Object Detection is one of the problematic Computer Vision (CV) problems with countless applications. We proposed a real-time object detection algorithm based on Improved You Only Look Once version 3 (YOLOv3) for detecting fish. The demand for monitoring the marine ecosystem is increasing day by day for a vigorous automated system, which has been beneficial for all of the researchers in order to collect information about marine life. This proposed work mainly approached the CV technique to detect and classify marine life. In this paper, we proposed improved YOLOv3 by increasing detection scale from 3 to 4, apply k-means clustering to increase the anchor boxes, novel transfer learning technique, and improvement in loss function to improve the model performance. We performed object detection on four fish species custom datasets by applying YOLOv3 architecture. We got 87.56% mean Average Precision (mAP). Moreover, comparing to the experimental analysis of the original YOLOv3 model with the improved one, we observed the mAP increased from 87.17% to 91.30. It showed that improved version outperforms than the original YOLOv3 model.

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Background and Objective N‐methyl‐D‐aspartate (NMDA) receptors are involved in pain signalling and neuroplasticity. Memantine has been shown to have analgesic properties in pre‐clinical and small clinical studies. We conducted a systematic review and meta‐analysis to assess the efficacy of memantine to prevent or reduce chronic pain. Databases and data treatment MEDLINE, EMBASE and CENTRAL databases were searched for comparative trials using memantine, either against placebo or active medications, for chronic pain in adults. Pain relief was considered our primary outcome. Meta‐analyses were conducted if outcomes were reported in two or more studies. Outcomes were reported as mean differences (MD) or risk ratios (RR) with 95% confidence intervals (CI). Quality was assessed using the GRADE approach. Results Among 454 citations, 15 studies were included with populations predominantly consisting of neuropathic conditions and fibromyalgia. Overall, we observed unclear reporting of randomization and allocation methods, apart from potential for publication bias. Among the 11 studies looking at chronic pain treatment, the difference in end pain score with memantine was not significant: MD = −0.58 units (95% CI −1.31, 0.14); I2 = 82% (low quality). In two surgical studies using memantine for pain prevention, memantine decreased pain intensity: MD = −1.02 units (95% CI −1.38, −0.66); I2 = 0%. Dizziness was significantly more common with memantine: RR = 4.90 (95% CI 1.26, 18.99); I2 = 52% (moderate quality). Conclusion The current evidence regarding the use of memantine for chronic pain is limited and uncertain. Despite its potential, pain relief achieved in clinical studies is small and is associated with an increase in dizziness. Significance Despite a sound rationale, the benefit of using memantine for chronic pain is unclear. Our systematic review and meta‐analysis show that memantine may have the potential to decrease pain. However, it can also increase common adverse effects. Considering the small number of studies with potential for bias and inconclusive evidence, there was low to very low certainty. Hence, no clear recommendations can be made about its routine clinical use until larger and more definitive studies are conducted.

BACKGROUND AND IMPORTANCE: We report the case history of solitary hypoglossal paraganglioma in a 64-year-old woman. The surgical difficulties encountered in the removal of this challenging tumor are discussed and as a literature review provided. CLINICAL PRESENTATION: A 64-year-old woman presented with a short history of dysphonia, occasional dysphagia, tinnitus, altered taste, and unilateral left-sided tongue wasting. On examination, there was left lower motor hypoglossal paralysis. Imaging showed a discrete enhancing lobulated mass, measuring 2 × 2 cm, in the region of the hypoglossal nerve extending into the hypoglossal canal suggestive of hypoglossal paraganglioma. A left dorsolateral suboccipital craniotomy was performed with the patient in the sitting position. The hypoglossal nerve appeared to be enlarged, and the jugular foramen was normal. Complete surgical debulking of the tumor was not attempted because of its vascular nature. The nerve was decompressed, and neuropathology confirmed a low-grade paraganglioma arising from the hypoglossal nerve. The patient was scheduled to receive stereotactic radiation for further management. CONCLUSION: When a case of solitary hypoglossal paraganglioma is encountered in clinical practice, the aim of management should be mainly focused on achieving a diagnosis and preserving the hypoglossal nerve function. If there is evidence of vascularity in the lesion noted on magnetic resonance imaging, a preoperative angiogram should be obtained with a view for embolization. We decompressed the hypoglossal canal and achieved good improvement in the patient's symptoms. We recommend stereotactic radiosurgery for remnant and small hypoglossal tumors and regular follow-up with magnetic resonance imaging scans.

Zooplankton is enormously diverse and fundamental group of microorganisms that exists in almost every freshwater body, determining its ecology and play a vital role in food chain. Considering the significance of zooplankton, the study of freshwater zooplankton is very essential which intensely relies on the classification of images. However, the routine manual analysis and classification is laborious, time consuming and expensive, and poses a significant challenge to experts. Thus, for recent decade much research is focused on the development of underwater imaging technologies and intelligent classification system of zooplankton. This work presents devotion to observation of freshwater zooplankton by designed underwater microscope and modeling the system for automatic classification among four different taxa. Unlike most of the existing zooplankton image classification systems, this model is trained on a comparatively small dataset collected from freshwater by designed underwater microscope. Transfer learning of pretrained AlexNet Convolutional Neural Network (CNN) model proved to be a potential approach in the system design. Among four networks trained over two datasets, the best overall classification accuracy of up to 93.1%, comparable to other existing systems was achieved on test dataset (92.5% for Calanoid and Cyclopoid (Female), 90% for Cyclopoid (Male) and 97.5% for Daphnia). Graphical User Interface (GUI) of the model constructed on MATLAB, makes it easy for the users to collect images for building database, train network and to classify images of different taxa. Moreover, the designed system is adaptable to the addition of more classes in the future.