SNE: Signed Network Embedding

Several network embedding models have been developed for unsigned networks. However, these models based on skip-gram cannot be applied to signed networks because they can only deal with one type of link. In this paper, we present our signed network embedding model called SNE. Our SNE adopts the log-bilinear model, uses node representations of all nodes along a given path, and further incorporates two signed-type vectors to capture the positive or negative relationship of each edge along the path. We conduct two experiments, node classification and link prediction, on both directed and undirected signed networks and compare with four baselines including a matrix factorization method and three state-of-the-art unsigned network embedding models. The experimental results demonstrate the effectiveness of our signed network embedding.Comment: To appear in PAKDD 201

MRI-based Surgical Planning for Lumbar Spinal Stenosis

The most common reason for spinal surgery in elderly patients is lumbar spinal stenosis(LSS). For LSS, treatment decisions based on clinical and radiological information as well as personal experience of the surgeon shows large variance. Thus a standardized support system is of high value for a more objective and reproducible decision. In this work, we develop an automated algorithm to localize the stenosis causing the symptoms of the patient in magnetic resonance imaging (MRI). With 22 MRI features of each of five spinal levels of 321 patients, we show it is possible to predict the location of lesion triggering the symptoms. To support this hypothesis, we conduct an automated analysis of labeled and unlabeled MRI scans extracted from 788 patients. We confirm quantitatively the importance of radiological information and provide an algorithmic pipeline for working with raw MRI scans

Post-operative outcomes and predictors of mortality after colorectal cancer surgery in the very elderly patients

Background: The frailty of the very elderly patients who undergo surgery for colorectal cancer negatively influences postoperative mortality. This study aimed to identify risk factors for postoperative mortality in octogenarian and nonagenarian patients who underwent surgical treatment for colorectal cancer. Methods: This is a single institution retrospective study. The primary outcomes were risk factors for postoperative mortality. The variables of the octogenarians and nonagenarians were compared by using t-test, chi-square test, and Fisher exact test. A multivariate logistic regression analysis was carried out on the combined cohorts. Results: we identified 319 octogenarians and 43 nonagenarians (N = 362) who underwent surgery for colorectal cancer at the Sant'Orsola-Malpighi university hospital in Bologna between 2011 and 2015. The 30-day post-operative mortality was 6% (N = 18) among octogenarians and 21% (N = 9) for the nonagenarians. The groups significantly differed in the type of surgery (elective vs. urgent surgery, p < 0.0001), ASA score (p = 0.0003) and rates of 30-day postoperative mortality (6% vs. 21%, p = 0.0003). In the multivariate analysis ASA > III (OR 2.37, 95% CI [1.43\u20133.93], p < 0,001), and urgent surgery (OR 2.17, 95% CI [1.17\u20134.04], p = 0.014) were associated to post-operative mortality. On the contrary, pre-operative albumin 653.4 g/dL (OR 0.14, 95% CI [0.05\u20130.52], p = 0.001) was associated with a protective effect on postoperative mortality. Conclusions: In the very elderly affected by colorectal cancer, preoperative nutritional status and pre-existing comorbidities, rather than age itself, should be considered as selection criteria for surgery. Preoperative improvement of nutritional status and ASA risk assessment may be beneficial for stratification of patients and ultimately for optimizing outcomes

Aerosol mass and black carbon concentrations, a two year record at NCO-P (5079 m, Southern Himalayas)

Aerosol mass and the absorbing fraction are important variables, needed to constrain the role of atmospheric particles in the Earth radiation budget, both directly and indirectly through CCN activation. In particular, their monitoring in remote areas and mountain sites is essential for determining source regions, elucidating the mechanisms of long range transport of anthropogenic pollutants, and validating regional and global models. Since March 2006, aerosol mass and black carbon concentration have been monitored at the Nepal Climate Observatory-Pyramid, a permanent high-altitude research station located in the Khumbu valley at 5079 m a.s.l. below Mt. Everest. The first two-year averages of PM<sub>1</sub> and PM<sub>1−10</sub> mass were 1.94 μg m<sup>−3</sup> and 1.88 μg m<sup>−3</sup>, with standard deviations of 3.90 μg m<sup>−3</sup> and 4.45 μg m<sup>−3</sup>, respectively, while the black carbon concentration average is 160.5 ng m<sup>−3</sup>, with a standard deviation of 296.1 ng m<sup>−3</sup>. Both aerosol mass and black carbon show well defined annual cycles, with a maximum during the pre-monsoon season and a minimum during the monsoon. They also display a typical diurnal cycle during all the seasons, with the lowest particle concentration recorded during the night, and a considerable increase during the afternoon, revealing the major role played by thermal winds in influencing the behaviour of atmospheric compounds over the high Himalayas. The aerosol concentration is subject to high variability: in fact, as well as frequent "background conditions" (55% of the time) when BC concentrations are mainly below 100 ng m<sup>−3</sup>, concentrations up to 5 μg m<sup>−3</sup> are reached during some episodes (a few days every year) in the pre-monsoon seasons. The variability of PM and BC is the result of both short-term changes due to thermal wind development in the valley, and long-range transport/synoptic circulation. At NCO-P, higher concentrations of PM<sub>1</sub> and BC are mostly associated with regional circulation and westerly air masses from the Middle East, while the strongest contributions of mineral dust arrive from the Middle East and regional circulation, with a special contribution from North Africa and South-West Arabian Peninsula in post-monsoon and winter season

