Computational Histological Staining and Destaining of Prostate Core Biopsy RGB Images with Generative Adversarial Neural Networks

Histopathology tissue samples are widely available in two states: paraffin-embedded unstained and non-paraffin-embedded stained whole slide RGB images (WSRI). Hematoxylin and eosin stain (H&E) is one of the principal stains in histology but suffers from several shortcomings related to tissue preparation, staining protocols, slowness and human error. We report two novel approaches for training machine learning models for the computational H&E staining and destaining of prostate core biopsy RGB images. The staining model uses a conditional generative adversarial network that learns hierarchical non-linear mappings between whole slide RGB image (WSRI) pairs of prostate core biopsy before and after H&E staining. The trained staining model can then generate computationally H&E-stained prostate core WSRIs using previously unseen non-stained biopsy images as input. The destaining model, by learning mappings between an H&E stained WSRI and a non-stained WSRI of the same biopsy, can computationally destain previously unseen H&E-stained images. Structural and anatomical details of prostate tissue and colors, shapes, geometries, locations of nuclei, stroma, vessels, glands and other cellular components were generated by both models with structural similarity indices of 0.68 (staining) and 0.84 (destaining). The proposed staining and destaining models can engender computational H&E staining and destaining of WSRI biopsies without additional equipment and devices.Comment: Accepted for publication at 2018 IEEE International Conference on Machine Learning and Applications (ICMLA

Machine Learning Algorithms for Classification of Microcirculation Images from Septic and Non-Septic Patients

Sepsis is a life-threatening disease and one of the major causes of death in hospitals. Imaging of microcirculatory dysfunction is a promising approach for automated diagnosis of sepsis. We report a machine learning classifier capable of distinguishing non-septic and septic images from dark field microcirculation videos of patients. The classifier achieves an accuracy of 89.45%. The area under the receiver operating characteristics of the classifier was 0.92, the precision was 0.92 and the recall was 0.84. Codes representing the learned feature space of trained classifier were visualized using t-SNE embedding and were separable and distinguished between images from critically ill and non-septic patients. Using an unsupervised convolutional autoencoder, independent of the clinical diagnosis, we also report clustering of learned features from a compressed representation associated with healthy images and those with microcirculatory dysfunction. The feature space used by our trained classifier to distinguish between images from septic and non-septic patients has potential diagnostic application.Comment: Accepted for publication at 2018 IEEE International Conference on Machine Learning and Applications (IEEE ICMLA

Kaons and antikaons in strong magnetic fields

The in-medium masses of the kaons and antikaons in strongly magnetized asymmetric nuclear matter are studied using a chiral SU(3) model. The medium modifications of the masses of these open strange pseudoscalar mesons arise due to their interactions with the nucleons and scalar mesons within the model. The proton, the charged nucleon, has effects from the Landau energy levels in the presence of the magnetic field. The anomalous magnetic moments (AMM) of the nucleons are taken into consideration in the present study and these are seen to be large at high magnetic fields and high densities. The isospin effects are appreciable at high densities. The density effects are observed to be the dominant medium effects, as compared to the effects from magnetic field and isospin asymmetry. ~Comment: 22 pages, 4 figures, version to be published in Eur. Phys. Jour. A. arXiv admin note: text overlap with arXiv:1712.0799

Timing of Surgery and Pre-operative Physiological Parameters as Clinical Predictors of Surgical Outcomes in Traumatic Subaxial Cervical Spine Fractures and Dislocations

