using multiplex interphase fluorescence in situ hybridization (miFISH), single-cell imaging and phylogenetic tree modelling: Telomerase reverse transcriptase (TERT), C-Myc (MYC), hepatocyte growth factor receptor tyrosine kinase (MET), tumor protein 53 (TP53), cyclin D1 (CCND1), human epidermal growth factor receptor 2 (HER2), the fragile histidine triad gene (FHIT) and FRA16D oxidoreductase (WWOX). Each NASH-HCC tumor had up to 14 distinct clonal signal patterns indicating multiclonality, which correlated with high tumor grade. Changes frequently observed were TP53 losses, 45%; MYC gains, 36%; WWOX losses, 36%; and HER2 gains, 18%. Whole-genome duplications were frequent (82%) with aberrant tetraploid cells evolving from diploid ancestors. Non-tumorous NAFLD/NASH biopsies did not harbor clonal copy number changes. Fine mapping of NASH-HCC using single-cell multiplex FISH shows that branched tumor evolution involves genome duplication and that multiclonality increases with tumor grade. The loss of oxidoreductase WWOX and HER2 gains could be potentially associated with NASH-induced hepatocellular carcinoma.

Development and applications of chromosome-specific cytogenetic BAC-FISH probes in Larimichthys crocea

Large yellow croaker Larimichthys crocea (Richardson) is an important member in family Sciaenidae, and one of most productive mariculture fishes in China. Fluorescence in situ hybridization is a useful tool for cytogenetics and genomics research. Here, we demonstrated that bacterial artificial chromosome (BAC) clones could be used to identify individual chromosomes in large yellow croaker, and also to study chromosome evolution in the related species. By using BAC paired-end sequencing and sequence alignment, 435 BAC clones were anchored to 24 pseudochromosomes of large yellow croaker genome. Among them, 72 BAC clones with low repeat content were selected and passed PCR test, and then grouped by chromosome for FISH test. As a results, there were 67 BAC clones, 2 to 3 BAC clones per chromosome, generated specific and stable signal at expected position. Then, a dual-color FISH probe cocktail composed of 48 of these BAC clones was formulated and used to hybridize metaphase chromosome spreads, resulting in distinct signal patterns on each chromosome pair, which help to distinguish all chromosomes in the metaphase spreads of large yellow croaker. The chromosome-specific BAC-FISH probes were also applied to a close relative of large yellow croaker, Collichthys lucidus, demonstrating that its Y chromosome originated from the fusion of Chr.1 and Chr.7. Thus, our study provides the first set of chromosome-specific FISH probes in family Sciaenidae, which will play an important role in cytogenetics and genomics research in the family.

The novel subclusters based on cancer-associated fibroblast for pancreatic adenocarcinoma.

Pancreatic adenocarcinoma (PAAD) is a fatal disease characterized by promoting connective tissue proliferation in the stroma. Activated cancer-associated fibroblasts (CAFs) play a key role in fibrogenesis in PAAD. CAF-based tumor typing of PAAD has not been explored. We extracted single-cell sequence transcriptomic data from GSE154778 and CRA001160 datasets from Gene Expression Omnibus or Tumor Immune Single-cell Hub to collect CAFs in PAAD. On the basis of Seurat packages and new algorithms in machine learning, CAF-related subtypes and their top genes for PAAD were analyzed and visualized. We used CellChat package to perform cell-cell communication analysis. In addition, we carried out functional enrichment analysis based on clusterProfiler package. Finally, we explored the prognostic and immunotherapeutic value of these CAF-related subtypes for PAAD. CAFs were divided into five new subclusters (CAF-C0, CAF-C1, CAF-C2, CAF-C3, and CAF-C4) based on their marker genes. The five CAF subclusters exhibited distinct signaling patterns, immune status, metabolism features, and enrichment pathways and validated in the pan-cancer datasets. In addition, we found that both CAF-C2 and CAF-C4 subgroups were negatively correlated with prognosis. With their top genes of each subclusters, the sub-CAF2 had significantly relations to immunotherapy response in the patients with pan-cancer and immunotherapy. We explored the heterogeneity of five subclusters based on CAF in signaling patterns, immune status, metabolism features, enrichment pathways, and prognosis for PAAD.

