James Marion Sims, M.D., LL.D.

Few physicians have been as celebrated and loathed as James Marion Sims, M.D., LL.D. Dr. Sims’ modest early life and education never could have predicted that he would one day be called the ‘‘Father of American Gynecology.’’1, 2 Lauded by contemporaries and early historians as ‘‘one of the most original and gifted of American surgeons,’’ he performed the first successful operation to correct vesicovaginal fistulas (a condition that prior to 1852 was known as the ‘‘stumbling-block of gynecology’’).1 Today, the same work that earned Dr. Sims fame, fortune, and innumerable honors has been called into question, because of the unethical manner in which he developed his surgical innovation

Adam Deep Learning with SOM for Human Sentiment Classification

Nowadays, with the improvement in communication through social network services, a massive amount of data is being generated from user's perceptions, emotions, posts, comments, reactions, etc., and extracting significant information from those massive data, like sentiment, has become one of the complex and convoluted tasks. On other hand, traditional Natural Language Processing (NLP) approaches are less feasible to be applied and therefore, this research work proposes an approach by integrating unsupervised machine learning (Self-Organizing Map), dimensionality reduction (Principal Component Analysis) and computational classification (Adam Deep Learning) to overcome the problem. Moreover, for further clarification, a comparative study between various well known approaches and the proposed approach was conducted. The proposed approach was also used in different sizes of social network data sets to verify its superior efficient and feasibility, mainly in the case of Big Data. Overall, the experiments and their analysis suggest that the proposed approach is very promissing

Hidden Markov model and Chapman Kolmogrov for protein structures prediction from images

Protein structure prediction and analysis are more significant for living organs to perfect asses the livingorgan functionalities. Several protein structure prediction methods use neural network (NN). However,the Hidden Markov model is more interpretable and effective for more biological data analysis comparedto the NN. It employs statistical data analysis to enhance the prediction accuracy. The current workproposed a protein prediction approach from protein images based on Hidden Markov Model andChapman Kolmogrov equation. Initially, a preprocessing stage was applied for protein imagesbinarization using Otsu technique in order to convert the protein image into binary matrix. Subsequently,two counting algorithms, namely the Flood fill and Warshall are employed to classify the proteinstructures. Finally, Hidden Markov model and Chapman Kolmogrov equation are applied on the classifiedstructures for predicting the protein structure. The execution time and algorithmic performances aremeasured to evaluate the primary, secondary and tertiary protein structure prediction

Quantitative Measurements of Alternating Finger Tapping in Parkinson's Disease Correlate With UPDRS Motor Disability and Reveal the Improvement in Fine Motor Control From Medication and Deep Brain Stimulation

Abstract: The Unified Parkinson's Disease Rating Scale (UP-DRS) is the primary outcome measure in most clinical trials of Parkinson's disease (PD) therapeutics. Each subscore of the motor section (UPDRS III) compresses a wide range of motor performance into a coarse-grained scale from 0 to 4; the assessment of performance can also be subjective. Quantitative digitography (QDG) is an objective, quantitative assessment of digital motor control using a computer-interfaced musical keyboard. In this study, we show that the kinematics of a repetitive alternating finger-tapping (RAFT) task using QDG correlate with the UPDRS motor score, particularly with the bradykinesia subscore, in 33 patients with PD. We show that dopaminergic medication and an average of 9.5 months of bilateral subthalamic nucleus deep brain stimulation (B-STN DBS) significantly improve UPDRS and QDG scores but may have different effects on certain kinematic parameters. This study substantiates the use of QDG to measure motor outcome in trials of PD therapeutics and shows that medication and B-STN DBS both improve fine motor control

Big DNA Datasets Analysis under Push down Automata

Consensus is a significant part that supports the identification of unknown information about animals, plants and insects around the globe. It represents a small part of Deoxyribonucleic acid (DNA) known as the DNA segment that carries all the information for investigation and verification. However, excessive datasets are the major challenges to mine the accurate meaning of the experiments. The datasets are increasing exponentially in ever seconds. In the present article, a memory saving consensus finding approach is organized. The principal component analysis (PCA) and independent component (ICA) are used to pre-process the training datasets. A comparison is carried out between these approaches with the Apriori algorithm. Furthermore, the push down automat (PDA) is applied for superior memory utilization. It iteratively frees the memory for storing targeted consensus by removing all the datasets that are not matched with the consensus. Afterward, the Apriori algorithm selects the desired consensus from limited values that are stored by the PDA. Finally, the Gauss-Seidel method is used to verify the consensus mathematically

