Time Segments Research Articles

Inequality in Children’s Physical Activity Across Time-Segmented Youth Sport Practice

ABSTRACT Purpose: Youth sport (YS) is a community system for promoting children’s physical activity (PA). Studies have examined mean PA during YS practices, but few have examined inequalities in the distribution of PA among children during practice time. This study examined PA inequality in time-segmented YS practices and differences in inequality by time segment characteristics. Methods: Children’s PA and YS practices were examined using accelerometer and video observation data from a sample of YS teams (n = 36 teams, n = 101 practices) for third- through sixth-grade children (n = 392), approximately eight to 12 years old, in two rural U.S. communities. Practices were time-segmented into smaller units (episodes; n = 991). Episodes were assigned codes for purpose (e.g. warm-up), member arrangement (e.g. whole group), and setting demand (i.e. fosters participation, creates exclusion). Group accelerometer data were paired with episodes, and the Gini coefficient quantified inequality in activity counts and minutes of moderate-to-vigorous PA (MVPA). Beta generalized estimating equations examined the influence of episode structure on PA inequality. Results: Warm-up (Gini = 0.22), fitness (Gini = 0.24), and sport skill (Gini = 0.24) episodes had significantly lower inequality (p < .05) in activity counts than other purpose types. Management (Gini = 0.32) and strategy (Gini = 0.40) episodes had significantly greater inequality (p < .05) in MVPA minutes than other purpose types. Episodes fostering participation (Gini = 0.32) had significantly lower activity count inequality (p < .05) than episodes creating exclusion (Gini = 0.35). Conclusion: PA inequality among children during YS varied by practice structure. Metrics such as the Gini coefficient can illuminate inequalities in PA and may be useful for guiding efforts to improve population PA in children. Trial Registration: This study is registered at www.clinicaltrials.gov (Identifier: NCT03380143).

Solving satellite image data downlink scheduling problem with family attribute via a bi-stage differential evolutionary algorithm

Due to the asynchronous development of observation and transition capabilities, the original image data (OID) obtained by a one-time observation cannot be completely transmitted in one transmission cycle between the EOS and GS, which is named a visible time window (VTW). First, it is needed to segment the OID into several segmented image datasets (SIDs), which are then transmitted in several VTWs. Furthermore, this can cause the satellite image data downlink scheduling problem (SIDSP). This study defines an innovative SIDSP as a satellite image data downlink scheduling problem with the family attribute (SIDSPFA), where massive OID are segmented using a fast segmentation operator, and all SIDs and other no-segmented OID are transmitted in the second step. In addition, two optimization objectives, namely the failure rate (FR) and Segmentation Time (ST) of the image data transmission, are defined to formalize the SIDSPFA as a bi-objective discrete optimization model. Further, a bi-stage differential evolutionary algorithm (DE-CD) is developed. The results of the extensive simulation tests verify the efficiency of the models, strategies, algorithms, and operators used in this study.

O-260 Identifying seminiferous tubules for sperm retrieval during microdissection testicular sperm extraction (M-TESE) using deep learning

