SMT-Based Contention-Free Task Mapping and Scheduling on 2D/3D SMART NoC with Mixed Dimension-Order Routing

SMART NoCs achieve ultra-low latency by enabling single-cycle multiple-hop transmission via bypass channels. However, contention along bypass channels can seriously degrade the performance of SMART NoCs by breaking the bypass paths. Therefore, contention-free task mapping and scheduling are essential for optimal system performance. In this article, we propose an SMT (Satisfiability Modulo Theories)-based framework to find optimal contention-free task mappings with minimum application schedule lengths on 2D/3D SMART NoCs with mixed dimension-order routing. On top of SMT’s fast reasoning capability for conditional constraints, we develop efficient search-space reduction techniques to achieve practical scalability. Experiments demonstrate that our SMT framework achieves 10× higher scalability than ILP (Integer Linear Programming) with 931.1× (ranges from 2.2× to 1532.1×) and 1237.1× (ranges from 4× to 4373.8×) faster average runtimes for finding optimum solutions on 2D and 3D SMART NoCs and our 2D and 3D extensions of the SMT framework with mixed dimension-order routing also maintain the improved scalability with the extended and diversified routing paths, resulting in reduced application schedule lengths throughout various application benchmarks.

Must Clinics Replace 2D by 3D Environments for an Efficient Training of Laparoscopic Novices? A Critical Analysis of the Learning Curve for Basic Skills

Background and AimsPublished studies repeatedly demonstrate an advantage of three-dimensional (3D) laparoscopic surgery over two-dimensional (2D) systems but with quite heterogeneous results. This raises the question whether clinics must replace 2D technologies to ensure effective training of future surgeons.MethodsWe recruited 45 students with no experience in laparoscopic surgery and comparable characteristics in terms of vision and frequency of video game usage. The students were randomly allocated to 3D (n = 23) or 2D (n = 22) groups and performed 10 runs of a laparoscopic “peg transfer” task in the Luebeck Toolbox. A repeated-measures ANOVA for operation times and a generalized linear mixed model for error rates were calculated. The main effects of laparoscopic condition and run, as well as the interaction term between the two, were examined.ResultsNo statistically significant differences in operation times and error rates were observed between 2D and 3D groups (p = 0.10 and p = 0.72, respectively). The learning curve showed a significant reduction in operation time and error rates (both p's < 0.001). No significant interactions between group and run were detected (operation time: p = 0.342, error rates: p = 0.83). With respect to both endpoints studied, the learning curves reached their plateau at the 7th run.ConclusionThe result of our study with laparoscopic novices revealed no significant difference between 2D and 3D technology with respect to performance time and the error rate in a simple standardized test. In the future, surgeons may thus still be trained in both techniques.

Fingerprint Database Enhancement by Applying Interpolation and Regression Techniques for IoT-based Indoor Localization

Most applied indoor localization is based on distance and fingerprint techniques. The distance-based technique converts specific parameters to a distance, while the fingerprint technique stores parameters as the fingerprint database. The widely used Internet of Things (IoT) technologies, e.g., Wi-Fi and ZigBee, provide the localization parameters, i.e., received signal strength indicator (RSSI). The fingerprint technique advantages over the distance-based method as it straightforwardly uses the parameter and has better accuracy. However, the burden in database reconstruction in terms of complexity and cost is the disadvantage of this technique. Some solutions, i.e., interpolation, image-based method, machine learning (ML)-based, have been proposed to enhance the fingerprint methods. The limitations are complex and evaluated only in a single environment or simulation. This paper proposes applying classical interpolation and regression to create the synthetic fingerprint database using only a relatively sparse RSSI dataset. We use bilinear and polynomial interpolation and polynomial regression techniques to create the synthetic database and apply our methods to the 2D and 3D environments. We obtain an accuracy improvement of 0.2m for 2D and 0.13m for 3D by applying the synthetic database. Adding the synthetic database can tackle the sparsity issues, and the offline fingerprint database construction will be less burden. Doi: 10.28991/esj-2021-SP1-012 Full Text: PDF

