Clustering with t-SNE, provably

Identifying low-dimensional trajectories of mechanically-ventilated patient systems: Empirical phenotypes of joint patient+care processes to enhance temporal analysis in ARDS research

Refined management of mechanically ventilation is an obvious target for improving patient outcomes, but is impeded by the nature of data for study and hypothesis generation. The connections between clinical outcomes and temporal development of iatrogenic injuries current lung-protective ventilator settings remain poorly understood. Analysis of lung-ventilator system (LVS) evolution at relevant timescales is frustrated by data volume and multiple sources of heterogeneity. This work motivates, presents, and validates a computational pipeline for resolving LVS systems into the joint evolution of data-conditioned model parameters and ventilator information. Applied to individuals, the workflow yields a concise low-dimensional representation of LVS behavior expressed in phenotypic breath waveforms suitable for analysis. The effectiveness of this approach is demonstrated through application to multi-day observational series of 35 patients. Individual patient analyses reveal multiple types of patient-oriented dynamics and breath behavior to expose the complexity of LVS evolution; less than 10% of phenotype changes related to ventilator settings changes. Dynamics are shown to including both stable and unstable phenotype transitions as well as both discrete and continuous changes unrelated to ventilator settings. At a cohort scale, 721 phenotypes constructed from individual data are condensed into a set of 16 groups that empirically organize around certain settings (positive end-expository pressure and ventilator mode) and structurally similar pressure-volume loop characterizations. Individual and cohort scale phenotypes, which may be refined by hypothesis-specific constructions, provide a common framework for ongoing temporal analysis and investigation of LVS dynamics.

AI-Enhanced evaluation of YouTube content on post-surgical incontinence following pelvic cancer treatment

Background

Several pelvic area cancers exhibit high incidence rates, and their surgical treatment can result in adverse effects such as urinary and fecal incontinence, significantly impacting patients' quality of life. Post-surgery incontinence is a significant concern, with prevalence rates ranging from 25 to 45% for urinary incontinence and 9-68% for fecal incontinence. Cancer survivors are increasingly turning to YouTube as a platform to connect with others, yet caution is warranted as misinformation is prevalent.

Objective

This study aims to evaluate the information quality in YouTube videos about post-surgical incontinence after pelvic area cancer surgery.

Methods

A YouTube search for "Incontinence after cancer surgery" yielded 108 videos, which were subsequently analyzed. To evaluate these videos, several quality assessment tools were utilized, including DISCERN, GQS, JAMA, PEMAT, and MQ-VET. Statistical analyses, such as descriptive statistics and intercorrelation tests, were employed to assess various video attributes, including characteristics, popularity, educational value, quality, and reliability. Also, artificial intelligence techniques like PCA, t-SNE, and UMAP were used for data analysis. HeatMap and Hierarchical Clustering Dendrogram techniques validated the Machine Learning results.

Results

The quality scales presented a high level of correlation one with each other (p < 0.01) and the Artificial Intelligence-based techniques presented clear clustering representations of the dataset samples, which were reinforced by the Heat Map and Hierarchical Clustering Dendrogram.

Conclusions

YouTube videos on "Incontinence after Cancer Surgery" present a "High" quality across multiple scales. The use of AI tools, like PCA, t-SNE, and UMAP, is highlighted for clustering large health datasets, improving data visualization, pattern recognition, and complex healthcare analysis.

High-risk event prone driver identification considering driving behavior temporal covariate shift

Drivers who perform frequent high-risk events (e.g., hard braking maneuvers) pose a significant threat to traffic safety. Existing studies commonly estimated high-risk event occurrence probabilities based upon the assumption that data collected from different time periods are independent and identically distributed (referred to as i.i.d. assumption). Such approach ignored the issue of driving behavior temporal covariate shift, where the distributions of driving behavior factors vary over time. To fill the gap, this study targets at obtaining time-invariant driving behavior features and establishing their relationships with high-risk event occurrence probability. Specifically, a generalized modeling framework consisting of distribution characterization (DC) and distribution matching (DM) modules was proposed. The DC module split the whole dataset into several segments with the largest distribution gaps, while the DM module identified time-invariant driving behavior features through learning common knowledge among different segments. Then, gated recurrent unit (GRU) was employed to conduct time-invariant driving behavior feature mining for high-risk event occurrence probability estimation. Moreover, modified loss functions were introduced for imbalanced data learning caused by the rarity of high-risk events. The empirical analyses were conducted utilizing online ride-hailing services data. Experiment results showed that the proposed generalized modeling framework provided a 7.2% higher average precision compared to the traditional i.i.d. assumption based approach. The modified loss functions further improved the model performance by 3.8%. Finally, benefits for the driver management program improvement have been explored by a case study, demonstrating a 33.34% enhancement in the identification precision of high-risk event prone drivers.

Universal Deoxidation of Semiconductor Substrates Assisted by Machine Learning and Real-Time Feedback Control

Substrate oxidation is inevitable when exposed to ambient atmosphere during semiconductor manufacturing, which is detrimental to the fabrication of state-of-the-art devices. Optimizing the deoxidation process in molecular beam epitaxy (MBE) for random substrates poses a multidimensional challenge and is sometimes controversial. Due to variations in substrates and growth processes, the determination of the deoxidation condition heavily relies on the individual's expertise, yielding inconsistent results. This study employs a machine learning model that integrates interpolation and vision transformer (Interpolation-ViT) techniques. The model utilizes reflection high-energy electron diffraction videos as input to predict the status of the substrate, enabling automated deoxidation within a controlled architecture for various substrates. Furthermore, we highlight the potential of models trained on data from specific MBE equipment to achieve high-accuracy deployment on different pieces of equipment. In contrast to traditional methods, our approach holds exceptional value, as it standardizes deoxidation temperatures across diverse equipment and substrates. This significantly advances the standardization of the semiconductor process. The concepts and methods presented are expected to revolutionize semiconductor manufacturing processes in the optoelectronic and microelectronic industries.

Integrated analysis of diverse cancer types reveals a breast cancer-specific serum miRNA biomarker through relative expression orderings analysis

PURPOSE

Serum microRNA (miRNA) holds great potential as a non-invasive biomarker for diagnosing breast cancer (BrC). However, most diagnostic models rely on the absolute expression levels of miRNAs, which are susceptible to batch effects and challenging for clinical transformation. Furthermore, current studies on liquid biopsy diagnostic biomarkers for BrC mainly focus on distinguishing BrC patients from healthy controls, needing more specificity assessment.

METHODS

We collected a large number of miRNA expression data involving 8465 samples from GEO, including 13 different cancer types and non-cancer controls. Based on the relative expression orderings (REOs) of miRNAs within each sample, we applied the greedy, LASSO multiple linear regression, and random forest algorithms to identify a qualitative biomarker specific to BrC by comparing BrC samples to samples of other cancers as controls.

RESULTS

We developed a BrC-specific biomarker called 7-miRPairs, consisting of seven miRNA pairs. It demonstrated comparable classification performance in our analyzed machine learning algorithms while requiring fewer miRNA pairs, accurately distinguishing BrC from 12 other cancer types. The diagnostic performance of 7-miRPairs was favorable in the training set (accuracy = 98.47%, specificity = 98.14%, sensitivity = 99.25%), and similar results were obtained in the test set (accuracy = 97.22%, specificity = 96.87%, sensitivity = 98.02%). KEGG pathway enrichment analysis of the 11 miRNAs within the 7-miRPairs revealed significant enrichment of target mRNAs in pathways associated with BrC.