Abstract Study question Is it possible to utilize artificial intelligence (AI) in the selection of seminiferous tubules for sperm retrieval during microdissection testicular sperm extraction (M-TESE)? Summary answer AI algorithm was able to segment seminiferous tubules deemed to be good candidates for sperm retrieval with IoU of 0.746 and F1 score of 72.5%. What is known already M-TESE is an advanced surgical procedure reserved for sperm retrieval in men with non-obstructive azoospermia (NOA). Meta-analysis have shown M-TESE might be superior in relation to sperm retrieval rates and postsurgical complications when compared to conventional techniques. Limitations of the technique are the relatively long learning curve for the surgeons and the longer intraoperative time required to identify the tubules with active spermatogenesis. Convolutional neural networks (CNNs) have been shown to successfully identify areas of interest in medical images such as CT and MRI scans. The possibility of utilizing trained CNNs in M-TESE procedures has so far not been studied. Study design, size, duration This is a cross sectional study analyzing image data gathered during M-TESE procedures including the first five men enrolled in a prospective study designed specifically to test the utilization of AI in the treatment of NOA. All participants signed written informed consent. Images were collected intraoperatively with low and high resolution cameras according to predefined protocol. Participants/materials, setting, methods CNN model with down and upsampling architecture was trained using patch-based technique with corresponding labelled truth image pairs based on combination of semi-automated hue saturation value thresholding and manual segmentation of seminiferous tubules. Tubules were allocated into two different classes by the operating surgeon according to the visual perception of their morphology as type 1 (i.e. larger, opaque, good candidates for micro dissection) and type 2 (i.e. sclerotic or atrophic; unlikely to harbor active spermatogenesis). Main results and the role of chance CNN models were trained on 1809 patches with corresponding ground truth segmented images. Two algorithms were tested: the first was trained solely to detect and semantically distinguish type 1 seminiferous tubules. The second was trained to semantically segment the major anatomical structures: tunica albuginea, testicular parenchyma, blood vessels; lobules and the two types of seminiferous tubules describe above. Validation was performed on 988 image patches not previously shown to the algorithm. After grid search for optimal learning rate and augmentation the first algorithm was able to segment type 1 tubules with F1 score of 72.5% and Jaccard Index (IoU) of 0.746. Training took 856 minutes on 48 core high performance computer (HPC) equipped with single graphics processing unit (GPU). Same computational environment was used in the training of the second algorithm. This model was able to segment anatomical structures such as parenchyma (IoU=0.732) and testicular lobules (IoU=0.683) but performed sub optimally on the clinically relevant type 1 tubules (IoU=0.432). The first trained algorithm was able to segment a single high resolution surgical image in 3.4 seconds on an HPC. However, the same procedure took 5.5 minutes on standard desktop computer with 8 core Intel processor without GPU. Limitations, reasons for caution The number of patients is relatively small and therefore more training data would be needed to achieve robust performance. Training data were labelled by single surgeon limiting the generalizability and possibly biasing the selection process. The need of HPC environment for real time segmentation can limit the clinical use. Wider implications of the findings This pilot study shows the potential of AI to help in choosing suitable tubules for micro dissection, shortening the learning curve for young andrological surgeons. The access to cluster based computation is getting easier by the day and can soon allow instant intraoperative AI segmentation, thus shortening the surgical procedure. Trial registration number NA

Loading Recognition for Lumbar Exoskeleton Based on Multi-Channel Surface Electromyography From Low Back Muscles.

Lumbar exoskeleton is an assistive robot, which can reduce the risk of injury and pain in low back muscles when lifting heavy objects. An important challenge it faces involves enhancing assistance with minimal muscle energy consumption. One of the viable solutions is to adjust the force or torque of assistance in response to changes in the load on the low back muscles. It requires accurate loading recognition, which has yet to yield satisfactory outcomes due to the limitations of available measurement tools and load classification methods. This study aimed to precisely identify muscle loading using a multi-channel surface electromyographic (sEMG) electrode array on the low back muscles, combined with a participant-specific load classification method. Ten healthy participants performed a stoop lifting task with objects of varying weights, while sEMG data was collected from the low back muscles using a 3x7 electrode array. Nineteen time segments of the lifting phase were identified, and time-domain sEMG features were extracted from each segment. Participant-specific classifiers were built using four classification algorithms to determine the object weight in each time segment, and the classification performance was evaluated using a 5-fold cross-validation method. The artificial neural network classifier achieved an impressive accuracy of up to 96%, consistently improving as the lifting phase progressed, peaking towards the end of the lifting movement. This study successfully achieves accurate recognition of load on low back muscles during the object lifting task. The obtained results hold significant potential in effectively reducing muscle energy consumption when wearing a lumbar exoskeleton.

