Machine Learning Models for 6-Month Survival Prediction after Surgical Resection of Glioblastoma

Introduction: The role of surgical resection for the treatment of glioblastoma multiforme is well established. Survival analysis after resective surgery in the literature comprises mostly of traditional statistical models. Machine learning models offer powerful predictive and analytical capability for varied datasets and offer improved generalizability and scalability. We analyzed survival data of patients with glioblastoma with various machine learning algorithms and compared it to binary logistic regression. Methods: We retrospectively identified cases of glioblastoma treated with surgical resection at our institution from 2012-2018. Feature scaling and one-hot encoding was used to better fit the models to the data and used the formula X’ = (X – Xmin)/(Xmax – Xmin). Feature selection was performed using chi-squared analysis (features with p Results: 582 patients fit the inclusion criteria and were used to build these models. 6-month mortality was 43.13%. Accuracy scores (AUC) for models used were 0.670 (logistic regression), 0.704 (Random Forest), 0.585 (Support Vector Machine), 0.560 (Naïve Bayes), 0.650 (XG Boost), 0.585 (Stochastic Gradient Descent Classifier), and 0.740 (Neural Network). 5-fold cross validation was used to ensure generalizability to an independent dataset. Conclusion: Machine learning methods for prediction of six-month survival for glioblastoma are promising analytical tools that we show can approach or exceed the accuracy of traditional logistic regression, particularly neural networks and the random forest algorithm. Improved prediction of 6-month survival using machine learning offers increased capabilities for patient education, adjuvant chemotherapy or radiation planning, and post-operative counseling, while maintaining increased adaptability and generalizability compared to regression models

Detecting Anterior Cruciate Ligament Tears and Posterolateral Corner Injuries on Magnetic Resonance Imaging

Introduction: Anterior Cruciate Ligament (ACL) tears are an extremely common orthopedic injury, with an incidence ranging from 39-52 per 100,000. Knee Magnetic Resonance Imaging (MRI) scans are the gold standard for diagnosing ACL tears and their comorbidities, such as posterolateral corner injuries; the results of these scans determine the appropriate treatment needed for patients. There is evidence that machine learning can be used to automate the detection of pathology on MRI, and we hypothesize that we can train a neural network machine learning model to accurately interpret ACL injuries and posterolateral corner injuries. Methods: We will be analyzing over 1000 knee MRIs including those that are normal, those with ACL tears, and those with ACL tears with posterolateral corner injuries. First, we will manually annotate the knee MRIs to classify them appropriately. We will then train a convoluted neural network machine learning model on ~80% of the data, and use the remaining ~20% to test its accuracy. We will compare the accuracy of our model to the accuracy of musculoskeletal radiologists. Results: We anticipate that our model will have comparable accuracy predicting ACL tears and posterolateral corner injuries to that of musculoskeletal radiologists. By having access to our model’s predictions, we expect radiologists will be able to detect ACL tears with posterolateral corner injuries with improved accuracy and speed. Discussion: While we do not have results yet, we anticipate that our model will be an early step to developing useful tools that aid radiologists. Our model will be trained on a large dataset which will increase its generalizability for future implementation. Radiologists can use our model’s predictions to aid them in diagnosis of pathology on knee MRI. We expect that improved diagnosis will improve patient treatment outcomes

