Black Hole Production by Cosmic Rays

Ultra-high energy cosmic rays create black holes in scenarios with extra dimensions and TeV-scale gravity. In particular, cosmic neutrinos will produce black holes deep in the atmosphere, initiating quasi-horizontal showers far above the standard model rate. At the Auger Observatory, hundreds of black hole events may be observed, providing evidence for extra dimensions and the first opportunity for experimental study of microscopic black holes. If no black holes are found, the fundamental Planck scale must be above 2 TeV for any number of extra dimensions.Comment: 4 pages, 4 figures, PRL versio

VarDict: a novel and versatile variant caller for next-generation sequencing in cancer research

Accurate variant calling in next generation sequencing (NGS) is critical to understand cancer genomes better. Here we present VarDict, a novel and versatile variant caller for both DNA- and RNA-sequencing data. VarDict simultaneously calls SNV, MNV, InDels, complex and structural variants, expanding the detected genetic driver landscape of tumors. It performs local realignments on the fly for more accurate allele frequency estimation. VarDict performance scales linearly to sequencing depth, enabling ultra-deep sequencing used to explore tumor evolution or detect tumor DNA circulating in blood. In addition, VarDict performs amplicon aware variant calling for polymerase chain reaction (PCR)-based targeted sequencing often used in diagnostic settings, and is able to detect PCR artifacts. Finally, VarDict also detects differences in somatic and loss of heterozygosity variants between paired samples. VarDict reprocessing of The Cancer Genome Atlas (TCGA) Lung Adenocarcinoma dataset called known driver mutations in KRAS, EGFR, BRAF, PIK3CA and MET in 16% more patients than previously published variant calls. We believe VarDict will greatly facilitate application of NGS in clinical cancer research

Leveraging GPT-4 for identifying cancer phenotypes in electronic health records: A performance comparison between GPT-4, GPT-3.5-turbo, Flan-T5, Llama-3-8B, and spaCy\u27s rule-based and machine learning-based methods

OBJECTIVE: Accurately identifying clinical phenotypes from Electronic Health Records (EHRs) provides additional insights into patients\u27 health, especially when such information is unavailable in structured data. This study evaluates the application of OpenAI\u27s Generative Pre-trained Transformer (GPT)-4 model to identify clinical phenotypes from EHR text in non-small cell lung cancer (NSCLC) patients. The goal was to identify disease stages, treatments and progression utilizing GPT-4, and compare its performance against GPT-3.5-turbo, Flan-T5-xl, Flan-T5-xxl, Llama-3-8B, and 2 rule-based and machine learning-based methods, namely, scispaCy and medspaCy. MATERIALS AND METHODS: Phenotypes such as initial cancer stage, initial treatment, evidence of cancer recurrence, and affected organs during recurrence were identified from 13 646 clinical notes for 63 NSCLC patients from Washington University in St. Louis, Missouri. The performance of the GPT-4 model is evaluated against GPT-3.5-turbo, Flan-T5-xxl, Flan-T5-xl, Llama-3-8B, medspaCy, and scispaCy by comparing precision, recall, and micro-F1 scores. RESULTS: GPT-4 achieved higher F1 score, precision, and recall compared to Flan-T5-xl, Flan-T5-xxl, Llama-3-8B, medspaCy, and scispaCy\u27s models. GPT-3.5-turbo performed similarly to that of GPT-4. GPT, Flan-T5, and Llama models were not constrained by explicit rule requirements for contextual pattern recognition. spaCy models relied on predefined patterns, leading to their suboptimal performance. DISCUSSION AND CONCLUSION: GPT-4 improves clinical phenotype identification due to its robust pre-training and remarkable pattern recognition capability on the embedded tokens. It demonstrates data-driven effectiveness even with limited context in the input. While rule-based models remain useful for some tasks, GPT models offer improved contextual understanding of the text, and robust clinical phenotype extraction

Metallothermic Reduction of Silica Nanoparticles to Porous Silicon for Drug Delivery Using New and Existing Reductants

