Deep learning based multimodal urban air quality prediction and traffic analytics.

Urban activities, particularly vehicle traffic, are contributing significantly to environmental pollution with detrimental effects on public health. The ability to anticipate air quality in advance is critical for public authorities and the general public to plan and manage these activities, which ultimately help in minimizing the adverse impact on the environment and public health effectively. Thanks to recent advancements in Artificial Intelligence and sensor technology, forecasting air quality is possible through the consideration of various environmental factors. This paper presents our novel solution for air quality prediction and its correlation with different environmental factors and urban activities, such as traffic density. To this aim, we propose a multi-modal framework by integrating real-time data from different environmental sensors and traffic density extracted from Closed Circuit Television footage. The framework effectively addresses data inconsistencies arising from sensor and camera malfunctions within a streaming dataset. The dataset exhibits real-world complexities, including abrupt camera or station activations/deactivations, noise interference, and outliers. The proposed system tackles the challenge of predicting air quality at locations having no sensors or experiencing sensor failures by training a joint model on the data obtained from nearby stations/sensors using a Particle Swarm Optimization (PSO)-based merit fusion of the sensor data. The proposed methodology is evaluated using various variants of the LSTM model including Bi-directional LSTM, CNN-LSTM, and Convolutions LSTM (ConvLSTM) obtaining an improvement of 48%, 67%, and 173% for short-term, medium-term, and long-term periods, respectively, over the ARIMA model.

Platelet Anti-Aggregator Drowns the Heart.

BACKGROUND: Anagrelide is a drug used for treatment of essential thrombocytosis especially when conventional therapy is insufficient. Adverse effects associated with anagrelide are palpitation, liver toxicity, renal failure and in few cases pericardial effusion. We here report a rare case of anagrelide induced pericardial effusion. CASE PRESENTATION: A 76-year-old male with past medical history of hypertension, hyperlipidemia, chronic obstructive pulmonary disease, and myeloproliferative disorder presented to the emergency department with dyspnea on rest and exertion. He was initially treated with hydroxyurea for thrombocytosis but was later switched to anagrelide. On examination patient had muffled heart sounds. Lab investigations identified hyperkalemia and transaminitis. Transthoracic echocardiogram identified a moderate sized pericardial effusion. The pericardial effusion and transaminitis were attributed to anagrelide toxicity as other causes were ruled out. Pericardiocentesis was performed and anagrelide was discontinued. Patient was discharged in a well-compensated state with outpatient follow-up in two to three weeks. CONCLUSION: Anagrelide is considered to be very effective treatment for essential thrombocytosis. It is, however, associated with serious adverse effects such as pericardial effusion, liver toxicity and palpitations. The mechanisms of these adverse drug reactions are still not completely understood. We suggest that patient taking anagrelide presenting with shortness of breath should have a transthoracic echocardiogram performed to rule out pericardial effusion. Liver enzymes should also be monitored closely and anagrelide discontinued immediately if the above-mentioned adverse events are noted.

Myoelectric Computer Interface Training for Reducing Co-Activation and Enhancing Arm Movement in Chronic Stroke Survivors: A Randomized Trial.

BACKGROUND: Abnormal muscle co-activation contributes to impairment after stroke. We developed a myoelectric computer interface (MyoCI) training paradigm to reduce abnormal co-activation. MyoCI provides intuitive feedback about muscle activation patterns, enabling decoupling of these muscles. OBJECTIVE: To investigate tolerability and effects of MyoCI training of 3 muscle pairs on arm motor recovery after stroke, including effects of training dose and isometric versus movement-based training. METHODS: We randomized chronic stroke survivors with moderate-to-severe arm impairment to 3 groups. Two groups tested different doses of isometric MyoCI (60 vs 90 minutes), and one group tested MyoCI without arm restraint (90 minutes), over 6 weeks. Primary outcome was arm impairment (Fugl-Meyer Assessment). Secondary outcomes included function, spasticity, and elbow range-of-motion at weeks 6 and 10. RESULTS: Over all 32 subjects, MyoCI training of 3 muscle pairs significantly reduced impairment (Fugl-Meyer Assessment) by 3.3 ± 0.6 and 3.1 ± 0.7 ( P < 10-4) at weeks 6 and 10, respectively. Each group improved significantly from baseline; no significant differences were seen between groups. Participants' lab-based and home-based function also improved at weeks 6 and 10 ( P ≤ .01). Spasticity also decreased over all subjects, and elbow range-of-motion improved. Both moderately and severely impaired patients showed significant improvement. No participants had training-related adverse events. MyoCI reduced abnormal co-activation, which appeared to transfer to reaching in the movement group. CONCLUSIONS: MyoCI is a well-tolerated, novel rehabilitation tool that enables stroke survivors to reduce abnormal co-activation. It may reduce impairment and spasticity and improve arm function, even in severely impaired patients.

The effect of myoelectric computer interface training on arm kinematics and function after stroke.

Abnormal co-activation patterns of arm muscles is a substantial cause of impaired arm function after stroke. We designed a myoelectric computer interface (MCI) training paradigm to help stroke survivors reduce this abnormal coactivation. Here, we evaluated the effects of MCI training on function and arm kinematics in 32 chronic stroke survivors. We compared the effects of training duration and isometric vs. movement-based training conditions in 3 different groups. All groups reduced abnormal co-activation in targeted muscles, and showed reduced arm impairment after 6 weeks of training. They also showed improvements in arm kinematics as well as functional scores. Moreover, the gains persisted, though most were reduced, at one month after training stopped. These results suggest that MCI training holds promise to improve arm function after stroke.

Primary Ewing's sarcoma of the orbit with intracranial extension abutting the temporal lobe: a rare case report.

Ewing's sarcoma is a small, blue, round-cell tumor of mesenchymal origin which typically presents itself during the first and the second decades of life. Typically, it is noticed in the long bones of the limbs, pelvis, or ribs. There have been few reports worldwide with none in Pakistan of this rare phenomenon. We report here the case of a 16-year-old female diagnosed with Ewing's sarcoma with intracranial extension abutting the temporal lobe resulting in proptosis and partial loss of vision in the left eye. The purpose of this case is to discuss the clinicoradiological presentation, microscopic description, and management of the patient.