Land Transport Emergency Response Technology Report

Sandia National Laboratories was tasked by the Japan Nuclear Cycle Development Institute (JNC) to provide assistance in developing an emergency response plan for radioactive material transportation activities. Those tasks included compiling radioactive materials (RAM) transportation accident data from the open literature and databases, investigating emergency response plans for radioactive materials transport in the United States, and developing specific recommendations for the JNC' nuclear material transport emergency response plan, based on information gathered during the first two tasks. These recommendations include developing a RAM database, a public transparency Internet website, an emergency response infrastructure designed specifically for transportation needs, and a clear set of directives to provide authority in the case of transportation accidents or incidents involving RAM

Clinical validation and utility of Percepta GSC for the evaluation of lung cancer

The Percepta Genomic Sequencing Classifier (GSC) was developed to up-classify as well as down-classify the risk of malignancy for lung lesions when bronchoscopy is non-diagnostic. We evaluated the performance of Percepta GSC in risk re-classification of indeterminate lung lesions. This multicenter study included individuals who currently or formerly smoked undergoing bronchoscopy for suspected lung cancer from the AEGIS I/ II cohorts and the Percepta Registry. The classifier was measured in normal-appearing bronchial epithelium from bronchial brushings. The sensitivity, specificity, and predictive values were calculated using predefined thresholds. The ability of the classifier to decrease unnecessary invasive procedures was estimated. A set of 412 patients were included in the validation (prevalence of malignancy was 39.6%). Overall, 29% of intermediate-risk lung lesions were down-classified to low-risk with a 91.0% negative predictive value (NPV) and 12.2% of intermediate-risk lesions were up-classified to high-risk with a 65.4% positive predictive value (PPV). In addition, 54.5% of low-risk lesions were down-classified to very low risk with >99% NPV and 27.3% of high-risk lesions were up-classified to very high risk with a 91.5% PPV. If the classifier results were used in nodule management, 50% of patients with benign lesions and 29% of patients with malignant lesions undergoing additional invasive procedures could have avoided these procedures. The Percepta GSC is highly accurate as both a rule-out and rule-in test. This high accuracy of risk re-classification may lead to improved management of lung lesions

Clinical validation and utility of Percepta GSC for the evaluation of lung cancer.

The Percepta Genomic Sequencing Classifier (GSC) was developed to up-classify as well as down-classify the risk of malignancy for lung lesions when bronchoscopy is non-diagnostic. We evaluated the performance of Percepta GSC in risk re-classification of indeterminate lung lesions. This multicenter study included individuals who currently or formerly smoked undergoing bronchoscopy for suspected lung cancer from the AEGIS I/ II cohorts and the Percepta Registry. The classifier was measured in normal-appearing bronchial epithelium from bronchial brushings. The sensitivity, specificity, and predictive values were calculated using predefined thresholds. The ability of the classifier to decrease unnecessary invasive procedures was estimated. A set of 412 patients were included in the validation (prevalence of malignancy was 39.6%). Overall, 29% of intermediate-risk lung lesions were down-classified to low-risk with a 91.0% negative predictive value (NPV) and 12.2% of intermediate-risk lesions were up-classified to high-risk with a 65.4% positive predictive value (PPV). In addition, 54.5% of low-risk lesions were down-classified to very low risk with >99% NPV and 27.3% of high-risk lesions were up-classified to very high risk with a 91.5% PPV. If the classifier results were used in nodule management, 50% of patients with benign lesions and 29% of patients with malignant lesions undergoing additional invasive procedures could have avoided these procedures. The Percepta GSC is highly accurate as both a rule-out and rule-in test. This high accuracy of risk re-classification may lead to improved management of lung lesions

Clinical validation and utility of Percepta GSC for the evaluation of lung cancer

The Percepta Genomic Sequencing Classifier (GSC) was developed to up-classify as well as down-classify the risk of malignancy for lung lesions when bronchoscopy is non-diagnostic. We evaluated the performance of Percepta GSC in risk re-classification of indeterminate lung lesions. This multicenter study included individuals who currently or formerly smoked undergoing bronchoscopy for suspected lung cancer from the AEGIS I/ II cohorts and the Percepta Registry. The classifier was measured in normal-appearing bronchial epithelium from bronchial brushings. The sensitivity, specificity, and predictive values were calculated using predefined thresholds. The ability of the classifier to decrease unnecessary invasive procedures was estimated. A set of 412 patients were included in the validation (prevalence of malignancy was 39.6%). Overall, 29% of intermediate-risk lung lesions were down-classified to low-risk with a 91.0% negative predictive value (NPV) and 12.2% of intermediate-risk lesions were up-classified to high-risk with a 65.4% positive predictive value (PPV). In addition, 54.5% of low-risk lesions were down-classified to very low risk with >99% NPV and 27.3% of high-risk lesions were up-classified to very high risk with a 91.5% PPV. If the classifier results were used in nodule management, 50% of patients with benign lesions and 29% of patients with malignant lesions undergoing additional invasive procedures could have avoided these procedures. The Percepta GSC is highly accurate as both a rule-out and rule-in test. This high accuracy of risk re-classification may lead to improved management of lung lesions

