Chondrosarcoma of the anterior chest wall: surgical resection and reconstruction, our institutional experience

Primary chest wall tumours are not very common. Chondrosarcomas is most common tumour arising from the chest wall. It occurs more often during the third and fourth decade of life. Chondrosarcomas are resistant to conventional chemotherapy and radiotherapy. Wide margin surgical excision remains the best available treatment approach. For chondrosarcomas involving the chest wall, surgical excision may result in chest wall defects that may require reconstruction to obliterate dead space, restore chest wall rigidity, preserve respiratory mechanics, maintain pulmonary function, protect intrathoracic organs, provide soft tissue coverage and minimize deformity. In this article we present a series of 3 cases of chondrosarcoma of anterior chest wall managed at government Royapettah hospital, Kilpauk medical college, Chennai. A 71-year-old male patient, a case of 22×20 cm giant chondrosarcoma arising from anterior left chest wall involving 2nd to 8th ribs. We did wide local excision and reconstruction of chest wall with a synthetic bone cement (methyl methacrylate) construct, sandwiched between two layers of polypropylene mesh.  A 38-year-old male patient, a case of 8×6 cm chondrosarcoma of left anterior chest wall involving 9th rib, we did wide excision of tumor along with 8th, 9th, 10th ribs and defect reconstructed with prolene mesh.  A 37-year-old male patient, a case of 5×4 cm chondrosarcoma arising from left 4th rib. We did wide excision along with 4th rib and primary closure. Patients with chondrosarcomas generally have a good prognosis when optimally diagnosed and treated. Our case series is interesting due to the different sizes of chondrosarcomas at presentation, which are managed differently. Complete resection with wide surgical margin remains the best available treatment, but post resection chest wall reconstruction is posing a great surgical challenge

Physiochemical changes during different stages of fruit ripening of climacteric fruit of mango (Mangifera indica L.) and non-climacteric of fruit cashew apple (Anacardium occidentale L.)

The present investigation was made to study the ripening behavior of climacteric fruit of mango (Mangifera indica L.)  and a non–climacteric fruit of  cashew apple (Anacardium occidentale L.) The different stages of fruit namely immature, mature, quarter ripen, half ripen, full ripen and over ripen were used for various analyses with pericarp tissues of mango and cashew apple fruits. Physio–Chemical parameters such as fruit firmness, total soluble solids, titratable acidity and pH. The fruit firmness and titratable acidity high at immature stage and low in over ripen stage. On the other hand, Total Soluble Solids and PH low at immature stage and high in over ripen stage

Design and Simulation of Blending Function for Landing Phase of a UAV

This paper aims to achieve the autonomous landing of unmanned air vehicle (UAV).  Itmainly deals with glide path design, flare path design, design of blending function, andinterfacing the glide and flare paths with the blending function. During transition from glideslope to flare path, a UAV will tend to the unstable region. In a manned aircraft, the pilotcontrols the unstability that occurs during the change of phase from glide slope to flare, butwhich is impossible in UAV till now. A blending function has been formulated for use in a UAVto overcome this unstability during transition. This simulation is done with the Matlab Simulinkand the results are reported

VIRTUAL SCREENING OF HETEROCYCLIC COMPOUNDS AGAINST ANGIOTENSIN-CONVERTING ENZYME FOR POTENTIAL ANTIHYPERTENSIVE INHIBITORS

Objective: The objective of this study was to investigate the antihypertensive activity of heterocyclic compounds against angiotensin-converting enzyme (ACE) through molecular docking studies. Methods: The X-ray crystal three-dimensional (3D) structure of human ACE complexed with lisinopril (PDB ID: 1O86) was retrieved from protein databank. The two-dimensional structures of 10 selected heterocyclic compounds were drawn in ACD-Chemsketch and converted into 3D structures. The 3D structures of compounds were virtually screened in the binding pockets of ACE using FlexX docking program. Further, the chemical entities revealing the molecular electronic structures of the best docked compound (Compound-4) were explored through density functional theory studies. Results: The Compound-4 showed the highest docking score of −26.6290 kJ/mol with ACE. The Hbond and non-bonded interactions are favored by phenylalanine, leucine, and arginine. The energy gap of 1.60 eV between highest occupied molecular orbital and lowest unoccupied molecular orbitals explained the presence of strong electron-acceptor group. Furthermore, the molecular electrostatic potential studies clearly envisaged the requirement of electropositive and electronegative groups are crucial for the ACE inhibitor activities. Conclusion: The identification of good ACE inhibitors requires the understanding of the current ACE inhibitors. Thus, the docking interactions of Compound-4 and its molecular electronic structure significantly imply its potential as antihypertensive agent. However, further clinical studies are required to ascertain its potential toxic effects

Adaptive filtering of radar images for autofocus applications

Autofocus techniques are being designed at the Jet Propulsion Laboratory to automatically choose the filter parameters (i.e., the focus) for the digital synthetic aperture radar correlator; currently, processing relies upon interaction with a human operator who uses his subjective assessment of the quality of the processed SAR data. Algorithms were devised applying image cross-correlation to aid in the choice of filter parameters, but this method also has its drawbacks in that the cross-correlation result may not be readily interpretable. Enhanced performance of the cross-correlation techniques of JPL was hypothesized given that the images to be cross-correlated were first filtered to improve the signal-to-noise ratio for the pair of scenes. The results of experiments are described and images are shown

