Fecal microbiota transplantation enhances cell therapy in a rat model of hypoganglionosis by SCFA-induced MEK1/2 signaling pathway.

Hirschsprung disease (HSCR), one of several neurocristopathies in children, is characterized by nerve loss in the large intestine and is mainly treated by surgery, which causes severe complications. Enteric neural crest-derived cell (ENCC) transplantation is a potential therapeutic strategy; however, so far with poor efficacy. Here, we assessed whether and how fecal microbiota transplantation (FMT) could improve ENCC transplantation in a rat model of hypoganglionosis; a condition similar to HSCR, with less intestinal innervation. We found that the hypoganglionosis intestinal microenvironment negatively influenced the ENCC functional phenotype in vitro and in vivo. Combining 16S rDNA sequencing and targeted mass spectrometry revealed microbial dysbiosis and reduced short-chain fatty acid (SCFA) production in the hypoganglionic gut. FMT increased the abundance of Bacteroides and Clostridium, SCFA production, and improved outcomes following ENCC transplantation. SCFAs alone stimulated ENCC proliferation, migration, and supported ENCC transplantation. Transcriptome-wide mRNA sequencing identified MAPK signaling as the top differentially regulated pathway in response to SCFA exposure, and inhibition of MEK1/2 signaling abrogated the SCFA-mediated effects on ENCC. This study demonstrates that FMT improves cell therapy for hypoganglionosis via short-chain fatty acid metabolism-induced MEK1/2 signaling.

A fitness training optimization system based on heart rate prediction under different activities.

Heart rate can be considered as an indicator of the exercise intensity in people's daily physical activities. Five heart rate zone theory is commonly adopted by individuals and professional athletes during their exercises and training. These heart rate zones are based upon percentages of people's maximal heart rate, which indicate different exercise intensities. The aim of paper is to propose an optimization training system based on dynamic heart rate prediction, which can predict people's heart rate under three different types of exercises: walking, running and rope jumping. The system can help people optimize their exercise by advising them to adjust the speed or workload to reach their predetermined training intensity under different activities. Four Long Short-Term Memory (LSTM) neural networks are deployed, one for human activity recognition (HAR) and three for heart rate prediction.

Deep-E Enhanced Photoacoustic Tomography Using Three-Dimensional Reconstruction for High-Quality Vascular Imaging.

Linear-array-based photoacoustic computed tomography (PACT) has been widely used in vascular imaging due to its low cost and high compatibility with current ultrasound systems. However, linear-array transducers have inherent limitations for three-dimensional imaging due to the poor elevation resolution. In this study, we introduced a deep learning-assisted data process algorithm to enhance the image quality in linear-array-based PACT. Compared to our earlier study where training was performed on 2D reconstructed data, here, we utilized 2D and 3D reconstructed data to train the two networks separately. We then fused the image data from both 2D and 3D training to get features from both algorithms. The numerical and in vivo validations indicate that our approach can improve elevation resolution, recover the true size of the object, and enhance deep vessels. Our deep learning-assisted approach can be applied to translational imaging applications that require detailed visualization of vascular features.

3D finger vein biometric authentication with photoacoustic tomography.

Biometric authentication is the recognition of human identity via unique anatomical features. The development of novel methods parallels widespread application by consumer devices, law enforcement, and access control. In particular, methods based on finger veins, as compared to face and fingerprints, obviate privacy concerns and degradation due to wear, age, and obscuration. However, they are two-dimensional (2D) and are fundamentally limited by conventional imaging and tissue-light scattering. In this work, for the first time, to the best of our knowledge, we demonstrate a method of three-dimensional (3D) finger vein biometric authentication based on photoacoustic tomography. Using a compact photoacoustic tomography setup and a novel recognition algorithm, the advantages of 3D are demonstrated via biometric authentication of index finger vessels with false acceptance, false rejection, and equal error rates <1.23%, <9.27%, and <0.13%, respectively, when comparing one finger, a false acceptance rateimprovement>10× when comparing multiple fingers, and <0.7% when rotating fingers ±30.

Robust Detection of Parkinson's Disease Using Harvested Smartphone Voice Data: A Telemedicine Approach.

