A Uncertainty Perspective on Qualitative Preference

Collaborative filtering has been successfully applied for predicting a person\u27s preference on an item, by aggregating community preference on the item. Typically, collaborative filtering systems are based on based on quantitative preference modeling, which requires users to express their preferences in absolute numerical ratings. However, quantitative user ratings are known to be biased and inconsistent and also significantly more burdensome to the user than the alternative qualitative preference modeling, requiring only to specify relative preferences between the item pair. More specifically, we identify three main components of collaborative filtering-- preference representation, aggregation, and similarity computation, and view each component from a qualitative perspective. From this perspective, we build a framework, which collects only qualitative feedbacks from users. Our rating-oblivious framework was empirically validated to have comparable prediction accuracies to an (impractical) upper bound accuracy obtained by collaborative filtering system using ratings

Carbon Dioxide-Catalyzed Stereoselective Cyanation Reaction

© 2019 American Chemical Society.We report a Michael-type cyanation reaction of coumarins by using CO2 as a catalyst. The delivery of the nucleophilic cyanide was realized by catalytic amounts of CO2, which forms cyanoformate and bicarbonate in the presence of water. Under ambient conditions, CO2-catalyzed reactions afforded high chemo- A nd diastereoselectivity of β-nitrile carbonyls, whereas only low reactivities were observed under argon or N2. Computational and experimental data suggest the catalytic role of CO2, which functions as a Lewis acid, and a protecting group to mask the reactivity of the product, suppressing byproducts and polymerization. The utility of this convenient method was demonstrated by preparing biologically relevant heterocyclic compounds with ease11sciescopu

Who Are Less Likely to Receive Subsequent Chemotherapy Beyond First-Line Therapy for Advanced Non-small Cell Lung Cancer?: Implications for Selection of Patients for Maintenance Therapy

BackgroundProspective studies have implied that maintenance therapy for non-small cell lung cancer (NSCLC) has its effect by giving active drugs earlier to patients who otherwise die without receiving second-line therapy. The purpose of this study was to select patients with NSCLC who could most benefit from maintenance therapy, by evaluating which patients would be less likely to receive second-line therapy.MethodsClinicopathologic data of patients with advanced NSCLC who received four cycles of first-line chemotherapy followed by time-off therapy and eventual disease progression or death were reviewed retrospectively. Patients were grouped into ones with first-line therapy only or ones with more than first-line therapy. Clinical characteristics between the two groups were compared.ResultsA total of 271 patients were eligible for analysis, and 39 patients (14.4%) received only first-line therapy. Patients significantly more likely to receive only first-line therapy had performance status of two or three after first-line therapy, large volume of initial target lesions (sum of long diameters ≥70 mm), or smaller decrease in target lesions (decrease <20%) after first-line therapy. Median overall survival of the 143 patients (52.8%) with at least one of these characteristics (16.3 months) was significantly shorter than that of patients without any of these characteristics (23.5 months, p = 0.007).ConclusionMaintenance therapy may be of greater benefit to patients with NSCLC who have clinical characteristics including poor performance status after first-line therapy, large initial target lesions, or smaller decrease in target lesions after first-line therapy

A graphene-based physiometer array for the analysis of single biological cells

A significant advantage of a graphene biosensor is that it inherently represents a continuum of independent and aligned sensor-units. We demonstrate a nanoscale version of a micro-physiometer – a device that measures cellular metabolic activity from the local acidification rate. Graphene functions as a matrix of independent pH sensors enabling subcellular detection of proton excretion. Raman spectroscopy shows that aqueous protons p-dope graphene – in agreement with established doping trajectories, and that graphene displays two distinct pKa values (2.9 and 14.2), corresponding to dopants physi- and chemisorbing to graphene respectively. The graphene physiometer allows micron spatial resolution and can differentiate immunoglobulin (IgG)-producing human embryonic kidney (HEK) cells from non-IgG-producing control cells. Population-based analyses allow mapping of phenotypic diversity, variances in metabolic activity, and cellular adhesion. Finally we show this platform can be extended to the detection of other analytes, e.g. dopamine. This work motivates the application of graphene as a unique biosensor for (sub)cellular interrogation.National Cancer Institute (U.S.) (Cancer Center Support (Core) Grant P30-CA14051)U.S. Army Research LaboratoryUnited States. Army Research Office. Institute for Soldier Nanotechnologies (Contract W911NF-13-D-0001)National Institute for Biomedical Imaging and Bioengineering (U.S.) (Grant P41EB015871-27)Skolkovo Institute of Science and Technolog

Pharmacogenomic profiling reveals molecular features of chemotherapy resistance in IDH wild-type primary glioblastoma