BlinkML: Efficient Maximum Likelihood Estimation with Probabilistic Guarantees

The rising volume of datasets has made training machine learning (ML) models a major computational cost in the enterprise. Given the iterative nature of model and parameter tuning, many analysts use a small sample of their entire data during their initial stage of analysis to make quick decisions (e.g., what features or hyperparameters to use) and use the entire dataset only in later stages (i.e., when they have converged to a specific model). This sampling, however, is performed in an ad-hoc fashion. Most practitioners cannot precisely capture the effect of sampling on the quality of their model, and eventually on their decision-making process during the tuning phase. Moreover, without systematic support for sampling operators, many optimizations and reuse opportunities are lost. In this paper, we introduce BlinkML, a system for fast, quality-guaranteed ML training. BlinkML allows users to make error-computation tradeoffs: instead of training a model on their full data (i.e., full model), BlinkML can quickly train an approximate model with quality guarantees using a sample. The quality guarantees ensure that, with high probability, the approximate model makes the same predictions as the full model. BlinkML currently supports any ML model that relies on maximum likelihood estimation (MLE), which includes Generalized Linear Models (e.g., linear regression, logistic regression, max entropy classifier, Poisson regression) as well as PPCA (Probabilistic Principal Component Analysis). Our experiments show that BlinkML can speed up the training of large-scale ML tasks by 6.26x-629x while guaranteeing the same predictions, with 95% probability, as the full model.Comment: 22 pages, SIGMOD 201

Handheld Co-Axial Bioprinting: Application to in situ surgical cartilage repair

Three-dimensional (3D) bioprinting is driving major innovations in the area of cartilage tissue engineering. Extrusion-based 3D bioprinting necessitates a phase change from a liquid bioink to a semi-solid crosslinked network achieved by a photo-initiated free radical polymerization reaction that is known to be cytotoxic. Therefore, the choice of the photocuring conditions has to be carefully addressed to generate a structure stiff enough to withstand the forces phisiologically applied on articular cartilage, while ensuring adequate cell survival for functional chondral repair. We recently developed a handheld 3D printer called Biopen . To progress towards translating this freeform biofabrication tool into clinical practice, we aimed to define the ideal bioprinting conditions that would deliver a scaffold with high cell viability and structural stiffness relevant for chondral repair. To fulfill those criteria, free radical cytotoxicity was confined by a co-axial Core/Shell separation. This system allowed the generation of Core/Shell GelMa/HAMa bioscaffolds with stiffness of 200KPa, achieved after only 10seconds of exposure to 700mW/cm2 of 365nm UV-A, containing \u3e90% viable stem cells that retained proliferative capacity. Overall, the Core/Shell handheld 3D bioprinting strategy enabled rapid generation of high modulus bioscaffolds with high cell viability, with potential for in situ surgical cartilage engineering

An Exploratory Analysis of the Latent Structure of Process Data via Action Sequence Autoencoder

Computer simulations have become a popular tool of assessing complex skills such as problem-solving skills. Log files of computer-based items record the entire human-computer interactive processes for each respondent. The response processes are very diverse, noisy, and of nonstandard formats. Few generic methods have been developed for exploiting the information contained in process data. In this article, we propose a method to extract latent variables from process data. The method utilizes a sequence-to-sequence autoencoder to compress response processes into standard numerical vectors. It does not require prior knowledge of the specific items and human-computers interaction patterns. The proposed method is applied to both simulated and real process data to demonstrate that the resulting latent variables extract useful information from the response processes.Comment: 28 pages, 13 figure

In vitro biosafety profile evaluation of multipotent mesenchymal stem cells derived from the bone marrow of sarcoma patients.

BACKGROUND: In osteosarcoma (OS) and most Ewing sarcoma (EWS) patients, the primary tumor originates in the bone. Although tumor resection surgery is commonly used to treat these diseases, it frequently leaves massive bone defects that are particularly difficult to be treated. Due to the therapeutic potential of mesenchymal stem cells (MSCs), OS and EWS patients could benefit from an autologous MSCs-based bone reconstruction. However, safety concerns regarding the in vitro expansion of bone marrow-derived MSCs have been raised. To investigate the possible oncogenic potential of MSCs from OS or EWS patients (MSC-SAR) after expansion, this study focused on a biosafety assessment of MSC-SAR obtained after short- and long-term cultivation compared with MSCs from healthy donors (MSC-CTRL). METHODS: We initially characterized the morphology, immunophenotype, and differentiation multipotency of isolated MSC-SAR. MSC-SAR and MSC-CTRL were subsequently expanded under identical culture conditions. Cells at the early (P3/P4) and late (P10) passages were collected for the in vitro analyses including: the sequencing of genes frequently mutated in OS and EWS, evaluation of telomerase activity, assessment of the gene expression profile and activity of major cancer pathways, cytogenetic analysis on synchronous MSC, and molecular karyotyping using a comparative genomic hybridization (CGH) array. RESULTS: MSC-SAR displayed comparable morphology, immunophenotype, proliferation rate, differentiation potential, and telomerase activity to MSC-CTRL. Both cell types displayed signs of senescence in the late stages of culture with no relevant changes in cancer gene expression. However, cytogenetic analysis detected chromosomal anomalies in the early and late stages of MSC-SAR and MSC-CTRL after culture. CONCLUSIONS: Our results demonstrated that the in vitro expansion of MSC does not influence or favor malignant transformation since MSC-SAR were not more prone than MSC-CTRL to deleterious changes during culture. However, the presence of chromosomal aberrations supports rigorous phenotypic, functional and genetic evaluation of the biosafety of MSCs, which is important for clinical applications