Introduction: Parkinson's disease affects over 10 million people globally, and ∼20% of patients with Parkinson's disease have not been diagnosed as such. The clinical diagnosis is costly: there are no specific tests or biomarkers and it can take days to diagnose as it relies on a holistic evaluation of the individual's symptoms. Existing research either predicts a Unified Parkinson Disease Rating Scale rating, uses other key Parkinsonian features such as tapping, gait, and tremor to diagnose an individual, or focuses on different audio features. Methods: In this article, we present a classification approach implemented as an iOS App to detect whether an individual has Parkinson's using 10-s audio clips of the individual saying "aaah" into a smartphone. Results: The 1,000 voice samples analyzed were obtained from the mPower (mobile Parkinson Disease) study, which collected 65,022 voice samples from 5,826 unique participants. Conclusions: The experimental results comparing 12 different methods indicate that our approach achieves 99.0% accuracy in under a second, which significantly outperforms both prior diagnosis methods in the accuracy achieved and the efficiency of clinical diagnoses.

Harnessing smartphone technology and three dimensional printing to create a mobile rehabilitation system, mRehab: assessment of usability and consistency in measurement.

BACKGROUND: Residual sensorimotor deficits are common following stroke. While it has been demonstrated that targeted practice can result in improvements in functional mobility years post stroke, there is little to support rehabilitation across the lifespan. The use of technology in home rehabilitation provides an avenue to better support self-management of recovery across the lifespan. We developed a novel mobile technology, capable of quantifying quality of movement with the purpose of providing feedback to augment rehabilitation and improve functional mobility. This mobile rehabilitation system, mRehab, consists of a smartphone embedded in three dimensional printed items representing functional objects found in the home. mRehab allows individuals with motor deficits to practice activities of daily living (ADLs) and receive feedback on their performance. The aim of this study was to assess the usability and consistency of measurement of the mRehab system. METHODS: To assess usability of the mRehab system, four older adults and four individuals with stroke were recruited to use the system, and complete surveys to discuss their opinions on the user interface of the smartphone app and the design of the 3D printed items. To assess the consistency of measurement by the mRehab system, 12 young adults were recruited and performed mRehab ADLs in three lab sessions within 1 week. Young adults were chosen for their expected high level of consistency in motor performance. RESULTS: Usability ratings from older adults and individuals with stroke led us to modify the design of the 3D printed items and improve the clarity of the mRehab app. The modified mRehab system was assessed for consistency of measurement and six ADLs resulted in coefficient of variation (CV) below 10%. This is a commonly used CV goal for consistency. Two ADLs ranged between 10 and 15% CV. Only two ADLs demonstrated high CV. CONCLUSIONS: mRehab is a client-centered technology designed for home rehabilitation that consistently measures performance. Development of the mRehab system provides a support for individuals working on recovering functional upper limb mobility that they can use across their lifespan.

Correlation of spatio-temporal characteristics of intestinal inflammation with IL-17 in a rat model of hypoganglionosis.

Interleukin 17 expression is increased in children with Hirschsprung disease, which is characterized by intestinal inflammation. This study designed to exploit the characteristics of intestinal inflammation and examine the correlation of interleukin 17 in this process of hypoganglionosis model established by benzalkonium chloride treatment. Colon sections from female rats were treated with benzalkonium chloride to induce hypoganglionosis or with saline alone as a sham control. C-reactive protein and tumor necrosis factor-ɑ were used as markers of inflammation. Expression of C-reactive protein, tumor necrosis factor-ɑ, and interleukin 17 was assessed in colon tissue and blood serum on days 7, 14 and 21 after treatment. The correlation between C-reactive protein, tumor necrosis factor-ɑ, and interleukin 17 expression was estimated using the Spearman's rank-correlation coefficient. C-reactive protein, tumor necrosis factor-ɑ, and interleukin 17 were strongly expressed in submucosa and mucosa layers and serum from treated animals. The expression of C-reactive protein, tumor necrosis factor-ɑ, and interleukin 17 maintained the highest level at Day 21. Only C-reactive protein and tumor necrosis factor-ɑ expression was increased in control animals and only on day 7. Spearman's rank correlation coefficient was significant in C-reactive protein, tumor necrosis factor-ɑ, and interleukin 17 at Day 7, 14 and 21. Concomitant upregulation of C-reactive protein, tumor necrosis factor-ɑ, and interleukin 17 and significant positive correlations between C-reactive protein, tumor necrosis factor-ɑ, and interleukin 17 may imply that interleukin 17 is involved in spatio-temporal inflammation induced by benzalkonium chloride.

ePave: A Self-Powered Wireless Sensor for Smart and Autonomous Pavement.