CONCLUSION

Our study provides evidence that utilizing serum miRNA pairs can offer significant advantages for BrC-specific diagnosis in clinical practice by directly comparing serum samples with BrC to other cancer types.

Development an electronic nose to recognize pesticides in groundwater

Timely detection of Groundwater pollution is essential to protect human health, especially for pesticide pollution. To solve this issue, we proposed a novel solution to realize the prediction of pesticide in groundwater by using the electronic nose (e-nose). The main work of this paper was divided into three steps: 1) checking whether sample was polluted by pesticides, 2) further predicting the pesticide type, brand and pollution degree when the sample was polluted by pesticides, and 3) optimizing the sensor array. Random forest was used to complete the first step, which had the best accuracy and sensitivity of 100 %. Support vector machine was applied to complete the second step, and the accuracy reaching 98.08 %. As for the third step, recursive feature elimination was used to optimize the sensor array. After optimization, the number of sensors was reduced from 26 to 8. In addition, the e-nose developed in this paper was compared with a commercial e-nose. The results showed that the cost of the developed e-nose was much lower than that of the commercial e-nose despite its slightly weaker prediction performance. Thus, this e-nose can be employed to recognize the pesticides in groundwater, and even can be integrated into the while drilling technology to realize the in-situ detection of groundwater.

An open set model for pest identification

Agricultural pest identification is a prerequisite for increasing crop production and meeting global food demands. Generally, numerous phenotypic and genotypic features are widely utilized for species-level pest identification. However, the approaches are time-consuming and require expert knowledge in relevant fields. Numerous image-based machine learning (ML) models also exist to identify insect pests in agricultural fields. The models are significantly rely on a large, manually curated dataset and are close-set in nature. Our study aims to develop an open set pest identification approach by adding the capability of rejecting any irrelevant inputs. Tephritid fruit flies (Diptera:Tephritidae) are considered as an example since they are the most economically important agricultural pests worldwide. Images of the fruit flies were collected from a publicly available database and filtered to exclude uninformative images using a deep learning model (Inception-V3) and an unsupervised k-means clustering method. For the closed-set identification task, our EfficientNet-B2 model classified four major genera of notorious tephritid flies, namely, Anastrepha, Ceratitis, Rhagoletis, and Bactrocera with an accuracy of 89.65%. We further improvise our proposed model for open-set recognition tasks to leverage the identification beyond the trained datasets. The open set model achieved an overall accuracy of 86.48% and a macro F1-score of 94.44% on the four genera and an unknown class. Our proposed model can be a practical and effective pest identification tool for harmful fruit flies. In addition, the model is easy to implement with existing agricultural pest control systems in an open-world scenario.

LSA-ac4C: A hybrid neural network incorporating double-layer LSTM and self-attention mechanism for the prediction of N4-acetylcytidine sites in human mRNA

N4-acetylcytidine (ac4C) is a vital constituent of the epitranscriptome and plays a crucial role in the regulation of mRNA expression. Numerous studies have established correlations between ac4C and the incidence, progression and prognosis of various cancers. Therefore, accurately predicting ac4C sites is an important step towards comprehending the biological functions of this modification and devising effective therapeutic interventions. Wet experiments are primary methods for studying ac4C, but computational methods have emerged as a promising supplement due to their cost-effectiveness and shorter research cycles. However, current models still have inherent limitations in terms of predictive performance and generalization ability. Here, we utilized automated machine learning technology to establish a reliable baseline and constructed a deep hybrid neural network, LSA-ac4C, which combines double-layer Long Short-Term Memory (LSTM) and self-attention mechanism for accurate ac4C sites prediction. Benchmarking comparisons demonstrate that LSA-ac4C exhibits superior performance compared to the current state-of-the-art method, with ACC, MCC and AUROC improving by 2.89 %, 5.96 % and 1.53 %, respectively, on an independent test set. Overall, LSA-ac4C serves as a powerful tool for predicting ac4C sites in human mRNA, thus benefiting research on RNA modification. For the convenience of the research community, a web server has been established at http://tubic.org/ac4C.

Novel SERS biosensor for rapid detection of breast cancer based on Ag2O-Ag-PSi nanochips

Ag2O-Ag-PSi (porous silicon) surface-enhanced Raman scattering (SERS) chip was successfully synthesized by electrochemical corrosion, in situ reduction and heat treatment technology. The influence of different heat treatment temperature on SERS performance of the chip is studied. The results show that the chip treated at 300 °C has the best SERS performance. The chip was composed of Ag2O-Ag nano core shell with a diameter of 40-60 nm and porous silicon substrate. Then, the optimized chip was used to perform SERS test on serum samples from 30 healthy volunteers and 30 early breast cancer patients, and the baseline was corrected by LabSpec6 software. Finally, the data were analyzed by principal component analysis combined with t-distributed Stochastic Neighbor Embedding (PCA-t-SNE). The results showed that the accuracy of the improved substrate combined with multivariate statistical method was 98%. The shelf life of the chips exceeded six months due to the presence of the Ag2O shell. This study provides a basis for developing a low-cost rapid and sensitive early screening technology for breast cancer.

A methodology of phenotyping ICU patients from EHR data: High-fidelity, personalized, and interpretable phenotypes estimation

OBJECTIVE

Computing phenotypes that provide high-fidelity, time-dependent characterizations and yield personalized interpretations is challenging, especially given the complexity of physiological and healthcare systems and clinical data quality. This paper develops a methodological pipeline to estimate unmeasured physiological parameters and produce high-fidelity, personalized phenotypes anchored to physiological mechanics from electronic health record (EHR).

METHODS

A methodological phenotyping pipeline is developed that computes new phenotypes defined with unmeasurable computational biomarkers quantifying specific physiological properties in real time. Working within the inverse problem framework, this pipeline is applied to the glucose-insulin system for ICU patients using data assimilation to estimate an established mathematical physiological model with stochastic optimization. This produces physiological model parameter vectors of clinically unmeasured endocrine properties, here insulin secretion, clearance, and resistance, estimated for individual patient. These physiological parameter vectors are used as inputs to unsupervised machine learning methods to produce phenotypic labels and discrete physiological phenotypes. These phenotypes are inherently interpretable because they are based on parametric physiological descriptors. To establish potential clinical utility, the computed phenotypes are evaluated with external EHR data for consistency and reliability and with clinician face validation.

RESULTS

The phenotype computation was performed on a cohort of 109 ICU patients who received no or short-acting insulin therapy, rendering continuous and discrete physiological phenotypes as specific computational biomarkers of unmeasured insulin secretion, clearance, and resistance on time windows of three days. Six, six, and five discrete phenotypes were found in the first, middle, and last three-day periods of ICU stays, respectively. Computed phenotypic labels were predictive with an average accuracy of 89%. External validation of discrete phenotypes showed coherence and consistency in clinically observable differences based on laboratory measurements and ICD 9/10 codes and clinical concordance from face validity. A particularly clinically impactful parameter, insulin secretion, had a concordance accuracy of 83%±27%.

CONCLUSION

The new physiological phenotypes computed with individual patient ICU data and defined by estimates of mechanistic model parameters have high physiological fidelity, are continuous, time-specific, personalized, interpretable, and predictive. This methodology is generalizable to other clinical and physiological settings and opens the door for discovering deeper physiological information to personalize medical care.

Noncontact remote sensing of abnormal blood pressure using a deep neural network: a novel approach for hypertension screening

Background

As the global burden of hypertension continues to increase, early diagnosis and treatment play an increasingly important role in improving the prognosis of patients. In this study, we developed and evaluated a method for predicting abnormally high blood pressure (HBP) from infrared (upper body) remote thermograms using a deep learning (DL) model.

