Localization of an experimental ecological unit in the Maradi region of Nigeria

A detailed topographical and geomorphological description of a specific ecological unit in the Maradi region of the Sahel in the Niger Republic is presented. Sandy structures are classified into active dunes and covered dunes and an extensives vocabulary is developed to describe sub-categories. The descriptions are based on meteorological data (anemometric and rainfall) from local weather stations, ground observations, aerial photographs and LANDSAT pictures. The problem of dune reactivation and desertification is discussed both from the standpoint of causes and possible counter measures

Rapid method for determination of antimicrobial susceptibilities pattern of urinary bacteria

Method determines bacterial sensitivity to antimicrobial agents by measuring level of adenosine triphosphate remaining in the bacteria. Light emitted during reaction of sample with a mixture of luciferase and luciferin is measured

Sensitive and Scalable Online Evaluation with Theoretical Guarantees

Multileaved comparison methods generalize interleaved comparison methods to provide a scalable approach for comparing ranking systems based on regular user interactions. Such methods enable the increasingly rapid research and development of search engines. However, existing multileaved comparison methods that provide reliable outcomes do so by degrading the user experience during evaluation. Conversely, current multileaved comparison methods that maintain the user experience cannot guarantee correctness. Our contribution is two-fold. First, we propose a theoretical framework for systematically comparing multileaved comparison methods using the notions of considerateness, which concerns maintaining the user experience, and fidelity, which concerns reliable correct outcomes. Second, we introduce a novel multileaved comparison method, Pairwise Preference Multileaving (PPM), that performs comparisons based on document-pair preferences, and prove that it is considerate and has fidelity. We show empirically that, compared to previous multileaved comparison methods, PPM is more sensitive to user preferences and scalable with the number of rankers being compared.Comment: CIKM 2017, Proceedings of the 2017 ACM on Conference on Information and Knowledge Managemen

No relationship between lean mass and functional asymmetry in high-level female tennis players

The relationship between lean mass and functional asymmetry in terms of their magnitude and direction was examined in 22 high-level female tennis players (20.9 ± 3.6 years). Lean mass of both upper and lower extremities was examined using Dual X-ray Absorptiometry. Functional asymmetry was assessed using a battery of field tests (handgrip strength, seated shot-put throw, plate tapping, single leg countermovement jump, single leg forward hop test, 6 m single leg hop test, and 505 change of direction (time and deficit)). Paired sample t-tests compared the dominant (overall highest/best (performance) value) against the non-dominant value (highest/best (performance) value of the opposing extremity). Linear regressions were used to explore the relationship between lean mass and functional asymmetry magnitudes. Kappa coefficients were used to examine the consistency in direction between the extremity displaying the highest lean mass value and the extremity performing dominantly across tests. Significant asymmetry magnitudes (p 0.05) were found for all upper and lower extremity lean mass and functional values. No relationship was apparent between lean mass and functional asymmetry magnitudes (p-value range = 0.131–0.889). Despite finding perfect consistency in asymmetry direction (k-value = 1.00) for the upper extremity, poor to fair consistency (k-value range = −0.00–0.21) was found for the lower extremity. In conclusion, lean mass and functional asymmetries should be examined independently

International vs. National female tennis players: a comparison of upper and lower extremity functional asymmetries

Background: Asymmetries have been reported to negatively impact sport performance. This study examined the magnitude and direction of whole-body functional asymmetry in international versus national female tennis players. Methods: Ten internationally and twelve nationally ranked tennis players participated. Upper extremity functional asymmetries (or side-to-side performance differences) were evaluated using handgrip strength, seated shot-put throw and plate tapping. Lower extremity functional asymmetries were determined using the single leg countermovement jump, single leg forward hop test, 6 m single leg hop test, 505 changes of direction (time and deficit), and Y-balance test (anterior, posteromedial, posterolateral). ANOVAs were used to compare the dominant (overall best or fastest result of a specific test) versus non-dominant performance values (best or fastest result of the corresponding extremity) within the internationally versus nationally ranked players. Functional asymmetry magnitudes differences (expressed as a %) were examined using Mann-Whitney U tests. Kappa coefficients examined the consistency as to which extremity performed dominantly across tests. Results: Significant asymmetries for every upper and lower extremity test were found. The functional asymmetry magnitude was significantly (p=0.020) higher on the single leg forward hop test for the nationally (6.3%) versus internationally ranked players (2.9%). Kappa coefficients showed perfect levels of consistency regarding all upper extremity tests (k=1.00), indicating true limb dominance whereas more variance was found as to which lower extremity performed dominantly across tests (k range=-0.067-0.174). Conclusions: The included female tennis players displayed significant whole-body functional asymmetries. Poor consistency as to which lower extremity performed dominantly across tests warrants individual asymmetry monitoring

