Three dimensional reconstruction of brain tumor along with space occupying in lesions

Multimedia Tools and Applications - The three-dimensional models of brain tumors serve as diagnostic assistance for physicians, surgeons, and radiologists. The proposed system establishes an...     

Designing optical glasses by machine learning coupled with a genetic algorithm

Engineering new glass compositions have experienced a sturdy tendency to move forward from (educated) trial-and-error to data- and simulation-driven strategies. In this work, we developed a computer program that combines data-driven predictive models (in this case, neural networks) with a genetic algorithm to design glass compositions with desired combinations of properties. First, we induced predictive models for the glass transition temperature (T_g) using a dataset of 45,302 compositions with 39 different chemical elements, and for the refractive index (n_d) using a dataset of 41,225 compositions with 38 different chemical elements. Then, we searched for relevant glass compositions using a genetic algorithm informed by a design trend of glasses having high n_d (1.7 or more) and low T_g (500 °C or less). Two candidate compositions suggested by the combined algorithms were selected and produced in the laboratory. These compositions are significantly different from those in the datasets used to induce the predictive models, showing that the used method is indeed capable of exploration. Both glasses met the constraints of the work, which supports the proposed framework. Therefore, this new tool can be immediately used for accelerating the design of new glasses. These results are a stepping stone in the pathway of machine learning-guided design of novel glasses.     

Syndecan-1 (CD138), Carcinomas and EMT

Cell surface proteoglycans are known to be important regulators of many aspects of cell behavior. The principal family of transmembrane proteoglycans is the syndecans, of which there are four in mammals. Syndecan-1 is mostly restricted to epithelia, and bears heparan sulfate chains that are capable of interacting with a large array of polypeptides, including extracellular matrix components and potent mediators of proliferation, adhesion and migration. For this reason, it has been studied extensively with respect to carcinomas and tumor progression. Frequently, but not always, syndecan-1 levels decrease as tumor grade, stage and invasiveness and dedifferentiation increase. This parallels experiments that show depletion of syndecan-1 can be accompanied by loss of cadherin-mediated adhesion. However, in some tumors, levels of syndecan-1 increase, but the characterization of its distribution is relevant. There can be loss of membrane staining, but acquisition of cytoplasmic and/or nuclear staining that is abnormal. Moreover, the appearance of syndecan-1 in the tumor stroma, either associated with its cellular component or the collagenous matrix, is nearly always a sign of poor prognosis. Given its relevance to myeloma progression, syndecan-1-directed antibody—toxin conjugates are being tested in clinical and preclinical trials, and may have future relevance to some carcinomas.     

Inhibition of Autophagy Does Not Re-Sensitize Acute Myeloid Leukemia Cells Resistant to Cytarabine

Elevated activation of the autophagy pathway is currently thought to be one of the survival mechanisms allowing therapy-resistant cancer cells to escape elimination, including for cytarabine (AraC)-resistant acute myeloid leukemia (AML) patients. Consequently, the use of autophagy inhibitors such as chloroquine (CQ) is being explored for the re-sensitization of AraC-resistant cells. In our study, no difference in the activity of the autophagy pathway was detected when comparing AraC-Res AML cell lines to parental AraC-sensitive AML cell lines. Furthermore, treatment with autophagy inhibitors CQ, 3-Methyladenine (3-MA), and bafilomycin A1 (BafA1) did not re-sensitize AraC-Res AML cell lines to AraC treatment. However, in parental AraC-sensitive AML cells, treatment with AraC did activate autophagy and, correspondingly, combination of AraC with autophagy inhibitors strongly reduced cell viability. Notably, the combination of these drugs also yielded the highest level of cell death in a panel of patient-derived AML samples even though not being additive. Furthermore, there was no difference in the cytotoxic effect of autophagy inhibition during AraC treatment in matched de novo and relapse samples with differential sensitivity to AraC. Thus, inhibition of autophagy may improve AraC efficacy in AML patients, but does not seem warranted for the treatment of AML patients that have relapsed with AraC-resistant disease.     

Analysis of energy saving in user equipment in LTE-A using stochastic modelling

Telecommunication Systems - Energy saving in User Equipment (UE) is one of the important issues for limited sources of power in the device. It is critical for the UE to maximize its energy...     