Molecular Characterization and Phylogenetic Study of Coxsackievirus A24v Causing Outbreaks of Acute Hemorrhagic Conjunctivitis (AHC) in Brazil

Coxsackievirus A24 variant (CA24v) is the most prevalent viral pathogen associated with acute hemorrhagic conjunctivitis (AHC) outbreaks. Sixteen years after its first outbreak in Brazil, this agent reemerged in 2003 in Brazil, spread to nearly all states and caused outbreaks until 2005. In 2009, a new outbreak occurred in the northeast region of the country. In this study, we performed a viral isolation in cell culture and characterized clinical samples collected from patients presenting symptoms during the outbreak of 2005 in Vitória, Espírito Santo State (ES) and the outbreak of 2009 in Recife, Pernambuco State (PE). We also performed a phylogenetic analysis of worldwide strains and all meaningful Brazilian isolates since 2003.Sterile cotton swabs were used to collect eye discharges, and all 210 clinical samples were used to inoculate cell cultures. Cytopathic effects in HEp-2 cells were seen in 58 of 180 (32%) samples from Vitória and 3 of 30 (10%) samples from Recife. Phylogenetic analysis based on a fragment of the VP1 and 3C gene revealed that the CA24v causing outbreaks in Brazil during the years 2003, 2004 and 2005 evolved from Asian isolates that had caused the South Korean outbreak of AHC during the summer of 2002. However, the 2009 outbreak of AHC in Pernambuco was originated from the reintroduction of a new CA24v strain that was circulating during 2007 in Asia, where CA24v outbreaks has been continuously reported since 1970.This study is the first phylogenetic analysis of AHC outbreaks caused by CA24v in Brazil. The results showed that Asian strains of CA24v were responsible for the outbreaks since 1987 and were independently introduced to Brazil in 2003 and 2009. Phylogenetic analysis of complete VP1 gene is a useful tool for studying the epidemiology of enteroviruses associated with outbreaks

Comparative Anatomical Analyses of the Forearm Muscles of Cebus libidinosus (Rylands et al. 2000): Manipulatory Behavior and Tool Use

The present study describes the flexor and extensor muscles in Cebus libidinosus' forearm and compares them with those from humans, chimpanzees and baboons. The data is presented in quantitative anatomical indices for similarity. The capuchin forearm muscles showed important similarities with chimpanzees and humans, particularly those that act on thumb motion and allow certain degree of independence from other hand structures, even though their configuration does not enable a true opposable thumb. The characteristics of Cebus' forearm muscles corroborate the evolutionary convergence towards an adaptive behavior (tool use) between Cebus genus and apes

A generative-oriented model-driven design environment for customizable video surveillance systems

To tackle the growing complexity and huge demand for tailored domestic video surveillance systems along with a high demanding time-to-market expectation, engineers at IVV Automação, LDAa are exploiting video surveillance domain as families of systems that can be developed following a pay-as-you-go fashion rather than developing an ex-nihilo new product. Several and different new functionalities are required for each new product’s hardware platforms (e.g., ranging from mobile phone, PDA to desktop PC) and operating systems (e.g., flavors of Linux, Windows and MAC OS X). Some of these functionalities have special economical constraints of development time and memory footprint. To better accommodate all the above listing requirements, a model-driven generative software development paradigm supported by mainstream tools is proposed to offer a significant leverage in hiding commonalities and configuring variabilities across families of video surveillance products while maintaining the new product quality.This work was funded through the Competitive Factors Operational Program COMPETE and through national funds though the Science and Technology Foundation - FCT, within the project: FCOMP-01-0124-FEDER-022674. This work was developed in cooperation with IVV Automation; all support and means provided by the company is acknowledged