Application of microwave digestion for complete dissolution of igneous silicate rock samples: A simple and quick sample preparation procedure.

In the area of geochemical analyses of rock solutions, achieving a complete sample dissolution is a fundamental prerequisite for obtaining accurate, precise and reliable analytical results. The challenge posed by the presence of resistant minerals such as zircon, rutile, corundum, spinel, tourmaline, beryl, chromite, and cassiterite in different silicate rocks is a well-recognized challenge in geological studies. These minerals, due to their resilient nature, demand additional efforts to ensure complete dissolution during sample preparation. The prevailing conventional sample digestion methods require several days of laboratory work and the handling of large amounts of multiple types of acids, which also increase sample blanks. Until recently, there was a widely held belief that microwave-assisted digestion, where microwave radiation is transformed to heat, faced limitations in achieving complete dissolution of refractory minerals. This prevailing opinion led to skepticism about the applicability of microwave-assisted digestion for sample preparation of e.g. igneous rock samples containing these minerals. This study introduces a novel, universal and quick closed-vessel (pressurized) high-temperature microwave-assisted digestion method appropriate for dissolution of all major types of igneous silicate rock samples, including rocks containing refractory minerals. This streamlined and expeditious procedure, comprising three steps, requires only a total time of ∼9 h. The method proves its versatility by successfully dissolving both, mafic igneous samples (e.g., basalt) with low-content of resistant minerals, and felsic igneous samples (e.g., granite) with relatively high-content of resistant minerals. To validate the reliability of this procedure, 36 trace elements were analyzed: Li, Be, Sc, V, Cr, Co, Ni, Cu, Zn, Ga, Rb, Sr, Y, Zr, Nb, Cs, Ba, La, Ce, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, Lu, Hf, Ta, Pb, Th and U in several geological Certified Reference Materials (CRMs). The CRMs including basalts JB-3, BCR-2, BHVO-2; andesites JA-2, AGV-2; granodiorite GSP-2; granite JG-2 and alkaline granite MGL-OShBO, were digested and analyzed using triple quadrupole Inductively Coupled-Plasma-Mass Spectrometer (ICP-QQQ). The results of the analysis demonstrate remarkable consistency, closely aligning with both certified and literature values.

Case report: ALK-positive histiocytosis presented as bilateral synchronous breast masses with long-term remission on crizotinib.

ALK-positive histiocytosis (APH) is a rare type of histiocytic neoplasm with characteristic ALK (Anaplastic Lymphoma Kinase) gene translocation and fusion, with only 27 reported cases in the literature. In this study, we report the first case of synchronous bilateral breast involvement of ALK-positive histiocytosis on initial presentation in a 46-year-old Hispanic woman. APH was diagnosed by the confirmation of clonal histiocyte proliferation with ALK overexpression on IHC and the presence of KIF5B-ALK gene fusion from her breast and lung biopsies. The patient in our study is currently under complete and long-term remission with crizotinib treatment (an ALK inhibitor). This report expands on the clinical manifestation of APH, emphasizes the importance of ALK detection in histiocytic diseases, and provides the efficacy and long-term prognosis of the ALK inhibitor therapy for APH.

Slow magnetic relaxation in a ferromagnetic CuII chain complex, induced by a phonon bottleneck effect.

The first slow magnetic relaxation in a ferromagnetic Cu(II) chain compound, Cu(dipic)(OH2)2 (dipicH2 = pyridine-2,6-dicarboxylic acid), induced by a phonon bottleneck effect under a magnetic field of 0.6 T, with a relaxation time of 2.2 s at 2.8 K, was observed.

Thermal, structural, optical, and photon shielding studies of cerium-doped barium tellurite glasses.

A series of tellurite-based glasses are prepared by using a melt-quenching method. The effect of cerium on the physical, thermal, structural, optical, spectroscopic, and shielding properties of barium tellurite glass samples is studied. It has been observed that the thermal stability factor increases with increasing cerium ion (Ce3+ ) concentration. The density and other physical parameters such as ion concentration and molar volume are calculated using the Archimedes principle. An increase in optical band gap and density suggests a decrement in non-bridging oxygens. These results are in accordance with Raman results. The blue emission in prepared glasses is studied in terms of International Commission on Illumination chromaticity coordinates. Moreover, various shielding properties such as mass attenuation coefficient, linear attenuation coefficient, effective atomic number, half-value layer, and tenth-value layer have also been determined to understand the photon shielding characteristics of as-prepared glass samples.

