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Dynamic behaviour of a micro-cantilever beam under periodic electro-thermal loading is studied in this paper. For certain applications the beam is required to vibrate at a particular frequency. Modal analysis using 3D finite element is used in order to find the geometrical parameters that makes fundamental frequency of the beam match the required frequency. Then non-linear dynamic thermoelastic analysis is conducted on the system to analyse the time-history (transient) behaviour of the beam and record its tip displacement. However, due to uncertainties and non-repeatabilities that are inherent properties of the system along with those associated with the manufacturing, final product is likely to have deviations from these estimated values (fundamental frequency and tip displacement). Thus, choosing a nominal (desired) design and studying the deviation in natural frequency and tip displacement via 2k factorial Design-of-Experiments (DOE), effect of uncertainties on the overall performance of the system is investigated. This allows finding the significance of individual parameters on the overall robustness of the design as well as potential interactions between various parameters. Finally, the expected behaviour of the micro-cantilever and its robustness to design and implementation uncertainties are elaborated and statements for robust design of this system are made. [ABSTRACT FROM AUTHOR]

In this article, a new analytical approach for the deflection analysis of yarns in woven materials under tensile loading is presented. A unit cell of a warp yarn surrounded by adjacent weft yarns is modeled using curved beams. Next, using the Winkler theory of curved beams, the warp yarn strain energy under tensile loading is derived; followed by the implementation of the variational method to obtain the governing equations as well as related boundary conditions. The exact displacement field solution was found and using the continuity of deflection condition at crossover points, the normal inter-yarn contact force was estimated. In the proposed analytical approach, shear forces and potential slippage of yarns at contact points are neglected. The validation of the solution was checked by comparing results of a benchmark example on steel textile preforms to two sets of finite element simulations. For further evaluation of the approach, the uniaxial extension response of a polypropylene/glass woven fabric composite with a noncircular yarn cross section is also considered under large deformation and predictions are compared to earlier data in the literature. [ABSTRACT FROM AUTHOR]

Interpretability in machine learning has become increasingly important as machine learning is being used in more and more applications, including those with high-stakes consequences such as healthcare where Interpretability has been regarded as a key to the successful adoption of machine learning models. However, using confounding/irrelevant information in making predictions by deep learning models, even the interpretable ones, poses critical challenges to their clinical acceptance. That has recently drawn researchers' attention to issues beyond the mere interpretation of deep learning models. In this paper, we first investigate application of an inherently interpretable prototype-based architecture, known as ProtoPNet, for breast cancer classification in digital pathology and highlight its shortcomings in this application. Then, we propose a new method that uses more medically relevant information and makes more accurate and interpretable predictions. Our method leverages the clustering concept and implicitly increases the number of classes in the training dataset. The proposed method learns more relevant prototypes without any pixel-level annotated data. To have a more holistic assessment, in addition to classification accuracy, we define a new metric for assessing the degree of interpretability based on the comments of a group of skilled pathologists. Experimental results on the BreakHis dataset show that the proposed method effectively improves the classification accuracy and interpretability by respectively 8 % and 18 % . Therefore, the proposed method can be seen as a step toward implementing interpretable deep learning models for the detection of breast cancer using histopathology images., (© 2024. The Author(s).)

Part-prototype networks have recently become methods of interest as an interpretable alternative to many of the current black-box image classifiers. However, the interpretability of these methods from the perspective of human users has not been sufficiently explored. In addition, previous works have had major issues with following proper experiment design and task representation that limit their reliability and validity. In this work, we have devised a framework for evaluating the interpretability of part-prototype-based models from a human perspective that solves these issues. The proposed framework consists of three actionable metrics and experiments. The results of these experiments will reveal important and reliable interpretability related properties of such models. To demonstrate the usefulness of our framework, we performed an extensive set of experiments using Amazon Mechanical Turk. They not only show the capability of our framework in assessing the interpretability of various part-prototype-based models, but they also are, to the best of our knowledge, the most comprehensive work on evaluating such methods in a unified framework., (© 2023. The Author(s).)

