Yahoo!

About: Yahoo! is a company organization based out in London, United Kingdom. It is known for research contribution in the topics: Population & Web search query. The organization has 26749 authors who have published 29915 publications receiving 732583 citations. The organization is also known as: Yahoo! Inc. & Maudwen-Yahoo! Inc.

Preservation of peri-implant soft and hard tissues using platform switching of implants placed in immediate extraction sockets: a proof-of-concept study with 12- to 36-month follow-up.

Abstract: PURPOSE: The purpose of this article is to evaluate the soft- and hard-tissue response to immediately placed implants. In addition, assessment was conducted of the soft tissue response to a transmucosal abutment which was narrower than the implant platform. MATERIALS AND METHODS: This study was conducted to evaluate 10 consecutively placed immediately loaded implants placed in extraction sockets in maxillae without compromised bone tissue. The infection control phase of periodontal therapy was completed in the areas of hopeless teeth prior to extraction. Implants with a 6-mm-platform diameter were placed immediately into the fresh extraction sockets. A provisional 4-mm-diameter transmucosal abutment was subsequently connected, and a provisional crown was adapted and adjusted for nonfunctional immediate positioning. Three months following implant placement, definitive prosthetic rehabilitation was performed. At the time of prosthesis insertion (baseline) and every 6 months thereafter, radiographic assessments, pocket probing depth (PPD), recession, and papilla height were measured. An image analysis software application was used to compare the radiographic bone crestal bone heights at the mesial and distal aspects of the implants. RESULTS: Nine patients with 10 sites were treated. Mean follow-up time was 22 months (range, 18 to 36 months). All 10 implants were found to be clinically osseointegrated. Software analysis of radiographic films showed a bone resorption of 0.78 +/- 0.36 mm. The mean values were significantly lower (P < or = .005) than a mean reference value of 1.7 mm. PPD did not exceed 3 mm in any site (average, 2.8 mm). Rather than recession, there was a mean gain in the buccal margin of 0.2 mm and a mean gain in papilla height of 0.25 mm. CONCLUSION: This proof-of-concept study suggests that immediate loading with platform switching can provide peri-implant hard tissue stability with soft tissue and papilla preservation. (Case Series)

Reduced spine density in specific regions of CA1 pyramidal neurons in two transgenic mouse models of Alzheimer's disease.

Abstract: One major hallmark of Alzheimer's disease (AD) is the massive loss of synapses that occurs at an early clinical stage of the disease. In this study, we characterize alterations in spine density and the expression of synapse-associated immediate early gene Arc (activity-regulated cytoskeleton-associated protein) in the hippocampal CA1 regions of two different amyloid precursor protein (APP) transgenic mouse lines before plaque development and their connection to performance in hippocampus-dependent memory tests. The density of mushroom-type spines was reduced by 34% in the basal dendrites proximal to the soma of CA1 pyramidal neurons in 5.5-month-old Tg2576 mice, carrying the Swedish mutation, compared with wild-type littermates. A similar reduction of 42% was confirmed in the same region of 8-month-old APP/Lo mice, carrying the London mutation. In this strain, the reduction extended to the distal dendritic spines (28%), although no differences were found in apical dendrites in either transgenic mouse line. Both transgenic mice lines presented a significant increase in Arc protein expression in CA1 compared with controls, suggesting rather an overactivity and increased spine turnover that was supported by a significant decrease in number of somatostatin-immunopositive inhibitory interneurons in the stratum oriens of CA1. Behaviorally, the transgenic mice showed decrease freezing in the fear contextual conditioning test and impairment in spatial memory assessed by Morris water maze test. These data indicate that cognitive impairment in APP transgenic mice is correlated with impairment of synaptic connectivity in hippocampal CA1, probably attributable to loss of inhibitory interneurons and subsequent hyperactivity.

A large-scale sentiment analysis for Yahoo! answers

Abstract: Sentiment extraction from online web documents has recently been an active research topic due to its potential use in commercial applications. By sentiment analysis, we refer to the problem of assigning a quantitative positive/negative mood to a short bit of text. Most studies in this area are limited to the identification of sentiments and do not investigate the interplay between sentiments and other factors. In this work, we use a sentiment extraction tool to investigate the influence of factors such as gender, age, education level, the topic at hand, or even the time of the day on sentiments in the context of a large online question answering site. We start our analysis by looking at direct correlations, e.g., we observe more positive sentiments on weekends, very neutral ones in the Science & Mathematics topic, a trend for younger people to express stronger sentiments, or people in military bases to ask the most neutral questions. We then extend this basic analysis by investigating how properties of the (asker, answerer) pair affect the sentiment present in the answer. Among other things, we observe a dependence on the pairing of some inferred attributes estimated by a user's ZIP code. We also show that the best answers differ in their sentiments from other answers, e.g., in the Business & Finance topic, best answers tend to have a more neutral sentiment than other answers. Finally, we report results for the task of predicting the attitude that a question will provoke in answers. We believe that understanding factors influencing the mood of users is not only interesting from a sociological point of view, but also has applications in advertising, recommendation, and search.

Mining Fashion Outfit Composition Using an End-to-End Deep Learning Approach on Set Data

Abstract: Composing fashion outfits involves deep under-standing of fashion standards while incorporating creativity for choosing multiple fashion items (e.g., jewelry, bag, pants, dress). In fashion websites, popular or high-quality fashion outfits are usually designed by fashion experts and followed by large audiences. In this paper, we propose a machine learning system to compose fashion outfits automatically. The core of the proposed automatic composition system is to score fashion outfit candidates based on the appearances and metadata. We propose to leverage outfit popularity on fashion-oriented websites to supervise the scoring component. The scoring component is a multimodal multiinstance deep learning system that evaluates instance aesthetics and set compatibility simultaneously. In order to train and evaluate the proposed composition system, we have collected a large-scale fashion outfit dataset with 195K outfits and 368K fashion items from Polyvore. Although the fashion outfit scoring and composition is rather challenging, we have achieved an AUC of 85% for the scoring component, and an accuracy of 77% for a constrained composition task.