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Rei-Chi Hsueh,1 Wan-Jung Wu,1 Chih-Lin Lin,2 Chun-Jen Liu,3 Yi-Wen Huang,4,5 Jui-Ting Hu,6 Chih-Feng Wu,1 Feng-Yu Sung,1 Wen-Jie Liu,1 Ming-Whei Yu1 1Graduate Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan; 2Department of Gastroenterology, Ren-Ai Branch, Taipei City Hospital, Taipei, Taiwan; 3Division of Gastroenterology, Department of Internal Medicine, National Taiwan University Hospital and Graduate Institute of Clinical Medicine, National Taiwan University College of Medicine, Taipei, Taiwan; 4Clinical Research Center, Liver Center and Division of Gastroenterology, Department of Internal Medicine, Taipei Medical University Hospital, Taipei, Taiwan; 5Division of Gastroenterology, Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan; 6Liver Center, Cathay General Hospital Medical Center, School of Medicine, Fu-Jen Catholic University College of Medicine, Taipei, TaiwanCorrespondence: Ming-Whei Yu, Graduate Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Room 522 No. 17, Xuzhou Road Zhongzheng District, Taipei City, 10055, Taiwan, Email yumw@ntu.edu.twBackground: Coexistence of hepatitis B and nonalcoholic fatty liver disease is common; however, little is known about the impact of hepatic steatosis and its major genetic determinants on the natural history of HBV infection. We aimed to study the effects of hepatic steatosis and PNPLA3 variant p.I148M on the risk of hepatocellular carcinoma (HCC) and the lifetime probability of HBsAg seroclearance, which is associated with functional remission and improved long-term outcome of HBV infection.Methods: We conducted a cohort study of 2385 male, HBsAg-positive Taiwanese civil servants recruited in 1989– 1992, and followed up until 2019. Cox regression with competing-risk models was used to estimate sub-distribution hazard ratios (sHRs) and 95% confidence intervals (CIs).Results: Of 2385 participants, 628 experienced HBsAg seroclearance and 217 developed HCC. Hepatic steatosis, excess body-mass index, and the PNPLA3-148M variant were significantly associated with higher HBsAg seroclearance rate. However, multivariate analyses accounting for HBsAg seroclearance and various HCC risk factors showed that, while steatosis was associated with decreased HCC risk (sHR [95% CI]: 0.49 [0.36– 0.66]), carriage of the PNPLA3-148M variant allele (vs II homozygotes: 1.64 [1.20– 2.25] for MI heterozygotes; 1.83 [1.20– 2.78] for MM homozygotes) and obesity (1.51 [1.07– 2.13]) were associated with increased risk. The inverse hepatic steatosis-HCC association persisted after additional adjustment for other viral factors or using different follow-up time cut-offs to account for reverse causality. Moreover, the PNPLA3 MM genotype was positively associated with elevations of ALT and AST and liver cirrhosis, while hepatic steatosis was positively associated with ALT but inversely associated with AST and liver cirrhosis.Conclusion: Hepatic steatosis and PNPLA3-148M variant appeared to have distinct impacts on the development of HBV-related progressive liver disease and HCC. PNPLA3 p.I148M, but not a diagnosis of hepatic steatosis, can help to identify HBV carriers with high-risk fatty liver disease in the progression to HCC.Keywords: chronic hepatitis B, HBsAg seroclearance, hepatic steatosis, hepatocellular carcinoma, liver cirrhosis, PNPLA3