The advent of recent high throughput sequencing technologies resulted in unexplored big data of genomics and transcriptomics that might help to answer various research questions in Parkinson's disease (PD) progression. While the literature has revealed various predictive models that use longitudinal clinical data for disease progression, there is no predictive model based on RNA-Sequence data of PD patients. This study investigates how to predict the PD Progression for a patient's next medical visit by capturing longitudinal temporal patterns in the RNA-Seq data. Data provided by Parkinson Progression Marker Initiative (PPMI) includes 423 PD patients without revealing any race, sex, or age information with a variable number of visits and 34,682 predictor variables for 4 years. We propose a predictive model based on deep Recurrent Neural Network (RNN) with the addition of dense connections and batch normalization into RNN layers. The results show that the proposed architecture can predict PD progression from high dimensional RNA-seq data with a Root Mean Square Error (RMSE) of 6.0 and a rank-order correlation of (r = 0.83, p < 0.0001) between the predicted and actual disease status of PD., (© 2022. The Author(s).)

Despite much progress in training artificial intelligence (AI) systems to imitate human language, building agents that use language to communicate intentionally with humans in interactive environments remains a major challenge. We introduce Cicero, the first AI agent to achieve human-level performance in Diplomacy , a strategy game involving both cooperation and competition that emphasizes natural language negotiation and tactical coordination between seven players. Cicero integrates a language model with planning and reinforcement learning algorithms by inferring players' beliefs and intentions from its conversations and generating dialogue in pursuit of its plans. Across 40 games of an anonymous online Diplomacy league, Cicero achieved more than double the average score of the human players and ranked in the top 10% of participants who played more than one game.

In many real-world scenarios, data from multiple modalities (sources) are collected during a development phase. Such data are referred to as multiview data. While additional information from multiple views often improves the performance, collecting data from such additional views during the testing phase may not be desired due to the high costs associated with measuring such views or, unavailability of such additional views. Therefore, in many applications, despite having a multiview training data set, it is desired to do performance testing using data from only one view. In this paper, we present a multiview feature selection method that leverages the knowledge of all views and use it to guide the feature selection process in an individual view. We realize this via a multiview feature weighting scheme such that the local margins of samples in each view are maximized and similarities of samples to some reference points in different views are preserved. Also, the proposed formulation can be used for cross-view matching when the view-specific feature weights are pre-computed on an auxiliary data set. Promising results have been achieved on nine real-world data sets as well as three biometric recognition applications. On average, the proposed feature selection method has improved the classification error rate by 31 percent of the error rate of the state-of-the-art.

Background and Purpose: Accumulating evidence suggest that lenalidomide, a structural analog of thalidomide, has neuro-modulatory and neuroprotective properties. In the present study, we investigated effects of acute administration of lenalidomide on clonic seizure threshold in mice induced by pentylenetetrazole (PTZ) and possible role of N-methyl-D-aspartic acid receptor (NMDAR) and nitric oxide (NO) pathway., Methods: We have utilized a clonic model of seizure in NMRI mice induced by PTZ to evaluate the potential effect of lenalidomide on seizure threshold. Different doses of lenalidomide (5, 10, 20, and 50 mg/kg, intraperitoneal [i.p.]) were administered 1 hour before PTZ. To evaluate probable role of NMDAR/NO signaling, the non-selective NO synthase inhibitor L- N G -nitroarginine methyl ester (L-NAME; 10 mg/kg, i.p.), neuronal NOS (nNOS) inhibitor 7-nitroindazole (7-NI; 30 mg/kg, i.p.), selective inducible NOS inhibitor aminoguanidine (AG; 100 mg/kg, i.p.), selective NMDAR antagonist MK-801 (0.01 mg/kg, i.p.), and selective NMDAR agonist D-serine (30 mg/kg, i.p.) were injected 15 minutes before lenalidomide., Results: Lenalidomide at 10 and 20 mg/kg significantly elevated the PTZ-induced seizure thresholds. Interestingly, L-NAME (10 mg/kg, i.p), 7-NI (30 mg/kg, i.p), and AG (100 mg/kg, i.p) reversed the anticonvulsive effect of lenalidomide (10 mg/kg). Moreover, treatment with the NMDAR agonist D-serine (30 mg/kg, i.p.) did not alter the anticonvulsive properties of lenalidomide (10 mg/kg, i.p). However, the NMDAR antagonist MK-801 (0.01 mg/kg, i.p) significantly reversed the anticonvulsive effects of lenalidomide (10 mg/kg)., Conclusions: Our study demonstrated a role for the NMDAR/NO pathway in the anticonvulsive effects of lenalidomide on the PTZ-induced clonic seizures in mice., Competing Interests: Conflict of Interest The authors declare that they have no conflicts of interest., (Copyright © 2021 Korean Epilepsy Society.)