Background Although temozolomide (TMZ) has been used as a standard adjuvant chemotherapeutic agent for primary glioblastoma (GBM), treating isocitrate dehydrogenase wild-type (IDH-wt) cases remains challenging due to intrinsic and acquired drug resistance. Therefore, elucidation of the molecular mechanisms of TMZ resistance is critical for its precision application. Methods We stratified 69 primary IDH-wt GBM patients into TMZ-resistant (n = 29) and sensitive (n = 40) groups, using TMZ screening of the corresponding patient-derived glioma stem-like cells (GSCs). Genomic and transcriptomic features were then examined to identify TMZ-associated molecular alterations. Subsequently, we developed a machine learning (ML) model to predict TMZ response from combined signatures. Moreover, TMZ response in multisector samples (52 tumor sectors from 18 cases) was evaluated to validate findings and investigate the impact of intra-tumoral heterogeneity on TMZ efficacy. Results In vitro TMZ sensitivity of patient-derived GSCs classified patients into groups with different survival outcomes (P = 1.12e−4 for progression-free survival (PFS) and 3.63e−4 for overall survival (OS)). Moreover, we found that elevated gene expression of EGR4, PAPPA, LRRC3, and ANXA3 was associated to intrinsic TMZ resistance. In addition, other features such as 5-aminolevulinic acid negative, mesenchymal/proneural expression subtypes, and hypermutation phenomena were prone to promote TMZ resistance. In contrast, concurrent copy-number-alteration in PTEN, EGFR, and CDKN2A/B was more frequent in TMZ-sensitive samples (Fishers exact P = 0.0102), subsequently consolidated by multi-sector sequencing analyses. Integrating all features, we trained a ML tool to segregate TMZ-resistant and sensitive groups. Notably, our method segregated IDH-wt GBM patients from The Cancer Genome Atlas (TCGA) into two groups with divergent survival outcomes (P = 4.58e−4 for PFS and 3.66e−4 for OS). Furthermore, we showed a highly heterogeneous TMZ-response pattern within each GBM patient usingin vitro TMZ screening and genomic characterization of multisector GSCs. Lastly, the prediction model that evaluates the TMZ efficacy for primary IDH-wt GBMs was developed into a webserver for public usage (http://www.wang-lab-hkust.com:3838/TMZEP) Conclusions We identified molecular characteristics associated to TMZ sensitivity, and illustrate the potential clinical value of a ML model trained from pharmacogenomic profiling of patient-derived GSC against IDH-wt GBMs

코드검색시스템을 위한 고차원 색인기법 설계

DoctorThis research addresses the problem of supporting scalable code similarity search systems for large-scale software repositories. While there are commercial code search engines available, they treat software as text and often fail to find semantically related code. Meanwhile, existing tools for semantic code clone searches take a “post-mortem” approach involving the detection of clones “after” the code development is completed, and hence, fail to return the results instantly. In clear contrast, the goal of this research is to combine the strength of these two lines of existing research.To achieve this goal, an indexing structure on vector abstractions of code is proposed. This index utilizes dimension reduction techniques to efficiently deal with the vector abstractions, which are naturally high-dimensional. This search system is then integrated into real-world development sessions. Such integration suggests that, by posing every code segment as a query to the software code corpus, developers can instantly reference relevant code segments at the time of generation to enhance productivity. This integration scenario creates the need for efficient similarity searches with the following requirements. First, a developer session translates into a sequence of evolving queries that need to be efficiently supported. Second, the quality of the results needs to be controlled, e.g., dealing with licenses requires that there be no false negatives. To satisfy these requirements, a workload-aware striping framework for high-dimensional evolving queries is proposed. This framework can be used to boost most existing high-dimensional indexes. In addition, to further enhance the scalability of code search systems, a workload-balancing distributed indexing structure is proposed. The goal of existing efforts in distributed indexing has been the localization of queries to data residing at a small number of nodes (i.e., locality-preserving indexing) to minimize communication cost. However, considering that workloads often correlate with data locality, such indexing often generates hotspots. Hence, workload-balancing is proposed as an optimization goal, and a distributed index that evenly distributes the workload is presented

Spatial Skyline Queries: An Efficient Geometric Algorithm

As more data-intensive applications emerge, advanced retrieval semantics, such as ranking and skylines, have attracted attention. Geographic information systems are such an application with massive spatial data. Our goal is to efficiently support skyline queries over massive spatial data. To achieve this goal, we first observe that the best known algorithm VS(2), despite its claim, may fail to deliver correct results. In contrast, we present a simple and efficient algorithm that computes the correct results. To validate the effectiveness and efficiency of our algorithm, we provide an extensive empirical comparison of our algorithm and VS(2) in several aspects.1111sciescopu

Aldehyde Carboxylation: A Concise DFT Mechanistic Study and a Hypothetical Role of CO (2) in the Origin of Life

Carbon dioxide is arguably one of the most stable carbon-based molecules, yet enzymatic carbon fixation processes enabled the sustainable life cycle on Earth. Chemical reactions involving CO (2) -functionalization often suffer from low efficiency with highly reactive substrates. We recently reported mild carboxylation of aldehydes to furnish -keto acids - a building block for chiral -amino acids via reductive amination. Here, we discuss potential reaction mechanisms of aldehyde carboxylation reactions based on two promoters: NHCs and KCN in the carboxylation reaction. New DFT mechanistic studies suggested a lower reaction barrier for a CO (2) -functionalization step, implying a potential role of CO (2) in prebiotic evolution of organic molecules in the primordial soup. 1 Introduction: Aldehydes, Benzoins, Carboxylic Acids 2 CO (2) -Activation: NHC, Cyanide, Lewis Acid and Water 3 A Breslow Intermediate: Benzoin Reaction vs. Carboxylation with CO (2) 4 Carboxylation in the Primordial Soup 5 Conclusio © Georg Thieme Verlag Stuttgart · New Yor