A novel myocardium segmentation approach based on neutrosophic active contour model

Automatic delineation of the myocardium in echocardiography can assist ra- diologists to diagnosis heart problems. However, it is still challenging to distinguish myocardium from other tissue due to a low signal-to-noise ratio, low contrast, vague boundary, and speckle noise

5,7,13,15-Tetra­oxo-2,2,10,10-tetra­kis­(trifluoro­meth­yl)-4,8,12,16-tetra­oxa-1(1,4),3(1,4),6(1,2),9(1,4),11(1,4),14(1,2)-hexa­benzenahexa­deca­phane tetra­hydro­furan monosolvate

The title compound, C46H24F12O8·C4H8O, consists of a cyclic aryl ester dimer and a tetra­hydro­furan mol­ecule. In the structure of the cyclic dimer, one carbonyl group stretches above the cavity and the other below

Analyzing Hidden Representations in End-to-End Automatic Speech Recognition Systems

Neural models have become ubiquitous in automatic speech recognition systems. While neural networks are typically used as acoustic models in more complex systems, recent studies have explored end-to-end speech recognition systems based on neural networks, which can be trained to directly predict text from input acoustic features. Although such systems are conceptually elegant and simpler than traditional systems, it is less obvious how to interpret the trained models. In this work, we analyze the speech representations learned by a deep end-to-end model that is based on convolutional and recurrent layers, and trained with a connectionist temporal classification (CTC) loss. We use a pre-trained model to generate frame-level features which are given to a classifier that is trained on frame classification into phones. We evaluate representations from different layers of the deep model and compare their quality for predicting phone labels. Our experiments shed light on important aspects of the end-to-end model such as layer depth, model complexity, and other design choices.Comment: NIPS 201

FAIRER: Fairness as Decision Rationale Alignment

Deep neural networks (DNNs) have made significant progress, but often suffer from fairness issues, as deep models typically show distinct accuracy differences among certain subgroups (e.g., males and females). Existing research addresses this critical issue by employing fairness-aware loss functions to constrain the last-layer outputs and directly regularize DNNs. Although the fairness of DNNs is improved, it is unclear how the trained network makes a fair prediction, which limits future fairness improvements. In this paper, we investigate fairness from the perspective of decision rationale and define the parameter parity score to characterize the fair decision process of networks by analyzing neuron influence in various subgroups. Extensive empirical studies show that the unfair issue could arise from the unaligned decision rationales of subgroups. Existing fairness regularization terms fail to achieve decision rationale alignment because they only constrain last-layer outputs while ignoring intermediate neuron alignment. To address the issue, we formulate the fairness as a new task, i.e., decision rationale alignment that requires DNNs' neurons to have consistent responses on subgroups at both intermediate processes and the final prediction. To make this idea practical during optimization, we relax the naive objective function and propose gradient-guided parity alignment, which encourages gradient-weighted consistency of neurons across subgroups. Extensive experiments on a variety of datasets show that our method can significantly enhance fairness while sustaining a high level of accuracy and outperforming other approaches by a wide margin

Experimental Study on Vertical Fire Spread of Thin Hanging Combustibles

AbstractIn order to study vertical fire spread law of thin combustibles hang in atrium, the vertical combustion characteristics of thin combustibles was tested using vertical combustion experimental equipment, limited oxygen index tester and calorific value analyzer. Combustion parameters such as oxygen index, calorific value, vertical fire spread rate, surface temperature, mass loss rate and heat release rate were obtained. Fitting line showed that vertical fire spread rate, mass loss rate and heat release rate can be expressed as a power function of burning time. Vertical fire spread was accelerated growth, which the fire spread rate is over ten times of horizontal fire spread rate. The maximum surface temperature of the combustion cotton was about 500°C, the maximum surface temperature of the combustion cardboard was about 700°C. Experimental results showed that the thin combustibles hang in atrium and other large spaces have a greater fire risk, its vertical fire spread is very fast, so fire prevention measures should be taken in practical applications

High optical transmittance of aluminum ultrathin film with hexagonal nanohole arrays as transparent electrode

We fabricate samples of aluminum ultrathin films with hexagonal nanohole arrays and characterize the transmission performance. High optical transmittance larger than 60% over a broad wavelength range from 430 nm to 750 nm is attained experimentally. The Fano-type resonance of the excited surface plasmon plaritons and the directly transmitted light attribute to both of the broadband transmission enhancement and the transmission suppression dips

Cationic liposomes induce cytotoxicity in HepG2 via regulation of lipid metabolism based on whole-transcriptome sequencing analysis