Direct comparison of non-osteoarthritic and osteoarthritic synovial fluid-induced intracellular chondrocyte signaling and phenotype changes

Since the joint microenvironment and tissue homeostasis are highly dependent on synovial fluid, we aimed to compare the essential chondrocyte signaling signatures of non-osteoarthritic vs end-stage osteoarthritic knee synovial fluid. Moreover, we determined the phenotypic consequence of the distinct signaling patterns on articular chondrocytes. Protein profiling of synovial fluid was performed using antibody arrays. Chondrocyte signaling and phenotypic changes induced by non-osteoarthritic and osteoarthritic synovial fluid were analyzed using a phospho-kinase array, luciferase-based transcription factor activity assays, and RT-qPCR. The origin of osteoarthritic synovial fluid signaling was evaluated by comparing the signaling responses of conditioned media from cartilage, synovium, infrapatellar fat pad and meniscus. Osteoarthritic synovial fluid induced pathway-phenotype relationships were evaluated using pharmacological inhibitors. Compared to non-osteoarthritic synovial fluid, osteoarthritic synovial fluid was enriched in cytokines, chemokines and growth factors that provoked differential MAPK, AKT, NFκB and cell cycle signaling in chondrocytes. Functional pathway analysis confirmed increased activity of these signaling events upon osteoarthritic synovial fluid stimulation. Tissue secretomes of osteoarthritic cartilage, synovium, infrapatellar fat pad and meniscus activated several inflammatory signaling routes. Furthermore, the distinct pathway signatures of osteoarthritic synovial fluid led to accelerated chondrocyte dedifferentiation via MAPK/ERK signaling, increased chondrocyte fibrosis through MAPK/JNK and PI3K/AKT activation, an elevated inflammatory response mediated by cPKC/NFκB, production of extracellular matrix-degrading enzymes by MAPK/p38 and PI3K/AKT routes, and enabling of chondrocyte proliferation. This study provides the first mechanistic comparison between non-osteoarthritic and osteoarthritic synovial fluid, highlighting MAPKs, cPKC/NFκB and PI3K/AKT as crucial OA-associated intracellular signaling routes.

Effects of site-directed mutagenesis of GLP-1 and glucagon receptors on signal transduction activated by dual and triple agonists.

The paradigm of one drug against multiple targets, known as unimolecular polypharmacology, offers the potential to improve efficacy while overcoming some adverse events associated with the treatment. This approach is best exemplified by targeting two or three class B1 G protein-coupled receptors, namely, glucagon-like peptide-1 receptor (GLP-1R), glucagon receptor (GCGR) and glucose-dependent insulinotropic polypeptide receptor for treatment of type 2 diabetes and obesity. Some of the dual and triple agonists have already shown initial successes in clinical trials, although the molecular mechanisms underlying their multiplexed pharmacology remain elusive. In this study we employed structure-based site-directed mutagenesis together with pharmacological assays to compare agonist efficacy across two key signaling pathways, cAMP accumulation and ERK1/2 phosphorylation (pERK1/2). Three dual agonists (peptide 15, MEDI0382 and SAR425899) and one triple agonist (peptide 20) were evaluated at GLP-1R and GCGR, relative to the native peptidic ligands (GLP-1 and glucagon). Our results reveal the existence of residue networks crucial for unimolecular agonist-mediated receptor activation and their distinct signaling patterns, which might be useful to the rational design of biased drug leads.

Impact of Laser Intensity Noise on Dual-Comb Absolute Ranging Precision.

Noise in mode-locked lasers has been a central issue for dual-comb metrological applications. In this work, we investigate the laser intensity noise on dual-comb absolute ranging precision. Two different dual-comb schemes based on linear optical sampling (LOS) and nonlinear asynchronous optical sampling (ASOPS) have been constructed. In the LOS scheme, the ranging precision deteriorates with the increase in laser relative intensity noise (RIN). This effect can be corrected by implementing a balanced photo-detection (BPD). In the ASOPS scheme, the experiment shows that the conversion from laser RIN to dual-comb ranging precision is negligible, making a balanced detection unnecessary for ranging precision improvement. The different manners of RIN’s impact on absolute ranging precision are attributed to the distinct cross-correlation signal patterns and the underlying time-of-flight (TOF) extraction algorithms.