Readmission Risk Assessment Tool for Stroke Patients

Introduction: Strokes are one of the leading causes of morbidity and mortality in the world and its cost of management has vastly increased; an effective prediction tool that utilizes artificial intelligence to lower the rate of stroke-related readmissions has the potential to lower healthcare costs and increase the quality of provider care. We hypothesize that machine learning techniques are superior to traditional statistics when determining the likelihood of 30-day readmission for Jefferson’s stroke patients. Methods: Jefferson’s existing data on stroke patients were cleaned, aggregated, and prepared to be split into train and test sets. Using the train sets, machine learning (ML) models such as Random Forest, Support Vector Machines, and Neural Networks were trained to assess the risk of readmission. Each model’s accuracy and precision were captured in the form of confusion matrices, AUCs, and more to reveal the most superior ML method in assessing this risk. These results were then compared to the readmission risk determined by traditional statistics. Results: After training the ML models, the test sets were inputted to determine how accurately they could predict a stroke patient’s chance of readmission with new data. Traditional statistics (in the form of logistic regression) showed an accuracy of 84%. The ML methods utilized resulted in the following accuracies: Random Forest at 95.50%, SVM at 94.79%, and Neural Networks at 95.40%. Discussion: This study not only demonstrates that machine learning methods are superior to traditional statistics in regard to determining the 30-day readmission risks for Jefferson stroke patients, but it also shows that the Random Forest model is the most accurate in doing so. The potential implications of this tool are large; its use can be seen at both the patient and the hospital levels by improving costs for the patient and the hospital as well as improving stroke education and care

Star Formation in Dwarf Galaxies of the Nearby Centaurus A Group

We present Halpha narrow-band imaging of 17 dwarf irregular galaxies (dIs) in the nearby Centaurus A Group. Although all large galaxies of the group have a current or recent enhanced star formation episode, the dIs have normal star formation rates and do not contain a larger fraction of dwarf starbursts than other nearby groups. Relative distances between dIs and larger galaxies of the group can be computed in 3D since most of them have now fairly accurately known distances. We find that the dI star formation rates do not depend on local environment, and in particular they do not show any correlation with the distance of the dI to the nearest large galaxy of the group. There is a clear morphology-density relation in the Centaurus A Group, similarly to the Sculptor and Local Groups, in the sense that dEs/dSphs tend to be at small distances from the more massive galaxies of the group, while dIs are on average at larger distances. We find four transition dwarfs in the Group, dwarfs that show characteristics of both dE/dSphs and dIs, and which contain cold gas but no current star formation. Interestingly the transition dwarfs have an average distance to the more massive galaxies which is intermediate between those of the dEs/dSphs and dIs, and which is quite large: 0.54 +- 0.31 Mpc. This large distance poses some difficulty for the most popular scenarios proposed for transforming a dI into a dE/dSph (ram-pressure with tidal stripping or galaxy harassment). If the observed transition dwarfs are indeed missing links between dIs and dE/dSphs, their relative isolation makes it less likely to have been produced by these mechanisms. We propose that an inhomogeneous IGM containing higher density clumps would be able to ram-pressure stripped the dIs at such large distances.Comment: 57 pages, 10 fi5gure

Predictions for high-frequency radio surveys of extragalactic sources

We present detailed predictions of the contributions of the various source populations to the counts at frequencies of tens of GHz. New evolutionary models are worked out for flat-spectrum radio quasars, BL Lac objects, and steep-spectrum sources. Source populations characterized by spectra peaking at high radio frequencies, such as extreme GPS sources, ADAF/ADIOS sources and early phases of gamma-ray burst afterglows are also dealt with. The counts of different populations of star-forming galaxies (normal spirals, starbursts, high-z galaxies detected by SCUBA and MAMBO surveys, interpreted as proto-spheroidal galaxies) are estimated taking into account both synchrotron and free-free emission, and dust re-radiation. Our analysis is completed by updated counts of Sunyaev-Zeldovich effects in clusters of galaxies and by a preliminary estimate of galactic-scale Sunyaev-Zeldovich signals associated to proto-galactic plasma.Comment: 12 pages, 14 figures, to be published in A&