Single-cell transcriptomics (scRNA-seq) has revolutionized the understanding of the spatial architecture of tissue structure and function. Advancing the "transcript-centric" view of scRNA-seq analyses is presently restricted by the limited resolution of proteomics and genome-wide techniques to analyze post-translational modifications. Here, by combining spatial cell sorting with transcriptomics and quantitative proteomics/phosphoproteomics, we established the spatially resolved proteome landscape of the liver endothelium, yielding deep mechanistic insight into zonated vascular signaling mechanisms. Phosphorylation of receptor tyrosine kinases was detected preferentially in the central vein area, resulting in an atypical enrichment of tyrosine phosphorylation. Prototypic biological validation identified Tie receptor signaling as a selective and specific regulator of vascular Wnt activity orchestrating angiocrine signaling, thereby controlling hepatocyte function during liver regeneration. Taken together, the study has yielded fundamental insight into the spatial organization of liver endothelial cell signaling. Spatial sorting may be employed as a universally adaptable strategy for multiomic analyses of scRNA-seq-defined cellular (sub)-populations., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.)

This study aimed to evaluate the antioxidant capacity of phosphatidylserine liposome (PS) against oxidative stress due to cyclosporine A (CsA) and concurrent administration of PS and CsA on the attenuation of immune response. The effect of oral PS was evaluated on biochemical and oxidative renal markers and histopathology of nephrotic rats receiving CsA. The effect of co-administration of PS with CsA was also assessed on DTH (delayed-type hypersensitivity) reaction of immunized rats. The cytokines production level of IL-2 (Interleukin-2) and IFN-γ (Interferon gamma) was measured in immunized rat's splenocytes. PS treatment significantly (P < 0.05) reduced Cr and BUN of serum and MDA (malondialdehyde) in kidney tissue, and increased SOD (superoxide dismutase) and CAT (Catalase) of kidney tissue in CsA-nephrotic rats. Histopathology data indicated significantly (P < 0.05) nephrotoxicity improvement after 25-day treatment with PS. Furthermore, CsA plus PS administration significantly reduced DTH response and cytokines production of IL-2 and IFN-γ in immunized rats. In conclusion, coadministration of CsA plus PS may overcome oxidative stress and improve the performance of organ transplantation or autoimmune therapy., (Copyright © 2020 Elsevier Inc. All rights reserved.)

The inflammatory factors of complete blood count (CBC) are associated with a decrease in the in vitro embryo production (IVP) outcome in women. The relation between the blood indices and in vitro fertilization (IVF) outcomes in bovines remains to be elucidated. Using ovum pick-up (OPU), oocytes were retrieved from heifers (n = 60) and inseminated separately with sperm. The blastocyst formation was recorded on day 7 after insemination for each animal and the blood indices were evaluated at the time of OPU. Then, heifers were classified on the basis of (1) blastocyst formation, cleaved vs. failed, or (2) inflammation, low-grade inflammation (lymphocyte counts > 5.6 × 10 9 /L) vs. no inflammation (lymphocyte counts < 5.6 × 10 9 /L). Oocytes derived from heifers with higher lymphocytes, red blood cells (RBC), platelets, hematocrit, red cell distribution width (RDW-SD) and plateletcrit values and lower monocytes, eosinophils, mean corpuscular hemoglobin (MCH) and MCH concentration (MCHC) successfully developed to the blastocyst stage. Heifers with low-grade inflammation numerically had a higher percentage of blastocyst formation than normal heifers. The principle component analysis (PCA) showed that blastocyst formation had the strongest positive association with RDW-cv and RDW-SD, while having a strong negative association with mean corpuscular volume (MCV), hemoglobin, MCHC and MCH. The PCA determined that the number of grade A COCs and the percentage of COCs reached the cleavage stage had a negative association with white blood cells (WBC), lymphocytes, basophils and monocytes, and a positive correlation with platelet to lymphocyte ratio, platelet distribution width (PDW) and plateletcrit. Network mapping detected close similarities between BFR and RDW-SD, MPV, and lymphocytes. The area under the receiver operating characteristic curve (AUC) identified that, eosinophils (AUC 0.80), RDW-SD (AUC 0.76), monocytes (AUC 0.76) and lymphocytes (AUC 0.76) had a good predictive ability to detect heifers with high OPU-IVP outcome (≥60%). In conclusion, these findings suggest that CBC indices at the time of OPU were associated with the IVF outcome and may be incorporated into protocols for the identification of heifers with high potential for blastocyst formation., Competing Interests: Declaration of competing interest The authors report no conflicts of interest., (Copyright © 2020 Elsevier Inc. All rights reserved.)

