Analyzing and clustering neural data

This thesis aims to analyze neural data in an overall effort by the Charles Stark Draper Laboratory to determine an underlying pattern in brain activity in healthy individuals versus patients with a brain degenerative disorder. The neural data comes from ECoG (electrocorticography) applied to either humans or primates. Each ECoG array has electrodes that measure voltage variations which neuroscientists claim correlates to neurons transmitting signals to one another. ECoG differs from the less invasive technique of EEG (electroencephalography) in that EEG electrodes are placed above a patients scalp while ECoG involves drilling small holes in the skull to allow electrodes to be closer to the brain. Because of this ECoG boasts an exceptionally high signal-to-noise ratio and less susceptibility to artifacts than EEG [6]. While wearing the ECoG caps, the patients are asked to perform a range of different tasks. The tasks performed by patients are partitioned into different levels of mental stress i.e. how much concentration is presumably required. The specific dataset used in this thesis is derived from cognitive behavior experiments performed on primates at MGH (Massachusetts General Hospital). The content of this thesis can be thought of as a pipelined process. First the data is collected from the ECoG electrodes, then the data is pre-processed via signal processing techniques and finally the data is clustered via unsupervised learning techniques. For both the pre-processing and the clustering steps, different techniques are applied and then compared against one another. The focus of this thesis is to evaluate clustering techniques when applied to neural data. For the pre-processing step, two types of bandpass filters, a Butterworth Filter and a Chebyshev Filter were applied. For the clustering step three techniques were applied to the data, K-means Clustering, Spectral Clustering and Self-Tuning Spectral Clustering. We conclude that for pre-processing the results from both filters are very similar and thus either filter is sufficient. For clustering we conclude that K- means has the lowest amount of overlap between clusters. K-means is also the most time-efficient of the three techniques and is thus the ideal choice for this application.2016-10-27T00:00:00

Asset Choice And Time Diversification Benefits

The issue of time diversification has been controversial. While some findings support time diversification, others do not. For example, Hodges, Taylor and Yoder (1997) find bonds outperform stocks, but Mukherji (2002) finds stocks provide time diversification benefits. This paper investigates whether the differences in the findings of Hodges, Taylor and Yoder (1997) and Mukherji (2002) stem from methodological variation. Results indicate that the differences in the procedure used to estimate the holding period returns may in fact be the reason for the difference in findings. Using a procedure to estimate holding period returns that is similar to Hodges, Taylor and Yoder (1997), and a performance measure that is similar to Mukherji (2002), we do not find that stocks provide time diversification benefits

Foreign body inhalation in children: A mixed bag of experiences over two years in a tertiary care center of Eastern India

Background: Foreign body aspiration (FBA) is a common medical emergency in childhood. It may have serious and lethal outcomes if not managed promptly. The clinician must suspect FBA on the grounds of history, even if clinical and radiological findings are not supportive. Objective: The objective of this study was to study the clinicoepidemiological profile of FBA in children. Materials and Methods: This hospital-based retrospective study was carried out in the Department of Pediatric Surgery between October 2017 and November 2019. A total of 15 cases of FBA reported in children were analyzed. All these cases presented in amedical emergency. History taking and clinical examination were followed by radiological evaluation and bronchoscopy in all the cases. Results: We reported 15 cases of FBA in children in our setting for 2 years. There were 11 boys (73%) and four girls (27%). Most of the cases reported within 3 days of aspiration, but the time of presentation was as long as 6 months. Rigid bronchoscopy was performed in all cases, and Magill forceps were used in two cases. Spontaneous expulsion of the FB occurred in two cases. All cases were subjected to check bronchoscopy. Conclusion: The age group <3 years have the highest risk for FBA. ronchoscopy is a skilled procedure and requires the utmost care to avoid lethal complications. The clinician must not get hesitant in doing repeat bronchoscopy in multiple settings to ensure successful retrieval of the FB

Waddling Gait: A complication of valproate therapy and a thought beyond vitamin D deficiency

Proximal muscle weakness is a common presentation in paediatric-orthopaedic clinics and is frequently paired with a vitamin D deficiency diagnosis. Recently, side effects of the extensive use of antiepileptic and antipsychotic drugs such as sodium valproate in childhood disorders are being documented. Sodium valproate causes a time-dependent, drug-induced proximal myopathy. We report a 13-year-old female patient who presented at the Orthopaedic Outpatient Department at Lady Hardinge Medical College, New Delhi, India, in 2019 with an abnormal gait. The patient was taking a combination therapy of sodium valproate, risperidone and trihexyphenidyl for absence seizures and a mood disorder. Following clinical investigations, the patient was diagnosed with proximal myopathy. As a result of elevated serum alkaline phosphatase and creatine kinase myocardial band levels, sodium valproate was replaced with ethosuximide and a carnitine supplementation was prescribed. The patient fully recovered and regained full mobility. Proximal myopathy had been incorrectly managed and assumed to be caused by a vitamin D deficiency.Keywords: Muscle Weakness; Carnitine; Myopathy; Valproic Acid; Vitamin D Deficiency; Gait; Case Report; India