Abstract Backgroud Cationic liposomes (CLs) can be used as non-viral vectors in gene transfer and drug delivery. However, the underlying molecular mechanism of its cytotoxicity has not been well elucidated yet. Methods We herein report a systems biology approach based on whole-transcriptome sequencing coupled with computational method to identify the predominant genes and pathways involved in the cytotoxicity of CLs in HepG2 cell line. Results Firstly, we validated the concentration-dependent cytotoxicity of CLs with an IC50 of 120 μg/ml in HepG2 exposed for 24 h. Subsequently, we used whole-transcriptome sequencing to identify 220 (77 up- and 143 down-regulated) differentially expressed genes (DEGs). Gene ontology (GO) and pathway analysis showed that these DEGs were mainly related to cholesterol, steroid, lipid biosynthetic and metabolic processes. Additionally, “key regulatory” genes were identified using gene act, pathway act and co-expression network analysis, and expression levels of 11 interested altered genes were confirmed by quantitative real time PCR. Interestingly, no cell cycle arrest was observed through flow cytometry. Conclusions These data are expected to provide deep insights into the molecular mechanism of CLs cytotoxicity.https://deepblue.lib.umich.edu/bitstream/2027.42/144776/1/40360_2018_Article_230.pd

Three-Dimensional Characterization of Mechanical Interactions between Endothelial Cells and Extracellular Matrix during Angiogenic Sprouting

We studied the three-dimensional cell-extracellular matrix interactions of endothelial cells that form multicellular structures called sprouts. We analyzed the data collected in-situ from angiogenic sprouting experiments and identified the differentiated interaction behavior exhibited by the tip and stalk cells. Moreover, our analysis of the tip cell lamellipodia revealed the diversity in their interaction behavior under certain conditions (e.g., when the heading of a sprout is switched approximately between the long-axis direction of two different lamellipodia). This study marks the first time that new characteristics of such interactions have been identified with shape changes in the sprouts and the associated rearrangements of collagen fibers. Clear illustrations of such changes are depicted in three-dimensional views.Singapore-MIT Alliance in Research and Technology (SMART

Effect of Wearable Sensor-Based Exercise on Musculoskeletal Disorders in Individuals With Neurodegenerative Diseases: A Systematic Review and Meta-Analysis

BackgroundThe application of wearable sensor technology in an exercise intervention provides a new method for the standardization and accuracy of intervention. Considering that the deterioration of musculoskeletal conditions is of serious concern in patients with neurodegenerative diseases, it is worthwhile to clarify the effect of wearable sensor-based exercise on musculoskeletal disorders in such patients compared with traditional exercise.MethodsFive health science-related databases, including PubMed, Cochrane Library, Embase, Web of Science, and Ebsco Cumulative Index to Nursing and Allied Health, were systematically searched. The protocol number of the study is PROSPERO CRD42022319763. Randomized controlled trials (RCTs) that were published up to March 2022 and written in English were included. Balance was the primary outcome measure, comprising questionnaires on postural stability and computerized dynamic posturography. The secondary outcome measures are motor symptoms, mobility ability, functional gait abilities, fall-associated self-efficacy, and adverse events. Stata version 16.0 was used for statistical analysis, and the weighted mean difference (WMD) was selected as the effect size with a 95% confidence interval (CI).ResultsFifteen RCTs involving 488 participants with mean ages ranging from 58.6 to 81.6 years were included in this review, with 14 of them being pooled in a quantitative meta-analysis. Only five included studies showed a low risk of bias. The Berg balance scale (BBS) was used in nine studies, and the pooled data showed a significant improvement in the wearable sensor-based exercise group compared with the traditional exercise group after 3–12-week intervention (WMD = 1.43; 95% CI, 0.50 to 2.36, P = 0.003). A significant change in visual score was found both post-assessment and at 1-month follow-up assessment (WMD = 4.38; 95% CI, 1.69 to 7.07, P = 0.001; I2 = 0.0%). However, no significant differences were found between the two groups in the secondary outcome measures (all p > 0.05). No major adverse events were reported.ConclusionThe wearable sensor-based exercise had advantages in improving balance in patients with neurodegenerative diseases, while there was a lack of evidence in motor symptoms, mobility, and functional gait ability enhancement. Future studies are recommended to construct a comprehensive rehabilitation treatment system for the improvement in both postural control and quality of life.Systematic Review Registrationhttp://www.crd.york.ac.uk/prospero/, identifier CRD42022319763

A SOM based Anomaly Detection Method for Wind Turbines Health Management through SCADA Data

In this paper, a data driven method for Wind Turbine system level anomaly detection and root sub-component identification is proposed. Supervisory control and data acquisition system (SCADA) data of WT is adopted and several parameters are selected based on physical knowledge in this domain and correlation coefficient analysis to build a normal behavior model. This model which is based on Self-organizing map (SOM) projects higher-dimensional SCADA data into a two-dimension-map. Afterwards, the Euclidean distance based indicator for system level anomalies is defined and a filter is created to screen out suspicious data points based on quantile function. Moreover, a failure data pattern based criterion is created for anomaly detection from system level. In order to track which sub-component should be responsible for an anomaly, a contribution proportion (CP) index is proposed. The method is tested with a two-month SCADA dataset with the measurement interval as 20 seconds. Results demonstrate capability and efficiency of the proposed method