STUDY OF THE EFFECTIVENESS OF THE APPLICATION OF COMPLEX GEOLOGICAL AND TECHNOLOGICAL MEASURES ON THE EXAMPLE OF GAS-CONDENSATE GENERATION IN THE CENTRAL PART OF THE PRIAXIAL ZONE OF THE DNIPROV-DNETZ BASIN

The field development system is the basis of hydrocarbon production, as it ensures the achievement of the necessary technical and economic indicators. Therefore, a high-perfor- mance development system is necessary to ensure maximum profit in the shortest possible time. Empirical studies, analysis and assessment of the effect of implementing a complex of geological and technological measures on the operational facility of a gas condensate field in the central part of the peri-axial zone of the Dnieper-Donets Basin were carried out in order to find methods for improving the existing gas and gas condensate field development systems and increasing gas extraction rates. It is shown that due to hydraulic fracturing, drilling and transfer of wells to the production facility during the five years of production of the produced gas, it is 7.8 % more than in the previous 21 hours, the rate of development of the field from 2015 to 2019 increased from 2.52 %. to 7.9 %, the average well yield increased by 83 % with an increase in the operational fund by 55 %. The results of studies of the effect of the application of geo- logical-technological measures are shown in the form of graphical dependences of the change in the rate of sampling on the number of production wells over time, the rate of daily gas sampling on the number of conducted geological-technological measures on the researched operational object over time. It has been determined that the main driver of the increase in gas production is the hydraulic fracturing of the formation in most of the operational wells, thanks to which the average increase in the output of the wells is 425 %. According to the results of geological and industrial data processing and analysis of changes in the rate of daily gas extraction over time, the effect of the performed operations is divided into three time segments lasting 15–18 months. The decrease in the duration of the effect from the increase in the fund of production wells and the conduct of hydraulic fracturing of the reservoir is due to the decrease in the volume of reservoir energy, therefore the investigated complex of measures will have the greatest effect when implemented at the initial stage of the development of a reservoir with similar geological parameters.

BiLSTM-MLAM: A Multi-Scale Time Series Prediction Model for Sensor Data Based on Bi-LSTM and Local Attention Mechanisms.

This paper introduces BiLSTM-MLAM, a novel multi-scale time series prediction model. Initially, the approach utilizes bidirectional long short-term memory to capture information from both forward and backward directions in time series data. Subsequently, a multi-scale patch segmentation module generates various long sequences composed of equal-length segments, enabling the model to capture data patterns across multiple time scales by adjusting segment lengths. Finally, the local attention mechanism enhances feature extraction by accurately identifying and weighting important time segments, thereby strengthening the model's understanding of the local features of the time series, followed by feature fusion. The model demonstrates outstanding performance in time series prediction tasks by effectively capturing sequence information across various time scales. Experimental validation illustrates the superior performance of BiLSTM-MLAM compared to six baseline methods across multiple datasets. When predicting the remaining life of aircraft engines, BiLSTM-MLAM outperforms the best baseline model by 6.66% in RMSE and 11.50% in MAE. In the LTE dataset, it achieves RMSE improvements of 12.77% and MAE enhancements of 3.06%, while in the load dataset, it demonstrates RMSE enhancements of 17.96% and MAE improvements of 30.39%. Additionally, ablation experiments confirm the positive impact of each module on prediction accuracy. Through segment length parameter tuning experiments, combining different segment lengths has resulted in lower prediction errors, affirming the effectiveness of the multi-scale fusion strategy in enhancing prediction accuracy by integrating information from multiple time scales.

Stability equivalence between regime‐switching jump diffusion delayed systems and corresponding systems with piecewise continuous arguments and application to discrete‐time feedback control

AbstractIn this paper, we mainly study the equivalence of exponential stability for regime‐switching jump diffusion delayed systems (RSJDDSs) and RSJDDSs with piecewise continuous arguments (RSJDDSs‐PCA). Our results show that if one of the RSJDDS and the RSJDDS‐PCA is th moment exponentially stable, then another system is also th moment exponentially stable when time delay and segment step size have a common upper bound, while both equations are almost surely exponentially stable, and we also provided a method to calculate this upper bound. In addition, as an application of the stability equivalence theorem, we design discrete‐time state and mode observations feedback control to stabilize unstable RSJDDSs and investigate that controllers of the drift, diffusion, and jump terms are all able to play a stabilizing effect on the controlled system.