Structural and luminescence studies of titanium co-doped SrY2 O4 :Eu phosphors.

Here, europium-doped (1 to 11 mol%) and titanium (1 to 5 mol%)co-doped SrY2 O4 :Eu phosphors were synthesized using solid-state reaction method. The synthesized samples were characterized using X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), Fourier transform infrared spectroscopy (FTIR), and photoluminescence (PL) techniques for structural, morphological, functional group, and photoluminescence studies, respectively. XRD patterns confirmed the formation of pure phase SrY2 O4 at 1300°C and structural parameters were further determined using Rietveld refinement. FESEM micrographs revealed that doped and co-doped samples had different morphological features. All the samples were excited at ultraviolet light excitation and emission spectra consisted of peaks corresponding to the Eu ions. The maximum PL intensity was observed for 9 mol% of Eu ions and 3 mol% co-doping of Ti ions. The synthesized phosphors have potential applications in optoelectronics and display devices.

Lightweight deep convolutional neural network for background sound classification in speech signals.

Recognizing background information in human speech signals is a task that is extremely useful in a wide range of practical applications, and many articles on background sound classification have been published. It has not, however, been addressed with background embedded in real-world human speech signals. Thus, this work proposes a lightweight deep convolutional neural network (CNN) in conjunction with spectrograms for an efficient background sound classification with practical human speech signals. The proposed model classifies 11 different background sounds such as airplane, airport, babble, car, drone, exhibition, helicopter, restaurant, station, street, and train sounds embedded in human speech signals. The proposed deep CNN model consists of four convolution layers, four max-pooling layers, and one fully connected layer. The model is tested on human speech signals with varying signal-to-noise ratios (SNRs). Based on the results, the proposed deep CNN model utilizing spectrograms achieves an overall background sound classification accuracy of 95.2% using the human speech signals with a wide range of SNRs. It is also observed that the proposed model outperforms the benchmark models in terms of both accuracy and inference time when evaluated on edge computing devices.

Low resolution thermal imaging dataset of sign language digits.

The dataset contains low resolution thermal images corresponding to various sign language digits represented by hand and captured using the Omron D6T thermal camera. The resolution of the camera is 32 × 32 pixels. Because of the low resolution of the images captured by this camera, machine learning models for detecting and classifying sign language digits face additional challenges. Furthermore, the sensor's position and quality have a significant impact on the quality of the captured images. In addition, it is affected by external factors such as the temperature of the surface in comparison to the temperature of the hand. The dataset consists of 3200 images corresponding to ten sign digits, 0-9. Thus, each sign language digit consists of 320 images collected from different persons. The hand is oriented in various ways to capture all of the variations in the dataset.

Updating thermal imaging dataset of hand gestures with unique labels.

An update to the previously published low resolution thermal imaging dataset is presented in this paper. The new dataset contains high resolution thermal images corresponding to various hand gestures captured using the FLIR Lepton 3.5 thermal camera and Purethermal 2 breakout board. The resolution of the camera is 160 × 120 with calibrated array of 19,200 pixels. The images captured by the thermal camera are light-independent. The dataset consists of 14,400 images with equal share from color and gray scale. The dataset consists of 10 different hand gestures. Each gesture has a total of 24 images from a single person with a total of 30 persons for the whole dataset. The dataset also contains the images captured under different orientations of the hand under different lighting conditions.

Causal theory on acceleration of seed germination in the vicinity of high voltage direct current transmission line.

Seed germination is the primary stage of growth in a seed. A wealth of experiments exist in literature to support the existence of correlation between seed germination to the electric and magnetic fields. This becomes more important as researchers have suggested to develop technologies to build ecologically clean and environment-friendly solutions to agricultural practices. Although the literature supports the existence of seed germination acceleration, the lack of a definite causal theory has been observed by numerous researchers over decades. After considering all the existing experimental data, we have formulated a causal theory to explain the factors influencing seed germination around high voltage DC transmission lines. This work opens new avenues of research in this field.

Detection of ventricular arrhythmia using hybrid time-frequency-based features and deep neural network.