Hepatocellular carcinoma (HCC), the most common type of liver cancer, poses significant challenges in detection and diagnosis. Medical imaging, especially computed tomography (CT), is pivotal in non-invasively identifying this disease, requiring substantial expertise for interpretation. This research introduces an innovative strategy that integrates two-dimensional (2D) and three-dimensional (3D) deep learning models within a federated learning (FL) framework for precise segmentation of liver and tumor regions in medical images. The study utilized 131 CT scans from the Liver Tumor Segmentation (LiTS) challenge and demonstrated the superior efficiency and accuracy of the proposed Hybrid-ResUNet model with a Dice score of 0.9433 and an AUC of 0.9965 compared to ResNet and EfficientNet models. This FL approach is beneficial for conducting large-scale clinical trials while safeguarding patient privacy across healthcare settings. It facilitates active engagement in problem-solving, data collection, model development, and refinement. The study also addresses data imbalances in the FL context, showing resilience and highlighting local models' robust performance. Future research will concentrate on refining federated learning algorithms and their incorporation into the continuous implementation and deployment (CI/CD) processes in AI system operations, emphasizing the dynamic involvement of clients. We recommend a collaborative human-AI endeavor to enhance feature extraction and knowledge transfer. These improvements are intended to boost equitable and efficient data collaboration across various sectors in practical scenarios, offering a crucial guide for forthcoming research in medical AI.

Monitoring dynamic balance during gait is critical for fall prevention in the elderly. The current study aimed to develop recurrent neural network models for extracting balance variables from a single inertial measurement unit (IMU) placed on the sacrum during walking. Thirteen healthy young and thirteen healthy older adults wore the IMU during walking and the ground truth of the inclination angles (IA) of the center of pressure to the center of mass vector and their rates of changes (RCIA) were measured simultaneously. The IA, RCIA, and IMU data were used to train four models (uni-LSTM, bi-LSTM, uni-GRU, and bi-GRU), with 10% of the data reserved to evaluate the model errors in terms of the root-mean-squared errors (RMSEs) and percentage relative RMSEs (rRMSEs). Independent t -tests were used for between-group comparisons. The sensitivity, specificity, and Pearson's r for the effect sizes between the model-predicted data and experimental ground truth were also obtained. The bi-GRU with the weighted MSE model was found to have the highest prediction accuracy, computational efficiency, and the best ability in identifying statistical between-group differences when compared with the ground truth, which would be the best choice for the prolonged real-life monitoring of gait balance for fall risk management in the elderly.

This study presents an architectural framework for the blockchain-based usage-based insurance (UBI) policy auction mechanism in the internet of vehicles (IoV) applications. The main objective of this study is to analyze and design the specific blockchain architecture and management considerations for the UBI environment. An auction mechanism is developed for the UBI blockchain platform to enhance consumer trust. The study identifies correlations between driving behaviors and associated risks to determine a driver's score. A decentralized bidding algorithm is proposed and implemented on a blockchain platform using elliptic curve cryptography and first-price sealed-bid auctions. Additionally, the model incorporates intelligent contract functionality to prevent unauthorized modifications and ensure that insurance prices align with the prevailing market value. An experimental study evaluates the system's efficacy by expanding the participant pool in the bidding process to identify the winning bidder and is investigated under scenarios where varying numbers of insurance companies submit bids. The experimental results demonstrate that as the number of insurance companies increases exponentially, the temporal overhead incurred by the system exhibits only marginal growth. Moreover, the allocation of bids is accomplished within a significantly abbreviated timeframe. These findings provide evidence that supports the efficiency of the proposed algorithm.

The rapid development of AIOT-related technologies has revolutionized various industries. The advantage of such real-time sensing, low costs, small sizes, and easy deployment makes extensive use of wireless sensor networks in various fields. However, due to the wireless transmission of data, and limited built-in power supply, controlling energy consumption and making the application of the sensor network more efficient is still an urgent problem to be solved in practice. In this study, we construct this problem as a tree structure wireless sensor network mathematical model, which mainly considers the QoS and fairness requirements. This study determines the probability of sensor activity, transmission distance, and transmission of the packet size, and thereby minimizes energy consumption. The Lagrangian Relaxation method is used to find the optimal solution with the lowest energy consumption while maintaining the network's transmission efficiency. The experimental results confirm that the decision-making speed and energy consumption can be effectively improved.