Griottes: a generalist tool for network generation from segmented tissue images

Microscopy techniques and image segmentation algorithms have improved dramatically this decade, leading to an ever increasing amount of biological images and a greater reliance on imaging to investigate biological questions. This has created a need for methods to extract the relevant information on the behaviors of cells and their interactions, while reducing the amount of computing power required to organize this information. This task can be performed by using a network representation in which the cells and their properties are encoded in the nodes, while the neighborhood interactions are encoded by the links. Here we introduce Griottes, an open-source tool to build the "network twin" of 2D and 3D tissues from segmented microscopy images. We show how the library can provide a wide range of biologically relevant metrics on individual cells and their neighborhoods, with the objective of providing multi-scale biological insights. The library's capacities are demonstrated on different image and data types. This library is provided as an open-source tool that can be integrated into common image analysis workflows to increase their capacities.

A Matrigel-based 3D construct of SH-SY5Y cells models the α-synuclein pathologies of Parkinson's disease

Parkinson's disease (PD) is featured with α-synuclein-based Lewy body pathology, which however was difficult to observe in conventional two-dimensional (2D) cell culture and even in animal models. We herein aimed to develop a three-dimensional (3D) cellular model of PD to recapitulate the α-synuclein pathologies. All-trans-retinoic acid-differentiated human SH-SY5Y cells and Matrigel were optimized for 3D construction. The 3D cultured cells displayed higher tyrosine hydroxylase expression and improved dopaminergic-like phenotypes than 2D cells as suggested by RNA-sequencing analyses. Multiple forms of α-synuclein, including monomer, low and high molecular weight oligomers, were differentially present in the 2D and 3D cells, but mostly remained unchanged upon the MPP+ or rotenone treatment. Phosphorylated α-synuclein was accumulated and detergent-insoluble α-synuclein fraction was observed in the neurotoxin-treated 3D cells. Importantly, Lewy body-like inclusions were captured in the 3D system, including proteinase K-resistant α-synuclein aggregates, ubiquitin aggregation, β-amyloid and β-sheet protein deposition. The study provides a unique and convenient 3D model of PD which recapitulates critical α-synuclein pathologies and should be useful in multiple PD-associated applications.

Immunogenicity of a Two-Dose Human Papillomavirus Vaccine Schedule in HIV-Infected Adolescents with Immune Reconstitution

HIV-infected patients are at increased risk of human papillomavirus (HPV) acquisition and HPV-associated diseases. This study set out to determine whether a two-dose (2D) HPV vaccination schedule was sufficient in HIV-infected adolescents with immune reconstitution (IR) following antiretroviral treatment. Participants aged 9–15 years who had CD4 cell counts > 500 cells/mm3 and HIV-1 RNA < 40 copies/mL for at least one year were assigned to the 2D schedule, while older participants or those without IR received a three-dose (3D) schedule. Antibodies to HPV-16 and -18 were measured using a pseudovirion-based neutralization assay. A total of 96 subjects were enrolled; 31.3% and 68.7% received the 2D and 3D schedule, respectively. Of these, 66.7% and 57.6% of the 2D and 3D participants, respectively, were male. The seroconversion rates for HPV-16 and HPV-18 were 100% in all cases, except for HPV-18 in males who received the 3D schedule (97.4%). In males, the anti-HPV-16 geometric mean titers (GMTs) were 6859.3 (95% confidence interval, 4394.3–10,707.1) and 7011.1 (4648.8–10,573.9) in the 2D and 3D groups (p = 0.946), respectively, and the anti-HPV-18 GMTs were 2039.3 (1432.2–2903.8) and 2859.8 (1810.0–4518.4) in the 2D and 3D (p = 0.313) groups, respectively. In females, the anti-HPV-16 GMTs were 15,758.7 (8868.0–28,003.4) and 26,241.6 (16,972.7–40,572.3) in the 2D and 3D groups (p = 0.197), respectively, and the anti-HPV-18 GMTs were 5971.4 (3026.8–11,780.6) and 9993.1 (5950.8–16,781.1) in the 2D and 3D groups (p = 0.271), respectively. In summary, a 2D schedule is as immunogenic in young adolescents with IR as a 3D schedule in older subjects and those without IR.