Lessons From The Conference: “Highlighting Massage Therapy In Complementary And Integrative Medicine”

A landmark conference, Highlighting Massage Therapy in Complimentary and Integrative Medicine, was held in Seattle, Washington, on May 13th–15th, 2010. The conference was designed to address the status of research related to massage therapy, as well as to have an open discussion regarding attitudes towards research and professional issues. Leaders from diverse manual therapy professions presented interesting and important data. The itinerary and summaries of the meeting can be found at http://www.massagetherapyfoundation.org/researchconference2010.html. In this brief report, rather than summarizing the presentations, we will share a combination of our observations and impressions, as well as suggestions for the direction of massage therapy research

Confidential Boosting with Random Linear Classifiers for Outsourced User-generated Data

User-generated data is crucial to predictive modeling in many applications. With a web/mobile/wearable interface, a data owner can continuously record data generated by distributed users and build various predictive models from the data to improve their operations, services, and revenue. Due to the large size and evolving nature of users data, data owners may rely on public cloud service providers (Cloud) for storage and computation scalability. Exposing sensitive user-generated data and advanced analytic models to Cloud raises privacy concerns. We present a confidential learning framework, SecureBoost, for data owners that want to learn predictive models from aggregated user-generated data but offload the storage and computational burden to Cloud without having to worry about protecting the sensitive data. SecureBoost allows users to submit encrypted or randomly masked data to designated Cloud directly. Our framework utilizes random linear classifiers (RLCs) as the base classifiers in the boosting framework to dramatically simplify the design of the proposed confidential boosting protocols, yet still preserve the model quality. A Cryptographic Service Provider (CSP) is used to assist the Cloud's processing, reducing the complexity of the protocol constructions. We present two constructions of SecureBoost: HE+GC and SecSh+GC, using combinations of homomorphic encryption, garbled circuits, and random masking to achieve both security and efficiency. For a boosted model, Cloud learns only the RLCs and the CSP learns only the weights of the RLCs. Finally, the data owner collects the two parts to get the complete model. We conduct extensive experiments to understand the quality of the RLC-based boosting and the cost distribution of the constructions. Our results show that SecureBoost can efficiently learn high-quality boosting models from protected user-generated data

Implicitly Constrained Semi-Supervised Least Squares Classification

We introduce a novel semi-supervised version of the least squares classifier. This implicitly constrained least squares (ICLS) classifier minimizes the squared loss on the labeled data among the set of parameters implied by all possible labelings of the unlabeled data. Unlike other discriminative semi-supervised methods, our approach does not introduce explicit additional assumptions into the objective function, but leverages implicit assumptions already present in the choice of the supervised least squares classifier. We show this approach can be formulated as a quadratic programming problem and its solution can be found using a simple gradient descent procedure. We prove that, in a certain way, our method never leads to performance worse than the supervised classifier. Experimental results corroborate this theoretical result in the multidimensional case on benchmark datasets, also in terms of the error rate.Comment: 12 pages, 2 figures, 1 table. The Fourteenth International Symposium on Intelligent Data Analysis (2015), Saint-Etienne, Franc

Optimal treatment allocations in space and time for on-line control of an emerging infectious disease

A key component in controlling the spread of an epidemic is deciding where, whenand to whom to apply an intervention.We develop a framework for using data to informthese decisionsin realtime.We formalize a treatment allocation strategy as a sequence of functions, oneper treatment period, that map up-to-date information on the spread of an infectious diseaseto a subset of locations where treatment should be allocated. An optimal allocation strategyoptimizes some cumulative outcome, e.g. the number of uninfected locations, the geographicfootprint of the disease or the cost of the epidemic. Estimation of an optimal allocation strategyfor an emerging infectious disease is challenging because spatial proximity induces interferencebetween locations, the number of possible allocations is exponential in the number oflocations, and because disease dynamics and intervention effectiveness are unknown at outbreak.We derive a Bayesian on-line estimator of the optimal allocation strategy that combinessimulation–optimization with Thompson sampling.The estimator proposed performs favourablyin simulation experiments. This work is motivated by and illustrated using data on the spread ofwhite nose syndrome, which is a highly fatal infectious disease devastating bat populations inNorth America