Ford Motor Company

About: Ford Motor Company is a company organization based out in Dearborn, Michigan, United States. It is known for research contribution in the topics: Internal combustion engine & Clutch. The organization has 36123 authors who have published 51450 publications receiving 855200 citations. The organization is also known as: Ford Motor & Ford Motor Corporation.

Multiwavelet grading of pathological images of prostate

Abstract: Histological grading of pathological images is used to determine the level of malignancy of cancerous tissues. This is a very important task in prostate cancer prognosis, since it is used for treatment planning. If infection of cancer is not rejected by noninvasive diagnostic techniques like magnetic resonance imaging, computed tomography scan, and ultrasound, then biopsy specimens of tissue are tested. For prostate, biopsied tissue is stained by hematoxyline and eosine method and viewed by pathologists under a microscope to determine its histological grade. Human grading is very subjective due to interobserver and intraobserver variations and in some cases difficult and time-consuming. Thus, an automatic and repeatable technique is needed for grading. The Gleason grading system is the most common method for histological grading of prostate tissue samples. According to this system, each cancerous specimen is assigned one of five grades. Although some automatic systems have been developed for analysis of pathological images, Gleason grading has not yet been automated; the goal of this research is to automate it. To this end, we calculate energy and entropy features of multiwavelet coefficients of the image. Then, we select most discriminative features by simulated annealing and use a k-nearest neighbor classifier to classify each image to appropriate grade (class). The leaving-one-out technique is used for error rate estimation. We also obtain the results using features extracted by wavelet packets and co-occurrence matrices and compare them with the multiwavelet method. Experimental results show the superiority of the multiwavelet transforms compared with other techniques. For multiwavelets, critically sampled preprocessing outperforms repeated-row preprocessing and has less sensitivity to noise for second level of decomposition. The first level of decomposition is very sensitive to noise and, thus, should not be used for feature extraction. The best multiwavelet method grades prostate pathological images correctly 97% of the time.

African Ancestry and Higher Prevalence of Triple-Negative Breast Cancer: Findings From an International Study

Abstract: BACKGROUND The study of breast cancer in women with African ancestry offers the promise of identifying markers for risk assessment and treatment of triple-negative disease.

Progesterone is neuroprotective after acute experimental spinal cord trauma in rats.

Abstract: STUDY DESIGN: A standardized rat contusion model was used to test the hypothesis that progesterone significantly improves neurologic recovery after a spinal cord injury that results in incomplete paraplegia. OBJECTIVES: To compare the effect of progesterone versus a variety of control agents to determine its effectiveness in promoting neurologic recovery after an incomplete rat spinal cord injury. SUMMARY OF BACKGROUND DATA: Progesterone is a neurosteroid, possessing a variety of functions in the central nervous system. Exogenous progesterone has been shown to improve neurologic function after focal cerebral ischemia and facilitates cognitive recovery after cortical contusion in rats. METHODS: A standardized rat contusion model of spinal cord injury using the New York University impactor that resulted in rats with incomplete paraplegia was used. Forty mature male Sprague-Dawley rats were randomly assigned to four groups: laminectomy with sham contusion, laminectomy with contusion without pharmacologic treatment, laminectomy with contusion treated with dimethylsulfoxide and dissolved progesterone, and laminectomy with contusion treated with dimethylsulfoxide. Functional status was assessed weekly using the Basso-Beattie-Bresnehan (BBB) locomotor rating scale for 6 weeks, after which the animals were killed for histologic studies. RESULTS: Rats treated with progesterone had better outcomes (P = 0.0017; P = 0.0172) with a BBB score of 15.5, compared with 10.0 in the dimethylsulfoxide control group and 12.0 in the spinal cord contusion without pharmacologic intervention group. This was corroborated in histologic analysis by relative sparing of white matter tissue at the epicenter of the injury in the progesterone-treated group (P < 0.05). CONCLUSIONS: Rats treated with progesterone had a better clinical and histologic outcome compared with the various control groups. These results indicate potential therapeutic properties of progesterone in the management of acute spinal cord injury.

Discovering urban activity patterns in cell phone data

Abstract: Massive and passive data such as cell phone traces provide samples of the whereabouts and movements of individuals. These are a potential source of information for models of daily activities in a city. The main challenge is that phone traces have low spatial precision and are sparsely sampled in time, which requires a precise set of techniques for mining hidden valuable information they contain. Here we propose a method to reveal activity patterns that emerge from cell phone data by analyzing relational signatures of activity time, duration, and land use. First, we present a method of how to detect stays and extract a robust set of geolocated time stamps that represent trip chains. Second, we show how to cluster activities by combining the detected trip chains with land use data. This is accomplished by modeling the dependencies between activity type, trip scheduling, and land use types via a Relational Markov Network. We apply the method to two different kinds of mobile phone datasets from the metropolitan areas of Vienna, Austria and Boston, USA. The former data includes information from mobility management signals, while the latter are usual Call Detail Records. The resulting trip sequence patterns and activity scheduling from both datasets agree well with their respective city surveys, and we show that the inferred activity clusters are stable across different days and both cities. This method to infer activity patterns from cell phone data allows us to use these as a novel and cheaper data source for activity-based modeling and travel behavior studies.