Model compilation: An approach to automated model derivation

An approach is introduced to automated model derivation for knowledge based systems. The approach, model compilation, involves procedurally generating the set of domain models used by a knowledge based system. With an implemented example, how this approach can be used to derive models of different precision and abstraction is illustrated, and models are tailored to different tasks, from a given set of base domain models. In particular, two implemented model compilers are described, each of which takes as input a base model that describes the structure and behavior of a simple electromechanical device, the Reaction Wheel Assembly of NASA's Hubble Space Telescope. The compilers transform this relatively general base model into simple task specific models for troubleshooting and redesign, respectively, by applying a sequence of model transformations. Each transformation in this sequence produces an increasingly more specialized model. The compilation approach lessens the burden of updating and maintaining consistency among models by enabling their automatic regeneration

Subtyping of Dengue Viruses using Return Time Distribution based Appproach

Dengue virus (DENV) is the causative agent of Dengue Hemorrhagic Fever and Dengue Shock Syndrome, and continues to represent a major public health hazard. DENVs are antigenically classified in four serotypes and each serotype is further divided into respective genotypes. The association between DENV subtypes and the kind & severity of disease caused by them is known. Experimental and computational approaches for subtyping are routinely used for the purpose of diagnosis and treatment of DENV, in addition to the study of phylodynamics. All virus-specific molecular subtyping tools make use of sequence alignments at backend. But as the volume of molecular data increases, alignment-dependent methods become computationally intensive. Hence, the need for alternative efficient approaches for subtyping of viruses becomes apparent. Recently, the concept of Return time distribution (RTD) was proposed and validated for alignment-free clustering and molecular phylogeny. The RTD-based approach is extended here for the subtyping of DENVs. 
Subtyping methodology involves compilation of curated genomic data of known subtypes, computing RTD of these sequences at different levels of k-mers, derivation a distance matrix and clustering. The subtype of the unknown is predicted based on its clustering with known subtypes.
Dataset consisting of 1359 DENV genomes with sequence identity (>92%) were clustered using the RTD based approach at k=5. Serotype specific clades, despite geographical and temporal variation in the dataset, were observed with 100% accuracy. The method was also found to be efficient in terms of time and implementation, apart from accuracy in the subtyping of DENV

Synthesis and Biological Evaluation of 4-(3-Hydroxy-Benzofuran-2-yl) Coumarins

Various 4-bromomethylcoumarins (1a-k) were reacted with methyl salicylate to yield 2-(2-oxo-2H-chromen-4-ylmethoxy)-benzoic acid methyl esters (2a-k). Formations of (3a-k) were achieved by using DBU under microwave irradiation. Structures of all the compounds were established on the basis of their spectral data. All the compounds were tested in vitro for their antimicrobial activity and cell cytotoxicity. All the tested compounds (2b-k) and (3a-k) were shown to be better activity against Staphylococcus aureus than the standard Ciprofloxacin. The compound (3k) (R = 6-OMe) was found to be more potent cytotoxic than the standard 5-fluorouracil

Synthesis and cytotoxic studies of a new series of pyridinoxymethylcoumarins

A series of 3-​(pyridin-​3-​yloxymethyl)​-​chromen-​2-​ones were synthesized by the reaction of substituted 4-​bromomethylcoumarins with 3-​hydroxypyridines. The synthesized compds. were screened for their cytotoxic activities against Dalton's ascitic lymphoma (DAL) and Ehrlich ascites carcinoma (EAC) cell lines. The 2-​(2-​Methyl-​pyridin-​3-​yloxymethyl)​-​benzo[f]​chromen-​3-​one was found to be the most cytotoxic against DAL cell line and 6-​Isopropyl-​3-​(2-​methyl-​pyridin-​3-​yloxymethyl)​-​chromen-​2-​one was found to be the most cytotoxic against EAC cell line

Increased Mobilisation of Circulating Endothelial Progenitors in von Hippel-Lindau Disease and Renal Cell Carcinoma

