The Probable Detection of SN 1923A: The Oldest Radio Supernova?

Based upon the results of VLA observations, we report the detection of two unresolved radio sources that are coincident with the reported optical position of SN 1923A in M83. For the source closest to the SN position, the flux density was determined to be 0.30 +/- 0.05 mJy at 20 cm and 0.093 +/- 0.028 mJy at 6 cm. The flux density of the second nearby source was determined to be 0.29 +/- 0.05 at 20 cm and 0.13 +/- 0.028 at 6 cm. Both sources are non-thermal with spectral indices of alpha = -1.0 +/- 0.30 and -0.69 +/- 0.24, respectively. SN 1923A has been designated as a Type II-P. No Type II-P (other than SN 1987A) has been detected previously in the radio. The radio emission from both sources appears to be fading with time. At an age of approximately 68 years when we observed it, this would be the oldest radio supernova (of known age) yet detected

MISR Global Aerosol Product Assessment by Comparison with AERONET

A statistical approach is used to assess the quality of the MISR Version 22 (V22) aerosol products. Aerosol Optical Depth (AOD) retrieval results are improved relative to the early post- launch values reported by Kahn et al. [2005a], varying with particle type category. Overall, about 70% to 75% of MISR AOD retrievals fall within 0.05 or 20% AOD of the paired validation data, and about 50% to 55% are within 0.03 or 10% AOD, except at sites where dust, or mixed dust and smoke, are commonly found. Retrieved particle microphysical properties amount to categorical values, such as three groupings in size: "small," "medium," and "large." For particle size, ground-based AERONET sun photometer Angstrom Exponents are used to assess statistically the corresponding MISR values, which are interpreted in terms of retrieved size categories. Coincident Single-Scattering Albedo (SSA) and fraction AOD spherical data are too limited for statistical validation. V22 distinguishes two or three size bins, depending on aerosol type, and about two bins in SSA (absorbing vs. non-absorbing), as well as spherical vs. non-spherical particles, under good retrieval conditions. Particle type sensitivity varies considerably with conditions, and is diminished for mid-visible AOD below about 0.15 or 0.2. Based on these results, specific algorithm upgrades are proposed, and are being investigated by the MISR team for possible implementation in future versions of the product

An Analysis of AERONET Aerosol Absorption Properties and Classifications Representative of Aerosol Source Regions

Partitioning of mineral dust, pollution, smoke, and mixtures using remote sensing techniques can help improve accuracy of satellite retrievals and assessments of the aerosol radiative impact on climate. Spectral aerosol optical depth (tau) and single scattering albedo (omega (sub 0) ) from Aerosol Robotic Network (AERONET) measurements are used to form absorption [i.e., omega (sub 0) and absorption Angstrom exponent (alpha(sub abs))] and size [i.e., extinction Angstrom exponent (alpha(sub ext)) and fine mode fraction of tau] relationships to infer dominant aerosol types. Using the long-term AERONET data set (1999-2010), 19 sites are grouped by aerosol type based on known source regions to: (1) determine the average omega (sub 0) and alpha(sub abs) at each site (expanding upon previous work); (2) perform a sensitivity study on alpha(sub abs) by varying the spectral omega (sub 0); and (3) test the ability of each absorption and size relationship to distinguish aerosol types. The spectral omega (sub 0) averages indicate slightly more aerosol absorption (i.e., a 0.0 < delta omega (sub 0) <= 0.02 decrease) than in previous work and optical mixtures of pollution and smoke with dust show stronger absorption than dust alone. Frequency distributions of alpha(sub abs) show significant overlap among aerosol type categories and at least 10% of the alpha(sub abs) retrievals in each category are below 1.0. Perturbing the spectral omega (sub 0) by +/- 0.03 induces significant alpha(sub abs) changes from the unperturbed value by at least approx. +/- 0.6 for Dust, approx. +/-0.2 for Mixed, and approx. +/-0.1 for Urban/Industrial and Biomass Burning. The omega (sub 0)440nm and alpha(sub ext) 440-870nm relationship shows the best separation among aerosol type clusters, providing a simple technique for determining aerosol type from surface- and future space-based instrumentation

Advancing Crop Transformation in the Era of Genome Editing

Plant transformation has enabled fundamental insights into plant biology and revolutionized commercial agriculture. Unfortunately, for most crops, transformation and regeneration remain arduous even after more than 30 years of technological advances. Genome editing provides novel opportunities to enhance crop productivity but relies on genetic transformation and plant regeneration, which are bottlenecks in the process. Here, we review the state of plant transformation and point to innovations needed to enable genome editing in crops. Plant tissue culture methods need optimization and simplification for efficiency and minimization of time in culture. Currently, specialized facilities exist for crop transformation. Single-cell and robotic techniques should be developed for high-throughput genomic screens. Plant genes involved in developmental reprogramming, wound response, and/or homologous recombination should be used to boost the recovery of transformed plants. Engineering universal Agrobacterium tumefaciens strains and recruiting other microbes, such as Ensifer or Rhizobium, could facilitate delivery of DNA and proteins into plant cells. Synthetic biology should be employed for de novo design of transformation systems. Genome editing is a potential game-changer in crop genetics when plant transformation systems are optimized

A Search for Radio Emission from Supernovae With Ages from About One Week to More Than 80 Years

