Machine Learning Assisted Electronic/Ionic Skin Recognition of Thermal Stimuli and Mechanical Deformation for Soft Robots.

Soft robots have the advantage of adaptability and flexibility in various scenarios and tasks due to their inherent flexibility and mouldability, which makes them highly promising for real-world applications. The development of electronic skin (E-skin) perception systems is crucial for the advancement of soft robots. However, achieving both exteroceptive and proprioceptive capabilities in E-skins, particularly in terms of decoupling and classifying sensing signals, remains a challenge. This study presents an E-skin with mixed electronic and ionic conductivity that can simultaneously achieve exteroceptive and proprioceptive, based on the resistance response of conductive hydrogels. It is integrated with soft robots to enable state perception, with the sensed signals further decoded using the machine learning model of decision trees and random forest algorithms. The results demonstrate that the newly developed hydrogel sensing system can accurately predict attitude changes in soft robots when subjected to varying degrees of pressing, hot pressing, bending, twisting, and stretching. These findings that multifunctional hydrogels combine with machine learning to decode signals may serve as a basis for improving the sensing capabilities of intelligent soft robots in future advancements.

Brd4::Nutm1 fusion gene initiates NUT carcinoma in vivo.

NUT carcinoma (NC) is an aggressive cancer with no effective treatment. About 70% of NUT carcinoma is associated with chromosome translocation events that lead to the formation of a BRD4::NUTM1 fusion gene. Because the BRD4::NUTM1 gene is unequivocally cytotoxic when ectopically expressed in cell lines, questions remain on whether the fusion gene can initiate NC. Here, we report the first genetically engineered mouse model for NUT carcinoma that recapitulates the human t(15;19) chromosome translocation in mice. We demonstrated that the mouse t(2;17) syntenic chromosome translocation, forming the Brd4::Nutm1 fusion gene, could induce aggressive carcinomas in mice. The tumors present histopathological and molecular features similar to human NC, with enrichment of undifferentiated cells. Similar to the reports of human NC incidence, Brd4::Nutm1 can induce NC from a broad range of tissues with a strong phenotypical variability. The consistent induction of poorly differentiated carcinoma demonstrated a strong reprogramming activity of BRD4::NUTM1. The new mouse model provided a critical preclinical model for NC that will lead to better understanding and therapy development for NC.

Stagewise Training With Exponentially Growing Training Sets.

In the world of big data, training large-scale machine learning problems has gained considerable attention. Numerous innovative optimization strategies have been presented in recent years to accelerate the large-scale training process. However, the possibility of further accelerating the training process of various optimization algorithms remains an unresolved subject. To begin addressing this difficult problem, we exploit the researched findings that when training data are independent and identically distributed, the learning problem on a smaller dataset is not significantly different from the original one. Upon that, we propose a stagewise training technique that grows the size of the training set exponentially while solving nonsmooth subproblem. We demonstrate that our stagewise training via exponentially growing the size of the training sets (STEGSs) are compatible with a large number of proximal gradient descent and gradient hard thresholding (GHT) techniques. Interestingly, we demonstrate that STEGS can greatly reduce overall complexity while maintaining statistical accuracy or even surpassing the intrinsic error introduced by GHT approaches. In addition, we analyze the effect of the training data growth rate on the overall complexity. The practical results of applying l2,1 -and l0 -norms to a variety of large-scale real-world datasets not only corroborate our theories but also demonstrate the benefits of our STEGS framework.

Yes Associated Transcriptional Regulator 1 (YAP1) and WW Domain Containing Transcription Regulator (WWTR1) are required for murine pregnancy initiation.