"Smart Pavement" is an emerging infrastructure for various on-road applications in transportation and road engineering. However, existing road monitoring solutions demand a certain periodic maintenance effort due to battery life limits in the sensor systems. To this end, we present an end-to-end self-powered wireless sensor-ePave-to facilitate smart and autonomous pavements. The ePave system includes a self-power module, an ultra-low-power sensor system, a wireless transmission module and a built-in power management module. First, we performed an empirical study to characterize the piezoelectric module in order to optimize energy-harvesting efficiency. Second, we developed an integrated sensor system with the optimized energy harvester. An adaptive power knob is designated to adjust the power consumption according to energy budgeting. Finally, we intensively evaluated the ePave system in real-world applications to examine the system's performance and explore the trade-off.

A Telemedicine Analytic Framework for Fully and Semi-automatic Alzheimer's Disease Screening using Clock Drawing Test.

More than 6 million Americans are at risk for Alzheimer's Disease Related Dementias (ADRD), most of whom are 65 or older. The clock drawing test (CDT) is a quick, simple, and effective technique that has the potential advantage of self-management and screening for ADRD patients. Current CDT-based ADRD screening studies focus more on efficacy, involving many handcrafted features, ignoring data modalities, and lacking validation. This paper aims to propose a unified telemedicine framework for fully and semi-automatic effective early ADRD screening based on multimodal and agile data fusion, focusing on the interpretability and validation of the model by using gradient-weighted class activation mapping (Grad-CAM) and locally linear embedding (LLE). The datasets for this work include 1,662 samples of CDT images and related demographic and cognitive information. The fully automatic case involving only CDT images can achieve the highest AUC of 81% with a 75% recall rate in binary screening. The multimodal data fusion in the semi-automatic case can achieve up to 90% AUC with an 83% recall rate. The visualization of the Convolutional Neural Networks (CNN) shows that it can automatically obtain critical information about the outline, scale, and clock hands from CDT images, and the analysis of structured features shows that the memory test is key to effective ADRD screening.

Unveiling intra-person fingerprint similarity via deep contrastive learning.

Fingerprint biometrics are integral to digital authentication and forensic science. However, they are based on the unproven assumption that no two fingerprints, even from different fingers of the same person, are alike. This renders them useless in scenarios where the presented fingerprints are from different fingers than those on record. Contrary to this prevailing assumption, we show above 99.99% confidence that fingerprints from different fingers of the same person share very strong similarities. Using deep twin neural networks to extract fingerprint representation vectors, we find that these similarities hold across all pairs of fingers within the same person, even when controlling for spurious factors like sensor modality. We also find evidence that ridge orientation, especially near the fingerprint center, explains a substantial part of this similarity, whereas minutiae used in traditional methods are almost nonpredictive. Our experiments suggest that, in some situations, this relationship can increase forensic investigation efficiency by almost two orders of magnitude.

Development and evaluation of visualizations of smoking data for integration into the Sense2Quit app for tobacco cessation.

IMPORTANCE: Due to insufficient smoking cessation apps for persons living with HIV, our study focused on designing and testing the Sense2Quit app, a patient-facing mHealth tool which integrated visualizations of patient information, specifically smoking use. OBJECTIVES: The purpose of this paper is to detail rigorous human-centered design methods to develop and refine visualizations of smoking data and the contents and user interface of the Sense2Quit app. The Sense2Quit app was created to support tobacco cessation and relapse prevention for people living with HIV. MATERIALS AND METHODS: Twenty people living with HIV who are current or former smokers and 5 informaticians trained in human-computer interaction participated in 5 rounds of usability testing. Participants tested the Sense2Quit app with use cases and provided feedback and then completed a survey. RESULTS: Visualization of smoking behaviors was refined through each round of usability testing. Further, additional features such as daily tips, games, and a homescreen were added to improve the usability of the app. A total of 66 changes were made to the Sense2Quit app based on end-user and expert recommendations. DISCUSSION: While many themes overlapped between usability testing with end-users and heuristic evaluations, there were also discrepancies. End-users and experts approached the app evaluation from different perspectives which ultimately allowed us to fill knowledge gaps and make improvements to the app. CONCLUSION: Findings from our study illustrate the best practices for usability testing for development and refinement of an mHealth-delivered consumer informatics tool for improving tobacco cessation yet further research is needed to fully evaluate how tools informed by target user needs improve health outcomes.

