Automated Big Text Security Classification

In recent years, traditional cybersecurity safeguards have proven ineffective against insider threats. Famous cases of sensitive information leaks caused by insiders, including the WikiLeaks release of diplomatic cables and the Edward Snowden incident, have greatly harmed the U.S. government's relationship with other governments and with its own citizens. Data Leak Prevention (DLP) is a solution for detecting and preventing information leaks from within an organization's network. However, state-of-art DLP detection models are only able to detect very limited types of sensitive information, and research in the field has been hindered due to the lack of available sensitive texts. Many researchers have focused on document-based detection with artificially labeled "confidential documents" for which security labels are assigned to the entire document, when in reality only a portion of the document is sensitive. This type of whole-document based security labeling increases the chances of preventing authorized users from accessing non-sensitive information within sensitive documents. In this paper, we introduce Automated Classification Enabled by Security Similarity (ACESS), a new and innovative detection model that penetrates the complexity of big text security classification/detection. To analyze the ACESS system, we constructed a novel dataset, containing formerly classified paragraphs from diplomatic cables made public by the WikiLeaks organization. To our knowledge this paper is the first to analyze a dataset that contains actual formerly sensitive information annotated at paragraph granularity.Comment: Pre-print of Best Paper Award IEEE Intelligence and Security Informatics (ISI) 2016 Manuscrip

Mechanisms matter: predicting the ecological impacts of global change

The ability of mechanistic models to reliably extrapolate to novel conditions could position them as the gold standard in understanding the impacts of global change, but exactly how mechanistic models can be used most effectively remains to be determined. In this issue, Desforges et al. present a mechanistic physiological model to understand the drivers of muskox population dynamics. We took this as an opportunity to discuss the potential for, and challenges of, using mechanistic models to predict ecological responses to environmental change

Consistency and Accuracy of CelebA Attribute Values

We report the first systematic analysis of the experimental foundations of facial attribute classification.Two annotators independently assigning attribute values shows that only 12 of 40 common attributes are assigned values with >= 95% consistency, and three (high cheekbones, pointed nose, oval face) have essentially random consistency. Of 5,068 duplicate face appearances in CelebA, attributes have contradicting values on from 10 to 860 of the 5,068 duplicates. Manual audit of a subset of CelebA estimates error rates as high as 40% for (no beard=false), even though the labeling consistency experiment indicates that no beard could be assigned with >= 95% consistency. Selecting the mouth slightly open (MSO) for deeper analysis, we estimate the error rate for (MSO=true) at about 20% and (MSO=false) at about 2%. A corrected version of the MSO attribute values enables learning a model that achieves higher accuracy than previously reported for MSO. Corrected values for CelebA MSO are available at https:// github.com/ HaiyuWu/ CelebAMSO

Individual-based modelling of elephant population dynamics using remote sensing to estimate food availability

Strategies for the conservation and management of many wild species requires an improved understanding of how population dynamics respond to changes in environmental conditions, including key drivers such as food availability. The development of mechanistic predictive models, in which the underlying processes of a system are modelled, enables a robust understanding of these demographic responses to dynamic environmental conditions. We present an individual-based energy budget model for a mega-herbivore, the African elephant (Loxodonta africana), which relates remotely measured changes in food availability to vital demographic rates of birth and mortality. Elephants require large spaces over which to roam in search of seasonal food, and thus are vulnerable to environmental changes which limit space use or alter food availability. The model is constructed using principles of physiological ecology; uncertain parameter values are calibrated using approximate Bayesian computation. The resulting model fits observed population dynamics data well. The model has critical value in being able to project elephant population size under future environmental conditions and is applicable to other mammalian herbivores with appropriate parameterisation

Human‐driven habitat conversion is a more immediate threat to Amboseli elephants than climate change

Global ecosystem change presents a major challenge to biodiversity conservation, which must identify and prioritize the most critical threats to species persistence given limited available funding. Mechanistic models enable robust predictions under future conditions and can consider multiple stressors in combination. Here we use an individual‐based model (IBM) to predict elephant population size in Amboseli, southern Kenya, under environmental scenarios incorporating climate change and anthropogenic habitat loss. The IBM uses projected food availability as a key driver of elephant population dynamics and relates variation in food availability to changes in vital demographic rates through an energy budget. Habitat loss, rather than climate change, represents the most significant threat to the persistence of the Amboseli elephant population in the 21st century and highlights the importance of collaborations and agreements that preserve space for Amboseli elephants to ensure the population remains resilient to environmental stochasticity

Near-infrared photoimmunotherapy targeting EGFR-Shedding new light on glioblastoma treatment

Glioblastomas (GBMs) are high-grade brain tumors, differentially driven by alterations (amplification, deletion or missense mutations) in the epidermal growth factor receptor (EGFR), that carry a poor prognosis of just 12–15 months following standard therapy. A combination of interventions targeting tumor-specific cell surface regulators along with convergent downstream signaling pathways may enhance treatment efficacy. Against this background, we investigated a novel photoimmunotherapy approach combining the cytotoxicity of photodynamic therapy with the specificity of immunotherapy. An EGFR-specific affibody (ZEGFR:03115) was conjugated to the phthalocyanine dye, IR700DX, which when excited with near-infrared light produces a cytotoxic response. ZEGFR:03115–IR700DX EGFR-specific binding was confirmed by flow cytometry and confocal microscopy. The conjugate showed effective targeting of EGFR positive GBM cells in the brain. The therapeutic potential of the conjugate was assessed both in vitro, in GBM cell lines and spheroids by the CellTiter-Glo® assay, and in vivo using subcutaneous U87-MGvIII xenografts. In addition, mice were imaged pre- and post-PIT using the IVIS/Spectrum/CT to monitor treatment response. Binding of the conjugate correlated to the level of EGFR expression in GBM cell lines. The cell proliferation assay revealed a receptor-dependent response between the tested cell lines. Inhibition of EGFRvIII+ve tumor growth was observed following administration of the immunoconjugate and irradiation. Importantly, this response was not seen in control tumors. In conclusion, the ZEGFR:03115–IR700DX showed specific uptake in vitro and enabled imaging of EGFR expression in the orthotopic brain tumor model. Moreover, the proof-of-concept in vivo PIT study demonstrated therapeutic efficacy of the conjugate in subcutaneous glioma xenografts