Methods

The data used in this cross-sectional study were drawn from a coronavirus disease 2019 (COVID-19) pilot cohort study comprising data from 252 volunteers recruited from 22 July to 4 September 2020. Original video files were cropped at 5 frame intervals to 3,800 frames per slice. Blood pressure (BP) information was measured using a Welch Allyn 71WT monitor prior to infrared imaging, and an abnormal increase in BP was defined as a systolic blood pressure (SBP) ≥140 mmHg and/or diastolic blood pressure (DBP) ≥90 mmHg. The PanycNet DL model was developed using a deep neural network to predict abnormal BP based on infrared thermograms.

Results

A total of 252 participants were included, of which 62.70% were male and 37.30% were female. The rate of abnormally high HBP was 29.20% of the total number. In the validation group (upper body), precision, recall, and area under the receiver operating characteristic curve (AUC) values were 0.930, 0.930, and 0.983 [95% confidence interval (CI): 0.904-1.000], respectively, and the head showed the strongest predictive ability with an AUC of 0.868 (95% CI: 0.603-0.994).

Conclusions

This is the first technique that can perform screening for hypertension without contact using existing equipment and data. It is anticipated that this technique will be suitable for mass screening of the population for abnormal BP in public places and home BP monitoring.

Utilization of the percutaneous left ventricular support as bridge to heart transplantation across the United States: In-depth UNOS database analysis

BACKGROUND

Intra-aortic balloon pump (IABP) and Impella device utilization as a bridge to heart transplantation (HTx) have risen exponentially. We aimed to explore the influence of device selection on HTx outcomes, considering regional practice variation.

METHODS

A retrospective longitudinal study was performed on a United Network for Organ Sharing (UNOS) registry dataset. We included adult patients listed for HTx between October 2018 and April 2022 as status 2, as justified by requiring IABP or Impella support. The primary end-point was successful bridging to HTx as status 2.

RESULTS

Of 32,806 HTx during the study period, 4178 met inclusion criteria (Impella n = 650, IABP n = 3528). Waitlist mortality increased from a nadir of 16 (in 2019) to a peak of 36 (in 2022) per thousand status 2 listed patients. Impella annual use increased from 8% in 2019 to 19% in 2021. Compared to IABP, Impella patients demonstrated higher medical acuity and lower success rate of transplantation as status 2 (92.1% vs 88.9%, p < 0.001). The IABP:Impella utilization ratio varied widely between regions, ranging from 1.77 to 21.31, with high Impella use in Southern and Western states. However, this difference was not justified by medical acuity, regional transplant volume, or waitlist time and did not correlate with waitlist mortality.

CONCLUSIONS

The shift in utilizing Impella as opposed to IABP did not improve waitlist outcomes. Our results suggest that clinical practice patterns beyond mere device selection determine successful bridging to HTx. There is a critical need for objective evidence to guide tMCS utilization and a paradigm shift in the UNOS allocation system to achieve equitable HTx practice across the United States.

Saturation mutagenesis of α-synuclein reveals monomer fold that modulates aggregation

α-Synuclein (aSyn) aggregation underlies neurodegenerative synucleinopathies. aSyn seeds are proposed to replicate and propagate neuronal pathology like prions. Seeding of aSyn can be recapitulated in cellular systems of aSyn aggregation; however, the mechanism of aSyn seeding and its regulation are not well understood. We developed an mEos-based aSyn seeding assay and performed saturation mutagenesis to identify with single-residue resolution positive and negative regulators of aSyn aggregation. We not only found the core regions that govern aSyn aggregation but also identified mutants outside of the core that enhance aggregation. We identified local structure within the N terminus of aSyn that hinders the fibrillization propensity of its aggregation-prone core. Based on the screen, we designed a minimal aSyn fragment that shows a ~4-fold enhancement in seeding activity and enabled discrimination of synucleinopathies. Our study expands the basic knowledge of aSyn aggregation and advances the design of cellular systems of aSyn aggregation to diagnose synucleinopathies based on protein conformation.

GoM DE: interpreting structure in sequence count data with differential expression analysis allowing for grades of membership

Parts-based representations, such as non-negative matrix factorization and topic modeling, have been used to identify structure from single-cell sequencing data sets, in particular structure that is not as well captured by clustering or other dimensionality reduction methods. However, interpreting the individual parts remains a challenge. To address this challenge, we extend methods for differential expression analysis by allowing cells to have partial membership to multiple groups. We call this grade of membership differential expression (GoM DE). We illustrate the benefits of GoM DE for annotating topics identified in several single-cell RNA-seq and ATAC-seq data sets.

GoM DE: interpreting structure in sequence count data with differential expression analysis allowing for grades of membership

Parts-based representations, such as non-negative matrix factorization and topic modeling, have been used to identify structure from single-cell sequencing data sets, in particular structure that is not as well captured by clustering or other dimensionality reduction methods. However, interpreting the individual parts remains a challenge. To address this challenge, we extend methods for differential expression analysis by allowing cells to have partial membership to multiple groups. We call this grade of membership differential expression (GoM DE). We illustrate the benefits of GoM DE for annotating topics identified in several single-cell RNA-seq and ATAC-seq data sets.

A methodology of phenotyping ICU patients from EHR data: high-fidelity, personalized, and interpretable phenotypes estimation

Objective

Computing phenotypes that provide high-fidelity, time-dependent characterizations and yield personalized interpretations is challenging, especially given the complexity of physiological and healthcare systems and clinical data quality. This paper develops a methodological pipeline to estimate unmeasured physiological parameters and produce high-fidelity, personalized phenotypes anchored to physiological mechanics from electronic health record (EHR).

Methods

A methodological phenotyping pipeline is developed that computes new phenotypes defined with unmeasurable computational biomarkers quantifying specific physiological properties in real time. Working within the inverse problem framework, this pipeline is applied to the glucose-insulin system for ICU patients using data assimilation to estimate an established mathematical physiological model with stochastic optimization. This produces physiological model parameter vectors of clinically unmeasured endocrine properties, here insulin secretion, clearance, and resistance, estimated for individual patient. These physiological parameter vectors are used as inputs to unsupervised machine learning methods to produce phenotypic labels and discrete physiological phenotypes. These phenotypes are inherently interpretable because they are based on parametric physiological descriptors. To establish potential clinical utility, the computed phenotypes are evaluated with external EHR data for consistency and reliability and with clinician face validation.

Results

The phenotype computation was performed on a cohort of 109 ICU patients who received no or short-acting insulin therapy, rendering continuous and discrete physiological phenotypes as specific computational biomarkers of unmeasured insulin secretion, clearance, and resistance on time windows of three days. Six, six, and five discrete phenotypes were found in the first, middle, and last three-day periods of ICU stays, respectively. Computed phenotypic labels were predictive with an average accuracy of 89%. External validation of discrete phenotypes showed coherence and consistency in clinically observable differences based on laboratory measurements and ICD 9/10 codes and clinical concordance from face validity. A particularly clinically impactful parameter, insulin secretion, had a concordance accuracy of 83% ± 27%.

Conclusion

The new physiological phenotypes computed with individual patient ICU data and defined by estimates of mechanistic model parameters have high physiological fidelity, are continuous, time-specific, personalized, interpretable, and predictive. This methodology is generalizable to other clinical and physiological settings and opens the door for discovering deeper physiological information to personalize medical care.

A Machine Learning Approach to Assess Patients with Deep Neck Infection Progression to Descending Mediastinitis: Preliminary Results

BACKGROUND

Deep neck infection (DNI) is a serious infectious disease, and descending mediastinitis is a fatal infection of the mediastinum. However, no study has applied artificial intelligence to assess progression to descending mediastinitis in DNI patients. Thus, we developed a model to assess the possible progression of DNI to descending mediastinitis.

