Electric bicycles as a new active transportation modality to promote health.

GOJANOVIC, B., J. WELKER, K. IGLESIAS, C. DAUCOURT, and G. GREMION. Electric Bicycles as a New Active Transportation Modality to Promote Health. Med. Sci. Sports Exerc., Vol. 43, No. 11, pp. 2204-2210, 2011. Electrically assisted bicycles (EAB) are an emerging transportation modality favored for environmental reasons. Some physical effort is required to activate the supporting engine, making it a potential active commuting option. Purpose: We hypothesized that using an EAB in a hilly city allows sedentary subjects to commute comfortably, while providing a sufficient effort for health-enhancing purposes. Methods: Sedentary subjects performed four different trips at a self-selected pace: walking 1.7 km uphill from the train station to the hospital (WALK), biking 5.1 km from the lower part of town to the hospital with a regular bike (BIKE), or EAB at two different power assistance settings (EAB(high), EAB(std)). HR, oxygen consumption, and need to shower were recorded. Results: Eighteen sedentary subjects (12 female, 6 male) age 36 +/- 10 yr were included, with (V) over dotO(2max) of 39.4 +/- 5.4 mL.min(-1).kg(-1). Time to complete the course was 22 (WALK), 19 (EAB(high)), 21 (EAB(std)), and 30 (BIKE) min. Mean %(V) over dotO(2max) was 59.0%, 54.9%, 65.7%, and 72.8%. Mean%HR(max) was 71.5%, 74.5%, 80.3%, and 84.0%. There was no significant difference between WALK and EAB(high), but all other comparisons were different (P < 0.05). Two subjects needed to shower after EAB(high), 3 needed to shower after WALK, 8 needed to shower after EAB(std), and all 18 needed to shower after BIKE. WALK and EAB(high) elicited 6.5 and 6.1 METs (no difference), whereas it was 7.3 and 8.2 for EAB(std) and BIKE. Conclusions: EAB is a comfortable and ecological transportation modality, helping sedentary people commute to work and meet physical activity guidelines. Subjects appreciated ease of use and mild effort needed to activate the engine support climbing hills, without the need to shower at work. EAB can be promoted in a challenging urban environment to promote physical activity and mitigate pollution issues.

Neurolysis using a carbohydrate polymer gel for the treatment of postoperative neuropathic pain.

Perineural and intraneural fibrosis is thought to be the main cause of failure of the many surgical treatments of neuropathic pain. We have used Adcon-T/N carbohydrate polymer gel for prevention of perineural fibrosis in several parts of the body. In this retrospective study, 54 patients who presented with postoperative neuropathic pain had microsurgical epineural neurolysis and relocation of a terminal neuroma. In 19 of them, the carbohydrate gel was applied at the same time. The mean follow-up was four years and the nerve distribution varied. Postoperative improvement in pain scores (visual analogue scale (VAS) and neuropathic pain scale inventory (NPSI)), sensitivity, overall improvement and satisfaction were equivalent in the two groups, with pain relief in about 80% of the patients. There was no significant beneficial effect in the carbohydrate gel group. Patients treated with this device had a higher infection rate (21 compared with 0, p = 0.01) and delayed wound healing (31.6 compared with 11.8, p = 0.2). We conclude that good long-term pain relief is obtained postoperatively independently of the addition of carbohydrate gel. There was a slight but not significant trend towards profound pain relief with the gel.

Surveillance active du cancer de la prostate [Active surveillance in prostate cancer].

Active surveillance in prostate cancer The spread of PSA in the screening of prostate cancer has almost doubled the incidence of this disease in the last twenty years. An improved understanding of the natural history of this cancer allows for risk stratification of the disease and to better predict insignificant prostate cancer. Active surveillance has recently been proposed as a new option to delay or avoid a radical treatment for patients with low-risk disease. The principle, results and future perspectives of this treatment modality are discussed in this review.

Using active learning for monitoring networks design: The example of wind power plants sites evaluation

Locating new wind farms is of crucial importance for energy policies of the next decade. To select the new location, an accurate picture of the wind fields is necessary. However, characterizing wind fields is a difficult task, since the phenomenon is highly nonlinear and related to complex topographical features. In this paper, we propose both a nonparametric model to estimate wind speed at different time instants and a procedure to discover underrepresented topographic conditions, where new measuring stations could be added. Compared to space filling techniques, this last approach privileges optimization of the output space, thus locating new potential measuring sites through the uncertainty of the model itself.