Endometrial stromal cell decidualization is required for pregnancy success. Although this process is integral to fertility, many of the intricate molecular mechanisms contributing to decidualization remain undefined. One pathway that has been implicated in endometrial stromal cell decidualization in humans in vitro is the Hippo signaling pathway. Two previously conducted studies showed that the effectors of the Hippo signaling pathway, YAP1 and WWTR1, were required for decidualization of primary stromal cells in culture. To investigate the in vivo role of YAP1 and WWTR1 in decidualization and pregnancy initiation, we generated a Progesterone Cre mediated partial double knockout (pdKO) of Yap1 and Wwtr1. Female pdKOs exhibited subfertility, a compromised decidualization response, partial interruption in embryo transport, blunted endometrial receptivity, delayed implantation and subsequent embryonic development, and a unique transcriptional profile. Bulk mRNA sequencing revealed aberrant maternal remodeling evidenced by significant alterations in extracellular matrix proteins at 7.5 days post-coitus in pdKO dams and enrichment for terms associated with fertility-compromising diseases like pre-eclampsia and endometriosis. Our results indicate a required role for YAP1 and WWTR1 for successful mammalian uterine function and pregnancy success.

Deep learning assists detection of esophageal cancer and precursor lesions in a prospective, randomized controlled study.

Endoscopy is the primary modality for detecting asymptomatic esophageal squamous cell carcinoma (ESCC) and precancerous lesions. Improving detection rate remains challenging. We developed a system based on deep convolutional neural networks (CNNs) for detecting esophageal cancer and precancerous lesions [high-risk esophageal lesions (HrELs)] and validated its efficacy in improving HrEL detection rate in clinical practice (trial registration ChiCTR2100044126 at www.chictr.org.cn). Between April 2021 and March 2022, 3117 patients ≥50 years old were consecutively recruited from Taizhou Hospital, Zhejiang Province, and randomly assigned 1:1 to an experimental group (CNN-assisted endoscopy) or a control group (unassisted endoscopy) based on block randomization. The primary endpoint was the HrEL detection rate. In the intention-to-treat population, the HrEL detection rate [28 of 1556 (1.8%)] was significantly higher in the experimental group than in the control group [14 of 1561 (0.9%), P = 0.029], and the experimental group detection rate was twice that of the control group. Similar findings were observed between the experimental and control groups [28 of 1524 (1.9%) versus 13 of 1534 (0.9%), respectively; P = 0.021]. The system's sensitivity, specificity, and accuracy for detecting HrELs were 89.7, 98.5, and 98.2%, respectively. No adverse events occurred. The proposed system thus improved HrEL detection rate during endoscopy and was safe. Deep learning assistance may enhance early diagnosis and treatment of esophageal cancer and may become a useful tool for esophageal cancer screening.

Rapid discrimination of four Salmonella enterica serovars: A performance comparison between benchtop and handheld Raman spectrometers.

Foodborne illnesses, particularly those caused by Salmonella enterica with its extensive array of over 2600 serovars, present a significant public health challenge. Therefore, prompt and precise identification of S. enterica serovars is essential for clinical relevance, which facilitates the understanding of S. enterica transmission routes and the determination of outbreak sources. Classical serotyping methods via molecular subtyping and genomic markers currently suffer from various limitations, such as labour intensiveness, time consumption, etc. Therefore, there is a pressing need to develop new diagnostic techniques. Surface-enhanced Raman spectroscopy (SERS) is a non-invasive diagnostic technique that can generate Raman spectra, based on which rapid and accurate discrimination of bacterial pathogens could be achieved. To generate SERS spectra, a Raman spectrometer is needed to detect and collect signals, which are divided into two types: the expensive benchtop spectrometer and the inexpensive handheld spectrometer. In this study, we compared the performance of two Raman spectrometers to discriminate four closely associated S. enterica serovars, that is, S. enterica subsp. enterica serovar dublin, enteritidis, typhi and typhimurium. Six machine learning algorithms were applied to analyse these SERS spectra. The support vector machine (SVM) model showed the highest accuracy for both handheld (99.97%) and benchtop (99.38%) Raman spectrometers. This study demonstrated that handheld Raman spectrometers achieved similar prediction accuracy as benchtop spectrometers when combined with machine learning models, providing an effective solution for rapid, accurate and cost-effective identification of closely associated S. enterica serovars.

Early hippocampal high-amplitude rhythmic spikes predict post-traumatic epilepsy in mice.