Background: Circulating endothelial cells (CECs) are a candidate biomarker for monitoring angiogenesis in cancer. Circulating endothelial cell subsets are mobilised by angiogenic mediators. Because of the highly angiogenic phenotype of renal cell carcinoma (RCC), we sought to assess the potential of CECs as a marker of RCC in patients with von Hippel-Lindau (VHL) disease and those with sporadic RCC. Methods: We performed multicolour flow cytometry to enumerate CECs in patients with RCC, patients with VHL disease with and without RCC, and normal subjects. Two subsets of CECs were evaluated: mature CECs (mCECs) and circulating endothelial progenitors (CEPs). Results: In patients with VHL disease and RCC and those with sporadic RCC (N=10), CEPs and the CEP:mCEC ratio were higher than in normal subjects (N=17) (median CEPs: 0.97 vs 0.19 cells $\mu l^{-1}$, respectively, P<0.01; median CEP:mCEC: 0.92 vs 0.58, respectively, P=0.04). However, in patients with VHL without RCC, CECs were not increased. In paired pre- and post-nephrectomy RCC patient samples (N=20), CEPs decreased after surgery (median difference 0.02 cells $\mu l^{-1}$, −0.06 to 1.2; P=0.05). Conclusion: Circulating endothelial progenitors were elevated in RCC, but not in patients with VHL without RCC. Circulating endothelial progenitor enumeration merits further investigation as a monitoring strategy for patients with VHL

A comparison of desflurane and sevoflurane in the recovery of cognitive function after general anesthesia in elderly patients

Background: The postoperative cognitive dysfunction (POCD) or psychomotor function disorder is known to be associated with the anesthetic agents, as well as the physiological changes resulting from the anesthesia. The known risk factors are old age, preexisting cerebral cardiac or vascular disease, alcohol abuse, intra and post-operative complications.Methods: 50 patients above 65 years of age falling into ASA Grade 1, 2, or 3 were catagrzed into 2 groups, one (Group A) wherein sevoflurane was given as the anesthetic agent and the other (Group B) where desflurane was administered. All had undergone physical and regular blood examination. MMSE score was taken for all patients for cognitive recognition before surgery and 1, 3, and 6 hours after surgery.  Results: Of the 50 patients, the MMSE score was above 27 for all before surgery, while, post-surgery it was below 27 after I hour in 100% of the cases. After 3 hours, in Group A, the mean MMSE was above 27 while it was still below 27 in Group B while it was above 27 in both the Groups after 6 hours post-surgery. There was only 1 cases of POCD after 6 hours in Group A and none in Group B. The recovery time was faster in Group B as compared to Group A.Conclusions: Desflurane was marginally a better anesthetic agent in terms or recovery to sevoflurane and sevoflurane was slightly better than the former when it came to cognitive recognition Therefore, we conclude that both the drugs are equally good anesthetic agents.

Speech Recognition on Raspberry Pi using TensorFlow Lite

A distributed computing paradigm called "edge computing" attempts to improve application performance and lower latency by positioning processing and data storage closer to the data source. The need for artificial intelligence (AI) applications on edge devices has grown as smart gadgets and the Internet of Things (IoT) has become more prevalent. Nevertheless, implementing AI models is hampered by these devices' low memory and processing capacity. Google's lightweight, cross-platform framework TensorFlow Lite solves these issues by making it possible for AI models to be deployed effectively on devices with limited resources. With the help of its on-device machine learning tools, developers may run models on microcontrollers and other embedded, and edge devices. This project aims to develop a speech recognition system for a single spoken word using a Convolutional Neural Network (CNN) model deployed on a Raspberry Pi for real-time detection by leveraging TensorFlow Lite. The process involves extracting features from audio files, specifically using Mel Frequency Cepstral Coefficients (MFCC), to represent the speech signals. Python libraries are utilized to compute MFCC samples, which are then used to train the CNN model for making predictions on real-time audio data