A sequential meta-transfer (SMT) learning to combat complexities of physics-informed neural networks: Application to composites autoclave processing

Physics-Informed Neural Networks (PINNs) have gained popularity in solving nonlinear partial differential equations (PDEs) via integrating physical laws into the training of neural networks, making them superior in many scientific and engineering applications. However, conventional PINNs still fall short in accurately approximating the solution of complex systems with strong nonlinearity, especially in long temporal domains. Besides, since PINNs are designed to approximate a specific realization of a given PDE system, they lack the necessary generalizability to efficiently adapt to new system configurations. This entails computationally expensive re-training from scratch for any new change in the system. To address these shortfalls, in this work a sequential meta-transfer (SMT) learning framework is proposed, offering a unified solution for both fast training and efficient adaptation of PINNs in highly nonlinear systems with long temporal domains. Specifically, the framework decomposes PDE's time domain into smaller time segments to create “easier” PDE problems for PINNs training. Then for each time interval, a meta-learner is assigned and trained to achieve an optimal initial state for rapid adaptation to a range of related tasks. Transfer learning principles are then leveraged across time intervals to further reduce the computational cost. Through a composites autoclave processing case study, it is shown that SMT is clearly able to enhance the adaptability of PINNs while significantly reducing computational cost, by a factor of 100.

A novel CNN-based image segmentation pipeline for individualized feline spinal cord stimulation modeling

Objective. Spinal cord stimulation (SCS) is a well-established treatment for managing certain chronic pain conditions. More recently, it has also garnered attention as a means of modulating neural activity to restore lost autonomic or sensory-motor function. Personalized modeling and treatment planning are critical aspects of safe and effective SCS (Rowald and Amft 2022 Front. Neurorobotics 16 983072, Wagner et al 2018 Nature 563 65–71). However, the generation of spine models at the required level of detail and accuracy requires time and labor intensive manual image segmentation by human experts. This study aims to develop a maximally automated segmentation routine capable of producing high-quality anatomical models, even with limited data, to facilitate safe and effective personalized SCS treatment planning. Approach. We developed an automated image segmentation and model generation pipeline based on a novel convolutional neural network (CNN) architecture trained on feline spinal cord magnetic resonance imaging data. The pipeline includes steps for image preprocessing, data augmentation, transfer learning, and cleanup. To assess the relative importance of each step in the pipeline and our choice of CNN architecture, we systematically dropped steps or substituted architectures, quantifying the downstream effects in terms of tissue segmentation quality (Jaccard index and Hausdorff distance) and predicted nerve recruitment (estimated axonal depolarization). Main results. The leave-one-out analysis demonstrated that each pipeline step contributed a small but measurable increment to mean segmentation quality. Surprisingly, minor differences in segmentation accuracy translated to significant deviations (ranging between 4% and 13% for each pipeline step) in predicted nerve recruitment, highlighting the importance of careful workflow design. Additionally, transfer learning techniques enhanced segmentation metric consistency and allowed generalization to a completely different spine region with minimal additional training data. Significance. To our knowledge, this work is the first to assess the downstream impacts of segmentation quality differences on neurostimulation predictions. It highlights the role of each step in the pipeline and paves the way towards fully automated, personalized SCS treatment planning in clinical settings.