Quality-Discriminative Localization of Multisensor Signals for Root Cause Analysis

Root cause analysis (RCA) methods for effectively identifying the critical causes of abnormal processes have attracted attention because manufacturing processes have grown in scale and complexity. However, the existing methods for building automatic RCA models suffer from the disadvantage of typically requiring expert knowledge. In addition, without a dataset representing the causal relationship of multivariate processes, it is difficult to provide useful information for RCA. Although data-driven RCA methods have been proposed, most are based on classification models. Given that product quality is defined as a continuous variable in many manufacturing industries, classification models are limited in deriving root causes affecting the product quality level. In this article, we propose a regression model-based RCA method, which we call quality-discriminative localization, consisting of a convolutional neural network (CNN)-based activation mapping of multisensor signal data. In our proposed method, the CNN predicts the product quality of a continuous variable. Activation mapping then extracts causal maps that highlight significant sensor signals for each product. To identify the root causes, we generate a root cause map from the weighted sum of quality and causal maps. We consider root causes as locations of abnormal processes and processing times from localized activation scores on the root cause map. We experimentally demonstrate the usefulness of the proposed method with simulated data and real process data from a steel manufacturing process. Our results show that the proposed method successfully identifies root causes with distinct sensor signal patterns.

Subcutaneous Adipose Tissue Edema in Lipedema Revealed by Noninvasive 3T MR Lymphangiography.

Lipedema exhibits excessive lower-extremity subcutaneous adipose tissue (SAT) deposition, which is frequently misidentified as obesity until lymphedema presents. MR lymphangiography may have relevance to distinguish lipedema from obesity or lymphedema. Hyperintensity profiles on 3T MR lymphangiography can identify distinct features consistent with SAT edema in participants with lipedema. Prospective cross-sectional study. Participants (48 females, matched for age [mean=44.8 years]) with lipedema (n=14), lipedema with lymphedema (LWL, n=12), cancer treatment-related lymphedema (lymphedema, n=8), and controls without these conditions (n=14). 3T MR lymphangiography (nontracer 3D turbo-spin-echo). Review of lymphangiograms in lower extremities by three radiologists was performed independently. Spatial patterns of hyperintense signal within the SAT were scored for extravascular (focal, diffuse, or not apparent) and vascular (linear, dilated, or not apparent) image features. Interreader reliability was computed using Fleiss Kappa. Fisher's exact test was used to evaluate the proportion of image features between study groups. Multinomial logistic regression was used to assess the relationship between image features and study groups. The odds ratio (OR) and 95% confidence interval (CI) of SAT extravascular and vascular features was reported in groups compared to lipedema. The threshold of statistical significance was P < 0.05. Reliable agreement was demonstrated between three independent, blinded reviewers (P < 0.001). The frequency of SAT hyperintensities in participants with lipedema (36% focal, 36% diffuse), LWL (42% focal, 33% diffuse), lymphedema (62% focal, 38% diffuse), and controls (43% focal, 0% diffuse) was significantly distinct. Compared with lipedema, SAT hyperintensities were less frequent in controls (focal: OR=0.63, CI=0.11-3.41; diffuse: OR=0.05, CI=0.00-1.27), similar in LWL (focal: OR=1.29, CI=0.19-8.89; diffuse: OR=1.05, CI=0.15-7.61), and more frequent in lymphedema (focal: OR=9.00, CI=0.30-274.12; diffuse: OR=5.73, CI=0.18-186.84). Noninvasive MR lymphangiography identifies distinct signal patterns indicating SAT edema and lymphatic load in participants with lipedema. 1 TECHNICAL EFFICACY: Stage 1.

Photoacoustic/Fluorescence Dual-Modality Probe for Biothiol Discrimination and Tumor Diagnosis in Cells and Mice.

Developing probes to simultaneously detect and discriminate biothiols is important, yet challenging. Activatable photoacoustic (PA) probes for discriminating biothiols in vivo are still lacking, and this hinders the diagnosis of thiol-related diseases. Herein we present the first PA and fluorescence dual-modality probe MB-NBD for discriminating different biothiol species. The probe has the advantages of both fluorescence imaging and PA imaging (high sensitivity and deep penetration) with distinct signal patterns toward hydrogen sulfide (H2S), cysteine/homocysteine (Cys/Hcy), and glutathione (GSH) treatment. The biothiol-activated product of MB-NBD exhibits enhancements in near-infrared fluorescence (NIRF) at 690 nm and absorbance/PA at 664 nm upon fast reaction, allowing it to selectively detect biothiol species over other reactive species. On the other hand, MB-NBD displays characteristic absorbance enhancement at 547 nm toward H2S, rendering specific detection of H2S. In addition, the specific enhancements in absorbance/PA at 470 nm and fluorescence at 550 nm toward Cys/Hcy treatment endows the probe with the capability of selectively detecting Cys/Hcy. Furthermore, MB-NBD is able to discriminate Cys and GSH by fluorescent imaging in live-cell and ratiometric PA imaging in mice experiments. MB-NBD has been successfully used to diagnose tumors by dual-channel ratiometric PA imaging.