Relationship between smoking and pulmonary tuberculosis based on positive sputum smears.

Introduction: Smoking and tuberculosis are the two major, global health problems. Not only active smokers but also passive smokers are at risk of becoming infected with tuberculosis. Through many mechanisms, smoking decreases immunity and predisposes to numerous infections.

Functional Outcome of Intertrochanteric Fractures of the Femur Treated With Standard Surgical Modalities.

Introduction: Among the most prevalent injuries among the elderly with trivial trauma are trochanteric fractures. Osteoporosis and female sex are additional risk factors. The patient's age, the fracture, their overall health, the amount of time between the fracture and treatment, the effectiveness of the treatment, any concurrent medical care, and the stability of the fixation all affect how well the patient responds to treatment.

"Preventing nail migration by stack nailing in forearm fractures" - A technical note.

Diaphyseal forearm fractures are typically treated with open reduction and plate fixation, which has been proven to be the most effective method in numerous trials. However, the risk of periosteal stripping, haemorrhage evacuation, nonunion, and infection is always present and may cause a delayed union and other surgical consequences. Although intramedullary nails have been mentioned in the literature for forearm bone fractures, issues regarding their common complication of nail migration, especially of the ulnar fracture, before the fracture has been consolidated is an issue, and has not been addressed.

Land cover classification from multi-temporal, multi-spectral remotely sensed imagery using patch-based recurrent neural networks.

Environmental sustainability research is dependent on accurate land cover information. Even with the increased number of satellite systems and sensors acquiring data with improved spectral, spatial, radiometric and temporal characteristics and the new data distribution policy, most existing land cover datasets are derived from a pixel-based, single-date multi-spectral remotely sensed image with an unacceptable accuracy. One major bottleneck for accuracy improvement is how to develop an accurate and effective image classification protocol.

A patch-based convolutional neural network for remote sensing image classification.

Availability of accurate land cover information over large areas is essential to the global environment sustainability; digital classification using medium-resolution remote sensing data would provide an effective method to generate the required land cover information. However, low accuracy of existing per-pixel based classification methods for medium-resolution data is a fundamental limiting factor. While convolutional neural networks (CNNs) with deep layers have achieved unprecedented improvements in object recognition applications that rely on fine image structures, they cannot be applied directly to medium-resolution data due to lack of such fine structures.