Oscillations, a highly conserved brain function across mammalian species, are pivotal in brain physiology and pathology. Traumatic brain injury (TBI) often leads to subacute and chronic brain oscillatory alterations associated with complications like post-traumatic epilepsy (PTE) in patients and animal models. Our recent work longitudinally recorded local field potential from the contralateral hippocampus of 12 strains of recombinant inbred Collaborative Cross (CC) mice alongside classical laboratory inbred C57BL/6J mice after lateral fluid percussion injury. In this study, we profiled the acute (<12 hr post-injury) and subacute (12-48 hr post-injury) hippocampal oscillatory responses to TBI and evaluated their predictive value for PTE. We found dynamic high-amplitude rhythmic spikes with elevated power density and reduced entropy that prevailed during the acute phase in CC031 mice who later developed PTE. This characteristic early brain oscillatory alteration is absent in CC031 sham controls or other CC and reference C57BL/6J strains that did not develop PTE after TBI. Our work provides quantitative measures linking early brain oscillation to PTE at a population level in mice under controlled experimental conditions. These findings will offer insights into circuit mechanisms and potential targets for neuromodulatory intervention.

Asynchronous Parallel Large-Scale Gaussian Process Regression.

Gaussian process regression (GPR) is an important nonparametric learning method in machine learning research with many real-world applications. It is well known that training large-scale GPR is a challenging task due to the required heavy computational cost and large volume memory. To address this challenging problem, in this article, we propose an asynchronous doubly stochastic gradient algorithm to handle the large-scale training of GPR. We formulate the GPR to a convex optimization problem, i.e., kernel ridge regression. After that, in order to efficiently solve this convex kernel problem, we first use the random feature mapping method to approximate the kernel model and then utilize two unbiased stochastic approximations, i.e., stochastic variance reduced gradient and stochastic coordinate descent, to update the solution asynchronously and in parallel. In this way, our algorithm scales well in both sample size and dimensionality, and speeds up the training computation. More importantly, we prove that our algorithm has a global linear convergence rate. Our experimental results on eight large-scale benchmark datasets with both regression and classification tasks show that the proposed algorithm outperforms the existing state-of-the-art GPR methods.

Obtaining Lower Query Complexities Through Lightweight Zeroth-Order Proximal Gradient Algorithms.

Zeroth-order (ZO) optimization is one key technique for machine learning problems where gradient calculation is expensive or impossible. Several variance, reduced ZO proximal algorithms have been proposed to speed up ZO optimization for nonsmooth problems, and all of them opted for the coordinated ZO estimator against the random ZO estimator when approximating the true gradient, since the former is more accurate. While the random ZO estimator introduces a larger error and makes convergence analysis more challenging compared to coordinated ZO estimator, it requires only O(1) computation, which is significantly less than O(d) computation of the coordinated ZO estimator, with d being dimension of the problem space. To take advantage of the computationally efficient nature of the random ZO estimator, we first propose a ZO objective decrease (ZOOD) property that can incorporate two different types of errors in the upper bound of convergence rate. Next, we propose two generic reduction frameworks for ZO optimization, which can automatically derive the convergence results for convex and nonconvex problems, respectively, as long as the convergence rate for the inner solver satisfies the ZOOD property. With the application of two reduction frameworks on our proposed ZOR-ProxSVRG and ZOR-ProxSAGA, two variance-reduced ZO proximal algorithms with fully random ZO estimators, we improve the state-of-the-art function query complexities from Omindn1/2ε2,dε3 to O˜n+dε2 under d>n12 for nonconvex problems, and from Odε2 to O˜nlog1ε+dε for convex problems. Finally, we conduct experiments to verify the superiority of our proposed methods.

Differential Analysis of Blood Routine Examination Parameters in Patients with Upper Gastrointestinal Bleeding and Lower Gastrointestinal Bleeding.