Mismatch negativity (MMN) is a component of the event-related potential (ERP) that is elicited through an odd-ball paradigm. The existence of the MMN in a coma patient has a good correlation with coma emergence; however, this component can be difficult to detect. Previously, MMN detection was based on visual inspection of the averaged ERPs by a skilled clinician, a process that is expensive and not always feasible in practice. In this paper, we propose a practical machine learning (ML) based approach for detection of MMN component, thus, improving the accuracy of prediction of emergence from coma. Furthermore, the method can operate on an automatic and continuous basis thus alleviating the need for clinician involvement. The proposed method is capable of the MMN detection over intervals as short as two minutes. This finer time resolution enables identification of waxing and waning cycles of a conscious state. An auditory odd-ball paradigm was applied to 22 healthy subjects and 2 coma patients. A coma patient is tested by measuring the similarity of the patient's ERP responses with the aggregate healthy responses. Because the training process for measuring similarity requires only healthy subjects, the complexity and practicality of training procedure of the proposed method are greatly improved relative to training on coma patients directly. Since there are only two coma patients involved with this study, the results are reported on a very preliminary basis. Preliminary results indicate we can detect the MMN component with an accuracy of 92.7% on healthy subjects. The method successfully predicted emergence in both coma patients when conventional methods failed. The proposed method for collecting training data using exclusively healthy subjects is a novel approach that may prove useful in future, unrelated studies where ML methods are used.

Language is one the earliest capacities affected by cognitive change. To monitor that change longitudinally, we have developed a web portal for remote linguistic data acquisition, called Talk2Me, consisting of a variety of tasks. In order to facilitate research in different aspects of language, we provide baselines including the relations between different scoring functions within and across tasks. These data can be used to augment studies that require a normative model; for example, we provide baseline classification results in identifying dementia. These data are released publicly along with a comprehensive open-source package for extracting approximately two thousand lexico-syntactic, acoustic, and semantic features. This package can be applied arbitrarily to studies that include linguistic data. To our knowledge, this is the most comprehensive publicly available software for extracting linguistic features. The software includes scoring functions for different tasks., Competing Interests: The authors of this manuscript have read the journal’s policy and have the following competing interests: KCF, MY, and FR are co-founders of a company, WinterLight Labs Incorporated, that commercializes the automated analysis of spontaneous speech and other language tasks. WinterLight Labs provided no influence or financial support, nor has WinterLight Labs received any benefit from this work beyond what is being made public. MY, who was a student at the inception of the study at the University of Toronto, was an employee of WinterLight Labs during the final editing of the document. There are no patents, products in development or marketed products associated with this research to declare. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

