The effects of periodontal therapy on serum antibody levels to Actinobacillus actinomycetemcomitans and Porphyromonas gingivalis (part II)

Levels of IgG and IgM antibodies were estimated against Actinobacillus actinomycetemcomitans and Porphyromonas gingivalis were determined by enzyme linked immunosorbent assay (ELISA) in 17 patients with juvenile periodontitis, 15 with adult periodontitis and 24 healthy controls at the beginning of treatment and 3 to 8 months after periodontal therapy. After treatment, antibodies to A. actinomycetemcomitans and P.gingivalis had decreased in patients, but the levels were still significantly higher than in healthy controls. Whether or not an of antibody level against a specific bacteria changes after periodontal treatment is however, still debatable.     

Fine grinding of thermoplastics by high speed friction grinding assisted by guar gum

High speed friction grinding has been used to grind plant and food substances in water but never been explored for grinding of thermoplastics like polylactic acid (PLA), low and high density polyethylene and polypropylene. Such grinding was investigated in this work and was made possible by using 0.5% guar gum solution instead of just water because increasing the viscosity of water reduced their settling and the speed of passing through the grinder. Tensile, flexural, and impact strengths of the plastics were studied and higher grinding efficiency of PLA could be explained by its low elongation-at-break compared to low density polyethylene, high density polyethylene, and polypropylene. The microplastics (2000–45 μm) were studied for mass and particle size distributions and by scanning electron microscopy, ¹³C CP/MAS NMR, Fourier transform infrared spectroscopy, differential scanning calorimetry, and thermogravimetric analysis. In addition, viscosity of guar gum and contact angles was measured. This new technology can produce finely ground microplastics (710–45 μm) for a variety of applications.     

Towards quality requirements via animated formal specifications

Assuring a high quality requirements specification document involves both an early validation process and an increased level of participation. An approach and its supporting environment which combines the benefits of a formal system specification and its subsequent execution via a rapid prototype is reported. The environment assists in the construction, clarification, validation and visualisation of a formal specification. An illustrative case study demonstrates the consequences of assertions about system properties at this early stage of software development. Our approach involves the pragmatic combination of technical benefits of formal systems engineering based techniques with the context‐sensitive notions of increased participation of both developer and user stakeholders to move us closer towards a quality requirements specification document.     

Chemical Element Profiling in the Sera and Brain of Bipolar Disorders Patients and Healthy Controls

Bipolar Disorder (BD) is a severe recurrent affective mood disorder characterized by a wide range of lifelong mood swings, varying between depressive and manic states. BD affects more than 1% of the world’s population irrespective of nationality, ethnic origin, or socioeconomic status and is one of the main causes of disability among young people, leading to cognitive and functional impairment and raised mortality, particularly death by suicide. Trace elements play a vital role in many biochemical and physiological processes. Compelling evidence shows that element toxicity might play a crucial role in the onset and progression of neurodegenerative disorders, but their involvement in mood disorders has been scarcely studied. In the present investigation, we determined the concentration of 26 elements in the serum of BD patients before and after treatment and in postmortem brain samples from BD patients and compared them with matched controls. The only element that was reduced significantly in the serum following treatment was vanadium (V). Furthermore, the concentration of Al, B, Cu, K, Mg and V were significantly lower in the pre-frontal cortex of BD patients compared with those of the controls. A comparison of Spearman’s rank correlation coefficients between the elements in the serum and brain of BD patients and control groups pointed to boron and aluminum as being involved in the disease. These results suggest that there is a disturbance in the elements’ homeostasis and the inter-elements’ relationship in the brain of BD patients and advocate a thorough examination of the possible involvement of chemical elements in different stages of the disease.     

Ensemble learning of diffractive optical networks

A plethora of research advances have emerged in the fields of optics and photonics that benefit from harnessing the power of machine learning.Specifically,there has been a revival of interest in optical computing hardware due to its potential advantages for machine learning tasks in terms of parallelization,power efficiency and computation speed.Diffractive deep neural networks(D²NNs)form such an optical computing framework that benefits from deep learning-based design of successive diffractive layers to all-optically process information as the input light diffracts through these passive layers.D²NNs have demonstrated success in various tasks,including object classification,the spectral encoding of information,optical pulse shaping and imaging.Here,we substantially improve the inference performance of diffractive optical networks using feature engineering and ensemble learning.After independently training 1252 D²NNs that were diversely engineered with a variety of passive input filters,we applied a pruning algorithm to select an optimized ensemble of D²NNs that collectively improved the image classification accuracy.Through this pruning,we numerically demonstrated that ensembles of N=14 and N=30 D²NNs achieve blind testing accuracies of 61.14±0.23%and 62.13±0.05%,respectively,on the classification of GFAR-10 test images,providing an inference improvennent of>16%compared to the average performance of the individual D²NNs within each ensemble.These results constitute the highest inference accuracies achieved to date by any diffractive optical neural network design on the same dataset and might provide a significant leap to extend the application space of diffractive optical image classification and machine vision systems.     

Reliability-Aware Heterogeneous 3D Chip Multiprocessor Design

Ability to stack separate chips in a single package enables three-dimensional integrated circuits (3D ICs). Heterogeneous 3D ICs provide even better opportunities to reduce the power and increase the performance per unit area. An important issue in designing a heterogeneous 3D IC is reliability. To achieve this, one needs to select the data mapping and processor layout carefully. This paper addresses this problem using an integer linear programming (ILP) approach. Specifically, on a heterogeneous 3D CMP, it explores how applications can be mapped onto 3D ICs to maximize reliability. Preliminary experiments indicate that the proposed technique generates promising results in both reliability and performance.