The authority of user personal health records (PHRs) is usually determined by the owner of a cloud computing system. When a PHR file is accessed, a dynamic access control algorithm must be used to authenticate the users. The proposed dynamic access control algorithm is based on a novel Lagrange interpolation polynomial with timestamps, mainly functioning to authenticate the users with key information. Moreover, the inclusion of timestamps allows user access within an approved time slot to enhance the security of the healthcare cloud system. According to the security analysis results, this healthcare cloud system can effectively resist common attacks, including external attacks, internal attacks, collaborative attacks and equation-based attacks. Furthermore, the overall computational complexity of establishing and updating the polynomials is O( n * m * (log m ) 2 ), which is a promising result, where m denotes the degree of $ polynomial~G\left(x, y\right) $ and n denotes the number of secure users in the hierarchy.

This paper proposes an encoder-decoder architecture for kidney segmentation. A hyperparameter optimization process is implemented, including the development of a model architecture, selecting a windowing method and a loss function, and data augmentation. The model consists of EfficientNet-B5 as the encoder and a feature pyramid network as the decoder that yields the best performance with a Dice score of 0.969 on the 2019 Kidney and Kidney Tumor Segmentation Challenge dataset. The proposed model is tested with different voxel spacing, anatomical planes, and kidney and tumor volumes. Moreover, case studies are conducted to analyze segmentation outliers. Finally, five-fold cross-validation and the 3D-IRCAD-01 dataset are used to evaluate the developed model in terms of the following evaluation metrics: the Dice score, recall, precision, and the Intersection over Union score. A new development and application of artificial intelligence algorithms to solve image analysis and interpretation will be demonstrated in this paper. Overall, our experiment results show that the proposed kidney segmentation solutions in CT images can be significantly applied to clinical needs to assist surgeons in surgical planning. It enables the calculation of the total kidney volume for kidney function estimation in ADPKD and supports radiologists or doctors in disease diagnoses and disease progression., Competing Interests: Declaration of Competing Interest The authors whose names are listed below certify that they have NO affiliations with or involvement in any organization or entity with any financial interest or non-financial interest (such as employment, consultancies, stock ownership, honoraria, paid expert testimony, patent applications/registrations, and grants or other funding). There is None Declared in the subject matter or materials discussed in this manuscript., (Copyright © 2022 The Author(s). Published by Elsevier B.V. All rights reserved.)

Previously, doctors interpreted computed tomography (CT) images based on their experience in diagnosing kidney diseases. However, with the rapid increase in CT images, such interpretations were required considerable time and effort, producing inconsistent results. Several novel neural network models were proposed to automatically identify kidney or tumor areas in CT images for solving this problem. In most of these models, only the neural network structure was modified to improve accuracy. However, data pre-processing was also a crucial step in improving the results. This study systematically discussed the necessary pre-processing methods before processing medical images in a neural network model. The experimental results were shown that the proposed pre-processing methods or models significantly improve the accuracy rate compared with the case without data pre-processing. Specifically, the dice score was improved from 0.9436 to 0.9648 for kidney segmentation and 0.7294 for all types of tumor detections. The performance was suitable for clinical applications with lower computational resources based on the proposed medical image processing methods and deep learning models. The cost efficiency and effectiveness were also achieved for automatic kidney volume calculation and tumor detection accurately., Competing Interests: Declaration of Competing Interest Authors declare that they have no conflict of interest., (Copyright © 2022 The Author(s). Published by Elsevier B.V. All rights reserved.)

Network slicing is a promising technology that network operators can deploy the services by slices with heterogeneous quality of service (QoS) requirements. However, an orchestrator for network operation with efficient slice resource provisioning algorithms is essential. This work stands on Internet service provider (ISP) to design an orchestrator analyzing the critical influencing factors, namely access control, scheduling, and resource migration, to systematically evolve a sustainable network. The scalability and flexibility of resources are jointly considered. The resource management problem is formulated as a mixed-integer programming (MIP) problem. A solution approach based on Lagrangian relaxation (LR) is proposed for the orchestrator to make decisions to satisfy the high QoS applications. It can investigate the resources required for access control within a cost-efficient resource pool and consider allocating or migrating resources efficiently in each network slice. For high system utilization, the proposed mechanisms are modeled in a pay-as-you-go manner. Furthermore, the experiment results show that the proposed strategies perform the near-optimal system revenue to meet the QoS requirement by making decisions.