Association between gut microbiota and Hirschsprung disease: a bidirectional two-sample Mendelian randomization study.

Background: Several studies have pointed to the critical role of gut microbiota (GM) and their metabolites in Hirschsprung disease (HSCR) pathogenesis. However, the detailed causal relationship between GM and HSCR remains unknown. Methods: In this study, we used two-sample Mendelian randomization (MR) analysis to investigate the causal relationship between GM and HSCR, based on the MiBioGen Consortium's genome-wide association study (GWAS) and the GWAS Catalog's HSCR data. Reverse MR analysis was performed subsequently, and the sensitivity analysis, Cochran's Q-test, MR pleiotropy residual sum, outlier (MR-PRESSO), and the MR-Egger intercept were used to analyze heterogeneity or horizontal pleiotropy. 16S rDNA sequencing and targeted mass spectrometry were developed for initial validation. Results: In the forward MR analysis, inverse-variance weighted (IVW) estimates suggested that Eggerthella (OR: 2.66, 95%CI: 1.23-5.74, p = 0.01) was a risk factor for HSCR, while Peptococcus (OR: 0.37, 95%CI: 0.18-0.73, p = 0.004), Ruminococcus2 (OR: 0.32, 95%CI: 0.11-0.91, p = 0.03), Clostridiaceae1 (OR: 0.22, 95%CI: 0.06-0.78, p = 0.02), Mollicutes RF9 (OR: 0.27, 95%CI: 0.09-0.8, p = 0.02), Ruminococcaceae (OR: 0.16, 95%CI: 0.04-0.66, p = 0.01), and Paraprevotella (OR: 0.45, 95%CI: 0.21-0.98, p = 0.04) were protective factors for HSCR, which had no heterogeneity or horizontal pleiotropy. However, reverse MR analysis showed that HSCR (OR: 1.02, 95%CI: 1-1.03, p = 0.049) is the risk factor for Eggerthella. Furthermore, some of the above microbiota and short-chain fatty acids (SCFAs) were altered in HSCR, showing a correlation. Conclusion: Our analysis established the relationship between specific GM and HSCR, identifying specific bacteria as protective or risk factors. Significant microbiota and SCFAs were altered in HSCR, underlining the importance of further study and providing new insights into the pathogenesis and treatment.

Visible-Light-Driven Iron-Catalyzed Decarboxylative C-N Coupling Reaction of Alkyl Carboxylic Acids with NaNO2.

An efficient visible-light-driven iron-catalyzed decarboxylative C-N coupling reaction of alkyl carboxylic acids with NaNO2 under mild conditions was developed. The reaction proceeds under photosensitizer-free conditions and features good to excellent yields, broad functional group tolerance, and an easy operation procedure. Preliminary mechanistic investigations showed that visible-light-driven iron catalysis not only achieved oxidative decarboxylation of alkyl carboxylic acids to alkyl radicals but also promoted the reduction of NO2- to NO, thus leading to the C-N radical coupling reaction.

Engaging in a home-based exercise program: a mixed-methods approach to identify motivators and barriers for individuals with stroke.

Community-dwelling individuals with chronic stroke used a novel, portable rehabilitation system, mRehab, that uses a smartphone app coupled with 3D printed objects resembling daily use items. The objectives of this study include evaluating participant's approach and nature of engagement with mRehab and identifying factors that influenced the users' engagement with mRehab. An explanatory mixed-method approach was used. In the first phase, 16 participants used mRehab at home for six weeks; six participants were recruited from the first phase for in-depth interviews. Participants were categorized into High, Moderate, and Low Frequency groups based on their frequency of mRehab use. High frequency of use was not related to improved performance on clinical assessments; instead, High Frequency users more commonly initiated performance of new activities after the mRehab program compared to participants with lower frequency of use. Useful activities that are challenging and meaningful to the participants, and availability of objective feedback for self-monitoring were some of the motivators for mRehab use. Difficulty with time management, lack of caregiver availability, and difficulties with the design of the system posed as barriers to mRehab use. Tailoring home programs to the recipients' needs including perceived meaningfulness of the activities is key for long-term rehabilitation.