METHODS

Between August 2017 and December 2022, 380 patients with DNI were enrolled; 75% of patients (n = 285) were assigned to the training group for validation, whereas the remaining 25% (n = 95) were assigned to the test group to determine the accuracy. The patients' clinical and computed tomography (CT) parameters were analyzed via the k-nearest neighbor method. The predicted and actual progression of DNI patients to descending mediastinitis were compared.

RESULTS

In the training and test groups, there was no statistical significance (all p > 0.05) noted at clinical variables (age, gender, chief complaint period, white blood cells, C-reactive protein, diabetes mellitus, and blood sugar), deep neck space (parapharyngeal, submandibular, retropharyngeal, and multiple spaces involved, ≥3), tracheostomy performance, imaging parameters (maximum diameter of abscess and nearest distance from abscess to level of sternum notch), or progression to mediastinitis. The model had a predictive accuracy of 82.11% (78/95 patients), with sensitivity and specificity of 41.67% and 87.95%, respectively.

CONCLUSIONS

Our model can assess the progression of DNI to descending mediastinitis depending on clinical and imaging parameters. It can be used to identify DNI patients who will benefit from prompt treatment.

The specious art of single-cell genomics

Dimensionality reduction is standard practice for filtering noise and identifying relevant features in large-scale data analyses. In biology, single-cell genomics studies typically begin with reduction to 2 or 3 dimensions to produce "all-in-one" visuals of the data that are amenable to the human eye, and these are subsequently used for qualitative and quantitative exploratory analysis. However, there is little theoretical support for this practice, and we show that extreme dimension reduction, from hundreds or thousands of dimensions to 2, inevitably induces significant distortion of high-dimensional datasets. We therefore examine the practical implications of low-dimensional embedding of single-cell data and find that extensive distortions and inconsistent practices make such embeddings counter-productive for exploratory, biological analyses. In lieu of this, we discuss alternative approaches for conducting targeted embedding and feature exploration to enable hypothesis-driven biological discovery.

Clustering Heterogeneous Conformational Ensembles of Intrinsically Disordered Proteins with t-Distributed Stochastic Neighbor Embedding

Intrinsically disordered proteins (IDPs) populate a range of conformations that are best described by a heterogeneous ensemble. Grouping an IDP ensemble into "structurally similar" clusters for visualization, interpretation, and analysis purposes is a much-desired but formidable task, as the conformational space of IDPs is inherently high-dimensional and reduction techniques often result in ambiguous classifications. Here, we employ the t-distributed stochastic neighbor embedding (t-SNE) technique to generate homogeneous clusters of IDP conformations from the full heterogeneous ensemble. We illustrate the utility of t-SNE by clustering conformations of two disordered proteins, Aβ42, and α-synuclein, in their APO states and when bound to small molecule ligands. Our results shed light on ordered substates within disordered ensembles and provide structural and mechanistic insights into binding modes that confer specificity and affinity in IDP ligand binding. t-SNE projections preserve the local neighborhood information, provide interpretable visualizations of the conformational heterogeneity within each ensemble, and enable the quantification of cluster populations and their relative shifts upon ligand binding. Our approach provides a new framework for detailed investigations of the thermodynamics and kinetics of IDP ligand binding and will aid rational drug design for IDPs.

Asymmetric gene expression and cell-type-specific regulatory networks in the root of bread wheat revealed by single-cell multiomics analysis

BACKGROUND

Homoeologs are defined as homologous genes resulting from allopolyploidy. Bread wheat, Triticum aestivum, is an allohexaploid species with many homoeologs. Homoeolog expression bias, referring to the relative contribution of homoeologs to the transcriptome, is critical for determining the traits that influence wheat growth and development. Asymmetric transcription of homoeologs has been so far investigated in a tissue or organ-specific manner, which could be misleading due to a mixture of cell types.

RESULTS

Here, we perform single nuclei RNA sequencing and ATAC sequencing of wheat root to study the asymmetric gene transcription, reconstruct cell differentiation trajectories and cell-type-specific gene regulatory networks. We identify 22 cell types. We then reconstruct cell differentiation trajectories that suggest different origins between epidermis/cortex and endodermis, distinguishing bread wheat from Arabidopsis. We show that the ratio of asymmetrically transcribed triads varies greatly when analyzing at the single-cell level. Hub transcription factors determining cell type identity are also identified. In particular, we demonstrate that TaSPL14 participates in vasculature development by regulating the expression of BAM1. Combining single-cell transcription and chromatin accessibility data, we construct the pseudo-time regulatory network driving root hair differentiation. We find MYB3R4, REF6, HDG1, and GATAs as key regulators in this process.

CONCLUSIONS

Our findings reveal the transcriptional landscape of root organization and asymmetric gene transcription at single-cell resolution in polyploid wheat.

An inflammation-related gene landscape predicts prognosis and response to immunotherapy in virus-associated hepatocellular carcinoma

Background

Due to the viral infection, chronic inflammation significantly increases the likelihood of hepatocellular carcinoma (HCC) development. Nevertheless, an inflammation-based signature aimed to predict the prognosis and therapeutic effect in virus-related HCC has rarely been established.

Method

Based on the integrated analysis, inflammation-associated genes (IRGs) were systematically assessed. We comprehensively investigated the correlation between inflammation and transcriptional profiles, prognosis, and immune cell infiltration. Then, an inflammation-related risk model (IRM) to predict the overall survival (OS) and response to treatment for virus-related HCC patients was constructed and verified. Also, the potential association between IRGs and tumor microenvironment (TME) was investigated. Ultimately, hub genes were validated in plasma samples and cell lines via qRT-PCR. After transfection with shCCL20 combined with overSLC7A2, morphological change of SMMC7721 and huh7 cells was observed. Tumorigenicity model in nude mouse was established.

Results

An inflammatory response-related gene signature model, containing MEP1A, CCL20, ADORA2B, TNFSF9, ICAM4, and SLC7A2, was constructed by conjoint analysis of least absolute shrinkage and selection operator (LASSO) Cox regression and gaussian finite mixture model (GMM). Besides, survival analysis attested that higher IRG scores were positively relevant to worse survival outcomes in virus-related HCC patients, which was testified by external validation cohorts (the ICGC cohort and GSE84337 dataset). Univariate and multivariate Cox regression analyses commonly proved that the IRG was an independent prognostic factor for virus-related HCC patients. Thus, a nomogram with clinical factors and IRG was also constructed to superiorly predict the prognosis of patients. Featured with microsatellite instability-high, mutation burden, and immune activation, lower IRG score verified a superior OS for sufferers. Additionally, IRG score was remarkedly correlated with the cancer stem cell index and drug susceptibility. The measurement of plasma samples further validated that CCL20 upexpression and SLC7A2 downexpression were positively related with virus-related HCC patients, which was in accord with the results in cell lines. Furthermore, CCL20 knockdown combined with SLC7A2 overexpression availably weakened the tumor growth in vivo.

Conclusions

Collectively, IRG score, serving as a potential candidate, accurately and stably predicted the prognosis and response to immunotherapy in virus-related HCC patients, which could guide individualized treatment decision-making for the sufferers.