An in-depth analysis of the variable cyclotron lines in GX 301−2

Context. The high-mass X-ray binary system GX 301−2 is a persistent source with a well-known variable cyclotron line centered at 35 keV. Recently, a second cyclotron line at 50 keV has been reported with a presumably different behavior than the 35 keV line. Aims. We investigate the presence of the newly discovered cyclotron line in the phase-averaged and phase-resolved spectra at higher luminosities than before. We further aim to determine the pulse-phase variability of both lines. Methods. We analyze a NuSTAR observation of GX 301−2 covering the pre-periastron flare, where the source luminosity reached its peak of ∼4 × 1037 erg s−1 in the 5–50 keV range. We analyze the phase-averaged spectra in the NuSTAR energy range from 3.5–79 keV for both the complete observation and three time segments of it. We further analyze the phase-resolved spectra and the pulse-phase variability of continuum and cyclotron line parameters. Results. We confirm that the description of the phase-averaged spectrum requires a second absorption feature at 51.5−1.0+1.1 keV besides the established line at 35 keV. The statistical significance of this feature in the phase-averaged spectrum is &gt; 99.999%. We further find that the 50 keV cyclotron line is present in three out of the eight phase bins. Conclusions. Based on the results of our analysis, we confirm that the detected absorption feature is very likely to be a cyclotron line. We discuss a variety of physical scenarios that could explain the proposed anharmonicity, but also outline circumstances under which the lines are harmonically related. We further present the cyclotron line history of GX 301−2 and evaluate concordance among each other. We also discuss an alternative spectral model including cyclotron line emission wings.

Traffic flow prediction: A 3D adaptive multi‐module joint modeling approach integrating spatial‐temporal patterns to capture global features

AbstractThe challenges in citywide traffic flow are intricate, encompassing various factors like temporal and spatial dependencies, holidays, and weather. Despite the complexity, there are still research gaps in effectively incorporating these spatio‐temporal relations through deep learning. Addressing these gaps is crucial for tackling issues such as traffic congestion, public safety, and efficient traffic management within cities. This paper underscores notable research gaps, including the development of models capable of handling both local and global traffic flow patterns, integrating multi‐modal data sources, and effectively managing spatio‐temporal dependencies. In this paper, we proposed a novel model named 3D spatial–temporal‐based adaptive modeling graph convolutional network (3D(STAMGCN)) that addresses for traffic flow data in better periodicity modeling. In contrast to earlier studies, 3D(STAMGCN) approaches the task of traffic flow prediction as a periodic residual learning problem. This is achieved by capturing the input variation between historical time segments and the anticipated output for future time segments. Forecasting traffic flow, as opposed to a direct approach, is significantly simpler when focusing on learning more stationary deviations. This, in turn, aids in the training of the model. Nevertheless, the networks enable residual generation at each time interval through learned variations between future conditions and their corresponding weekly observations. Consequently, this significantly contributes to achieving more accurate forecasts for multiple steps ahead. We executed extensive experiments on two real‐world datasets and compared the performance of our model to state‐of‐the‐art (SOTA) techniques.

Seizure detection using nonlinear measures over EEG frequency bands and deep learning classifiers

Epilepsy is a brain disorder that causes patients to suffer from convulsions, which affects their behavior and way of life. Epilepsy can be detected with electroencephalograms (EEGs), which record brain neural activity. Traditional approaches for detecting epileptic seizures from an EEG signal are time-consuming and annoying. To supersede these traditional methods, a myriad of automated seizure detection frameworks based on machine learning techniques have recently been developed. Feature extraction and classification are the two essential phases for seizure detection. The classifier assigns the proper class label after feature extraction lowers the input pattern space while maintaining useful features. This paper proposes a new feature extraction method based on calculating nonlinear features from the most relevant EEG frequency bands. The EEG signal is first decomposed into smaller time segments from which a vector of nonlinear features is computed and supplied to machine learning (ML) and deep learning (DL) classifiers. Experiments on the Bonn dataset reveals an accuracy of 99.7% reached in classifying normal and ictal EEG signals; and an accuracy of 98.8% in the discrimination of ictal and interictal EEG signals. Furthermore, a performance of 100% is achieved on the Hauz Khas dataset. The classification results of the proposed approach were compared to those from published state of the art techniques. Our results are equivalent to or better than some recent studies appeared in the literature.