Deep Learning Enhanced Volumetric Photoacoustic Imaging of Vasculature in Human.

The development of high-performance imaging processing algorithms is a core area of photoacoustic tomography. While various deep learning based image processing techniques have been developed in the area, their applications in 3D imaging are still limited due to challenges in computational cost and memory allocation. To address those limitations, this work implements a 3D fully-dense (3DFD) U-net to linear array based photoacoustic tomography and utilizes volumetric simulation and mixed precision training to increase efficiency and training size. Through numerical simulation, phantom imaging, and in vivo experiments, this work demonstrates that the trained network restores the true object size, reduces the noise level and artifacts, improves the contrast at deep regions, and reveals vessels subject to limited view distortion. With these enhancements, 3DFD U-net successfully produces clear 3D vascular images of the palm, arms, breasts, and feet of human subjects. These enhanced vascular images offer improved capabilities for biometric identification, foot ulcer evaluation, and breast cancer imaging. These results indicate that the new algorithm will have a significant impact on preclinical and clinical photoacoustic tomography.

Pilot Testing of an mHealth App for Tobacco Cessation in People Living With HIV: Protocol for a Pilot Randomized Controlled Trial.

BACKGROUND: An estimated 40% of people living with HIV smoke cigarettes. Although smoking rates in the United States have been declining in recent years, people living with HIV continue to smoke cigarettes at twice the rate of the general population. Mobile health (mHealth) technology is an effective tool for people living with a chronic illness, such as HIV, as currently 84% of households in the United States report that they have a smartphone. Although many studies have used mHealth interventions for smoking cessation, few studies have recruited people living with HIV who smoke. OBJECTIVE: The objective of the pilot randomized controlled trial (RCT) is to examine the feasibility, acceptability, and preliminary efficacy of the Sense2Quit App as a tool for people living with HIV who are motivated to quit smoking. METHODS: The Sense2Quit study is a 2-arm RCT for people living with HIV who smoke cigarettes (n=60). Participants are randomized to either the active intervention condition, which consists of an 8-week supply of nicotine replacement therapy, standard smoking cessation counseling, and access to the Sense2Quit mobile app and smartwatch, or the control condition, which consists of standard smoking cessation counseling and a referral to the New York State Smokers' Quitline. The Sense2Quit app is a mobile app connected through Bluetooth to a smartwatch that tracks smoking gestures and distinguishes them from other everyday hand movements. In the Sense2Quit app, participants can view their smoking trends, which are recorded through their use of the smartwatch, including how often or how much they smoke and the amount of money that they are spending on cigarettes, watch videos with quitting tips, information, and distractions, play games, set reminders, and communicate with a study team member. RESULTS: Enrollment of study participants began in March 2023 and is expected to end in October 2023. All data collection is expected to be completed by the end of January 2024. This RCT will test the difference in outcomes between the control and intervention arms. The primary outcome will be the percentage of participants with biochemically verified 7-day point prevalence smoking or tobacco abstinence at their 12-week follow-up. Results from this pilot study will be disseminated to the research community following the completion of all data collection. CONCLUSIONS: The Sense2Quit study leverages mHealth so that it can help smokers improve their efforts at smoking cessation. Our research has the potential to not only increase quitting rates among people living with HIV who may need a prolonged, tailored intervention but also inform further development of mHealth for people living with HIV. This mHealth study will contribute significant findings to the greater mHealth research community, providing evidence as to how mHealth should be developed and tested among the target population. TRIAL REGISTRATION: ClinicalTrials.gov NCT05609032; https://clinicaltrials.gov/study/NCT05609032. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/49558.

Identifying the potential transcriptional regulatory network in Hirschsprung disease by integrated analysis of microarray datasets.