As wireless sensor networks have become more prevalent, data from sensors in daily life are constantly being recorded. Due to cost or energy consumption considerations, optimization-based approaches are proposed to reduce deployed sensors and yield results within the error tolerance. The correlation-aware method is also designed in a mathematical model that combines theoretical and practical perspectives. The sensor deployment strategies, including XGBoost, Pearson correlation, and Lagrangian Relaxation (LR), are determined to minimize deployment costs while maintaining estimation errors below a given threshold. Moreover, the results significantly ensure the accuracy of the gathered information while minimizing the cost of deployment and maximizing the lifetime of the WSN. Furthermore, the proposed solution can be readily applied to sensor distribution problems in various fields.

A combined edge and core cloud computing environment is a novel solution in 5G network slices. The clients' high availability requirement is a challenge because it limits the possible admission control in front of the edge cloud. This work proposes an orchestrator with a mathematical programming model in a global viewpoint to solve resource management problems and satisfying the clients' high availability requirements. The proposed Lagrangian relaxation-based approach is adopted to solve the problems at a near-optimal level for increasing the system revenue. A promising and straightforward resource management approach and several experimental cases are used to evaluate the efficiency and effectiveness. Preliminary results are presented as performance evaluations to verify the proposed approach's suitability for edge and core cloud computing environments. The proposed orchestrator significantly enables the network slicing services and efficiently enhances the clients' satisfaction of high availability.

Despite considerable knowledge of viral pathogenesis, the pathophysiological changes related to the multifactorial, multistep process of hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) development remains unclear. Longitudinal metabolomics study can reveal biological process for disease progression. We performed metabolite profiling with longitudinal prediagnostic plasma samples from two nested case-control studies of hepatitis B surface antigen carriers participating in ultrasound screening for HCC, one within a government employee cohort (870 samples from 109 HCC cases and 107 controls) and the other within a hospital-based cohort (266 samples from 63 HCC cases and 114 controls). Of the 34 measured metabolites, tyrosine, isoleucine, and glutamine were consistently associated with HCC. In analyses combining longitudinal data, a high metabolic risk score based on the three amino acids was robustly associated with increased risk of HCC (OR = 3.71, 95% confidence interval: 2.53-5.42), even after adjustment for clinical factors, or when assessed for different times up to ≥8 years before diagnosis. Similar association was observed in an independent, prospective analysis comprising 633 randomly selected individuals of the government employee cohort. More importantly, this metabolite signature was longitudinally influenced by HBV-infection phase and involved in gradual progression to liver fibrosis and cirrhosis. Furthermore, mediation analysis showed that the score mediated substantial proportions of the associations of key viral factors, insulin resistance, and diabetes status with HCC risk. Our results suggest that an amino-acid dysregulation metabotype may play a role in HBV-related HCC development, and may also be linked to common pathways that mediate increased HCC risks., (© 2020 Wiley Periodicals LLC.)

This paper focuses on the use of an attention-based encoder-decoder model for the task of breathing sound segmentation and detection. This study aims to accurately segment the inspiration and expiration of patients with pulmonary diseases using the proposed model. Spectrograms of the lung sound signals and labels for every time segment were used to train the model. The model would first encode the spectrogram and then detect inspiratory or expiratory sounds using the encoded image on an attention-based decoder. Physicians would be able to make a more precise diagnosis based on the more interpretable outputs with the assistance of the attention mechanism.The respiratory sounds used for training and testing were recorded from 22 participants using digital stethoscopes or anti-noising microphone sets. Experimental results showed a high 92.006% accuracy when applied 0.5 second time segments and ResNet101 as encoder. Consistent performance of the proposed method can be observed from ten-fold cross-validation experiments.