Rapid discrimination of Shigella spp. and Escherichia coli via label-free surface enhanced Raman spectroscopy coupled with machine learning algorithms

Shigella and enterotoxigenic Escherichia coli (ETEC) are major bacterial pathogens of diarrheal disease that is the second leading cause of childhood mortality globally. Currently, it is well known that Shigella spp., and E. coli are very closely related with many common characteristics. Evolutionarily speaking, Shigella spp., are positioned within the phylogenetic tree of E. coli. Therefore, discrimination of Shigella spp., from E. coli is very difficult. Many methods have been developed with the aim of differentiating the two species, which include but not limited to biochemical tests, nucleic acids amplification, and mass spectrometry, etc. However, these methods suffer from high false positive rates and complicated operation procedures, which requires the development of novel methods for accurate and rapid identification of Shigella spp., and E. coli. As a low-cost and non-invasive method, surface enhanced Raman spectroscopy (SERS) is currently under intensive study for its diagnostic potential in bacterial pathogens, which is worthy of further investigation for its application in bacterial discrimination. In this study, we focused on clinically isolated E. coli strains and Shigella species (spp.), that is, S. dysenteriae, S. boydii, S. flexneri, and S. sonnei, based on which SERS spectra were generated and characteristic peaks for Shigella spp., and E. coli were identified, revealing unique molecular components in the two bacterial groups. Further comparative analysis of machine learning algorithms showed that, the Convolutional Neural Network (CNN) achieved the best performance and robustness in bacterial discrimination capacity when compared with Random Forest (RF) and Support Vector Machine (SVM) algorithms. Taken together, this study confirmed that SERS paired with machine learning could achieve high accuracy in discriminating Shigella spp., from E. coli, which facilitated its application potential for diarrheal prevention and control in clinical settings.

The Mechanism of Li Deposition on the Cu Substrates in the Anode-Free Li Metal Batteries

Due to the rapid growth in the demand for high-energy-density Lithium (Li) batteries and insufficient global Li reserves, the anode-free Li metal batteries are receiving increasing attention. Various strategies, such as surface modification and structural design of copper (Cu) current collectors, have been proposed to stabilize the anode-free Li metal batteries. Unfortunately, the mechanism of Li deposition on the Cu surfaces with the different Miller indices is poorly understood, especially on the atomic scale. Here, the large-scale molecular dynamics simulations of Li deposition on the Cu substrates are performed in the anode-free Li metal batteries. The results show that the surface properties of the Li panel can be altered through the different Cu substrate surfaces. Through surface similarity analysis, potential energy distributions,and inhomogeneous deposition simulations, it is found that the Li atoms exhibit different potential energy variances and kinetic characteristics on the different Cu surfaces. Furthermore, a proposal to reduce the fraction of the (110) facet in commercial Cu foils is made to improve the reversibility and stability of Li plating/stripping in the anode-free Li metal batteries.

Ribosomal Stress Couples with the Hypoxia Response in Dec1-Dependent Orthodontic Tooth Movement

This study characterized the effects of a deficiency of the hypoxia-responsive gene, differentiated embryonic chondrocyte gene 1 (Dec1), in attenuating the biological function of orthodontic tooth movement (OTM) and examined the roles of ribosomal proteins in the hypoxic environment during OTM. HIF-1α transgenic mice and control mice were used for hypoxic regulation of periodontal ligament (PDL) fibroblasts. Dec1 knockout (Dec1KO) and wild-type (WT) littermate C57BL/6 mice were used as in vivo models of OTM. The unstimulated contralateral side served as a control. In vitro, human PDL fibroblasts were exposed to compression forces for 2, 4, 6, 24, and 48 h. HIF-1α transgenic mice had high expression levels of Dec1, HSP105, and ribosomal proteins compared to control mice. The WT OTM mice displayed increased Dec1 expression in the PDL fibroblasts. Micro-CT analysis showed slower OTM in Dec1KO mice compared to WT mice. Increased immunostaining of ribosomal proteins was observed in WT OTM mice compared to Dec1KO OTM mice. Under hypoxia, Dec1 knockdown caused a significant suppression of ribosomal protein expression in PDL fibroblasts. These results reveal that the hypoxic environment in OTM could have implications for the functions of Dec1 and ribosomal proteins to rejuvenate periodontal tissue homeostasis.

Urban Road Surface Discrimination by Tire-Road Noise Analysis and Data Clustering

The surface condition of roadways has direct consequences on a wide range of processes related to the transportation technology, quality of road facilities, road safety, and traffic noise emissions. Methods developed for detection of road surface condition are crucial for maintenance and rehabilitation plans, also relevant for driving environment detection for autonomous transportation systems and e-mobility solutions. In this paper, the clustering of the tire-road noise emission features is proposed to detect the condition of the wheel tracks regions during naturalistic driving events. This acoustic-based methodology was applied in urban areas under nonstop real-life traffic conditions. Using the proposed method, it was possible to identify at least two groups of surface status on the inspected routes over the wheel-path interaction zone. The detection rate on urban zone reaches 75% for renewed lanes and 72% for distressed lanes.

binny: an automated binning algorithm to recover high-quality genomes from complex metagenomic datasets

The reconstruction of genomes is a critical step in genome-resolved metagenomics and for multi-omic data integration from microbial communities. Here, we present binny, a binning tool that produces high-quality metagenome-assembled genomes (MAG) from both contiguous and highly fragmented genomes. Based on established metrics, binny outperforms or is highly competitive with commonly used and state-of-the-art binning methods and finds unique genomes that could not be detected by other methods. binny uses k-mer-composition and coverage by metagenomic reads for iterative, nonlinear dimension reduction of genomic signatures as well as subsequent automated contig clustering with cluster assessment using lineage-specific marker gene sets. When compared with seven widely used binning algorithms, binny provides substantial amounts of uniquely identified MAGs and almost always recovers the most near-complete ($\gt 95\%$ pure, $\gt 90\%$ complete) and high-quality ($\gt 90\%$ pure, $\gt 70\%$ complete) genomes from simulated datasets from the Critical Assessment of Metagenome Interpretation initiative, as well as substantially more high-quality draft genomes, as defined by the Minimum Information about a Metagenome-Assembled Genome standard, from a real-world benchmark comprised of metagenomes from various environments than any other tested method.

Identification of a novel m5C/m6A-related gene signature for predicting prognosis and immunotherapy efficacy in lung adenocarcinoma

Lung adenocarcinoma (LUAD) is the most prevalent subtype of non-small cell lung cancer (NSCLC) and is associated with high mortality rates. However, effective methods to guide clinical therapeutic strategies for LUAD are still lacking. The goals of this study were to analyze the relationship between an m5C/m6A-related signature and LUAD and construct a novel model for evaluating prognosis and predicting drug resistance and immunotherapy efficacy. We obtained data from LUAD patients from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. Based on the differentially expressed m5C/m6A-related genes, we identified distinct m5C/m6A-related modification subtypes in LUAD by unsupervised clustering and compared the differences in functions and pathways between different clusters. In addition, a risk model was constructed using multivariate Cox regression analysis based on prognostic m5C/m6A-related genes to predict prognosis and immunotherapy response. We showed the landscape of 36 m5C/m6A regulators in TCGA-LUAD samples and identified 29 differentially expressed m5C/m6A regulators between the normal and LUAD groups. Two m5C/m6A-related subtypes were identified in 29 genes. Compared to cluster 2, cluster 1 had lower m5C/m6A regulator expression, higher OS (overall survival), higher immune activity, and an abundance of infiltrating immune cells. Four m5C/m6A-related gene signatures consisting of HNRNPA2B1, IGF2BP2, NSUN4, and ALYREF were used to construct a prognostic risk model, and the high-risk group had a worse prognosis, higher immune checkpoint expression, and tumor mutational burden (TMB). In patients treated with immunotherapy, samples with high-risk scores had higher expression of immune checkpoint genes and better immunotherapeutic efficacy than those with low-risk scores. We concluded that the m5C/m6A regulator-related risk model could serve as an effective prognostic biomarker and predict the therapeutic sensitivity of chemotherapy and immunotherapy.

