Breast cancer detection using ensemble of convolutional neural networks

Early detection leading to timely treatment in the initial stages of cancer may decrease the breast cancer death rate. We propose deep learning techniques along with image processing for the detection of tumors. The availability of online datasets and advances in graphical processing units (GPU) have promoted the application of deep learning models for the detection of breast cancer. In this paper, deep learning models using convolutional neural network (CNN) have been built to automatically classify mammograms into benign and malignant. Issues like overfitting and dataset imbalance are overcome. Experimentation has been done on two publicly available datasets, namely mammographic image analysis society (MIAS) database and digital database for screening mammography (DDSM). Robustness of the models is accomplished by merging the datasets. In our experimentation, MatConvNet has achieved an accuracy of 94.2% on the merged dataset, performing the best amongst all the CNN models used individually. Hungarian optimization algorithm is employed for selection of individual CNN models to form an ensemble. Ensemble of CNN models led to an improved performance, resulting in an accuracy of 95.7%

Detecting Evasive Malicious URL Using Graph Algorithm

The present disclosure describes systems and methods to enable inspecting of byte streams using a bipartite match algorithm to detect use of evasive techniques to defeat Uniform Resource (URL) filtering. According to an aspect of the present disclosure, a two stage dynamic programming algorithm is provided that matches signatures once and only once just like DFA in the first stage, and uses polynomial runtime to select a matching pattern in the second stage. According to an example implementation of the present disclosure, the patterns are compiled into an n*m binary matrix plus one additional column to describe the modifiers, where n is the number of patterns and m is the number of unique signatures used in the patterns. In the first stage of runtime, an n*m binary matrix is built where n is the number of tokens matched against the DFA and m is the number of signatures such that e(i,j)=true if and only if the ith token matches the jth signature. In the second stage of runtime, the sub runtime n*m matrix is obtained for a particular pattern such that e(i,j)=true if and only if the ith token matches the jth signatures in the pattern. A bipartite graph can be built where there are two sets of vertex, n and m, where n is token and m is signature. There is a weight 1 edge between an ith vertex in n and an nth vertex in m if e(i,j) in the matrix is true

BioHealthBase: informatics support in the elucidation of influenza virus host–pathogen interactions and virulence

The BioHealthBase Bioinformatics Resource Center (BRC) (http://www.biohealthbase.org) is a public bioinformatics database and analysis resource for the study of specific biodefense and public health pathogens—Influenza virus, Francisella tularensis, Mycobacterium tuberculosis, Microsporidia species and ricin toxin. The BioHealthBase serves as an extensive integrated repository of data imported from public databases, data derived from various computational algorithms and information curated from the scientific literature. The goal of the BioHealthBase is to facilitate the development of therapeutics, diagnostics and vaccines by integrating all available data in the context of host–pathogen interactions, thus allowing researchers to understand the root causes of virulence and pathogenicity. Genome and protein annotations can be viewed either as formatted text or graphically through a genome browser. 3D visualization capabilities allow researchers to view proteins with key structural and functional features highlighted. Influenza virus host–pathogen interactions at the molecular/cellular and systemic levels are represented. Host immune response to influenza infection is conveyed through the display of experimentally determined antibody and T-cell epitopes curated from the scientific literature or as derived from computational predictions. At the molecular/cellular level, the BioHealthBase BRC has developed biological pathway representations relevant to influenza virus host–pathogen interaction in collaboration with the Reactome database (http://www.reactome.org)

Increased treatment durations lead to greater improvements in non-weight bearing dorsiflexion range of motion for asymptomatic individuals immediately following an anteroposterior grade IV mobilisation of the talus