Analysis of geologic terrain models for determination of optimum SAR sensor configuration and optimum information extraction for exploration of global non-renewable resources. Pilot study: Arkansas Remote Sensing Laboratory, part 1, part 2, and part 3

Computer-generated radar simulations and mathematical geologic terrain models were used to establish the optimum radar sensor operating parameters for geologic research. An initial set of mathematical geologic terrain models was created for three basic landforms and families of simulated radar images were prepared from these models for numerous interacting sensor, platform, and terrain variables. The tradeoffs between the various sensor parameters and the quantity and quality of the extractable geologic data were investigated as well as the development of automated techniques of digital SAR image analysis. Initial work on a texture analysis of SEASAT SAR imagery is reported. Computer-generated radar simulations are shown for combinations of two geologic models and three SAR angles of incidence

Causal Graphs Underlying Generative Models: Path to Learning with Limited Data

Training generative models that capture rich semantics of the data and interpreting the latent representations encoded by such models are very important problems in unsupervised learning. In this work, we provide a simple algorithm that relies on perturbation experiments on latent codes of a pre-trained generative autoencoder to uncover a causal graph that is implied by the generative model. We leverage pre-trained attribute classifiers and perform perturbation experiments to check for influence of a given latent variable on a subset of attributes. Given this, we show that one can fit an effective causal graph that models a structural equation model between latent codes taken as exogenous variables and attributes taken as observed variables. One interesting aspect is that a single latent variable controls multiple overlapping subsets of attributes unlike conventional approach that tries to impose full independence. Using a pre-trained RNN-based generative autoencoder trained on a dataset of peptide sequences, we demonstrate that the learnt causal graph from our algorithm between various attributes and latent codes can be used to predict a specific property for sequences which are unseen. We compare prediction models trained on either all available attributes or only the ones in the Markov blanket and empirically show that in both the unsupervised and supervised regimes, typically, using the predictor that relies on Markov blanket attributes generalizes better for out-of-distribution sequences

Industrial fisheries off Saurashtra coast based on exploratory survey during 1985-'88

In Saurashtra waters, where fishery resource is currently being well exploited by private sector, exploratory survey programmes are being conducted by Government of India. The analysis, based on 4 year survey (1985-'88), with a view to provide information and to extend our knowledge about the spatial and seasonal distribution of various industrially important fishes along the Saurashtra coast. Ribbon fish and sciaenids which constituted the bulk of the catch together formed more than 60% of the total catch and catch rate were 14.7 and 14.5 kg/hr respectively. Area-wise analysis of data revealed that maximum effort was expended in 21° 69 0 and the effort was very low in 21° 70° and 23° 68° Depth wise analysis revealed that the maximum catch rate of ribbon fish and other sciaenids was obtained at 21-30 m depth. The catch rate of elasmobranch, carangid and Lactarius lactarius was maximum at 41-50 m, cat fish, pomfret and perch at 51-60 m and carangid and cephalopod at 61-70 m depth

Crop classification using multidate/multifrequency radar data

Both C- and L-band radar data acquired over a test site near Colby, Kansas during the summer of 1978 were used to identify three types of vegetation cover and bare soil. The effects of frequency, polarization, and the look angle on the overall accuracy of recognizing the four types of ground cover were analyzed. In addition, multidate data were used to study the improvement in recognition accuracy possible with the addition of temporal information. The soil moisture conditions had changed considerably during the temporal sequence of the data; hence, the effects of soil moisture on the ability to discriminate between cover types were also analyzed. The results provide useful information needed for selecting the parameters of a radar system for monitoring crops

Artificial neural network modeling of the tensile properties of indigeneously developed 15Cr-15Ni-2.2Mo-Ti modified austenitic stainless steel

The severe and hostile operating conditions of fast breeder reactors demand the development of new austenitic stainless steels that possess higher resistance to void swelling and irradiation embrittlement. This paper discusses the efforts made in the laboratory and industrial scale development of a 15Cr-15Ni-2.2Mo-Ti modified austenitic stainless steel and the evaluation of tensile properties. Melting and casting were carried out in a vacuum induction furnace and the data on recovery of various alloying elements was obtained for charge calculations. Based on the recovery data and decarburisation behavicur under different vacuum levels, a series of alloys with close chemistry variations were prepared. Heat treatment was optimised for these special steels to control the grain size at required level. The ingots were thermo-mechanically processed and tensile properties were evaluated. This experimental data has been used to train and test an artificial neural network. The input parameters of the neural network are chemical compositions and test temperature while the yield strength, ultimate tensile strength and uniform elongation were obtained as output. A multilayer perceptron (MLP) based feed-forward network with back-propagation learning algorithm has been employed. A very good performance of the developed network is obtained. The model can be used as a guideline for new alloy development