Automatic Mallampati Classification Using Active Appearance Models

Difficult tracheal intubation assessment is an important research topic in anesthesia as failed intubations are important causes of mortality in anesthetic practice. The modified Mallampati score is widely used, alone or in conjunction with other criteria, to predict the difficulty of intubation. This work presents an automatic method to assess the modified Mallampati score from an image of a patient with the mouth wide open. For this purpose we propose an active appearance models (AAM) based method and use linear support vector machines (SVM) to select a subset of relevant features obtained using the AAM. This feature selection step proves to be essential as it improves drastically the performance of classification, which is obtained using SVM with RBF kernel and majority voting. We test our method on images of 100 patients undergoing elective surgery and achieve 97.9% accuracy in the leave-one-out crossvalidation test and provide a key element to an automatic difficult intubation assessment system.

Small ubiquitin-like modifier conjugating enzyme with active site mutation acts as dominant negative inhibitor of SUMO conjugation in arabidopsis(F).

Small ubiquitin-like modifier (SUMO) conjugation affects a broad range of processes in plants, including growth, flower initiation, pathogen defense, and responses to abiotic stress. Here, we investigate in vivo and in vitro a SUMO conjugating enzyme with a Cys to Ser change in the active site, and show that it has a dominant negative effect. In planta expression significantly perturbs normal development, leading to growth retardation, early flowering and gene expression changes. We suggest that the mutant protein can serve as a probe to investigate sumoylation, also in plants for which poor genetic infrastructure precludes analysis via loss-of-function mutants.

Kaposi sarcoma herpes virus antibody response and viremia following highly active antiretroviral therapy in the Swiss HIV Cohort study.

OBJECTIVE: To describe the effect of HAART on Kaposi sarcoma herpes virus (KSHV) antibody response and viremia among HIV-positive MSM. DESIGN: A follow-up study of 272 HIV-positive MSM (including 22 with Kaposi sarcoma) who first initiated HAART between January 1996 and July 2004 in the Swiss HIV Cohort Study. METHODS: For each individual, two serum samples, one at HAART initiation and another 24 months later, were tested for latent and lytic KSHV antibodies using immunofluorescence assays, and for KSHV viremia using PCR. Factors associated with changes in KSHV antibody titers and viremia were evaluated. RESULTS: At HAART initiation, 69.1 and 75.0% of patients were seropositive to latent and lytic KSHV antibodies, respectively. Seropositivity was associated with the presence of Kaposi sarcoma, older age, lower CD8 cell count and higher CD4/CD8 ratio. Prevalence of KSHV viremia at HAART initiation was 6.4%, being significantly higher among patients with Kaposi sarcoma (35.0%), and those with HIV viral loads 100 000 copies/ml (11.7%) or higher. At 24-month follow-up, geometric mean titers (GMTs) among KSHV seropositive patients increased and antibody seroprevalence was higher. Having Kaposi sarcoma and/or CD4 cell counts less than 50 cells/microl at HAART initiation was associated both with higher probability for antibody titers to increase (including seroconversion) and larger increases in GMTs. Only one of 17 viremic patients at HAART initiation had viremia at 24-month follow-up. CONCLUSION: HAART increases KSHV-specific humoral immune response and clearance of viremia among HIV-infected MSM, consistent with the dramatic protection offered by HAART against Kaposi sarcoma.

HCF-1 is cleaved in the active site of O-GlcNAc transferase.

Host cell factor-1 (HCF-1), a transcriptional co-regulator of human cell-cycle progression, undergoes proteolytic maturation in which any of six repeated sequences is cleaved by the nutrient-responsive glycosyltransferase, O-linked N-acetylglucosamine (O-GlcNAc) transferase (OGT). We report that the tetratricopeptide-repeat domain of O-GlcNAc transferase binds the carboxyl-terminal portion of an HCF-1 proteolytic repeat such that the cleavage region lies in the glycosyltransferase active site above uridine diphosphate-GlcNAc. The conformation is similar to that of a glycosylation-competent peptide substrate. Cleavage occurs between cysteine and glutamate residues and results in a pyroglutamate product. Conversion of the cleavage site glutamate into serine converts an HCF-1 proteolytic repeat into a glycosylation substrate. Thus, protein glycosylation and HCF-1 cleavage occur in the same active site.

A Survey of Active Learning Algorithms for Supervised Remote Sensing Image Classification

Defining an efficient training set is one of the most delicate phases for the success of remote sensing image classification routines. The complexity of the problem, the limited temporal and financial resources, as well as the high intraclass variance can make an algorithm fail if it is trained with a suboptimal dataset. Active learning aims at building efficient training sets by iteratively improving the model performance through sampling. A user-defined heuristic ranks the unlabeled pixels according to a function of the uncertainty of their class membership and then the user is asked to provide labels for the most uncertain pixels. This paper reviews and tests the main families of active learning algorithms: committee, large margin, and posterior probability-based. For each of them, the most recent advances in the remote sensing community are discussed and some heuristics are detailed and tested. Several challenging remote sensing scenarios are considered, including very high spatial resolution and hyperspectral image classification. Finally, guidelines for choosing the good architecture are provided for new and/or unexperienced user.