Multi-scale 3D-CRU for EEG emotion recognition* *This research has been supported by the National Natural Science Foundation of China Joint Fund Key Project (U2003207), National Natural Science Foundation of China (6220100261902064), Natural Science Foundation of Anhui Province (No. 1908085MF209), Key Projects of Natural Science Foundation of Anhui Province Universities (No. KJ2018A0018).

In this paper, we propose a novel multi-scale 3D-CRU model, with the goal of extracting more discriminative emotion feature from EEG signals. By concurrently exploiting the relative electrode locations and different frequency subbands of EEG signals, a three-dimensional feature representation is reconstructed wherein the Delta (δ) frequency pattern is included. We employ a multi-scale approach, termed 3D-CRU, to concurrently extract frequency and spatial features at varying levels of granularity within each time segment. In the proposed 3D-CRU, we introduce a multi-scale 3D Convolutional Neural Network (3D-CNN) to effectively capture discriminative information embedded within the 3D feature representation. To model the temporal dynamics across consecutive time segments, we incorporate a Gated Recurrent Unit (GRU) module to extract temporal representations from the time series of combined frequency-spatial features. Ultimately, the 3D-CRU model yields a global feature representation, encompassing comprehensive information across time, frequency, and spatial domains. Numerous experimental assessments conducted on publicly available DEAP and SEED databases provide empirical evidence supporting the enhanced performance of our proposed model in the domain of emotion recognition. These findings underscore the efficacy of the features extracted by the proposed multi-scale 3D-GRU model, particularly with the incorporation of the Delta (δ) frequency pattern. Specifically, on the DEAP dataset, the accuracy of Valence and Arousal are 93.12% and 94.31%, respectively, while on the SEED dataset, the accuracy is 92.25%.

A general modeling scheme for spatiotemporal DGGS with emphasis on encoding and operating multiscale time grids

AbstractOne of the basic scientific problems concerning geographic information science is how to rapidly organize, query, and compute spatiotemporal big data. The spatiotemporal discrete global grid system (DGGS) provides a homogenized discrete structure for processing multiscale and multitype spatiotemporal data. To date, most research in spatiotemporal DGGS has focused on spatial discretization while neglecting temporal discretization. Here, we propose a general modeling scheme for spatiotemporal DGGS with emphasis on encoding and operating multiscale time grids. We subdivide continuous time into multiscale temporal grids, which are then encoded as integers. Moreover, we designed integer code operations, including hierarchical traversal, neighborhood finding, and temporal relationship calculations. Compared to the multiscale time segment integer coding (MTSIC) approach, the proposed method resulted in 22% higher encoding efficiency, 10.92 times faster decoding, 2.81 times better parent code finding efficiency, 41% improved efficiency, 100% accuracy in finding children codes (compared to less than 100% with MTSIC), and a 62% enhancement in temporal relationship calculation efficiency. The application of querying spatiotemporal trajectory data validates the feasibility and practicality of substituting conventional string‐based time and floating‐point location coordinates with spatiotemporal integer codes to query data. The time encoding and operation methods proposed here indicate high efficiency, superior accuracy, and broad application prospects.

Focus effect unveils children’s local processing of pronouns and reflexives

ABSTRACT Studies on young children’s comprehension have shown that children can experience problems interpreting object pronouns, even when reflexive interpretation is already adult-like. Compared to resolving reflexives, linking pronouns to a referent is considered a more “intensive” process, because it also involves non-syntactic factors like discourse context. This could explain why children experience more difficulties with pronouns than with reflexives. Using eye-tracking and a truth value judgement task, we investigated the effect of focus via it-clefts on the processing of reflexives and pronouns in German-speaking children and adults. We analyzed gaze data of two time segments: before and during the mention of the pronoun/reflexive. The cleft segment revealed similar processing of it-clefts in children and adults. In the subsequent reflexive/pronoun segment, clefts caused adults to pay overall more attention to the local referent, while children fixated the clefted non-local referent more. The difference in focus effect, that is, children attend the clefted referent more, while adults pay more attention to the non-clefted referent, helped uncover processing differences between children and adults. That is, unlike adults, children consider only the local discourse context during referential processing. We argue that these processing differences cause children’s interpretation difficulties. However, the offline data showed no effect of information structure, suggesting that whether the processing differences transfer to the final interpretation depends on the language-specific function of the pronoun system, which may aid in restricting referential links.