Clustering Analysis, Structure Fingerprint Analysis, and Quantum Chemical Calculations of Compounds from Essential Oils of Sunflower (Helianthus annuus L.) Receptacles

Sunflower (Helianthus annuus L.) is an appropriate crop for current new patterns of green agriculture, so it is important to change sunflower receptacles from waste to useful resource. However, there is limited knowledge on the functions of compounds from the essential oils of sunflower receptacles. In this study, a new method was created for chemical space network analysis and classification of small samples, and applied to 104 compounds. Here, t-SNE (t-Distributed Stochastic Neighbor Embedding) dimensions were used to reduce coordinates as node locations and edge connections of chemical space networks, respectively, and molecules were grouped according to whether the edges were connected and the proximity of the node coordinates. Through detailed analysis of the structural characteristics and fingerprints of each classified group, our classification method attained good accuracy. Targets were then identified using reverse docking methods, and the active centers of the same types of compounds were determined by quantum chemical calculation. The results indicated that these compounds can be divided into nine groups, according to their mean within-group similarity (MWGS) values. The three families with the most members, i.e., the d-limonene group (18), α-pinene group (10), and γ-maaliene group (nine members) determined the protein targets, using PharmMapper. Structure fingerprint analysis was employed to predict the binding mode of the ligands of four families of the protein targets. Thence, quantum chemical calculations were applied to the active group of the representative compounds of the four families. This study provides further scientific information to support the use of sunflower receptacles.

Personalized driving assistance algorithms: Case study of federated learning based forward collision warning

Current designs of advanced driving assistance systems (ADAS) mainly developed uniform collision warning algorithms, which ignore the heterogeneity of driving behaviors, thus lead to low drivers' trust in. To address this issue, developing personalized driving assistance algorithms is a promising approach. However, current personalization systems were mainly implemented through manually adjusting warning trigger thresholds, which would be less feasible for overall drivers as certain domain expertise is required to set personal thresholds accurately. Other personalization techniques exploited individual drivers' data to build personalized models. Such approach could learn personal behavior but requires impractical large-scale individual data collections. To fill up the gaps, self-adaptive algorithms for personalized forward collision warning (FCW) based on federated learning were proposed in this study. A baseline model was developed by long short-term memory (LSTM) for FCW. Federated learning framework was then introduced to collect knowledge from multiple drivers with privacy preserving. Specifically, a general cloud server model was trained by collecting updated parameters from individual vehicle server models rather than collecting raw data. Besides, a driver-specific batch normalization (BN) layer was added into each vehicle server model to address the heterogeneity of driving behaviors. Experiments show empirically that the proposed federated-based personalized models with the BN layer showed to have the best performance. The average modeling accuracy has reached 84.88% and the performance is comparable to conventional total data collection training approach, where the additional BN layer could increase the accuracy by 3.48%. Finally, applications of the proposed framework and its further investigations have been discussed.

Mapping circuit dynamics during function and dysfunction

Neural circuits can generate many spike patterns, but only some are functional. The study of how circuits generate and maintain functional dynamics is hindered by a poverty of description of circuit dynamics across functional and dysfunctional states. For example, although the regular oscillation of a central pattern generator is well characterized by its frequency and the phase relationships between its neurons, these metrics are ineffective descriptors of the irregular and aperiodic dynamics that circuits can generate under perturbation or in disease states. By recording the circuit dynamics of the well-studied pyloric circuit in Cancer borealis, we used statistical features of spike times from neurons in the circuit to visualize the spike patterns generated by this circuit under a variety of conditions. This approach captures both the variability of functional rhythms and the diversity of atypical dynamics in a single map. Clusters in the map identify qualitatively different spike patterns hinting at different dynamic states in the circuit. State probability and the statistics of the transitions between states varied with environmental perturbations, removal of descending neuromodulatory inputs, and the addition of exogenous neuromodulators. This analysis reveals strong mechanistically interpretable links between complex changes in the collective behavior of a neural circuit and specific experimental manipulations, and can constrain hypotheses of how circuits generate functional dynamics despite variability in circuit architecture and environmental perturbations.

Increased oxidative stress contributes to impaired peripheral CD56dimCD57+ NK cells from patients with systemic lupus erythematosus

Background

Systemic lupus erythematosus (SLE) is characterized by loss of immune tolerance and imbalance of immune cell subsets. Natural killer (NK) cells contribute to regulate both the innate and adaptive immune response. In this study, we aimed to detect alterations of peripheral NK cells and explore intrinsic mechanisms involving in NK cell abnormality in SLE.

Methods

Blood samples from healthy controls (HCs) and patients with SLE and rheumatoid arthritis (RA) were collected. The NK count, NK subsets (CD56^bright, CD56^dimCD57⁻, and CD56^dimCD57⁺), phenotypes, and apoptosis were evaluated with flow cytometer. Mitochondrial reactive oxygen species (mtROS) and total ROS levels were detected with MitoSOX Red and DCFH-DA staining respectively. Published data (GSE63829 and GSE23695) from Gene Expression Omnibus (GEO) was analyzed by Gene Set Enrichment Analysis (GSEA).

Results

Total peripheral NK count was down-regulated in untreated SLE patients in comparison to that in untreated RA patients and HCs. SLE patients exhibited a selective reduction in peripheral CD56^dimCD57⁺ NK cell proportion, which was negatively associated with disease activity and positively correlated with levels of complement(C)3 and C4. Compared with HCs, peripheral CD56^dimCD57⁺ NK cells from SLE patients exhibited altered phenotypes, increased endogenous apoptosis and higher levels of mtROS and ROS. In addition, when treated with hydrogen peroxide (H₂O₂), peripheral CD56^dimCD57⁺ NK cell subset was more prone to undergo apoptosis than CD56^dimCD57⁻ NK cells. Furthermore, this NK cell subset from SLE patients exhibited impaired cytotoxicity in response to activated CD4⁺ T cells in vitro.

Conclusion

Our study demonstrated a selective loss of mature CD56^dimCD57⁺ NK cell subset in SLE patients, which may caused by preferential apoptosis of this subset under increased oxidative stress in SLE. The attenuated in vitro cytotoxicity of CD56^dimCD57⁺ NK cells may contribute to the impaired ability of eliminating pathogenic CD4⁺ T cells in SLE.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13075-022-02731-y.

Revisiting Dimensionality Reduction Techniques for Visual Cluster Analysis: An Empirical Study

Dimensionality Reduction (DR) techniques can generate 2D projections and enable visual exploration of cluster structures of high-dimensional datasets. However, different DR techniques would yield various patterns, which significantly affect the performance of visual cluster analysis tasks. We present the results of a user study that investigates the influence of different DR techniques on visual cluster analysis. Our study focuses on the most concerned property types, namely the linearity and locality, and evaluates twelve representative DR techniques that cover the concerned properties. Four controlled experiments were conducted to evaluate how the DR techniques facilitate the tasks of 1) cluster identification, 2) membership identification, 3) distance comparison, and 4) density comparison, respectively. We also evaluated users' subjective preference of the DR techniques regarding the quality of projected clusters. The results show that: 1) Non-linear and Local techniques are preferred in cluster identification and membership identification; 2) Linear techniques perform better than non-linear techniques in density comparison; 3) UMAP (Uniform Manifold Approximation and Projection) and t-SNE (t-Distributed Stochastic Neighbor Embedding) perform the best in cluster identification and membership identification; 4) NMF (Nonnegative Matrix Factorization) has competitive performance in distance comparison; 5) t-SNLE (t-Distributed Stochastic Neighbor Linear Embedding) has competitive performance in density comparison.

