Sidewall depletion in nano-patterned LAO/STO heterostructures

We report the fabrication of nanostructures from the quasi-two-dimensional electron gas (q2DEG) formed at the LaAlO$_{3}$/ SrTiO$_{3}$ (LAO/STO) interface. The process uses electron beam lithography in combination with reactive ion etching. This technique allows to pattern high-quality structures down to lateral dimensions as small as $100$nm while maintaining the conducting properties without inducing conductivity in the STO substrate. Temperature dependent transport properties of patterned Hall bars of various widths show only a small size dependence of conductivity at low temperature as well as at room temperature. The deviation can be explained by a narrow lateral depletion region. All steps of the patterning process are fully industry compatible.Comment: 5 pages, 4 figure

The state of SQL-on-Hadoop in the cloud

Managed Hadoop in the cloud, especially SQL-on-Hadoop, has been gaining attention recently. On Platform-as-a-Service (PaaS), analytical services like Hive and Spark come preconfigured for general-purpose and ready to use. Thus, giving companies a quick entry and on-demand deployment of ready SQL-like solutions for their big data needs. This study evaluates cloud services from an end-user perspective, comparing providers including: Microsoft Azure, Amazon Web Services, Google Cloud, and Rackspace. The study focuses on performance, readiness, scalability, and cost-effectiveness of the different solutions at entry/test level clusters sizes. Results are based on over 15,000 Hive queries derived from the industry standard TPC-H benchmark. The study is framed within the ALOJA research project, which features an open source benchmarking and analysis platform that has been recently extended to support SQL-on-Hadoop engines. The ALOJA Project aims to lower the total cost of ownership (TCO) of big data deployments and study their performance characteristics for optimization. The study benchmarks cloud providers across a diverse range instance types, and uses input data scales from 1GB to 1TB, in order to survey the popular entry-level PaaS SQL-on-Hadoop solutions, thereby establishing a common results-base upon which subsequent research can be carried out by the project. Initial results already show the main performance trends to both hardware and software configuration, pricing, similarities and architectural differences of the evaluated PaaS solutions. Whereas some providers focus on decoupling storage and computing resources while offering network-based elastic storage, others choose to keep the local processing model from Hadoop for high performance, but reducing flexibility. Results also show the importance of application-level tuning and how keeping up-to-date hardware and software stacks can influence performance even more than replicating the on-premises model in the cloud.This work is partially supported by the Microsoft Azure for Research program, the European Research Council (ERC) under the EUs Horizon 2020 programme (GA 639595), the Spanish Ministry of Education (TIN2015-65316-P), and the Generalitat de Catalunya (2014-SGR-1051).Peer ReviewedPostprint (author's final draft

Ethanol production from Sorghum bicolor using both separate and simultaneous saccharification and fermentation in batch and fed batch systems

The objective of this work was to find the best combination of different experimental conditions during pre-treatment, enzymatic saccharification, detoxification of inhibitors and fermentation of Sorghum bicolor straw for ethanol production. The optimization of pre-treatment using different concentrations of dilute sulfuric acid, various temperatures and residence times was achieved at 121°C, 1% acid concentration, 60 min residence time and enzyme saccharification using cellulase (celluclast 1.5 L) and -glucosidase (Novozyme 188) at 50°C and pH 4.8 for 48 h. Different surfactants were used in order toincrease the monomeric sugar during enzymatic hydrolysis and it has been observed that the addition of these surfactants contributed significantly in cellulosic conversion but no effect was shown onhemicellulosic hydrolysis. Fermentability of hydrolyzate was tested using Saccharomyces cerevisiae Ethanol RedTM and it was observed that simultaneous saccharification and fermentation (SSF) with bothbatch and fed batch resulted in better ethanol yield as compared to separate hydrolysis and fermentation (SHF). Detoxification of furan during SHF facilitated reduction in fermentation time from 96to 48 h. 98.5% theoretical yield was achieved in SHF with detoxification experiment attaining an ethanol concentration and yield of 23.01 gL-1 and 0.115 gg-1 DM respectively. During the SSF batch and fed batch fermentation, the maximum yields of ethanol per gram of dry matter were 0.1257 and 0.1332 g respectively

Rare novel CYP2U1 and ZFYVE26 variants identified in two Pakistani families with spastic paraplegia

BAKGROUND: Hereditary Spastic paraplegias (HSPs) are a clinically and genetically heterogeneous group of degenerative disorders characterized by progressive spasticity and weakness of the lower limbs. This study aimed to identify causative gene variants in two unrelated consanguineous Pakistani families presented with 2 different forms of HSP. METHODS: Whole exome sequencing (WES) was performed in the two families and variants were validated by Sanger sequencing and segregation analysis. ANALYSIS: In family A, a homozygous pathogenic variant in ZFYVE26 was identified in one family. While in family B, a frameshift variant in CYP2U1 was identified in 4 affected individuals presented with clinical features of SPG56. Our study is the first report of ZFYVE26 mutations causing HSP in the Pakistani population and the second report of CYP2U1 in a Pakistani family. CONCLUSIONS: Our findings enhance the clinical and genetic variability associated with two rare autosomal recessive HSP genes, highlighting the complexity of HSPs. These findings further emphasize the usefulness of WES as a powerful diagnostic tool