Sudden cardiac death (SCD) is a major cause of death among patients with heart diseases. It occurs mainly due to ventricular tachyarrhythmia (VTA) which includes ventricular tachycardia (VT) and ventricular fibrillation (VF) conditions. The main challenging task is to predict the VTA condition at a faster rate and timely application of automatic external defibrillator (AED) for saving lives. In this study, a VF/VT classification scheme has been proposed using a deep neural network (DNN) approach using hybrid time-frequency-based features. Two annotated public domain ECG databases (CUDB and VFDB) were used as training, test, and validation of datasets. The main motivation of this study was to implement a deep learning model for the classification of the VF/VT conditions and compared the results with other standard machine learning algorithms. The signal is decomposed with the wavelet transform, empirical mode decomposition (EMD) and variable mode decomposition (VMD) approaches and twenty-four are extracted to form a hybrid model from a window of length 5 s length. The DNN classifier achieved an accuracy (Acc) of 99.2%, sensitivity (Se) of 98.8%, and specificity (Sp) of 99.3% which is comparatively better than the results of the standard classifier. The proposed algorithm can detect VTA conditions accurately, hence could reduce the rate of misinterpretations by human experts and improves the efficiency of cardiac diagnosis by ECG signal analysis.

Low temperature synthesis of NbC/C nano-composites as visible light photoactive catalyst.

A facile carbothermal route was adopted to obtain niobium carbide nanoparticles (NPs) embedded in carbon network from Nb2O5 to study photocatalytic behavior. Optimization of synthesis parameters to obtain single phase NbC NPs has been successfully done. The phase identification, morphology and nature of carbon were determined with the help of X-ray diffraction, transmission electron microscopy (TEM) and Raman spectroscopy. X-ray photoelectron spectroscopy (XPS) suggested the presence of multiple oxidation states of Nb associated to NbC and NbCxOy centers on the surface of NPs. Due to the presence of NbCxOy on the surface of NPs, absorption under visible region of EM spectrum has been observed by UV-visible spectroscopy. Different organic dyes (RhB, MB and MO) were used to study the effect of holding time on the photocatalytic performance of as-synthesized samples. RhB dye was found to be the most sensitive organic molecule among all the considered dyes and degraded 78% in 120 min.

A Nasal Brush-based Classifier of Asthma Identified by Machine Learning Analysis of Nasal RNA Sequence Data.

Asthma is a common, under-diagnosed disease affecting all ages. We sought to identify a nasal brush-based classifier of mild/moderate asthma. 190 subjects with mild/moderate asthma and controls underwent nasal brushing and RNA sequencing of nasal samples. A machine learning-based pipeline identified an asthma classifier consisting of 90 genes interpreted via an L2-regularized logistic regression classification model. This classifier performed with strong predictive value and sensitivity across eight test sets, including (1) a test set of independent asthmatic and control subjects profiled by RNA sequencing (positive and negative predictive values of 1.00 and 0.96, respectively; AUC of 0.994), (2) two independent case-control cohorts of asthma profiled by microarray, and (3) five cohorts with other respiratory conditions (allergic rhinitis, upper respiratory infection, cystic fibrosis, smoking), where the classifier had a low to zero misclassification rate. Following validation in large, prospective cohorts, this classifier could be developed into a nasal biomarker of asthma.

Influence of CuO/MgO ratio on the gene expression, cytocompatibilty, and antibacterial/anticancerous/analgesic drug loading kinetics for (15-x) CuO-xMgO-10P2 O5 -60SiO2 -10CaO-5ZnO (2.5 ≤ x ≤ 12.5) mesoporous bioactive glasses.