Abstract Objective To evaluate the risk factors and outcomes in patients surgically treated for subaxial cervical spine injuries with respect of the timing of surgery and preoperative physiological parameters of the patient. Methods 26 patients with sub-axial cervical spine fractures and dislocations were enrolled. Demographic data of patients, appropriate radiological investigation, and physiological parameters like respiratory rate, blood pressure, heart rate, PaO2 and ASIA impairment scale were documented. They were divided pre-operatively into 2 groups. Group U with patients having abnormal physiological parameters and Group S including patients having physiological parameters within normal range. They were further subdivided into early and late groups according to the timing of surgery as Uearly, Ulate, Searly and Slate. All the patients were called for follow-up at 1, 6 and 12 months. Results 56 percent of patients in Group S had neurological improvement by one ASIA grade and a good outcome irrespective of the timing of surgery. Patients in Group U having unstable physiological parameters and undergoing early surgical intervention had poor outcomes. Conclusion This study concludes that early surgical intervention in physiologically unstable patients had a strong association as a risk factor in the final outcome of the patients in terms of mortality and morbidity. Also, no positive association of improvement in physiologically stable patients with respect to the timing of surgery could be established

In silico interaction of Berberine with some immunomodulatory targets: A docking analysis

Plant, mineral, and animal products have been utilized as medications from the beginning of time to cure a variety of ailments. Use of medicinal herbs to modulate immune function has a rich history. Natural products serve as the foundation for contemporary pharmaceutical ingredients. Immunomodulation alters an individual's immune system by interfering with its normal processes. Immunomodulators derived from natural sources have been extensively studied in order to modify the immune system and prevent illness. Berberine is an alkaloid has been identified for its anti-inflammatory properties. In animal studies, Berberine was found to demonstrate analgesic properties. The current work is aimed to explore the in silico interactions of Berberine with various chemokines and inflammatory pathways. Berberine was docked with TNF-α, IL-1β, IL-6, and NOs in this investigation. Docking study demonstrated notable interactions with these targets. The present research provides insight into the development of new compounds for immunomodulation and the management of inflammatory illnesses. More research on Berberine and related flavonoids is necessary to assess its safety. As a result, Berberine can be regarded as a candidate for the advancement of an immunomodulatory agent

Multi-band Extension of the Wideband Timing Technique

The wideband timing technique enables the high-precision simultaneous estimation of Times of Arrival (ToAs) and Dispersion Measures (DMs) while effectively modeling frequency-dependent profile evolution. We present two novel independent methods that extend the standard wideband technique to handle simultaneous multi-band pulsar data incorporating profile evolution over a larger frequency span to estimate DMs and ToAs with enhanced precision. We implement the wideband likelihood using the libstempo python interface to perform wideband timing in the tempo2 framework. We present the application of these techniques to the dataset of fourteen millisecond pulsars observed simultaneously in Band 3 (300 - 500 MHz) and Band 5 (1260 - 1460 MHz) of the upgraded Giant Metrewave Radio Telescope (uGMRT) as a part of the Indian Pulsar Timing Array (InPTA) campaign. We achieve increased ToA and DM precision and sub-microsecond root mean square post-fit timing residuals by combining simultaneous multi-band pulsar observations done in non-contiguous bands for the first time using our novel techniques.Comment: Submitted to MNRA

Noise analysis of the Indian Pulsar Timing Array data release I

The Indian Pulsar Timing Array (InPTA) collaboration has recently made its first official data release (DR1) for a sample of 14 pulsars using 3.5 years of uGMRT observations. We present the results of single-pulsar noise analysis for each of these 14 pulsars using the InPTA DR1. For this purpose, we consider white noise, achromatic red noise, dispersion measure (DM) variations, and scattering variations in our analysis. We apply Bayesian model selection to obtain the preferred noise models among these for each pulsar. For PSR J1600$-$3053, we find no evidence of DM and scattering variations, while for PSR J1909$-$3744, we find no significant scattering variations. Properties vary dramatically among pulsars. For example, we find a strong chromatic noise with chromatic index $\sim$ 2.9 for PSR J1939+2134, indicating the possibility of a scattering index that doesn't agree with that expected for a Kolmogorov scattering medium consistent with similar results for millisecond pulsars in past studies. Despite the relatively short time baseline, the noise models broadly agree with the other PTAs and provide, at the same time, well-constrained DM and scattering variations.Comment: Accepted for publication in PRD, 30 pages, 17 figures, 4 table