Evaluation of the Response of Intracranial Xenografts to VEGF Signaling Inhibition Using Multiparametric MRI.

Vascular endothelial growth factor A (VEGF-A) is considered one of the most important factors in tumor angiogenesis, and consequently, a number of therapeutics have been developed to inhibit VEGF signaling. Therapeutic strategies to target brain malignancies, both primary brain tumors, particularly in pediatric patients, and metastases, are lacking, but targeting angiogenesis may be a promising approach. Multiparametric MRI was used to investigate the response of orthotopic SF188luc pediatric glioblastoma xenografts to small molecule pan-VEGFR inhibitor cediranib and the effects of both cediranib and cross-reactive human/mouse anti-VEGF-A antibody B20-4.1.1 in intracranial MDA-MB-231 LM2-4 breast cancer xenografts over 48 hours. All therapeutic regimens resulted in significant tumor growth delay. In cediranib-treated SF188luc tumors, this was associated with lower Ktrans (compound biomarker of perfusion and vascular permeability) than in vehicle-treated controls. Cediranib also induced significant reductions in both Ktrans and apparent diffusion coefficient (ADC) in MDA-MB-231 LM2-4 tumors associated with decreased histologically assessed perfusion. B20-4.1.1 treatment resulted in decreased Ktrans, but in the absence of a change in perfusion; a non-significant reduction in vascular permeability, assessed by Evans blue extravasation, was observed in treated tumors. The imaging responses of intracranial MDA-MB-231 LM2-4 tumors to VEGF/VEGFR pathway inhibitors with differing mechanisms of action are subtly different. We show that VEGF pathway blockade resulted in tumor growth retardation and inhibition of tumor vasculature in preclinical models of pediatric glioblastoma and breast cancer brain metastases, suggesting that multiparametric MRI can provide a powerful adjunct to accelerate the development of antiangiogenic therapies for use in these patient populations

The effectiveness of case management for comorbid diabetes type 2 patients; the CasCo study. Design of a randomized controlled trial

BACKGROUND: More than half of the patients with type 2 diabetes (T2DM) patients are diagnosed with one or more comorbid disorders. They can participate in several single-disease oriented disease management programs, which may lead to fragmented care because these programs are not well prepared for coordinating care between programs. Comorbid patients are therefore at risk for suboptimal treatment, unsafe care, inefficient use of health care services and unnecessary costs. Case management is a possible model to counteract fragmented care for comorbid patients. It includes evidence-based optimal care, but is tailored to the individual patients' preferences.The objective of this study is to examine the effectiveness of a case management program, in addition to a diabetes management program, on the quality of care for comorbid T2DM patients. METHODS/DESIGN: The study is a randomized controlled trial among patients with T2DM and at least one comorbid chronic disease (N=230), who already participate in a diabetes management program. Randomization will take place at the level of the patients in general practices. Trained practice nurses (case managers) will apply a case management program in addition to the diabetes management program. The case management intervention is based on the Guided Care model and includes six elements; assessing health care needs, planning care, create access to other care providers and community resources, monitoring, coordinating care and recording of all relevant information. Patients in the control group will continue their participation in the diabetes management program and receive care-as-usual from their general practitioner and other care providers. DISCUSSION: We expect that the case management program, which includes better structured care based on scientific evidence and adjusted to the patients' needs and priorities, will improve the quality of care coordination from both the patients' and caregivers' perspective and will result in less consumption of health care services. TRIAL REGISTRATION: Netherlands Trial Register (NTR): NTR1847. (aut. ref.

Combined MYC and P53 defects emerge at medulloblastoma relapse and define rapidly progressive, therapeutically targetable disease

We undertook a comprehensive clinical and biological investigation of serial medulloblastoma biopsies obtained at diagnosis and relapse. CombinedMYCfamily amplifications and P53 pathway defects commonly emerged at relapse, and all patients in this group died of rapidly progressive disease postrelapse. To study this interaction, we investigated a transgenic model of MYCN-driven medulloblastoma and found spontaneous development ofTrp53inactivating mutations. Abrogation of p53 function in this model produced aggressive tumors that mimicked characteristics of relapsed human tumors with combined P53-MYC dysfunction. Restoration of p53 activity and genetic and therapeutic suppression of MYCN all reduced tumor growth and prolonged survival. Our findings identify P53-MYC interactions at medulloblastoma relapse as biomarkers of clinically aggressive disease that may be targeted therapeutically.Additional co-authors: Louise Howell, Colin Kwok, Abhijit Joshi, Sarah Leigh Nicholson, Stephen Crosier, David W. Ellison, Stephen B. Wharton, Keith Robson, Antony Michalski, Darren Hargrave, Thomas S. Jacques, Barry Pizer, Simon Bailey, Fredrik J. Swartling, William A. Weiss, Louis Chesler, Steven C. Cliffor