In the present study, novel mesoporous bioactive glasses (MBGs) (15-x)CuO-xMgO-10P2 O5 -60SiO2 -10CaO-5ZnO (2.5 ≤ x ≤ 12.5, varying in steps of 2) are synthesized using the sol-gel technique. The structural phases of the glasses/glass ceramics were studied by XRD. The pH variation and simulated body fluids (SBF) studies demonstrated the in-vitro bioactivity of all the MBGs. MBGs possess surface area variation between 98.22 and 442.41 cm2 /g. The pore size of MBGs lies in the range of 5.8-8.8 nm. The cytotoxicity assays were conducted for MG63 human osteosarcoma cell line depicting non-toxic behavior of all MBGs at 7.8125 µg/ml. In addition to this, the effect of the magnesium on the gene expression was also investigated using reverse transcription-polymerase chain reaction (RT-qPCR). The MBGs were loaded with the antibacterial (vancomycin/amoxicillin), anticancerous (doxorubicin), and analgesic (Iburofen) drugs. Ibuprofen and amoxicillin drugs were almost fully loaded in all the MBGs, whereas doxorubicin and vancomycin drugs illustrated variation in loading with decreasing copper content. © 2018 Wiley Periodicals, Inc. J Biomed Mater Res Part A: 106A: 2116-2130, 2018.

Using machine learning to identify air pollution exposure profiles associated with early cognitive skills among U.S. children.

Data-driven machine learning methods present an opportunity to simultaneously assess the impact of multiple air pollutants on health outcomes. The goal of this study was to apply a two-stage, data-driven approach to identify associations between air pollutant exposure profiles and children's cognitive skills. Data from 6900 children enrolled in the Early Childhood Longitudinal Study, Birth Cohort, a national study of children born in 2001 and followed through kindergarten, were linked to estimated concentrations of 104 ambient air toxics in the 2002 National Air Toxics Assessment using ZIP code of residence at age 9 months. In the first-stage, 100 regression trees were learned to identify ambient air pollutant exposure profiles most closely associated with scores on a standardized mathematics test administered to children in kindergarten. In the second-stage, the exposure profiles frequently predicting lower math scores were included within linear regression models and adjusted for confounders in order to estimate the magnitude of their effect on math scores. This approach was applied to the full population, and then to the populations living in urban and highly-populated urban areas. Our first-stage results in the full population suggested children with low trichloroethylene exposure had significantly lower math scores. This association was not observed for children living in urban communities, suggesting that confounding related to urbanicity needs to be considered within the first-stage. When restricting our analysis to populations living in urban and highly-populated urban areas, high isophorone levels were found to predict lower math scores. Within adjusted regression models of children in highly-populated urban areas, the estimated effect of higher isophorone exposure on math scores was -1.19 points (95% CI -1.94, -0.44). Similar results were observed for the overall population of urban children. This data-driven, two-stage approach can be applied to other populations, exposures and outcomes to generate hypotheses within high-dimensional exposure data.

Thermal and structural studies of carbon coated Mo2C synthesized via in-situ single step reduction-carburization.

Carbon coated nano molybdenum carbide (Mo2C) has been synthesized at 800 °C through single step reduction route using molybdenum trioxide (MoO3) as a precursor, polypropylene (P.P) as a carbon source and magnesium (Mg) as a catalyst in an autoclave. The synthesized samples were characterized by X-ray diffraction (XRD), thermal analysis techniques (TG/DTA/DTG), field emission scanning electron microscopy (FESEM) and transmission electron microscopy (TEM). Williamson- Hall (W-H) analysis has been done to estimate various parameters like strain, stress and strain energy density. Multi-stage kinetic analysis of the product phase has been studied to establish the nature of the thermal decomposition. Coats-Redfern method applied to determine the mechanism involved in the decomposition of the product phase shows that initial and final stage follow F1 mechanism whereas middle stage follow F3 mechanism. The activation energy (E a) and pre-exponential factor (A) has also been determined. The morphological studies shows that the particles have partially spherical/faceted shape, with carbon coated having wide particle size distribution. The surface chemistry and surface area analysis were studied by X-ray photoelectron spectroscopy (XPS) and Brunauer-Emmet-Teller (BET), respectively. The formation mechanism of carbon coated Mo2C nano particles has been predicted based on the XRD, TG/DTA & DTG and microstructural results.

Effect of strontium substitution on the cytocompatibility and 3-D scaffold structure for the xSrO-(10-x) MgO-60SiO2-20CaO-10 P2O5 (2 ≤ x ≤ 8) sol-gel glasses.