Quantum Chemistry Common Driver and Databases (QCDB) and Quantum Chemistry Engine (QCEngine): Automation and interoperability among computational chemistry programs

Community efforts in the computational molecular sciences (CMS) are evolving toward modular, open, and interoperable interfaces that work with existing community codes to provide more functionality and composability than could be achieved with a single program. The Quantum Chemistry Common Driver and Databases (QCDB) project provides such capability through an application programming interface (API) that facilitates interoperability across multiple quantum chemistry software packages. In tandem with the Molecular Sciences Software Institute and their Quantum Chemistry Archive ecosystem, the unique functionalities of several CMS programs are integrated, including CFOUR, GAMESS, NWChem, OpenMM, Psi4, Qcore, TeraChem, and Turbomole, to provide common computational functions, i.e., energy, gradient, and Hessian computations as well as molecular properties such as atomic charges and vibrational frequency analysis. Both standard users and power users benefit from adopting these APIs as they lower the language barrier of input styles and enable a standard layout of variables and data. These designs allow end-to-end interoperable programming of complex computations and provide best practices options by default

SARS-CoV-2 infects the human kidney and drives fibrosis in kidney organoids

This work was supported by grants of the German Research Foundation (DFG: KR 4073/11-1; SFBTRR219, 322900939; and CRU344, 428857858, and CRU5011 InteraKD 445703531), a grant of the European Research Council (ERC-StG 677448), the Federal Ministry of Research and Education (BMBF NUM-COVID19, Organo-Strat 01KX2021), the Dutch Kidney Foundation (DKF) TASK FORCE consortium (CP1805), the Else Kroener Fresenius Foundation (2017_A144), and the ERA-CVD MENDAGE consortium (BMBF 01KL1907) all to R.K.; DFG (CRU 344, Z to I.G.C and CRU344 P2 to R.K.S.); and the BMBF eMed Consortium Fibromap (to V.G.P, R.K., R.K.S., and I.G.C.). R.K.S received support from the KWF Kankerbestrijding (11031/2017–1, Bas Mulder Award) and a grant by the ERC (deFiber; ERC-StG 757339). J.J. is supported by the Netherlands Organisation for Scientific Research (NWO Veni grant no: 091 501 61 81 01 36) and the DKF (grant no. 19OK005). B.S. is supported by the DKF (grant: 14A3D104) and the NWO (VIDI grant: 016.156.363). R.P.V.R. and G.J.O. are supported by the NWO VICI (grant: 16.VICI.170.090). P.B. is supported by the BMBF (DEFEAT PANDEMIcs, 01KX2021), the Federal Ministry of Health (German Registry for COVID-19 Autopsies-DeRegCOVID, www.DeRegCOVID.ukaachen.de; ZMVI1-2520COR201), and the German Research Foundation (DFG; SFB/TRR219 Project-IDs 322900939 and 454024652). S.D. received DFG support (DJ100/1-1) as well as support from VGP and TBH (SFB1192). M.d.B,R.R., N.S., and A.A. are supported by an ERC Advanced Investigator grant (H2020-ERC-2017-ADV-788982-COLMIN) to N.S. A.A. is supported by the NWO (VI.Veni.192.094). We thank Saskia de Wildt, Jeanne Pertijs (Radboudumc, Department of Pharmacology), and Robert M. Verdijk (Erasmus Medical Center, Department of Pathology) for providing tissue controls (Erasmus MC Tissue Bank) and Christian Drosten (Charite´ Universitatsmedizin Berlin, Institute of € Virology) and Bart Haagmans (Erasmus Medical Center, Rotterdam) for providing the SARS-CoV-2 isolate. We thank Kioa L. Wijnsma (Department of Pediatric Nephrology, Radboud Institute for Molecular Life Sciences, Amalia Children’s Hospital, Radboud University Medical Center) for support with statistical analysis regarding the COVID-19 patient cohort.Peer reviewedPublisher PD