Background: This study addresses the critical need for differentiating between upper and lower gastrointestinal bleeding by focusing on blood routine parameters to enhance diagnostic precision. Objective: This study aims to identify and compare specific blood routine parameters to determine their efficacy in distinguishing between upper and lower gastrointestinal bleeding for improved clinical decision-making. Methods: This retrospective study analyzed 119 patients with gastrointestinal bleeding (GIB) admitted to our hospital between January 2017 and June 2020. Among them, 86 were diagnosed with upper GIB (UGIB) and 33 with lower GIB (LGIB). After admission, peripheral blood samples were collected for a comprehensive blood routine examination, including white blood cell count (WBC), red blood cell count (RBC), hemoglobin (Hb), platelet count (PLT), blood urea nitrogen (BUN), creatinine (Cr), and BUN to Cr ratio (BUN/Cr ratio). Differences in blood routine parameters were compared between the UGIB and LGIB groups. Receiver Operating Characteristic (ROC) curve analysis was conducted to assess the efficacy of blood routine examinations in differentiating between UGIB and LGIB. Results: The study revealed no significant differences in WBC and Cr levels between LGIB and UGIB patients (P > .05). However, UGIB patients exhibited statistically lower levels of RBC, Hb, and PLT, along with higher BUN and BUN/Cr ratio levels compared to LGIB patients (P < .05). Pearson correlation coefficient analysis indicated an inverse correlation of BUN/Cr with RBC, Hb, and PLT in GIB patients and a positive association between BUN/Cr and BUN (P < .05). ROC analysis demonstrated that RBC, Hb, PLT, BUN, and BUN/Cr ratios were effective in distinguishing UGIB from LGIB (P < .05). Conclusions: Blood routine parameters, including RBC, Hb, PLT, BUN, and BUN/Cr ratio, are valuable in differentiating between UGIB and LGIB. These parameters can serve as early evaluation indexes for GIB, facilitating timely intervention and treatment to enhance therapeutic outcomes.

When local governments' stay-at-home orders meet the white house's "opening up america again".

In the current polarized political climate, citizens frequently face conflicting directives from their local and federal government officials. For instance, on April 16th, 2020, The White House launched the "Opening up America Again" (OuAA) campaign while many U.S. counties had stay-at-home orders. We created a panel data set of U.S. counties to study the impact of U.S. counties' stay-at-home orders on community mobility before and after The White House's campaign to reopen the country. Our results suggest that before the OuAA campaign, stay-at-home orders substantially decreased the time spent in retail and recreation businesses. However, after the launch of the OuAA campaign, the time spent at retail and recreational businesses in a typical conservative county increased significantly more than in liberal counties (23% increase in a typical conservative county vs. 9% increase in a typical liberal county). We also found that in conservative counties with stay-at-home orders, time spent at retail and recreational businesses increased less than in those without stay-at-home orders. These findings illuminate that when federal and local government policies are at odds, residents decide which policies to adhere to based on the alignment between their political ideology and the government body. Our findings highlight the substantial importance of each government body in forming citizens' behaviors, offering practical implications for policy makers during natural disasters.

Direct visualization and 3D reconstruction of conductive filaments in aSiO2 material-based memristive device.

Observation of conductive filaments has greatly aided the development of theoretical models of memristive devices. In this work, we visualized and reconstructed the conductive filaments in a Cu/Cu-doped SiO2/W device employing a focused ion beam (FIB) as a milling technique. The SEM images taken from the device after 150 DC sweep cycles showed that Joule heat played a vital role in determining the morphology of a conductive filament, where the vaporization of the conductive filament resulted in the creation of defects, including particles, voids, and cavities. The competition between the formation and vaporization of conductive filaments generally induces a remarkable current fluctuation. Since Cu-doped SiO2 was utilized as the electrolyte, the vapors exfoliated adjacent single layers. FIB milling proceeded in top-down and front-back modes; thus, a 3D model of conductive filaments and defects was constructed according to a series of FIB-SEM images. This methodology is promising for a future failure analysis of memristive devices.

The Updated Mouse Universal Genotyping Array Bioinformatic Pipeline Improves Genetic QC in Laboratory Mice.

The MiniMUGA genotyping array is a popular tool for genetic QC of laboratory mice and genotyping of samples from most types of experimental crosses involving laboratory strains, particularly for reduced complexity crosses. The content of the production version of the MiniMUGA array is fixed; however, there is the opportunity to improve array's performance and the associated report's usefulness by leveraging thousands of samples genotyped since the initial description of MiniMUGA in 2020. Here we report our efforts to update and improve marker annotation, increase the number and the reliability of the consensus genotypes for inbred strains and increase the number of constructs that can reliably be detected with MiniMUGA. In addition, we have implemented key changes in the informatics pipeline to identify and quantify the contribution of specific genetic backgrounds to the makeup of a given sample, remove arbitrary thresholds, include the Y Chromosome and mitochondrial genome in the ideogram, and improve robust detection of the presence of commercially available substrains based on diagnostic alleles. Finally, we have made changes to the layout of the report, to simplify the interpretation and completeness of the analysis and added a table summarizing the ideogram. We believe that these changes will be of general interest to the mouse research community and will be instrumental in our goal of improving the rigor and reproducibility of mouse-based biomedical research.