Manual therapy aims to minimise pain and restore joint mobility and function. Joint mobilisations are integral to these techniques, with anteroposterior (AP) talocrural joint mobilisations purported to increase dorsiflexion range of motion (DF-ROM). This study aimed to determine whether different treatment durations of single grade IV anteroposterior talocrural joint mobilisations elicit statistically significant differences in DF-ROM. Sixteen asymptomatic male football players (age = 27.1 ± 5.3 years) participated in the study. Non-weight bearing (NWB) and weight bearing (WB) DF-ROM was measured before and after 4 randomised treatment conditions: control treatment, 30 s, 1 min, 2 min. NWB DF-ROM was measured using a universal goniometer, and WB DF-ROM using the weight-bearing lunge test. A within-subjects design was employed so that all participants received each of the treatment conditions. A 4 × 4 balanced Latin square design and 1 week interval between sessions reduced any residual effects. Two-way repeated measures ANOVA revealed a significant improvement in DF-ROM following all AP mobilisation treatments (p < 0.001). The within subjects contrasts showed that increases in treatment duration was associated with statistically significant improvements in DF-ROM (NWB DF-ROM control = 0.01%, 30 s = 14.2%, 1 min = 21.6%, 2 min = 32.8%; WB DF-ROM control = 0.01%, 30 s = 5.0%, 1 min = 7.6%, 2 min = 10.9%; p < 0.05). However, WB DF-ROM improvements were below the minimal detectable change scores needed to conclude that improvements were not a consequence of measurement error. This research shows that single session mobilisations can elicit NWB DF-ROM improvements in asymptomatic individuals in the absence of pain, whilst increases in treatment duration confer greater improvements in NWB DF-ROM within this population

Albiglutide and cardiovascular outcomes in patients with type 2 diabetes and cardiovascular disease (Harmony Outcomes): a double-blind, randomised placebo-controlled trial

Background: Glucagon-like peptide 1 receptor agonists differ in chemical structure, duration of action, and in their effects on clinical outcomes. The cardiovascular effects of once-weekly albiglutide in type 2 diabetes are unknown. We aimed to determine the safety and efficacy of albiglutide in preventing cardiovascular death, myocardial infarction, or stroke. Methods: We did a double-blind, randomised, placebo-controlled trial in 610 sites across 28 countries. We randomly assigned patients aged 40 years and older with type 2 diabetes and cardiovascular disease (at a 1:1 ratio) to groups that either received a subcutaneous injection of albiglutide (30–50 mg, based on glycaemic response and tolerability) or of a matched volume of placebo once a week, in addition to their standard care. Investigators used an interactive voice or web response system to obtain treatment assignment, and patients and all study investigators were masked to their treatment allocation. We hypothesised that albiglutide would be non-inferior to placebo for the primary outcome of the first occurrence of cardiovascular death, myocardial infarction, or stroke, which was assessed in the intention-to-treat population. If non-inferiority was confirmed by an upper limit of the 95% CI for a hazard ratio of less than 1·30, closed testing for superiority was prespecified. This study is registered with ClinicalTrials.gov, number NCT02465515. Findings: Patients were screened between July 1, 2015, and Nov 24, 2016. 10 793 patients were screened and 9463 participants were enrolled and randomly assigned to groups: 4731 patients were assigned to receive albiglutide and 4732 patients to receive placebo. On Nov 8, 2017, it was determined that 611 primary endpoints and a median follow-up of at least 1·5 years had accrued, and participants returned for a final visit and discontinuation from study treatment; the last patient visit was on March 12, 2018. These 9463 patients, the intention-to-treat population, were evaluated for a median duration of 1·6 years and were assessed for the primary outcome. The primary composite outcome occurred in 338 (7%) of 4731 patients at an incidence rate of 4·6 events per 100 person-years in the albiglutide group and in 428 (9%) of 4732 patients at an incidence rate of 5·9 events per 100 person-years in the placebo group (hazard ratio 0·78, 95% CI 0·68–0·90), which indicated that albiglutide was superior to placebo (p&lt;0·0001 for non-inferiority; p=0·0006 for superiority). The incidence of acute pancreatitis (ten patients in the albiglutide group and seven patients in the placebo group), pancreatic cancer (six patients in the albiglutide group and five patients in the placebo group), medullary thyroid carcinoma (zero patients in both groups), and other serious adverse events did not differ between the two groups. There were three (&lt;1%) deaths in the placebo group that were assessed by investigators, who were masked to study drug assignment, to be treatment-related and two (&lt;1%) deaths in the albiglutide group. Interpretation: In patients with type 2 diabetes and cardiovascular disease, albiglutide was superior to placebo with respect to major adverse cardiovascular events. Evidence-based glucagon-like peptide 1 receptor agonists should therefore be considered as part of a comprehensive strategy to reduce the risk of cardiovascular events in patients with type 2 diabetes. Funding: GlaxoSmithKline

