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Summary: Background: Haploinsufficiency (HI) resulting from deletion of the long arm of chromosome 5 [del(5q)] and the accompanied loss of heterozygosity are likely key pathogenic factors in del(5q) myeloid neoplasia (MN) although the consequences of del(5q) have not been yet clarified. Methods: Here, we explored mutations, gene expression and clinical phenotypes of 388 del(5q) vs. 841 diploid cases with MN [82% myelodysplastic syndromes (MDS)]. Findings: Del(5q) resulted as founder (better prognosis) or secondary hit (preceded by TP53 mutations). Using Bayesian prediction analyses on 57 HI marker genes we established the minimal del(5q) gene signature that distinguishes del(5q) from diploid cases. Clusters of diploid cases mimicking the del(5q) signature support the overall importance of del(5q) genes in the pathogenesis of MDS in general. Sub-clusters within del(5q) patients pointed towards the inherent intrapatient heterogeneity of HI genes. Interpretation: The underlying clonal expansion drive results from a balance between the “HI-driver” genes (e.g., CSNK1A1, CTNNA1, TCERG1) and the proapoptotic “HI-anti-drivers” (e.g., RPS14, PURA, SIL1). The residual essential clonal expansion drive allows for selection of accelerator mutations such as TP53 (denominating poor) and CSNK1A1 mutations (with a better prognosis) which overcome pro-apoptotic genes (e.g., p21, BAD, BAX), resulting in a clonal expansion. In summary, we describe the complete picture of del(5q) MN identifying the crucial genes, gene clusters and clonal hierarchy dictating the clinical course of del(5q) patients. Funding: Torsten Haferlach Leukemia Diagnostics Foundation. US National Institute of Health (NIH) grants R35 HL135795, R01HL123904, R01 HL118281, R01 HL128425, R01 HL132071, and a grant from Edward P. Evans Foundation.

Solvents purification mainly used in pharmaceutical field such as acetone and methyl ethyl ketone (MEK) were performed through hybrid silica membranes and from binary and multi-components mixtures. Two hybrid silica membranes—zirconia doped bis(triethoxysilyl)methane and bis(triethoxysilyl)ethane (BTESE)—were studied. Flux, permeance, and separation factor were evaluated depending on temperature, composition, and number of organic compounds in the feed. Dehydration tests of acetone were operated at 30 and 45 °C following by acetone and MEK purification at 50 °C from multi-components hydro-organic mixtures where hydrophilic compounds (water, methanol) but also hydrophobic (dichloromethane (DCM) and/or toluene) were present. Results showed that the presence of Zr nanoparticles affected flux and improved selectivity in the case of dehydration. Experiments related to acetone and MEK purification, revealed a mass transfer alteration and a decrease of performance, from 99 to 97 wt% and from 98 to 95 wt% respectively, when the number of compounds in the initial feed grown up and more precisely, in the presence of DCM and toluene thus highlighting a possible coupling effect.

The regeneration of volatile organic solvents via dehydration tests, from 90 wt %, was evaluated by pervaporation using an on-line near-infrared (NIR) spectrometer. Experiments were performed using a bis(triethoxysilyl)methane (BTESM)-based ceramic HybSi® membrane at temperatures of 20, 30 and 40 °C. The presence of an on-line NIR allows continuous monitoring of the process without sampling, and quickly estimates mass fractions of species in the retentate. Dehydration tests were performed at 30 °C in order to confirm the on-line NIR reproducibility, and closely matched results obtained with an off-line densimeter. These results validated the usefulness of the on-line NIR and provided the same precision whatever the mass fraction in the retentate. A good on-line reproducibility was found, with an agreement between the on-line NIR and off-line densimeter, obtaining an average deviation of ±0.058 wt %, ±0.17 wt % and ±0.049 wt %, respectively, at 20, 30 and 40 °C.

Cyber-Physical System (CPS) is an integration of distributed sensor networks with computational devices. CPS claims many promising applications, such as traffic observation, battlefield surveillance, and sensor-network-based monitoring. One important topic in CPS research is about the atypical event analysis, that is, retrieving the events from massive sensor data and analyzing them with spatial, temporal, and other multidimensional information. Many traditional methods are not feasible for such analysis since they cannot describe the complex atypical events. In this paper, we propose a novel model of atypical cluster to effectively represent such events and efficiently retrieve them from massive data. The basic cluster is designed to summarize an individual event, and the macrocluster is used to integrate the information from multiple events. To facilitate scalable, flexible, and online analysis, the atypical cube is constructed, and a guided clustering algorithm is proposed to retrieve significant clusters in an efficient manner. We conduct experiments on real sensor datasets with the size of more than 50 GB; the results show that the proposed method can provide more accurate information with only 15% to 20% time cost of the baselines.