This paper proposes an anaesthesia monitoring system that accurately measures the depth of anaesthesia through 40-Hz auditory steady-state response. With accurate and fast depth of anaesthesia measuring, the monitor can reduce the incidence of awareness during surgical operation. The proposed denoising method for extracting 40-Hz auditory steady-state cycles, adaptive multilevel wavelet denoising, enabled the system to extract auditory steady-state response cycles from fewer epochs and over short periods of time which is of crucial importance in monitoring anaesthesia. The noise estimation scheme, adaptive threshold levels, rearranging, and multilevel denoising of frames increase the accuracy and signal to noise ratio of the extracted cycles. The modified fuzzy c-means clustering scheme, proposed to improve clustering performance in noisy data bases where no prior information about the level of noise and signal energy is available, is used for clustering the auditory steady-state cycles. Weighting the features with a novel algorithm and based on their differentiating role in clustering, the modified fuzzy c-means improves fuzziness in cluster partitions and the geometrical structure of the data. An index called depth of anaesthesia index is defined and determined at each cycle based on the clustering information of the cycle and the previous ones. The algorithm is applied to auditory steady-state response signals recorded from 20 human subjects during surgical operations with Propofol-induced general anaesthesia. The accuracy of the depth of anaesthesia index is validated through the subjects' medical markers, clinical parameters, and the recorded bispectral index during the induction phase. Depth of anaesthesia index is verified to be accurate and able to detect fast transitions between different levels of anaesthesia. The computed depth of anaesthesia indices detected the induction of anaesthesia on average 55 s faster than bispectral index and 17 s earlier than loss of eyelash reflex.

Home-based programs have been shown to be effective in improving health conditions, patient self-management, quality of life, and health outcomes. However, there is mixedevidence on the effectiveness of these programs due to limitations in the intervention tools that are used, primarily the burden that is placed on the user, especially among seniors. In this paper we developed a novel home-based package that measures critically important physiological information such that neither active compliance or interaction from the user is required. To this end, we embedded passive sensors (including load cells, electrodes, pulse sensor and color sensors) into common household items such as tiling, furniture and wall. The smart package measures subject's electrocardiogram (ECG), photoplethysmogram (PPG), ballistocardiogram (BCG), electromyogram (EMG) and imaging photoplethysmogram (IPPG). In contrast to the previous studies, the proposed package measures all the physiological information unobtrusively, simultaneously and in a synchronized manner such that all the data samples corresponding to different intervals of a specific cardiovascular cycle can be identified. Such information can be analyzed by a clinician or be used for a higher level information extraction such as beat-to-beat blood pressure estimation. In addition, the proposed package is the first and only homebased technology that can simultaneously and unobtrusively capture both mechanical and electrical characteristics of user's heart activities. This results in a more accurate home-based vital parameters monitoring.

Electrocardiogram (ECG) and transient evoked otoacoustic emission (TEOAE) are among the physiological signals that have attracted significant interest in biometric community due to their inherent robustness to replay and falsification attacks. However, they are time-dependent signals and this makes them hard to deal with in across-session human recognition scenario where only one session is available for enrollment. This paper presents a novel feature selection method to address this issue. It is based on an auxiliary dataset with multiple sessions where it selects a subset of features that are more persistent across different sessions. It uses local information in terms of sample margins while enforcing an across-session measure. This makes it a perfect fit for aforementioned biometric recognition problem. Comprehensive experiments on ECG and TEOAE variability due to time lapse and body posture are done. Performance of the proposed method is compared against seven state-of-the-art feature selection algorithms as well as another six approaches in the area of ECG and TEOAE biometric recognition. Experimental results demonstrate that the proposed method performs noticeably better than other algorithms.

Conventional feature selection algorithms assign a single common feature set to all regions of the sample space. In contrast, this paper proposes a novel algorithm for localized feature selection for which each region of the sample space is characterized by its individual distinct feature subset that may vary in size and membership. This approach can therefore select an optimal feature subset that adapts to local variations of the sample space, and hence offer the potential for improved performance. Feature subsets are computed by choosing an optimal coordinate space so that, within a localized region, within-class distances and between-class distances are, respectively, minimized and maximized. Distances are measured using a logistic function metric within the corresponding region. This enables the optimization process to focus on a localized region within the sample space. A local classification approach is utilized for measuring the similarity of a new input data point to each class. The proposed logistic localized feature selection (lLFS) algorithm is invariant to the underlying probability distribution of the data; hence, it is appropriate when the data are distributed on a nonlinear or disjoint manifold. lLFS is efficiently formulated as a joint convex/increasing quasi-convex optimization problem with a unique global optimum point. The method is most applicable when the number of available training samples is small. The performance of the proposed localized method is successfully demonstrated on a large variety of data sets. We demonstrate that the number of features selected by the lLFS method saturates at the number of available discriminative features. In addition, we have shown that the Vapnik-Chervonenkis dimension of the localized classifier is finite. Both these factors suggest that the lLFS method is insensitive to the overfitting issue, relative to other methods.