Effect of heat treatment and zircon addition on the properties of low-cement castables based on recycled alumina grog

In this investigation, low-cement castables were prepared using 70% alumina grog aggregates obtained from crushed alumina brick waste. The aggregates were thermally treated at 1550 °C for 3 h. Four types of low-cement castables were prepared with various types of aggregates (alumina grog with or without thermal treatment) and fillers (with or without zircon addition), and they were evaluated in terms of their physical, thermal, and chemical properties. Microstructural analysis via scanning electron microscopy (SEM) was performed on the castables before and after slag attack. Compared to the other fabricated castables, the thermally treated alumina grog castables with zircon showed better physical properties, such as a higher bulk density, cold crushing strength, and modulus of rupture and a lower apparent porosity and water absorption. In addition, they had a higher positive linear thermal expansion, refractoriness under load, permanent linear change, and hot modulus of rupture. The results of the SEM with energy dispersive X-ray analysis of the prepared castables confirmed that the mullite and anorthite phases were predominant when zircon was not added and the zircon–mullite phase additionally appeared upon the incorporation of zircon. A quantitative elemental analysis via X-ray fluorescence spectroscopy was employed to determine the composition of the castables. X-ray diffraction analysis showed that the alumina grog castables had a high mullite and low anorthite content, and the thermally treated alumina grog had a high anorthite, low mullite, and high zircon content. The improvement in the mechanical and thermo-mechanical properties of the castables with thermally treated alumina grog and added zircon can be attributed to the formation of the zircon–mullite phase with a low mullite phase content.     

Feature selection methods for root-cause analysis among top-level product attributes

Manufacturing companies not only strive to deliver flawless products but also monitor product failures in the field to identify potential quality issues. When product failures occur, quality engineers must identify the root cause to improve any affected product and process. This root-cause analysis can be supported by feature selection methods that identify relevant product attributes, such as manufacturing dates with an increased number of product failures. In this paper, we present different methods for feature selection and evaluate their ability to identify relevant product attributes in a root-cause analysis. First, we compile a list of feature selection methods. Then, we summarize the properties of product attributes in warranty case data and discuss these properties regarding the challenges they pose for machine learning algorithms. Next, we simulate datasets of warranty cases, which emulate these product properties. Finally, we compare the feature selection methods based on these simulated datasets. In the end, the univariate filter information gain is determined to be a suitable method for a wide range of applications. The comparison based on simulated data provides a more general result than other publications, which only focus on a single use case. Due to the generic nature of the simulated datasets, the results can be applied to various root-cause analysis processes in different quality management applications and provide a guideline for readers who wish to explore machine learning methods for their analysis of quality data.     

Effects of feed composition,protein denaturation and storage of milk serum protein/lactose powders on lactosylation

During whey powder production, the feed is subjected to several heat treatments which can cause lactosylation of proteins. In this study, lactosylation of whey proteins was evaluated in spray-dried powders before and after storage by varying the native protein fraction as well as the serum protein/lactose ratio in the powders. The lactosylation of native α-lactalbumin and β-lactoglobulin in the powders before storage was not affected to a large extent by the protein denaturation or if the feed had been heat treated in a high or low lactose environment. After storage (relative humidity of 23.5%, 30 °C, 25 days), the kinetic of lactosylation tended to increase with increasing native protein fraction and bulk protein content in the powders. An explanation could be that proteins dissolved in the lactose glassy structure might have a lower reactivity, while proteins present in the protein glassy structure with dissolved lactose may display higher lactosylation reactivity.     

A review of phosphorescent and fluorescent phosphors for fingerprint detection

Currently, the efficient detection of fingerprints is essential for the crime investigations. Revealing fingerprints is commonly achieved with fluorescent organic compounds but they are not efficient for fingerprint detection on porous or reflective surfaces. In order to solve the problem of collecting fingerprints on porous/reflective surfaces, inorganic phosphors have been employed, since they have characteristics of variable color emission, afterglow, high chemical stability and nano-size, which allow the fingerprint detection on any porous or non-porous surfaces. Due to these last properties, this review presents a summary about the use of phosphorescent and fluorescent phosphors for the detection of latent fingerprints. First, we discussed the main physical and chemical characteristics of the fingerprints which permit their detection and collection from any surface. After this, we presented the main morphological, structural and luminescent properties of the phosphorescent and fluorescent phosphors that allow their use for fingerprint detection. Later, we demonstrated with pictures of fingerprints (with and without light emission from the phosphors deposited on them) that both, phosphorescent and fluorescent phosphors can be used to visualize fingerprints with high resolution and high contrast without interference of the background surface, which is ideal for its collection and registration in the Automated Fingerprint Identification System (AFIS). We believe that this review could be useful to understand how to select an appropriate phosphorescent or fluorescent material for fingerprint detection depending on the type of surface (porous or non-porous, reflective or not reflective) where the fingerprint is deposited.     

Multicriteria Minimax Problems: Localization of the Pareto Set and Suboptimal Control Design

Automation and Remote Control - We consider multicriteria minimax optimization problems with criteria in the form of the maxima of functionals given by the induced norms of linear operators taking...