Unlabelled: To evaluate how hepatitis B virus (HBV) genetic variation affected progression from chronic carrier state to hepatocellular carcinoma (HCC), we analyzed HBV full-length sequences in blood obtained <1-20 years before diagnosis from 117 HCC cases and 118 controls nested in a cohort of 4,841 HBV carriers, for whom HBV genotypes B and C are predominant. The relationship between each viral single-nucleotide polymorphism (SNP) and HCC development was assessed using ordinal logistic models according to five periods of time to diagnosis (TTD). Thirty-one HBV-SNPs showed significant association with TTD after adjustment for HBV genotype, 24 of which could also be analyzed with an extended analysis on the full-length data in conjunction with 512 partial sequences (nucleotides 2,436-1,623) from the cohort. The obtained 10 robust candidate HBV-SNPs (P ≤ 0.0304), which showed odds ratios ranging from 1.89 to 8.68, were further confirmed in 163 GenBank HBV-HCC sequences from nine Asia regions, assayed after HCC diagnosis, representing the end stage of progressive hepatic diseases. The prevalence of these HBV-SNPs and their cumulative number, presented in terms of mutation score, increased with time approaching HCC diagnosis, with an odds ratio of 2.17, 4.21, 8.15, and 19.15, respectively, for the mutation score of 1, 2, 3, and ≥4 versus 0. The mutation score for predicting short-term HCC risk outperformed other factors, including HBV-DNA levels, viral genotype, and various combinations of risk factors, and revealed increasing accuracy with shorter TTD (<4.5 years before diagnosis: area under the curve = 0.83-0.89; sensitivity = 72.7%-94.1%; specificity = 58.3%-70.5%; conditioned on optimized cutoff for genotype B and C, respectively)., Conclusions: Identifying and tracking viral mutations is important for monitoring hepatitis B progression and early detection of HCC. (Hepatology 2016;64:720-731)., (© 2016 by the American Association for the Study of Liver Diseases.)

Objective: The hepatitis B virus (HBV)-polymerase region overlaps pre-S/S genes with high epitope density and plays an essential role in viral replication. We investigated whether genetic variation in the polymerase region determined long-term dynamics of viral load and the risk of hepatitis B progression in a population-based cohort study., Methods: We sequenced the HBV-polymerase region using baseline plasma from treatment-naïve individuals with HBV-DNA levels≥1000 copies/mL in a longitudinal viral-load study of participants with chronic HBV infection followed-up for 17 years, and obtained sequences from 575 participants (80% with HBV genotype Ba and 17% with Ce)., Results: Patterns of viral sequence diversity across phases (i.e., immune-tolerant, immune-clearance, non/low replicative, and hepatitis B e antigen (HBeAg)-negative hepatitis phases) of HBV-infection, which were associated with viral and clinical features at baseline and during follow-up, were similar between HBV genotypes, despite greater diversity for genotype Ce vs. Ba. Irrespective of genotypes, however, HBeAg-negative participants had 1.5-to-2-fold higher levels of sequence diversity than HBeAg-positive participants (P<0.0001). Furthermore, levels of viral genetic divergence from the population consensus sequence, estimated by numbers of nucleotide substitutions, were inversely associated with long-term viral load even in HBeAg-negative participants. A mixed model developed through analysis of the entire HBV-polymerase region identified 153 viral load-associated single nucleotide polymorphisms in overall and 136 in HBeAg-negative participants, with distinct profiles between HBV genotypes. These polymorphisms were most evident at sites within or flanking T-cell epitopes. Seven polymorphisms revealed associations with both enhanced viral load and a more than 4-fold increased risk of hepatocellular carcinoma and/or liver cirrhosis., Conclusions: The data highlight a role of viral genetic divergence in the natural course of HBV-infection. Interindividual differences in the long-term dynamics of viral load is not only associated with accumulation of mutations in HBV-polymerase region, but differences in specific viral polymorphisms which differ between genotypes.