In this study, the influence of metals (Mg, Al, and Ca) and reaction conditions (time, temperature, and metal grain size) on the metallothermic reduction of Stöber silica nanoparticles (NPs) to form porous Si has been explored. Mg metal was found to be an effective reducing agent even at temperatures below its melting point; however, it also induced a high degree of structural damage and morphology change. Al was effective in reducing silica NPs only at its melting point or above, but the resulting particles retained a higher degree of structural morphology as compared to those reduced using Mg. Ca was found to be ineffective in reducing silica. A new reductant, a mixture of 70 % Mg and 30 % Al, was found to induce the least amount of morphology change, and the reactions proceeded at a temperature (450 °C) lower than those required with Mg or Al individually. Furthermore, porous Si NPs obtained using Mg, Al, and the mixture of 70 % Mg and 30 % Al as reductants have been investigated as carriers for ibuprofen loading and release. Porous Si obtained from reductions with Mg and the Mg/Al mixture showed higher drug loading and a sustained drug release profile, whereas porous Si obtained from Al reduction had lower loading and showed a conventional release profile over 24 h

A comparison of collision cross section values obtained via travelling wave ion mobility-mass spectrometry and ultra high performance liquid chromatography-ion mobility-mass spectrometry : application to the characterisation of metabolites in rat urine

A comprehensive Collision Cross Section (CCS) library was obtained via Travelling Wave Ion Guide mobility measurements through direct infusion (DI). The library consists of CCS and Mass Spectral (MS) data in negative and positive ElectroSpray Ionisation (ESI) mode for 463 and 479 endogenous metabolites, respectively. For both ionisation modes combined, TWCCSN2 data were obtained for 542 non-redundant metabolites. These data were acquired on two different ion mobility enabled orthogonal acceleration QToF MS systems in two different laboratories, with the majority of the resulting TWCCSN2 values (from detected compounds) found to be within 1% of one another. Validation of these results against two independent, external TWCCSN2 data sources and predicted TWCCSN2 values indicated to be within 1-2% of these other values. The same metabolites were then analysed using a rapid reversed-phase ultra (high) performance liquid chromatographic (U(H)PLC) separation combined with IM and MS (IM-MS) thus providing retention time (tr), m/z and TWCCSN2 values (with the latter compared with the DI-IM-MS data). Analytes for which TWCCSN2 values were obtained by U(H)PLC-IM-MS showed good agreement with the results obtained from DI-IM-MS. The repeatability of the TWCCSN2 values obtained for these metabolites on the different ion mobility QToF systems, using either DI or LC, encouraged the further evaluation of the U(H)PLC-IM-MS approach via the analysis of samples of rat urine, from control and methotrexate-treated animals, in order to assess the potential of the approach for metabolite identification and profiling in metabolic phenotyping studies. Based on the database derived from the standards 63 metabolites were identified in rat urine, using positive ESI, based on the combination of tr, TWCCSN2 and MS data.</p

Numerical relativity with characteristic evolution, using six angular patches

The characteristic approach to numerical relativity is a useful tool in evolving gravitational systems. In the past this has been implemented using two patches of stereographic angular coordinates. In other applications, a six-patch angular coordinate system has proved effective. Here we investigate the use of a six-patch system in characteristic numerical relativity, by comparing an existing two-patch implementation (using second-order finite differencing throughout) with a new six-patch implementation (using either second- or fourth-order finite differencing for the angular derivatives). We compare these different codes by monitoring the Einstein constraint equations, numerically evaluated independently from the evolution. We find that, compared to the (second-order) two-patch code at equivalent resolutions, the errors of the second-order six-patch code are smaller by a factor of about 2, and the errors of the fourth-order six-patch code are smaller by a factor of nearly 50.Comment: 12 pages, 5 figures, submitted to CQG (special NFNR issue

A Powassan virus domain III nanoparticle immunogen elicits neutralizing and protective antibodies in mice

Powassan virus (POWV) is an emerging tick borne flavivirus (TBFV) that causes severe neuroinvasive disease. Currently, there are no approved treatments or vaccines to combat POWV infection. Here, we generated and characterized a nanoparticle immunogen displaying domain III (EDIII) of the POWV E glycoprotein. Immunization with POWV EDIII presented on nanoparticles resulted in significantly higher serum neutralizing titers against POWV than immunization with monomeric POWV EDIII. Furthermore, passive transfer of EDIII-reactive sera protected against POWV challenge in vivo. We isolated and characterized a panel of EDIII-specific monoclonal antibodies (mAbs) and identified several that potently inhibit POWV infection and engage distinct epitopes within the lateral ridge and C-C\u27 loop of the EDIII. By creating a subunit-based nanoparticle immunogen with vaccine potential that elicits antibodies with protective activity against POWV infection, our findings enhance our understanding of the molecular determinants of antibody-mediated neutralization of TBFVs