A study of diaphyseal nutrient foramina in human femur

Background: The external opening of the nutrient canal in a bone is the nutrient foramen (NF). It is clinically important to have an understanding of the location, number, direction and caliber of diaphyseal nutrient foramina in femur, especially in orthopedic surgical procedures. Here we study the diaphyseal nutrient foramina of femur in detail.Methods: This study was conducted on 312, (154 right and 158 left), macerated specimens of adult human femur. All the important parameters were studied using osteometric board, vernier calipers and other precision measuring instruments.Results: The mean number of nutrient foramina per femur bone was 1.64 and mean distance of NF from upper end of femur was 19.48 cms. The foraminal index obtained was 45.01%. The most common location of NF was on the medial lip of linea aspera (40.9%). 44.6% femur had only one NF, while 49.4% had two NF, 3.8% femur had three NFs and 2.24% femur had four NFs. 50.48% of NFs were of big size caliber, 26.6% were of medium size and 22.8% were of small caliber. So 77.1% NFs in femur were dominant foramina.  In all the bones studied the direction of the nutrient foramina was always directed upwards.Conclusions: The findings of this study on nutrient foramina adds to the information from studies in the past by other authors but the importance of this study lies in the large sample size and the detailed study of caliber of the nutrient foramen for the first time.

LAR-1: Affirmative influences on Energy-Conservation and Network Lifetime in MANET

Nowadays, reduction of energy-consumption in Mobile Ad-Hoc Networks (MANETs) has been a herculean task. The LAR-1 position-based scheme in Ad-Hoc networks has recently invoked interest in the domain of energy conservation as it extends the network lifetime of the networks. The main objective of this new approach is, to achieve energy-aware routing by utilizing the sleep mode condition of mobile nodes in MANETs. In this paper, developments of performance-metrics using this new approach have been presented. Amazing results have been achieved through energy-aware routing by utilizing the sleep mode condition of mobile nodes in Ad-Hoc Networks. Using these developments, this approach has been found to be better than other protocols like DSR in networks

Deep learning enabled laser speckle wavemeter with a high dynamic range

Funding: This work was supported by a Medical Research Scotland PhD studentship PhD 873-2015 awarded to R.K.G, and grant funding from Leverhulme Trust (RPG-2017-197) and UK Engineering and Physical Sciences Research Council (grant EP/P030017/1).The speckle pattern produced when a laser is scattered by a disordered medium has recently been shown to give a surprisingly accurate or broadband measurement of wavelength. Here it is shown that deep learning is an ideal approach to analyse wavelength variations using a speckle wavemeter due to its ability to identify trends and overcome low signal to noise ratio in complex datasets. This combination enables wavelength measurement at high precision over a broad operating range in a single step, with a remarkable capability to reject instrumental and environmental noise, which has not been possible with previous approaches. It is demonstrated that the noise rejection capabilities of deep learning provide attometre-scale wavelength precision over an operating range from 488 nm to 976 nm. This dynamic range is six orders of magnitude beyond the state of the art.Publisher PDFPeer reviewe

Extended Kalman Filtering Projection Method to Reduce the 3σ Noise Value of Optical Biosensors

Optical biosensors have experienced a rapid growth over the past decade because of their high sensitivity and the fact that they are label-free. Many optical biosensors rely on tracking the change in a resonance signal or an interference pattern caused by the change in refractive index that occurs upon binding to a target biomarker. The most commonly used method for tracking such a signal is based on fitting the data with an appropriate mathematical function, such as a harmonic function or a Fano, Gaussian, or Lorentz function. However, these functions have limited fitting efficiency because of the deformation of data from noise. Here, we introduce an extended Kalman filter projection (EKFP) method to address the problem of resonance tracking and demonstrate that it improves the tolerance to noise, reduces the 3σ noise value, and lowers the limit of detection (LOD). We utilize the method to process the data of experiments for detecting the binding of C-reactive protein in a urine matrix with a chirped guided mode resonance sensor and are able to improve the LOD from 10 to 1 pg/mL. Our method reduces the 3σ noise value of this measurement compared to a simple Fano fit from 1.303 to 0.015 pixels. These results demonstrate the significant advantage of the EKFP method to resolving noisy data of optical biosensors

Label-free optical hemogram of granulocytes enhanced by artificial neural networks

Funding: Medical Research Scotland (PhD873-2015) and the UK Engineering and Physical Sciences Research Council through grants EP/R004854/1 and EP/P030017/1.An outstanding challenge for immunology is the classification of immune cells in a label-free fashion with high speed. For this purpose, optical techniques such as Raman spectroscopy or digital holographic microscopy have been used successfully to identify immune cell subsets. To achieve high accuracy, these techniques require a post-processing step using linear methods of multivariate processing, such as principal component analysis. Here we demonstrate for the first time a comparison between artificial neural networks and principal component analysis (PCA) to classify the key granulocyte cell lineages of neutrophils and eosinophils using both digital holographic microscopy and Raman spectroscopy. Artificial neural networks can offer advantages in terms of classification accuracy and speed over a PCA approach. We conclude that digital holographic microscopy with convolutional neural networks based analysis provides a route to a robust, stand-alone and high-throughput hemogram with a classification accuracy of 91.3 % at a throughput rate of greater than 100 cells per second.Publisher PDFPeer reviewe

Applications of machine learning in biophotonics and laser metrology

Recently, optical technologies have found several applications in fields including biophotonics, precision metrology and wavelength scale sensors. However, to gather statistically relevant information and analysis these methods require large amount of measurements. Current linear multivariate methods such as principal component analysis or linear discriminant analysis are not sufficient to analyze these big datasets with non-linear variability. Recently, the application of deep learning based artificial neural networks have found an upsurge in various areas of science ranging from quantum physics to evolutionary biology, providing an enhancement in the efficiency of various techniques. This thesis focuses on the applications of machine learning with the goal to enhance different aspects of biophotonics. Firstly, this thesis explores the application machine learning to enhance the label-free characterization of cells of the immune system using Raman spectroscopy and digital holographic microscopy. The combination of deep learning with digital holographic microscopy provides a route towards a high throughput hemogram device which would be useful for the classification of clinically important immune cells with morphological similarities but different functions. Following this, the applications of deep learning are explored in the regime of precision optical metrology for the development of a laser speckle wavemeter with a high dynamic range with an additional application for the development of a binary speckle based spectrometer. Finally, the application of machine learning based methods are also explored to improve the sensitivity of the chirped guided mode biosensor. A comparison between the linear method of principal component analysis and direct Fano fitting is drawn which is followed by the application of multi layered perceptron for further improvement."This work was supported by Medical Research Scotland [Grant Ph.D. 873-2015], which provided an opportunity of an industrial PhD with M Squared Lasers." -- Acknowledgement

SIMULATION STUDY OF BLACKHOLE ATTACK IN THE MOBILE AD HOC NETWORKS

A wireless ad hoc network is a temporary network set up by wireless nodes usually moving randomly and communicating without a network infrastructure. Due to security vulnerabilities of the routing protocols, however, wireless ad hoc networks may be unprotected against attacks by the malicious nodes. In this study we investigated the effects of Blackhole attacks on the network performance. We simulated Blackhole attacks in Qualnet Simulator and measured the packet loss in the network with and without a blackhole. The simulation is done on AODV (Ad hoc On Demand Distance Vector) Routing Protocol. The network performance in the presence of a blackhole is reduced up to 26%