Objective: Hirschsprung disease (HSCR) is one of the common neurocristopathies in children, which is associated with at least 20 genes and involves a complex regulatory mechanism. Transcriptional regulatory network (TRN) has been commonly reported in regulating gene expression and enteric nervous system development but remains to be investigated in HSCR. This study aimed to identify the potential TRN implicated in the pathogenesis and diagnosis of HSCR. Methods: Based on three microarray datasets from the Gene Expression Omnibus database, the multiMiR package was used to investigate the microRNA (miRNA)-target interactions, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Then, we collected transcription factors (TFs) from the TransmiR database to construct the TF-miRNA-mRNA regulatory network and used cytoHubba to identify the key modules. Finally, the receiver operating characteristic (ROC) curve was determined and the integrated diagnostic models were established based on machine learning by the support vector machine method. Results: We identified 58 hub differentially expressed microRNAs (DEMis) and 16 differentially expressed mRNAs (DEMs). The robust target genes of DEMis and DEMs mainly enriched in several GO/KEGG terms, including neurogenesis, cell-substrate adhesion, PI3K-Akt, Ras/mitogen-activated protein kinase and Rho/ROCK signaling. Moreover, 2 TFs (TP53 and TWIST1), 4 miRNAs (has-miR-107, has-miR-10b-5p, has-miR-659-3p, and has-miR-371a-5p), and 4 mRNAs (PIM3, CHUK, F2RL1, and CA1) were identified to construct the TF-miRNA-mRNA regulatory network. ROC analysis revealed a strong diagnostic value of the key TRN regulons (all area under the curve values were more than 0.8). Conclusion: This study suggests a potential role of the TF-miRNA-mRNA network that can help enrich the connotation of HSCR pathogenesis and diagnosis and provide new horizons for treatment.

Theoretically Guided Iterative Design of the Sense2Quit App for Tobacco Cessation in Persons Living with HIV.

The use of mobile health (mHealth technology) can be an effective intervention when considering chronic illnesses. Qualitative research methods were used to identify specific content and features for a mobile app for smoking cessation amongst people living with HIV (PWH). We conducted five focus group sessions followed by two Design Sessions with PWH who were or are currently chronic cigarette smokers. The first five groups focused on the perceived barriers and facilitators to smoking cessation amongst PWH. The two Design Sessions leveraged the findings from the focus group sessions and were used to determine the optimal features and user interface of a mobile app to support smoking cessation amongst PWH. Thematic analysis was conducted using the Health Belief Model and Fogg's Functional Triad. Seven themes emerged from our focus group sessions: history of smoking, triggers, consequences of quitting smoking, motivation to quit, messages to help quit, quitting strategies, and mental health-related challenges. Functional details of the app were identified during the Design Sessions and used to build a functional prototype.

Dual-Scan Photoacoustic Tomography for the Imaging of Vascular Structure on Foot.

Chronic leg ulcers are affecting approximately 6.5 million Americans, and they are associated with significant mortality, reduced quality of life, and high treatment costs. Since many chronic ulcers have underlying vascular insufficiency, accurate assessment of tissue perfusion is critical to treatment planning and monitoring. This study introduces a dual-scan photoacoustic (PA) tomography (PAT) system that can simultaneously image the dorsal and plantar sides of the foot to reduce imaging time. To account for the unique shape of the foot, the system employs height-adjustable and articulating baseball stages that can scan along the foot's contour. In vivo results from healthy volunteers demonstrate the system's ability to acquire clear images of foot vasculature, and results from patients indicate that the system can image patients with various ulcer conditions. We also investigated various PA features and examined their correlation with the foot condition. Our preliminary results indicate that vessel sharpness, occupancy, intensity, and density could all be used to assess tissue perfusion. This research demonstrated the potential of PAT for routine clinical tissue perfusion assessment.

Cluster Size Optimization in Sensor Networks with Decentralized Cluster-Based Protocols.

Network lifetime and energy-efficiency are viewed as the dominating considerations in designing cluster-based communication protocols for wireless sensor networks. This paper analytically provides the optimal cluster size that minimizes the total energy expenditure in such networks, where all sensors communicate data through their elected cluster heads to the base station in a decentralized fashion. LEACH, LEACH-Coverage, and DBS comprise three cluster-based protocols investigated in this paper that do not require any centralized support from a certain node. The analytical outcomes are given in the form of closed-form expressions for various widely-used network configurations. Extensive simulations on different networks are used to confirm the expectations based on the analytical results. To obtain a thorough understanding of the results, cluster number variability problem is identified and inspected from the energy consumption point of view.