Global Model Selection for Semi-Supervised Support Vector Machine via Solution Paths.

Semi-supervised support vector machine (S 3 VM) is important because it can use plentiful unlabeled data to improve the generalization accuracy of traditional SVMs. In order to achieve good performance, it is necessary for S 3 VM to take some effective measures to select hyperparameters. However, model selection for semi-supervised models is still a key open problem. Existing methods for semi-supervised models to search for the optimal parameter values are usually computationally demanding, especially those ones with grid search. To address this challenging problem, in this article, we first propose solution paths of S 3 VM (SPS 3 VM), which can track the solutions of the nonconvex S 3 VM with respect to the hyperparameters. Specifically, we apply incremental and decremental learning methods to update the solution and let it satisfy the Karush-Kuhn-Tucker (KKT) conditions. Based on the SPS 3 VM and the piecewise linearity of model function, we can find the model with the minimum cross-validation (CV) error for the entire range of candidate hyperparameters by computing the error path of S 3 VM. Our SPS 3 VM is the first solution path algorithm for nonconvex optimization problem of semi-supervised learning models. We also provide the finite convergence analysis and computational complexity of SPS 3 VM. Experimental results on a variety of benchmark datasets not only verify that our SPS 3 VM can globally search the hyperparameters (regularization and ramp loss parameters) but also show a huge reduction of computational time while retaining similar or slightly better generalization performance compared with the grid search approach.

GENERATION OF TOLEROGENIC DENDRITIC CELLS UNDER THE PERSISTENT INFLAMMATION STIMULATION.

ABSTRACT: Immunosuppression, commonly accompanied by persistent inflammation, is a key feature in the later phase of sepsis. However, the pathophysiological mechanisms underlying this phenomenon remain unclear. Dendritic cells (DCs), specifically tolerogenic DCs (tolDCs), play a crucial role in this process by regulating immune responses through inducing T cell anergy and releasing anti-inflammatory cytokines. Nevertheless, the existing cell models are inadequate for investigating tolDCs during the immunosuppressive phase of sepsis. Therefore, this study aimed to develop a novel in vitro model to generate tolDCs under chronic inflammatory conditions. We have successfully generated tolDCs by exposing them to sublethal lipopolysaccharide (LPS) for 72 h while preserving cell viability. Considering that IL-10-induced tolDCs (IL-10-tolDCs) are well-established models, we compared the immunological tolerance between LPS-tolDCs and IL-10-tolDCs. Our findings indicated that both LPS-tolDCs and IL-10-tolDCs exhibited reduced expression of maturation markers, whereas their levels of inhibitory markers were elevated. Furthermore, the immunoregulatory activities of LPS-tolDCs and IL-10-tolDCs were found to be comparable. These dysfunctions include impaired antigen presenting capacity and suppression of T cell activation, proliferation, and differentiation. Notably, compared with IL-10-tolDCs, LPS-tolDCs showed a reduced response in maturation and cytokine production upon stimulation, indicating their potential as a better model for research. Overall, in comparison with IL-10-tolDCs, our data suggest that the immunological dysfunctions shown in LPS-tolDCs could more effectively elucidate the increased susceptibility to secondary infections during sepsis. Consequently, LPS-tolDCs have emerged as promising therapeutic targets for ameliorating the immunosuppressed state in septic patients.

On the Number of Linear Regions of Convolutional Neural Networks With Piecewise Linear Activations.

