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The present study proposes a cognitive prosthesis device for face memory impairment as a proof-of-concept for the domain-specific cognitive prosthesis. Healthy subjects (n = 6) and a patient with poor face memory were enrolled. An acquaintance face recognition test with and without the use of cognitive prosthesis for face memory impairment, face recognition tests, quality of life, neuropsychological assessments, and machine learning performance of the cognitive prosthesis were followed-up throughout four weeks of real-world device use by the patient. The healthy subjects had an accuracy of 92.38 ± 4.41% and reaction time of 1.27 ± 0.12 s in the initial attempt of the acquaintance face recognition test, which changed to 80.48 ± 6.23% (p = 0.06) and 2.11 ± 0.20 s (p < 0.01) with prosthesis use. The patient had an accuracy of 74.29% and a reaction time of 6.65 s, which improved to 94.29% and 3.28 s with prosthesis use. After four weeks, the patient's unassisted accuracy and reaction time improved to 100% and 1.23 s. Functional MRI study revealed activation of the left superior temporal lobe during face recognition task without prosthesis use and activation of the right precentral motor area with prosthesis use. The prosthesis could improve the patient's performance by bypassing the brain area inefficient for facial recognition and employing the area more efficiently for the cognitive task. [ABSTRACT FROM AUTHOR]

The article focuses on the interest of semiconductor and light-emitting diode (LED) manufacturers to automate film frame handling process to increase yield of back-end-of-line (BEOL) process. It mentions that the drive of semiconductor industry for film-frame-handling automation is due to cope with the timing requirements of BEOL test applications and inspection. It adds that cost-effective and customizable automation solution is the one needed to address the technical automation challenges.

Fibrous dysplasia (FD) is a skeletal disease caused by somatic activating mutations of the cyclic adenosine monophosphate (cAMP)-regulating protein, α-subunit of the Gs stimulatory protein (Gsα). These mutations lead to replacement of normal bone by proliferative osteogenic precursors, resulting in deformity, fracture, and pain. Medical treatment has been ineffective in altering the disease course. Receptor activator of NF-κB ligand (RANKL) is a cell-surface protein involved in many cellular processes, including osteoclastogenesis, and is reported to be overexpressed in FD-like bone cells. Denosumab is a humanized monoclonal antibody to RANKL approved for treatment of osteoporosis and prevention of skeletal-related events from bone metastases. We present the case of a 9-year-old boy with severe FD who was treated with denosumab for a rapidly expanding femoral lesion. Immunohistochemical staining on a pretreatment bone biopsy specimen revealed marked RANKL expression. He was started on monthly denosumab, with an initial starting dose of 1 mg/kg and planned 0.25 mg/kg dose escalations every 3 months. Over 7 months of treatment he showed marked reduction in pain, bone turnover markers (BTMs), and tumor growth rate. Denosumab did not appear to impair healing of a femoral fracture that occurred while on treatment. With initiation of treatment he developed hypophosphatemia and secondary hyperparathyroidism, necessitating supplementation with phosphorus, calcium, and calcitriol. BTMs showed rapid and sustained suppression. With discontinuation there was rapid and dramatic rebound of BTMs with cross-linked C-telopeptide (reflecting osteoclast activity) exceeding pretreatment levels, accompanied by severe hypercalcemia. In this child, denosumab lead to dramatic reduction of FD expansion and FD-related bone pain. Denosumab was associated with clinically significant disturbances of mineral metabolism both while on treatment and after discontinuation. Denosumab treatment of FD warrants further study to confirm efficacy and determine potential morbidity, as well as to determine the mechanism of RANKL in the pathogenesis of FD and related bone marrow stromal cell diseases. © 2012 American Society for Bone and Mineral Research. [ABSTRACT FROM AUTHOR]

A multiobjective genetic algorithm is designed to optimize a computer-aided detection (CAD) system for identifying colonic polyps. Colonic polyps appear as elliptical protrusions on the inner surface of the colon. Curvature-based features for colonic polyp detection have proved to be successful in several CT colonography (CTC) CAD systems. Our CTC CAD program uses a sequential classifier to form initial polyp detections on the colon surface. The classifier utilizes a set of thresholds on curvature-based features to cluster suspicious colon surface regions into polyp candidates. The thresholds were previously chosen experimentally by using feature histograms. The chosen thresholds were effective for detecting polyps sized 10 mm or larger in diameter. However, many medium-sized polyps, 6–9 mm in diameter, were missed in the initial detection procedure. In this paper, the task of finding optimal thresholds as a multiobjective optimization problem was formulated, and a genetic algorithm to solve it was utilized by evolving the Pareto front of the Pareto optimal set. The new CTC CAD system was tested on 792 patients. The sensitivities of the optimized system improved significantly, from 61.68% to 74.71% with an increase of 13.03% (95% CI [6.57%, 19.5%], p=7.78×10-5) for the size category of 6–9 mm polyps, from 65.02% to 77.4% with an increase of 12.38% (95% CI [6.23%, 18.53%], p=7.95×10-5) for polyps 6 mm or larger, and from 82.2% to 90.58% with an increase of 8.38% (95%CI [0.75%, 16%], p=0.03) for polyps 8 mm or larger at comparable false positive rates. The sensitivities of the optimized system are nearly equivalent to those of expert radiologists. [ABSTRACT FROM AUTHOR]

A multiobjective genetic algorithm is designed to optimize a computer-aided detection (CAD) system for identifying colonic polyps. Colonic polyps appear as elliptical protrusions on the inner surface of the colon. Curvature-based features for colonic polyp detection have proved to be successful in several CT colonography (CTC) CAD systems. Our CTC CAD program uses a sequential classifier to form initial polyp detections on the colon surface. The classifier utilizes a set of thresholds on curvature-based features to cluster suspicious colon surface regions into polyp candidates. The thresholds were previously chosen experimentally by using feature histograms. The chosen thresholds were effective for detecting polyps sized 10 mm or larger in diameter. However, many medium-sized polyps, 6-9 mm in diameter, were missed in the initial detection procedure. In this paper, the task of finding optimal thresholds as a multiobjective optimization problem was formulated, and a genetic algorithm to solve it was utilized by evolving the Pareto front of the Pareto optimal set. The new CTC CAD system was tested on 792 patients. The sensitivities of the optimized system improved significantly, from 61.68% to 74.71% with an increase of 13.03% (95% CI [6.57%, 19.5%], p = 7.78 x 10(-5)) for the size category of 6-9 mm polyps, from 65.02% to 77.4% with an increase of 12.38% (95% CI [6.23%, 18.53%], p = 7.95 x 10(-5)) for polyps 6 mm or larger, and from 82.2% to 90.58% with an increase of 8.38% (95% CI [0.75%, 16%], p = 0.03) for polyps 8 mm or larger at comparable false positive rates. The sensitivities of the optimized system are nearly equivalent to those of expert radiologists.