Foreground simulations for the LOFAR - Epoch of Reionization Experiment

Future high redshift 21-cm experiments will suffer from a high degree of contamination, due both to astrophysical foregrounds and to non-astrophysical and instrumental effects. In order to reliably extract the cosmological signal from the observed data, it is essential to understand very well all data components and their influence on the extracted signal. Here we present simulated astrophysical foregrounds datacubes and discuss their possible statistical effects on the data. The foreground maps are produced assuming 5 deg x 5 deg windows that match those expected to be observed by the LOFAR Epoch-of-Reionization (EoR) key science project. We show that with the expected LOFAR-EoR sky and receiver noise levels, which amount to ~52 mK at 150 MHz after 300 hours of total observing time, a simple polynomial fit allows a statistical reconstruction of the signal. We also show that the polynomial fitting will work for maps with realistic yet idealised instrument response, i.e., a response that includes only a uniform uv coverage as a function of frequency and ignores many other uncertainties. Polarized galactic synchrotron maps that include internal polarization and a number of Faraday screens along the line of sight are also simulated. The importance of these stems from the fact that the LOFAR instrument, in common with all current interferometric EoR experiments has an instrumentally polarized response.Comment: 18 figures, 3 tables, accepted to be published in MNRA

Serendipitous discovery of a dying Giant Radio Galaxy associated with NGC 1534, using the Murchison Widefield Array

This article has been accepted for publication in Monthly Notices of the Royal Astronomical Society. © 2015 The Authors. Published by Oxford University Press on behalf of the Royal Astronomical Society.Recent observations with the Murchison Widefield Array at 185~MHz have serendipitously unveiled a heretofore unknown giant and relatively nearby ($z = 0.0178$) radio galaxy associated with NGC\,1534. The diffuse emission presented here is the first indication that NGC\,1534 is one of a rare class of objects (along with NGC\,5128 and NGC\,612) in which a galaxy with a prominent dust lane hosts radio emission on scales of $\sim$700\,kpc. We present details of the radio emission along with a detailed comparison with other radio galaxies with disks. NGC1534 is the lowest surface brightness radio galaxy known with an estimated scaled 1.4-GHz surface brightness of just 0.2\,mJy\,arcmin$^{-2}$. The radio lobes have one of the steepest spectral indices yet observed: $\alpha=-2.1\pm0.1$, and the core to lobe luminosity ratio is $Peer reviewe

An Antiviral Response Directed by PKR Phosphorylation of the RNA Helicase A

The double-stranded RNA-activated protein kinase R (PKR) is a key regulator of the innate immune response. Activation of PKR during viral infection culminates in phosphorylation of the α subunit of the eukaryotic translation initiation factor 2 (eIF2α) to inhibit protein translation. A broad range of regulatory functions has also been attributed to PKR. However, as few additional PKR substrates have been identified, the mechanisms remain unclear. Here, PKR is shown to interact with an essential RNA helicase, RHA. Moreover, RHA is identified as a substrate for PKR, with phosphorylation perturbing the association of the helicase with double-stranded RNA (dsRNA). Through this mechanism, PKR can modulate transcription, as revealed by its ability to prevent the capacity of RHA to catalyze transactivating response (TAR)–mediated type 1 human immunodeficiency virus (HIV-1) gene regulation. Consequently, HIV-1 virions packaged in cells also expressing the decoy RHA peptides subsequently had enhanced infectivity. The data demonstrate interplay between key components of dsRNA metabolism, both connecting RHA to an important component of innate immunity and delineating an unanticipated role for PKR in RNA metabolism

Mechanisms of Mycotoxin-Induced Neurotoxicity through Oxidative Stress-Associated Pathways

Among many mycotoxins, T-2 toxin, macrocyclic trichothecenes, fumonisin B1 (FB1) and ochratochin A (OTA) are known to have the potential to induce neurotoxicity in rodent models. T-2 toxin induces neuronal cell apoptosis in the fetal and adult brain. Macrocyclic trichothecenes bring about neuronal cell apoptosis and inflammation in the olfactory epithelium and olfactory bulb. FB1 induces neuronal degeneration in the cerebral cortex, concurrent with disruption of de novo ceramide synthesis. OTA causes acute depletion of striatal dopamine and its metabolites, accompanying evidence of neuronal cell apoptosis in the substantia nigra, striatum and hippocampus. This paper reviews the mechanisms of neurotoxicity induced by these mycotoxins especially from the viewpoint of oxidative stress-associated pathways