We report VLA radio observations of 29 SNe with ages ranging from 10 days to about 90 years past explosion. These observations significantly contribute to the existing data pool on such objects. Included are detections of known radio SNe~1950B, 1957D, 1970G, 1983N, the suspected radio SN 1923A, and the possible radio SN 1961V. None of the remaining 23 observations resulted in detections, providing further evidence to support the observed trend that most SNe are not detectable radio emitters. To investigate the apparent lack of radio emission from the SNe reported here, we have followed standard practice and used Chevalier's ``standard model'' to derive (upper limits to) the mass-loss rates for the super nova progenitors. These upper limits to the fluxes are consistent with a lack of circumstellar material needed to provide detectable radio emission for SNe at these ages and distances. Comparison of the radio luminosities of these supernovae as a function of age past explosion to other well-observed radio SNe indicates that the Type II SNe upper limits are more consistent with the extrapolated light curves of SN 1980K than of SN 1979C, suggesting that SN 1980K may be a more typical radio emitter than SN 1979C. For completeness, we have included an appendix where the results of analyses of the non-SN radio sources are presented. Where possible, we make (tentative) identifications of these sources using various methods.Comment: 42 pages, 9 fiugres, 5 tables; To appear in Ap

Progenitors of Type Ia Supernovae: Binary Stars with White Dwarf Companions

Type Ia SNe (SNe Ia) are thought to come from carbon-oxygen white dwarfs that accrete mass from binary companions until they approach the Chandrasekhar limit, ignite carbon, and undergo complete thermonuclear disruption. A survey of the observed types of binaries that contain white dwarfs is presented. We propose that certain systems that seem most promising as SN Ia progenitors should be more intensively observed and modeled, to determine whether the white dwarfs in these systems will be able to reach the Chandrasekhar limit. In view of the number of promising single-degenerate systems and the dearth of promising double-degenerate systems, we suspect that single-degenerates produce most or perhaps all SNe Ia, while double-degenerates produce some or perhaps none.Comment: 34 pages, to appear in New Astronomy Review

Data Publication with the Structural Biology Data Grid Supports Live Analysis

Access to experimental X-ray diffraction image data is fundamental for validation and reproduction of macromolecular models and indispensable for development of structural biology processing methods. Here, we established a diffraction data publication and dissemination system, Structural Biology Data Grid (SBDG; data.sbgrid.org), to preserve primary experimental data sets that support scientific publications. Data sets are accessible to researchers through a community driven data grid, which facilitates global data access. Our analysis of a pilot collection of crystallographic data sets demonstrates that the information archived by SBDG is sufficient to reprocess data to statistics that meet or exceed the quality of the original published structures. SBDG has extended its services to the entire community and is used to develop support for other types of biomedical data sets. It is anticipated that access to the experimental data sets will enhance the paradigm shift in the community towards a much more dynamic body of continuously improving data analysis

Virchow: A Million-Slide Digital Pathology Foundation Model

Computational pathology uses artificial intelligence to enable precision medicine and decision support systems through the analysis of whole slide images. It has the potential to revolutionize the diagnosis and treatment of cancer. However, a major challenge to this objective is that for many specific computational pathology tasks the amount of data is inadequate for development. To address this challenge, we created Virchow, a 632 million parameter deep neural network foundation model for computational pathology. Using self-supervised learning, Virchow is trained on 1.5 million hematoxylin and eosin stained whole slide images from diverse tissue groups, which is orders of magnitude more data than previous works. When evaluated on downstream tasks including tile-level pan-cancer detection and subtyping and slide-level biomarker prediction, Virchow outperforms state-of-the-art systems both on internal datasets drawn from the same population as the pretraining data as well as external public datasets. Virchow achieves 93% balanced accuracy for pancancer tile classification, and AUCs of 0.983 for colon microsatellite instability status prediction and 0.967 for breast CDH1 status prediction. The gains in performance highlight the importance of pretraining on massive pathology image datasets, suggesting pretraining on even larger datasets could continue improving performance for many high-impact applications where limited amounts of training data are available, such as drug outcome prediction

Short-Term Variability of the QT Interval Can be Used for the Prediction of Imminent Ventricular Arrhythmias in Patients With Primary Prophylactic Implantable Cardioverter Defibrillators

Background Short-term variability of the QT interval (STVQT) has been proposed as a novel electrophysiological marker for the prediction of imminent ventricular arrhythmias in animal models. Our aim is to study whether STVQT can predict imminent ventricular arrhythmias in patients. Methods and Results In 2331 patients with primary prophylactic implantable cardioverter defibrillators, 24-hour ECG Holter recordings were obtained as part of the EU-CERT-ICD (European Comparative Effectiveness Research to Assess the Use of Primary Prophylactic Implantable Cardioverter Defibrillators) study. ECG Holter recordings showing ventricular arrhythmias of >4 consecutive complexes were selected for the arrhythmic groups (n=170), whereas a control group was randomly selected from the remaining Holter recordings (n=37). STVQT was determined from 31 beats with fiducial segment averaging and calculated as [Formula: see text], where Dn represents the QT interval. STVQT was determined before the ventricular arrhythmia or 8:00 am in the control group and between 1:30 and 4:30 am as baseline. STVQT at baseline was 0.84±0.47 ms and increased to 1.18±0.74 ms (P<0.05) before the ventricular arrhythmia, whereas the STVQT in the control group remained unchanged. The arrhythmic patients were divided into three groups based on the severity of the arrhythmia: (1) nonsustained ventricular arrhythmia (n=32), (2) nonsustained ventricular tachycardia (n=134), (3) sustained ventricular tachycardia (n=4). STVQT increased before nonsustained ve