In the present study, novel glasses xSrO-(10-x) MgO-60SiO2-20CaO-10 P2O5 (2 ≤ x ≤ 8, in steps of 2) are synthesized via sol-gel method. The current work focusses on the evaluation of mechanical, physical and biocompatible properties for sol-gel glasses. The pore size and surface area of these glasses were studied using BET analysis. The structural aspect of the glasses/glass ceramics was studied by XRD and Raman spectroscopy. The cytotoxicity assays were conducted for MG63 human osteosarcoma cell line. Furthermore, the as prepared glasses were used for the fabrication of 3-D porous scaffolds via polymer replication method. The loaded green bodies have been sintered at 700, 800 and 900 °C and were kept for 6 h to densify the glass network. The effect of sintering temperature on the structure and properties of as prepared scaffolds were analyzed via scanning electron microscopy (SEM) and porosity calculations.

Antibacterial and anticancerous drug loading kinetics for (10-x)CuO-xZnO-20CaO-60SiO2-10P2O5 (2 ≤ x ≤ 8) mesoporous bioactive glasses.

In the present study, antibacterial and anticancerous drug loading kinetics for the (10-x)CuO-xZnO-20CaO-60SiO2-10P2O5 (2≤x≤8, varying in steps of 2) mesoporous bioactive glasses (MBGs) have been studied. XRD analysis of the as prepared glass samples proved its amorphous nature. Scanning electron microscopy (SEM) revealed the apatite layer formation on the surface of the MBGs after soaking for 15 days in SBF. Ion dissolution studies of calcium, phosphorous and silicon have been performed using inductively coupled plasma (ICP). FTIR and Raman analysis depicted about the presence of various bonds and groups present in the glasses. The pore size of MBGs lies in the range of 4.2-9.7 nm. Apart from this, specific surface area of the MBGs varied from 263 to 402 cm2/g. The MBGs were loaded with Doxorubicin (DOX), Vancomycin (VANCO) and Tetracycline (TETRA) drugs among which, the decreasing copper content influenced the loading properties of doxorubicin and tetracycline drugs. Vancomycin was fully loaded almost in all the MBGs, whereas other drugs depicted varying loading with respect to the copper content.

Predicting protein function and other biomedical characteristics with heterogeneous ensembles.

Prediction problems in biomedical sciences, including protein function prediction (PFP), are generally quite difficult. This is due in part to incomplete knowledge of the cellular phenomenon of interest, the appropriateness and data quality of the variables and measurements used for prediction, as well as a lack of consensus regarding the ideal predictor for specific problems. In such scenarios, a powerful approach to improving prediction performance is to construct heterogeneous ensemble predictors that combine the output of diverse individual predictors that capture complementary aspects of the problems and/or datasets. In this paper, we demonstrate the potential of such heterogeneous ensembles, derived from stacking and ensemble selection methods, for addressing PFP and other similar biomedical prediction problems. Deeper analysis of these results shows that the superior predictive ability of these methods, especially stacking, can be attributed to their attention to the following aspects of the ensemble learning process: (i) better balance of diversity and performance, (ii) more effective calibration of outputs and (iii) more robust incorporation of additional base predictors. Finally, to make the effective application of heterogeneous ensembles to large complex datasets (big data) feasible, we present DataSink, a distributed ensemble learning framework, and demonstrate its sound scalability using the examined datasets. DataSink is publicly available from https://github.com/shwhalen/datasink.

A review of bioactive glasses: Their structure, properties, fabrication and apatite formation.

Bioactive glass and glass-ceramics are used in bone repair applications and are being developed for tissue engineering applications. Bioactive glasses/Bioglass are very attractive materials for producing scaffolds devoted to bone regeneration due to their versatile properties, which can be properly designed depending on their composition. An important feature of bioactive glasses, which enables them to work for applications in bone tissue engineering, is their ability to enhance revascularization, osteoblast adhesion, enzyme activity and differentiation of mesenchymal stem cells as well as osteoprogenitor cells. An extensive amount of research work has been carried out to develop silicate, borate/borosilicate bioactive glasses and phosphate glasses. Along with this, some metallic glasses have also been investigated for biomedical and technological applications in tissue engineering. Many trace elements have also been incorporated in the glass network to obtain the desired properties, which have beneficial effects on bone remodeling and/or associated angiogenesis. The motivation of this review is to provide an overview of the general requirements, composition, structure-property relationship with hydroxyapatite formation and future perspectives of bioglasses.Attention has also been given to developments of metallic glasses and doped bioglasses along with the techniques used for their fabrication.