Smart nanocrystals of artemether: fabrication, characterization, and comparative in vitro and in vivo antimalarial evaluation

YesArtemether (ARTM) is a very effective antimalarial drug with poor solubility and consequently low bioavailability. Smart nanocrystals of ARTM with particle size of 161±1.5 nm and polydispersity index of 0.172±0.01 were produced in <1 hour using a wet milling technology, Dena® DM-100. The crystallinity of the processed ARTM was confirmed using differential scanning calorimetry and powder X-ray diffraction. The saturation solubility of the ARTM nanocrystals was substantially increased to 900 µg/mL compared to the raw ARTM in water (145.0±2.3 µg/mL) and stabilizer solution (300.0±2.0 µg/mL). The physical stability studies conducted for 90 days demonstrated that nanocrystals stored at 2°C-8°C and 25°C were very stable compared to the samples stored at 40°C. The nanocrystals were also shown to be stable when processed at acidic pH (2.0). The solubility and dissolution rate of ARTM nanocrystals were significantly increased (P<0.05) compared to those of its bulk powder form. The results of in vitro studies showed significant antimalarial effect (P<0.05) against Plasmodium falciparum and Plasmodium vivax. The IC50 (median lethal oral dose) value of ARTM nanocrystals was 28- and 54-fold lower than the IC50 value of unprocessed drug and 13- and 21-fold lower than the IC50 value of the marketed tablets, respectively. In addition, ARTM nanocrystals at the same dose (2 mg/kg) showed significantly (P<0.05) higher reduction in percent parasitemia (89%) against P. vivax compared to the unprocessed (27%), marketed tablets (45%), and microsuspension (60%). The acute toxicity study demonstrated that the LD50 value of ARTM nanocrystals is between 1,500 mg/kg and 2,000 mg/kg when given orally. This study demonstrated that the wet milling technology (Dena® DM-100) can produce smart nanocrystals of ARTM with enhanced antimalarial activities

Malignant Mesothelioma subtyping via sampling driven multiple instance prediction on tissue image and cell morphology data

Malignant Mesothelioma is a difficult to diagnose and highly lethal cancer usually associated with asbestos exposure. It can be broadly classified into three subtypes: Epithelioid, Sarcomatoid, and a hybrid Biphasic subtype in which significant components of both of the previous subtypes are present. Early diagnosis and identification of the subtype informs treatment and can help improve patient outcome. However, the subtyping of malignant mesothelioma, and specifically the recognition of transitional features from routine histology slides has a high level of inter-observer variability. In this work, we propose an end-to-end multiple instance learning (MIL) approach for malignant mesothelioma subtyping. This uses an adaptive instance-based sampling scheme for training deep convolutional neural networks on bags of image patches that allows learning on a wider range of relevant instances compared to max or top-N based MIL approaches. We also investigate augmenting the instance representation to include aggregate cellular morphology features from cell segmentation. The proposed MIL approach enables identification of malignant mesothelial subtypes of specific tissue regions. From this a continuous characterisation of a sample according to predominance of sarcomatoid vs epithelioid regions is possible, thus avoiding the arbitrary and highly subjective categorisation by currently used subtypes. Instance scoring also enables studying tumor heterogeneity and identifying patterns associated with different subtypes. We have evaluated the proposed method on a dataset of 234 tissue micro-array cores with an AUROC of 0.89±0.05 for this task. The dataset and developed methodology is available for the community at: https://github.com/measty/PINS

MesoGraph: automatic profiling of mesothelioma subtypes from histological images

Mesothelioma is classified into three histological subtypes, epithelioid, sarcomatoid, and biphasic, according to the relative proportions of epithelioid and sarcomatoid tumor cells present. Current guidelines recommend that the sarcomatoid component of each mesothelioma is quantified, as a higher percentage of sarcomatoid pattern in biphasic mesothelioma shows poorer prognosis. In this work, we develop a dual-task graph neural network (GNN) architecture with ranking loss to learn a model capable of scoring regions of tissue down to cellular resolution. This allows quantitative profiling of a tumor sample according to the aggregate sarcomatoid association score. Tissue is represented by a cell graph with both cell-level morphological and regional features. We use an external multicentric test set from Mesobank, on which we demonstrate the predictive performance of our model. We additionally validate our model predictions through an analysis of the typical morphological features of cells according to their predicted score

MesoGraph: Automatic profiling of mesothelioma subtypes from histological images.

Mesothelioma is classified into three histological subtypes, epithelioid, sarcomatoid, and biphasic, according to the relative proportions of epithelioid and sarcomatoid tumor cells present. Current guidelines recommend that the sarcomatoid component of each mesothelioma is quantified, as a higher percentage of sarcomatoid pattern in biphasic mesothelioma shows poorer prognosis. In this work, we develop a dual-task graph neural network (GNN) architecture with ranking loss to learn a model capable of scoring regions of tissue down to cellular resolution. This allows quantitative profiling of a tumor sample according to the aggregate sarcomatoid association score. Tissue is represented by a cell graph with both cell-level morphological and regional features. We use an external multicentric test set from Mesobank, on which we demonstrate the predictive performance of our model. We additionally validate our model predictions through an analysis of the typical morphological features of cells according to their predicted score