Farmacologia clínica da doença de Parkinson: Clinical pharmacology of Parkinson's disease

As patologias neurodegenerativas cursam com depleção progressiva e irreversível dos neurônios existentes em regiões específicas do cérebro. A Doença de Parkinson (DP) é um protótipo, na qual o extravio neuronal do hipocampo e do córtex resulta em déficit de memória e disfunção cognitiva. O seguinte artigo objetivou descrever de modo narrativo as considerações clínicas da doença de Parkinson que justifiquem a ação farmacológica dos fármacos empregados em sua terapêutica. Atualmente, a intervenção farmacológica e a cirúrgica não são capazes de reverter o quadro clínico, mas evitam a progressão da morbimortalidade da DP. O tratamento é individual, baseado na reação específica, o quadro clínico, resposta farmacológica e aspectos socioeconômicos, ocupacionais e emocionais. A finalidade se baseia em perpetuar a autonomia e funcionalidade, o máximo de tempo possível. A escolha dos fármacos mais apropriados para cada paciente e o início do tratamento e o acompanhamento ao longo da evolução são etapas difíceis. Devido a cronicidade, o tratamento deve continuar por toda a vida, considerando que os fármacos e suas doses mudam com o tempo, o surgimento de efeitos adversos

Causality Bounds on Dissipative General-Relativistic Magnetohydrodynamics

We determine necessary and sufficient conditions under which a large class of relativistic generalizations of Braginskii's magnetohydrodynamics, described using Israel-Stewart theory, are causal and strongly hyperbolic in the fully nonlinear regime in curved spacetime. Our new nonlinear analysis provides stricter constraints on the dynamical variables that cannot be obtained via a standard linear expansion around equilibrium. Causality severely constrains the size of shear-viscous corrections, placing a bound on the far-from-equilibrium dynamics of magnetized weakly collisional relativistic plasmas, which rules out the onset of the firehose instability in such systems

Formulation and test of a model of positional distortion fields. Third International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences, Quebec City [document available at www.ncgia.ucsb.edu/vital

BSTRACT A simple model of positional distortion in vector databases is presented in which positions are distorted by the addition of a vector field. Given two data sets it is possible to observe values of the field at sample points, and to build a complete model by interpolation. We argue that discontinuities in the distortion field are common, though not observed in the absence of features that cross them. A piecewise-constant model is proposed. A variogram is constructed based on distortion directions, using a novel measure, and calibrated using a novel clustering technique based on the properties of the angular variogram.

Novel risk index for the identification of age-related macular degeneration using radon transform and DWT features

Age-related Macular Degeneration (AMD) affects the central vision of aged people. It can be diagnosed due to the presence of drusen, Geographic Atrophy (GA) and Choroidal Neovascularization (CNV) in the fundus images. It is labor intensive and time-consuming for the ophthalmologists to screen these images. An automated digital fundus photography based screening system can overcome these drawbacks. Such a safe, non-contact and cost-effective platform can be used as a screening system for dry AMD. In this paper, we are proposing a novel algorithm using Radon Transform (RT), Discrete Wavelet Transform (DWT) coupled with Locality Sensitive Discriminant Analysis (LSDA) for automated diagnosis of AMD. First the image is subjected to RT followed by DWT. The extracted features are subjected to dimension reduction using LSDA and ranked using t-test. The performance of various supervised classifiers namely Decision Tree (DT), Support Vector Machine (SVM), Probabilistic Neural Network (PNN) and k-Nearest Neighbor (k-NN) are compared to automatically discriminate to normal and AMD classes using ranked LSDA components. The proposed approach is evaluated using private and public datasets such as ARIA and STARE. The highest classification accuracy of 99.49%, 96.89% and 100% are reported for private, ARIA and STARE datasets. Also, AMD index is devised using two LSDA components to distinguish two classes accurately. Hence, this proposed system can be extended for mass AMD screening