Recent advance in wireless sensor network (WSN) applications such as the Internet of Things (IoT) have attracted a lot of attention. Sensor nodes have to monitor and cooperatively pass their data, such as temperature, sound, pressure, etc. through the network under constrained physical or environmental conditions. The Quality of Service (QoS) is very sensitive to network delays. When resources are constrained and when the number of receivers increases rapidly, how the sensor network can provide good QoS (measured as end-to-end delay) becomes a very critical problem. In this paper; a solution to the wireless sensor network multicasting problem is proposed in which a mathematical model that provides services to accommodate delay fairness for each subscriber is constructed. Granting equal consideration to both network link capacity assignment and routing strategies for each multicast group guarantees the intra-group and inter-group delay fairness of end-to-end delay. Minimizing delay and achieving fairness is ultimately achieved through the Lagrangean Relaxation method and Subgradient Optimization Technique. Test results indicate that the new system runs with greater effectiveness and efficiency.

This study aimed to investigate the association of fasting insulin and glucose levels with hepatocellular carcinoma (HCC) risk in a case-cohort study within a cohort (1989-2006) of 2903 male government employees chronically infected with hepatitis B virus (HBV) in Taiwan. Insulin, glucose and HBV-related factors were assayed in baseline plasma among 124 HCC cases and a random subcohort of 1084 of the total cohort. After adjustment for demographics and HBV-related factors, including viral load and genotype, the HCC risk was higher for the highest [>6.10 μU/ml, hazard ratio (HR) = 2.36, 95% confidence interval (CI): 1.43-3.90] and lowest (<2.75 μU/ml, HR = 1.57, 95% CI: 0.96-2.58) categories of insulin, compared with insulin of 2.75-4.10 μU/ml. The dose-response relationship between insulin and HCC varied by follow-up time, with stronger association for the HCC cases that occurred ≥8 years after baseline (P for trend <0.0001). The effect of higher insulin on HCC risk remained after adjustment for other metabolic factors, and was fairly consistent across strata of age, body mass index, and HBV genotypic variants. However, it was more profound among those with viral load <4.39 log(10) copies/ml at recruitment (>6.10 μU/ml, HR = 6.15, 95% CI: 2.48-15.22). Higher insulin was also associated with an increased risk for cirrhosis diagnosed by ultrasonography and elevated alanine aminotransferase. No association with either cirrhosis or HCC was noted for glucose or diabetes after adjusting for insulin. In conclusion, elevated insulin levels are an independent risk factor for HCC among HBV carriers, especially for those with lower viral load.

In this paper we propose an energy-efficient object tracking algorithm in wireless sensor networks (WSNs). Such sensor networks have to be designed to achieve energy-efficient object tracking for any given arbitrary topology. We consider in particular the bi-directional moving objects with given frequencies for each pair of sensor nodes and link transmission cost. This problem is formulated as a 0/1 integer-programming problem. A Lagrangean relaxation-based (LR-based) heuristic algorithm is proposed for solving the optimization problem. Experimental results showed that the proposed algorithm achieves near optimization in energy-efficient object tracking. Furthermore, the algorithm is very efficient and scalable in terms of the solution time.

Background & Aims: We assessed the influence of genetic variants in the hepatitis B virus (HBV) core, which is a principal immunologic target, on the progression to hepatocellular carcinoma (HCC) in a cohort of 4841 male HBV carriers followed up for 16 years., Methods: First, baseline sera from 116 HCC cases and 154 controls nested within the cohort were used for sequencing of the HBV core gene to screen for variants with effects on HCC progression. By applying a high-throughput assay for detecting viral single nucleotide substitutions, we then used a longitudinal study (n = 1143) to examine whether 2 identified variants that lie in the region within or flanking epitopes affected the natural course of hepatitis B through investigating their relationships with time trends for viral load and clinical features., Results: In the nested case-control study, there were 6 core variants associated with decreased risk of HCC after accounting for viral genotype; 5 lie in the region within or flanking epitopes (P < .04). Each variant correlated with a 0.7- to 1-log decrease in viral load and hepatitis B virus e antigen negativity at baseline. The longitudinal study further showed that the appearance of 2 such variants (T1938C and T2045A) was preceded by long-term diminished viral load and decreased rate of liver abnormalities and was significantly less frequent in individuals with a prolonged immune clearance phase that associated with spectrum of liver disease than those in inactive carrier or reactivation phase., Conclusions: HBV core variants affecting the kinetics of host-virus interplay may influence longitudinal viral load and HCC progression.