Identification of a basement membrane-related gene signature for predicting prognosis and estimating the tumor immune microenvironment in breast cancer

INTRODUCTION

Breast cancer (BC) is the most common malignancy in the world and has a high cancer-related mortality rate. Basement membranes (BMs) guide cell polarity, differentiation, migration and survival, and their functions are closely related to tumor diseases. However, few studies have focused on the association of basement membrane-related genes (BMRGs) with BC. This study aimed to explore the prognostic features of BMRGs in BC and provide new directions for the prevention and treatment of BC.

METHODS

We collected transcriptomic and clinical data of BC patients from TCGA and GEO datasets and constructed a predictive signature for BMRGs by using univariate, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis. The reliability of the model was further evaluated and validated by Kaplan-Meier survival curves and receiver operating characteristic curves (ROC). Column line plots and corresponding calibration curves were constructed. Possible biological pathways were investigated by enrichment analysis. Afterward, we assessed the mutation status by tumor mutational burden (TMB) analysis and compared different subtypes using cluster analysis. Finally, we examined drug treatment sensitivity and immunological correlation to lay the groundwork for more in-depth studies in this area.

RESULTS

The prognostic risk model consisted of 7 genes (FBLN5, ITGB2, LAMC3, MMP1, EVA1B, SDC1, UNC5A). After validation, we found that the model was highly reliable and could accurately predict the prognosis of BC patients. Cluster analysis showed that patients with cluster 1 had more sensitive drugs and had better chances of better clinical outcomes. In addition, TMB, immune checkpoint, immune status, and semi-inhibitory concentrations were significantly different between high and low-risk groups, with lower-risk patients having the better anti-cancer ability.

DISCUSSION

The basement membrane-related gene signature that we established can be applied as an independent prognostic factor for BC and can provide a reference for individualized treatment of BC patients.

Unsupervised feature learning for electrocardiogram data using the convolutional variational autoencoder

Most existing electrocardiogram (ECG) feature extraction methods rely on rule-based approaches. It is difficult to manually define all ECG features. We propose an unsupervised feature learning method using a convolutional variational autoencoder (CVAE) that can extract ECG features with unlabeled data. We used 596,000 ECG samples from 1,278 patients archived in biosignal databases from intensive care units to train the CVAE. Three external datasets were used for feature validation using two approaches. First, we explored the features without an additional training process. Clustering, latent space exploration, and anomaly detection were conducted. We confirmed that CVAE features reflected the various types of ECG rhythms. Second, we applied CVAE features to new tasks as input data and CVAE weights to weight initialization for different models for transfer learning for the classification of 12 types of arrhythmias. The f1-score for arrhythmia classification with extreme gradient boosting was 0.86 using CVAE features only. The f1-score of the model in which weights were initialized with the CVAE encoder was 5% better than that obtained with random initialization. Unsupervised feature learning with CVAE can extract the characteristics of various types of ECGs and can be an alternative to the feature extraction method for ECGs.

Development and validation of a neural network for NAFLD diagnosis

Non-Alcoholic Fatty Liver Disease (NAFLD) affects about 20–30% of the adult population in developed countries and is an increasingly important cause of hepatocellular carcinoma. Liver ultrasound (US) is widely used as a noninvasive method to diagnose NAFLD. However, the intensive use of US is not cost-effective and increases the burden on the healthcare system. Electronic medical records facilitate large-scale epidemiological studies and, existing NAFLD scores often require clinical and anthropometric parameters that may not be captured in those databases. Our goal was to develop and validate a simple Neural Network (NN)-based web app that could be used to predict NAFLD particularly its absence. The study included 2970 subjects; training and testing of the neural network using a train–test-split approach was done on 2869 of them. From another population consisting of 2301 subjects, a further 100 subjects were randomly extracted to test the web app. A search was made to find the best parameters for the NN and then this NN was exported for incorporation into a local web app. The percentage of accuracy, area under the ROC curve, confusion matrix, Positive (PPV) and Negative Predicted Value (NPV) values, precision, recall and f1-score were verified. After that, Explainability (XAI) was analyzed to understand the diagnostic reasoning of the NN. Finally, in the local web app, the specificity and sensitivity values were checked. The NN achieved a percentage of accuracy during testing of 77.0%, with an area under the ROC curve value of 0.82. Thus, in the web app the NN evidenced to achieve good results, with a specificity of 1.00 and sensitivity of 0.73. The described approach can be used to support NAFLD diagnosis, reducing healthcare costs. The NN-based web app is easy to apply and the required parameters are easily found in healthcare databases.

A Role for Translational Regulation by S6 Kinase and a Downstream Target in Inflammatory Pain

BACKGROUND AND PURPOSE

Translational controls pervade neurobiology. Nociceptors play an integral role in the detection and propagation of pain signals. Nociceptors can undergo persistent changes in their intrinsic excitability. Pharmacologic disruption of nascent protein synthesis diminishes acute and chronic forms of pain-associated behaviors. Yet, the targets of translational controls that facilitate plasticity in nociceptors are unclear.

EXPERIMENTAL APPROACH

We used ribosome profiling to probe the translational landscape in DRG neurons after treatment of the inflammatory mediators NGF and IL-6. We validated the expression dynamics of c-Fos using immunoblotting and immunohistochemistry. Given that inflammation is known to stimulate mTOR signaling, we reasoned that downstream factors (e.g., ribosomal protein S6 kinase 1, S6K1) might control c-Fos levels. We utilized small-molecule inhibitors of S6K1 (DG2) or c-Fos (T-5224) to probe their effects on nociceptor activity in vitro using multi-electrode arrays (MEAs) and pain behavior in vivo using a hyperalgesic priming model.

KEY RESULTS

We demonstrate that c-Fos is expressed in sensory neurons. Inflammatory mediators that promote pain in both humans and rodents promote c-Fos translation. We demonstrate that the mTOR effector S6K1 is essential for c-Fos biosynthesis. Inhibition of S6K1 or c-Fos with small molecules diminish mechanical and thermal hypersensitivity in response to inflammatory cues. Additionally, both inhibitors reduce evoked nociceptor activity.

CONCLUSION

Our data reveal a novel role of S6K1 in modulating rapid response to inflammatory mediators, with c-Fos being one key downstream target. Targeting the S6 kinase pathway or c-Fos is an exciting new avenue for pain-modulating compounds.

RFCell: A Gene Selection Approach for scRNA-seq Clustering Based on Permutation and Random Forest

In recent years, the application of single cell RNA-seq (scRNA-seq) has become more and more popular in fields such as biology and medical research. Analyzing scRNA-seq data can discover complex cell populations and infer single-cell trajectories in cell development. Clustering is one of the most important methods to analyze scRNA-seq data. In this paper, we focus on improving scRNA-seq clustering through gene selection, which also reduces the dimensionality of scRNA-seq data. Studies have shown that gene selection for scRNA-seq data can improve clustering accuracy. Therefore, it is important to select genes with cell type specificity. Gene selection not only helps to reduce the dimensionality of scRNA-seq data, but also can improve cell type identification in combination with clustering methods. Here, we proposed RFCell, a supervised gene selection method, which is based on permutation and random forest classification. We first use RFCell and three existing gene selection methods to select gene sets on 10 scRNA-seq data sets. Then, three classical clustering algorithms are used to cluster the cells obtained by these gene selection methods. We found that the gene selection performance of RFCell was better than other gene selection methods.