Assessment of temporal changes in glomerular filtration rate following clamp ischemia in male and female rats using transcutaneous FITC-sinistrin clearance

Warm arterial clamping with reperfusion in rats is a commonly utilized model of ischemic kidney injury (IRI). Traditionally, changes in plasma creatinine or creatinine clearance have been utilized to assess kidney function in this model. A limitation of these approaches, however, is that changes in plasma creatinine are relatively slow to occur, resulting in low temporal fidelity. This limits our ability to relate changes in kidney function to histo-pathological changes observed during reperfusion of the kidneys. Transcutaneous assessment of FITC-sinistrin clearance enables accurate, repeated measurement of glomerular filtration rate in conscious animals over short segments of time (hours) without drawing blood. The goal of the current study was to characterize temporal changes in renal function in male and female rats after reperfusion from bi-lateral warm arterial clamp ischemia using transcutaneous FITC-sinistrin clearance. Our study was performed in age matched male (n=8) and female (n=5) Sprague Dawley rats (12-20wks of age). FITC sinistrin clearance was measured by affxing a miniaturized transcutaneous measurement device between the shoulder blades (MediBeacon) of rats. 50mg/kg of FITC-sinistrin was then injected i.v via the tail vein and the device allowed to record for ~3 hours. Measurements were taken either at pre-surgery (baseline), 1-3 hours, 24 hours or 7 days following reperfusion of the kidneys from warm bilateral arterial clamp ischemia. For the ischemic surgery, animals were anesthetized with isoflurane (2-5%, 95% O2) and the right and left kidneys were accessed by flank incisions. After being exposed, the renal arteries were separated from the renal veins via dissection. Both renal arteries were then clamped with microserrefines for 45 minutes. After the ischemic period, the clamps were removed, allowing blood flow to be restored to the kidneys. Body temperature was measured via a rectal probe and maintained at 37°C throughout the procedure by a servo-controlled heating table and UV heating lamp. The wounds were then closed, and the animals allowed to recover. Buprenorphine was administered as an analgesic. For each measurement, the ½ life of FITC sinistrin was determined using a 1-compartment model (MB Lab software) and glomerular filtration rate (GFR) per 100g of body weight rate calculated. Baseline GFR was 0.94±0.03ml/min/100g in males and 1.09±0.02/min/100g in females (PTTEST=0.04). Following 45 minutes of warm bilateral arterial ischemia and reperfusion, GFR fell to 0.24±0.08ml/min/100g in male and 0.36±0.15ml/min/100g in females within 1-3 hours of reperfusion. At 24 hours of reperfusion, GFR averaged 0.06±0.03ml/min/100g in males and 0.28±0.16ml/min/100g in females. GFR recovered in males and females by 7 days of reperfusion averaging 0.86±0.10ml/min/100g in males and 0.83±0.00ml/min/100g in females. There was no significant difference between FITC-sinistrin clearance between males and females at any time point after IRI. Our data indicate that most of the reduction in GFR is present immediately after releasing the clamp. This is consistent with continued vascular disturbances mediating the initial decline in glomerular filtration rate in this model (i.e. vasospasm). The absence of sex differences in GFR decline using this method suggests that sex-differences detected using plasma creatinine may be, at least in part, secondary to the more rapid production of creatinine in males than in females or greater baseline filtration/body weight ratio in females compared to males rather than differences in renal function decline to ischemia. American Heart Association Transformational Project Award 970585 to Paul O'Connor. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Anomaly Detection Model of Time Segment Power Usage Behavior Using Unsupervised Learning