By eliminating redundant data flows, data aggregation capabilities in wireless sensor networks could transmit less data to reduce the total energy consumption. However, additional data collisions incur extra data retransmissions. These data retransmissions not only increase the system energy consumption, but also increase link transmission delays. The decision of when and where to aggregate data depends on the trade-off between data aggregation and data retransmission. The challenges of this problem need to address the routing (layer 3) and the MAC layer retransmissions (layer 2) at the same time to identify energy-efficient data-aggregation routing assignments, and in the meantime to meet the delay QoS. In this paper, for the first time, we study this cross-layer design problem by using optimization-based heuristics. We first model this problem as a non-convex mathematical programming problem where the objective is to minimize the total energy consumption subject to the data aggregation tree and the delay QoS constraints. The objective function includes the energy in the transmission mode (data transmissions and data retransmissions) and the energy in the idle mode (to wait for data from downstream nodes in the data aggregation tree). The proposed solution approach is based on Lagrangean relaxation in conjunction with a number of optimization-based heuristics. From the computational experiments, it is shown that the proposed algorithm outperforms existing heuristics that do not take MAC layer retransmissions and the energy consumption in the idle mode into account.

Embedding data-aggregation capabilities into sensor nodes of wireless networks could save energy by reducing redundant data flow transmissions. Existing research describes the construction of data aggregation trees to maximize data aggregation times in order to reduce data transmission of redundant data. However, aggregation of more nodes on the same node will incur significant collisions. These MAC (Media Access Control) layer collisions introduce additional data retransmissions that could jeopardize the advantages of data aggregation. This paper is the first to consider the energy consumption tradeoffs between data aggregation and retransmissions in a wireless sensor network. By using the existing CSMA/CA (Carrier Sense Multiple Access with Collision Avoidance) MAC protocol, the retransmission energy consumption function is well formulated. This paper proposes a novel non-linear mathematical formulation, whose function is to minimize the total energy consumption of data transmission subject to data aggregation trees and data retransmissions. This solution approach is based on Lagrangean relaxation, in conjunction with optimization-based heuristics. From the computational experiments, it is shown that the proposed algorithms could construct MAC aware data aggregation trees that are up to 59% more energy efficient than existing data aggregation algorithms.

Epidermolysis bullosa (EB) pruriginosa is a subtype of dominant dystrophic EB (DDEB), characterized by severe pruritus and blistering localized to the extensor surface of the extremities. EB pruriginosa exhibits extensive clinical heterogeneity with variable expression and delayed age of onset. Mutations in the COL7A1 gene, especially in glycine residues within Gly-X-Y repeats, have been shown to cause this form of DDEB. Here, we report a novel COL7A1 mutation in a Taiwanese pedigree with EB pruriginosa. Using PCR and direct sequence analysis we have identified a G-->T transversion at nucleotide 7097 in exon 92 of COL7A1, converting a glycine residue to valine (G2366V). The mutation resides within a consecutive, uninterrupted stretch of 17 Gly-X-Y residues in the triple-helical domain of type VII collagen. Interestingly, an affected member of this family also displayed elevated IgE levels, previously reported in some patients with this disorder. Our finding further implicates COL7A1 mutation in the pathogenesis of EB pruriginosa and underscores the heterogeneous clinical symptoms of glycine mutations in DDEB.