Single-Trial Kernel-Based Functional Connectivity for Enhanced Feature Extraction in Motor-Related Tasks

Motor learning is associated with functional brain plasticity, involving specific functional connectivity changes in the neural networks. However, the degree of learning new motor skills varies among individuals, which is mainly due to the between-subject variability in brain structure and function captured by electroencephalographic (EEG) recordings. Here, we propose a kernel-based functional connectivity measure to deal with inter/intra-subject variability in motor-related tasks. To this end, from spatio-temporal-frequency patterns, we extract the functional connectivity between EEG channels through their Gaussian kernel cross-spectral distribution. Further, we optimize the spectral combination weights within a sparse-based ℓ2-norm feature selection framework matching the motor-related labels that perform the dimensionality reduction of the extracted connectivity features. From the validation results in three databases with motor imagery and motor execution tasks, we conclude that the single-trial Gaussian functional connectivity measure provides very competitive classifier performance values, being less affected by feature extraction parameters, like the sliding time window, and avoiding the use of prior linear spatial filtering. We also provide interpretability for the clustered functional connectivity patterns and hypothesize that the proposed kernel-based metric is promising for evaluating motor skills.

Automatic Image-Based Event Detection for Large-N Seismic Arrays Using a Convolutional Neural Network

Passive seismic experiments have been proposed as a cost-effective and non-invasive alternative to controlled-source seismology, allowing body–wave reflections based on seismic interferometry principles to be retrieved. However, from the huge volume of the recorded ambient noise, only selected time periods (noise panels) are contributing constructively to the retrieval of reflections. We address the issue of automatic scanning of ambient noise data recorded by a large-N array in search of body–wave energy (body–wave events) utilizing a convolutional neural network (CNN). It consists of computing first both amplitude and frequency attribute values at each receiver station for all divided portions of the recorded signal (noise panels). The created 2-D attribute maps are then converted to images and used to extract spatial and temporal patterns associated with the body–wave energy present in the data to build binary CNN-based classifiers. The ensemble of two multi-headed CNN models trained separately on the frequency and amplitude attribute maps demonstrates better generalization ability than each of its participating networks. We also compare the prediction performance of our deep learning (DL) framework with a conventional machine learning (ML) algorithm called XGBoost. The DL-based solution applied to 240 h of ambient seismic noise data recorded by the Kylylahti array in Finland demonstrates high detection accuracy and the superiority over the ML-based one. The ensemble of CNN-based models managed to find almost three times more verified body–wave events in the full unlabelled dataset than it was provided at the training stage. Moreover, the high-level abstraction features extracted at the deeper convolution layers can be used to perform unsupervised clustering of the classified panels with respect to their visual characteristics.

Natural Language Processing Based Method for Clustering and Analysis of Aviation Safety Narratives

The complexity of commercial aviation operations has grown substantially in recent years, together with a diversification of techniques for collecting and analyzing flight data. As a result, data-driven frameworks for enhancing flight safety have grown in popularity. Data-driven techniques offer efficient and repeatable exploration of patterns and anomalies in large datasets. Text-based flight safety data presents a unique challenge in its subjectivity, and relies on natural language processing tools to extract underlying trends from narratives. In this paper, a methodology is presented for the analysis of aviation safety narratives based on text-based accounts of in-flight events and categorical metadata parameters which accompany them. An extensive pre-processing routine is presented, including a comparison between numeric models of textual representation for the purposes of document classification. A framework for categorizing and visualizing narratives is presented through a combination of k-means clustering and 2-D mapping with t-Distributed Stochastic Neighbor Embedding (t-SNE). A cluster post-processing routine is developed for identifying driving factors in each cluster and building a hierarchical structure of cluster and sub-cluster labels. The Aviation Safety Reporting System (ASRS), which includes over a million de-identified voluntarily submitted reports describing aviation safety incidents for commercial flights, is analyzed as a case study for the methodology. The method results in the identification of 10 major clusters and a total of 31 sub-clusters. The identified groupings are post-processed through metadata-based statistical analysis of the learned clusters. The developed method shows promise in uncovering trends from clusters that are not evident in existing anomaly labels in the data and offers a new tool for obtaining insights from text-based safety data that complement existing approaches.

Enhanced synchronization-inspired clustering for high-dimensional data

The synchronization-inspired clustering algorithm (Sync) is a novel and outstanding clustering algorithm, which can accurately cluster datasets with any shape, density and distribution. However, the high-dimensional dataset with high dimensionality, high noise, and high redundancy brings some new challenges for the synchronization-inspired clustering algorithm, resulting in a significant increase in clustering time and a decrease in clustering accuracy. To address these challenges, an enhanced synchronization-inspired clustering algorithm, namely SyncHigh, is developed in this paper to quickly and accurately cluster the high-dimensional datasets. First, a PCA-based (Principal Component Analysis) dimension purification strategy is designed to find the principal components in all attributes. Second, a density-based data merge strategy is constructed to reduce the number of objects participating in the synchronization-inspired clustering algorithm, thereby speeding up clustering time. Third, the Kuramoto Model is enhanced to deal with mass differences between objects caused by the density-based data merge strategy. Finally, extensive experimental results on synthetic and real-world datasets show the effectiveness and efficiency of our SyncHigh algorithm.

t-viSNE: Interactive Assessment and Interpretation of t-SNE Projections

t-Distributed Stochastic Neighbor Embedding (t-SNE) for the visualization of multidimensional data has proven to be a popular approach, with successful applications in a wide range of domains. Despite their usefulness, t-SNE projections can be hard to interpret or even misleading, which hurts the trustworthiness of the results. Understanding the details of t-SNE itself and the reasons behind specific patterns in its output may be a daunting task, especially for non-experts in dimensionality reduction. In this article, we present t-viSNE, an interactive tool for the visual exploration of t-SNE projections that enables analysts to inspect different aspects of their accuracy and meaning, such as the effects of hyper-parameters, distance and neighborhood preservation, densities and costs of specific neighborhoods, and the correlations between dimensions and visual patterns. We propose a coherent, accessible, and well-integrated collection of different views for the visualization of t-SNE projections. The applicability and usability of t-viSNE are demonstrated through hypothetical usage scenarios with real data sets. Finally, we present the results of a user study where the tool's effectiveness was evaluated. By bringing to light information that would normally be lost after running t-SNE, we hope to support analysts in using t-SNE and making its results better understandable.

Semi-supervised labelling of the femur in a whole-body post-mortem CT database using deep learning

A deep learning pipeline was developed and used to localize and classify a variety of implants in the femur contained in whole-body post-mortem computed tomography (PMCT) scans. The results provide a proof-of-principle approach for labelling content not described in medical/autopsy reports. The pipeline, which incorporated residual networks and an autoencoder, was trained and tested using n = 450 full-body PMCT scans. For the localization component, Dice scores of 0.99, 0.96, and 0.98 and mean absolute errors of 3.2, 7.1, and 4.2 mm were obtained in the axial, coronal, and sagittal views, respectively. A regression analysis found the orientation of the implant to the scanner axis and also the relative positioning of extremities to be statistically significant factors. For the classification component, test cases were properly labelled as nail (N⁺), hip replacement (H⁺), knee replacement (K⁺) or without-implant (I^-) with an accuracy >97%. The recall for I^- and H⁺ cases was 1.00, but fell to 0.82 and 0.65 for cases with K⁺ and N⁺. This semi-automatic approach provides a generalized structure for image-based labelling of features, without requiring time-consuming segmentation.