Study of underlying particle spectrum during huge X-ray flare of Mkn 421 in April 2013

Context: In April 2013, the nearby (z=0.031) TeV blazar, Mkn 421, showed one of the largest flares in X-rays since the past decade. Aim: To study all multiwavelength data available during MJD 56392 to 56403, with special emphasis on X-ray data, and understand the underlying particle energy distribution. Methods: We study the correlations between the UV and gamma bands with the X-ray band using the z-transformed discrete correlation function. We model the underlying particle spectrum with a single population of electrons emitting synchrotron radiation, and do a statistical fitting of the simultaneous, time-resolved data from the Swift-XRT and the NuSTAR. Results: There was rapid flux variability in the X-ray band, with a minimum doubling timescale of $1.69 \pm 0.13$ hrs. There were no corresponding flares in UV and gamma bands. The variability in UV and gamma rays are relatively modest with $\sim 8 \%$ and $\sim 16 \%$ respectively, and no significant correlation was found with the X-ray light curve. The observed X-ray spectrum shows clear curvature which can be fit by a log parabolic spectral form. This is best explained to originate from a log parabolic electron spectrum. However, a broken power law or a power law with an exponentially falling electron distribution cannot be ruled out either. Moreover, the excellent broadband spectrum from $0.3-79$ keV allows us to make predictions of the UV flux. We find that this prediction is compatible with the observed flux during the low state in X-rays. However, during the X-ray flares, the predicted flux is a factor of $2-50$ smaller than the observed one. This suggests that the X-ray flares are plausibly caused by a separate population which does not contribute significantly to the radiation at lower energies. Alternatively, the underlying particle spectrum can be much more complex than the ones explored in this work.Comment: 11 pages, 7 figures, Accepted in A&

Software Defect Prediction using Deep Learning by Correlation Clustering of Testing Metrics

The software industry has made significant efforts in recent years to enhance software quality in businesses. The use of proactively defect prediction in the software will assist programmers and white box testing in detecting issues early, saving time and money. Conventional software defect prediction methods focus on traditional source code metrics such as code complexities, lines of code, and so on. These capabilities, unfortunately, are unable to retrieve the semantics of source code. In this paper, we have presented a novel Correlation Clustering fine-tuned CNN (CCFT-CNN) model based on testing Metrics. CCFT-CNN can predict the regions of source code that contain faults, errors, and bugs. Abstract Syntax Tree (AST) tokens are extracted as testing Metrics vectors from the source code. The correlation among AST testing Metrics is performed and clustered as a more relevant feature vector and fed into Convolutional Neural Network (CNN). Then, to enhance the accuracy of defect prediction, fine-tuning of the CNN model is performed by applying hyperparameters. The result analysis is performed on the PROMISE dataset that contains samples of open-source Java applications such as Camel Dataset, Jedit dataset, Poi dataset, Synapse dataset, Xerces dataset, and Xalan dataset. The result findings show that the CCFT- CNN model increases the average F-measure by 2% when compared to the baseline model

Skateboards: Are they really perilous? A retrospective study from a district hospital

<p>Abstract</p> <p>Background</p> <p>Skateboarding has been a popular sport among teenagers even with its attendant associated risks. The literature is packed with articles regarding the perils of skateboards. Is the skateboard as dangerous as has been portrayed?</p> <p>Methods</p> <p>This was a retrospective study conducted over a 5 year period. All skateboard related injuries seen in the Orthopaedic unit were identified and data collated on patient demographics, mechanism & location of injury, annual incidence, type of injury, treatment needed including hospitalisation.</p> <p>Results</p> <p>We encountered 50 patients with skateboard related injuries. Most patients were males and under the age of 15. The annual incidence has remained low at about 10. The upper limb was predominantly involved with most injuries being fractures. Most injuries occurred during summer. The commonest treatment modality was plaster immobilisation. The distal radius was the commonest bone to be fractured. There were no head & neck injuries, open fractures or injuries requiring surgical intervention.</p> <p>Conclusion</p> <p>Despite its negative image among the medical fraternity, the skateboard does not appear to be a dangerous sport with a low incidence and injuries encountered being not severe. Skateboarding should be restricted to supervised skateboard parks and skateboarders should wear protective gear. These measures would reduce the number of skateboarders injured in motor vehicle collisions, reduce the personal injuries among skateboarders, and reduce the number of pedestrians injured in collisions with skateboarders.</p