&lt;p&gt;In Taiwan, the current electricity prices for residential users remain relatively low. This results in a diminished incentive for these users to invest in energy-saving improvements. Consequently, devising strategies to encourage residential users to adopt energy-saving measures becomes a vital research area. Grounded in behavioral science, this study introduces a feasible approach where an energy management system provides alerts and corresponding energy-saving recommendations to residential users upon detecting abnormal electricity consumption behavior. To pinpoint anomalous electricity usage within specific time segments, this research employs an unsupervised machine learning method, developing an anomaly detection model for the overall electricity consumption behavior of residential users. The model focuses on analyzing 2-hour intervals of electricity consumption, enabling more effective detection of abnormal usage patterns. It is trained using power consumption data collected from five actual residential users as part of an experimental study. The results indicate that the proposed anomaly detection model achieves performance metrics such as Precision, Recall, and F1-score of 0.90 or above, showcasing its potential for practical implementation.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;

Latent Heat Loss Through Fabrics During an Alternate Simulated Work–Rest Sequence

Humans produce different rates of sweating depending on the intensity level of a given activity. The clothing worn during the activity has a significant effect on the latent heat loss that can occur before, during, and after the activity. The water adsorption and spreading properties of a material yield differences in the amount of heat exchange that can occur between the person and the environment. Current test methods evaluate this using a value known as evaporative resistance, which is used to determine the heat exchange potential of a material. However, this value is only taken once the material has reached “steady state” and does not consider the sweating period before steady state is reached, or the drying period after steady state is reached. Therefore, an area under the curve (AUC) value was derived during these periods to compare the heat exchange properties of different materials. A sweating guarded hot plate was used to simulate different sweat rates, and therefore activity levels, to compare materials constructed of cotton, viscose, polyester, and wool. The overall latent heat loss of the hydrophobic wool was much less compared to the other samples with a gentler slope and lower AUC values than the other samples. It was found that the segment of time analyzed has a significant impact on the conclusion drawn about the latent heat loss of a material, p &lt; 0.0001. Sweat rate also has a significant impact on the heat loss value, p &lt; 0.0001, but is perhaps less useful when comparing samples to each other. It was found that comparing the different periods of the test could yield different conclusions when comparing materials for latent heat loss. Several current test methods do not consider phases outside of the steady state period and therefore miss valuable information pertinent to user comfort during these phases, especially in the post-exercise phase.

Using word evolution to predict drug repurposing

BackgroundTraditional literature based discovery is based on connecting knowledge pairs extracted from separate publications via a common mid point to derive previously unseen knowledge pairs. To avoid the over generation often associated with this approach, we explore an alternative method based on word evolution. Word evolution examines the changing contexts of a word to identify changes in its meaning or associations. We investigate the possibility of using changing word contexts to detect drugs suitable for repurposing.ResultsWord embeddings, which represent a word’s context, are constructed from chronologically ordered publications in MEDLINE at bi-monthly intervals, yielding a time series of word embeddings for each word. Focusing on clinical drugs only, any drugs repurposed in the final time segment of the time series are annotated as positive examples. The decision regarding the drug’s repurposing is based either on the Unified Medical Language System (UMLS), or semantic triples extracted using SemRep from MEDLINE.ConclusionsThe annotated data allows deep learning classification, with a 5-fold cross validation, to be performed and multiple architectures to be explored. Performance of 65% using UMLS labels, and 81% using SemRep labels is attained, indicating the technique’s suitability for the detection of candidate drugs for repurposing. The investigation also shows that different architectures are linked to the quantities of training data available and therefore that different models should be trained for every annotation approach.

Research on the Recognition and Application of Montreal Forced Aligner for Singing Audio

This paper discusses the feasibility of obtaining phoneme-aligned time segments for singing audio using the Montreal Forced Aligner (MFA) tool. Initially, the recognition effectiveness of singing audio data is tested using the open-source MFA model. Subsequently, samples with high recognition accuracy are manually annotated and used to train the MFA model. Finally, the recognition effectiveness of the open-source MFA model and the MFA model trained on singing audio data is observed. It is found that the performance of the trained MFA model is significantly improved. This also confirms the feasibility of MFA in recognizing singing audio.