One fundamental problem in deep learning is understanding the excellent performance of deep Neural Networks (NNs) in practice. An explanation for the superiority of NNs is that they can realize a large family of complicated functions, i.e., they have powerful expressivity. The expressivity of a Neural Network with Piecewise Linear activations (PLNN) can be quantified by the maximal number of linear regions it can separate its input space into. In this paper, we provide several mathematical results needed for studying the linear regions of Convolutional Neural Networks with Piecewise Linear activations (PLCNNs), and use them to derive the maximal and average numbers of linear regions for one-layer PLCNNs. Furthermore, we obtain upper and lower bounds for the number of linear regions of multi-layer PLCNNs. Our results suggest that deeper PLCNNs have more powerful expressivity than shallow PLCNNs, while PLCNNs have more expressivity than fully-connected PLNNs per parameter, in terms of the number of linear regions.

Perturbation diversity certificates robust generalization.

Whilst adversarial training has been proven to be one most effective defending method against adversarial attacks for deep neural networks, it suffers from over-fitting on training adversarial data and thus may not guarantee the robust generalization. This may result from the fact that the conventional adversarial training methods generate adversarial perturbations usually in a supervised way so that the resulting adversarial examples are highly biased towards the decision boundary, leading to an inhomogeneous data distribution. To mitigate this limitation, we propose to generate adversarial examples from a perturbation diversity perspective. Specifically, the generated perturbed samples are not only adversarial but also diverse so as to certify robust generalization and significant robustness improvement through a homogeneous data distribution. We provide theoretical and empirical analysis, establishing a foundation to support the proposed method. As a major contribution, we prove that promoting perturbations diversity can lead to a better robust generalization bound. To verify our methods' effectiveness, we conduct extensive experiments over different datasets (e.g., CIFAR-10, CIFAR-100, SVHN) with different adversarial attacks (e.g., PGD, CW). Experimental results show that our method outperforms other state-of-the-art (e.g., PGD and Feature Scattering) in robust generalization performance.

Genetic diversity drives extreme responses to traumatic brain injury and post-traumatic epilepsy.

Traumatic brain injury (TBI) is a complex and heterogeneous condition that can cause wide-spectral neurological sequelae such as behavioral deficits, sleep abnormalities, and post-traumatic epilepsy (PTE). However, understanding the interaction of TBI phenome is challenging because few animal models can recapitulate the heterogeneity of TBI outcomes. We leveraged the genetically diverse recombinant inbred Collaborative Cross (CC) mice panel and systematically characterized TBI-related outcomes in males from 12 strains of CC and the reference C57BL/6J mice. We identified unprecedented extreme responses in multiple clinically relevant traits across CC strains, including weight change, mortality, locomotor activity, cognition, and sleep. Notably, we identified CC031 mouse strain as the first rodent model of PTE that exhibit frequent and progressive post-traumatic seizures after moderate TBI induced by lateral fluid percussion. Multivariate analysis pinpointed novel biological interactions and three principal components across TBI-related modalities. Estimate of the proportion of TBI phenotypic variability attributable to strain revealed large range of heritability, including >70% heritability of open arm entry time of elevated plus maze. Our work provides novel resources and models that can facilitate genetic mapping and the understanding of the pathobiology of TBI and PTE.

Shape Anisotropy-Governed High-Performance Nanomagnetosol for In Vivo Magnetic Particle Imaging of Lungs.

Caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), coronavirus disease 2019 (COVID-19) has shown extensive lung manifestations in vulnerable individuals, putting lung imaging and monitoring at the forefront of early detection and treatment. Magnetic particle imaging (MPI) is an imaging modality, which can bring excellent contrast, sensitivity, and signal-to-noise ratios to lung imaging for the development of new theranostic approaches for respiratory diseases. Advances in MPI tracers would offer additional improvements and increase the potential for clinical translation of MPI. Here, a high-performance nanotracer based on shape anisotropy of magnetic nanoparticles is developed and its use in MPI imaging of the lung is demonstrated. Shape anisotropy proves to be a critical parameter for increasing signal intensity and resolution and exceeding those properties of conventional spherical nanoparticles. The 0D nanoparticles exhibit a 2-fold increase, while the 1D nanorods have a > 5-fold increase in signal intensity when compared to VivoTrax. Newly designed 1D nanorods displayed high signal intensities and excellent resolution in lung images. A spatiotemporal lung imaging study in mice revealed that this tracer offers new opportunities for monitoring disease and guiding intervention.