What time to adapt? The role of discretionary time in sustaining the climate change value-action gap

We investigate the role discretionary (non-working) time plays in sustaining the gap between individuals’ concern about climate change and their propensity to act on this concern by adopting sustainable consumption practices. Using recent Australian survey data on climate change adaptation, we find that while discretionary time is unrelated to concern about climate change, it is positively correlated with the propensity to adopt mitigating behavior. Moreover, we find that increasing discretionary time is associated with significant reductions in the gap between the concern that individuals express about climate change and their reporting of engagement in sustainable consumption practices

Spitzer observations of the Massive star forming complex S254-S258: structure and evolution

We present Spitzer-IRAC, NOAO 2.1meter-Flamingos, Keck-NIRC, and FCRAO-SEQUOIA observations of the massive star forming complex S254-S258, covering an area of 25x20 arc-minutes. Using a combination of the IRAC and NIR data, we identify and classify the young stellar objects (YSO) in the complex. We detect 510 sources with near or mid IR-excess, and we classify 87 Class I, and 165 Class II sources. The YSO are found in clusters surrounded by isolated YSO in a low-density distributed population. The ratio of clustered to total YSO is 0.8. We identify six new clusters in the complex. One of them, G192.63-00, is located around the ionizing star of the HII region S255. We hypothesize that the ionizing star of S255 was formed in this cluster. We also detect a southern component of the cluster in HII region S256. The cluster G192.54-0.15, located inside HII region S254 has a VLSR of 17 km/s with respect to the main cloud, and we conclude that it is located in the background of the complex. The structure of the molecular cloud is examined using 12CO and 13CO, as well as a near-IR extinction map. The main body of the molecular cloud has VLSR between 5 and 9 km/s. The arc-shaped structure of the molecular cloud, following the border of the HII regions, and the high column density in the border of the HII regions support the idea that the material has been swept up by the expansion of the HII regions.Comment: Accepted for publication in The Astrophysical Journa

Application of deep learning on mammographies to discriminate between low and high-risk DCIS for patient participation in active surveillance trials

Background: Ductal Carcinoma In Situ (DCIS) can progress to invasive breast cancer, but most DCIS lesions never will. Therefore, four clinical trials (COMET, LORIS, LORETTA, AND LORD) test whether active surveillance for women with low-risk Ductal carcinoma In Situ is safe (E. S. Hwang et al., BMJ Open, 9: e026797, 2019, A. Francis et al., Eur J Cancer. 51: 2296–2303, 2015, Chizuko Kanbayashi et al. The international collaboration of active surveillance trials for low-risk DCIS (LORIS, LORD, COMET, LORETTA), L. E. Elshof et al., Eur J Cancer, 51, 1497–510, 2015). Low-risk is defined as grade I or II DCIS. Because DCIS grade is a major eligibility criteria in these trials, it would be very helpful to assess DCIS grade on mammography, informed by grade assessed on DCIS histopathology in pre-surgery biopsies, since surgery will not be performed on a significant number of patients participating in these trials. Objective: To assess the performance and clinical utility of a convolutional neural network (CNN) in discriminating high-risk (grade III) DCIS and/or Invasive Breast Cancer (IBC) from low-risk (grade I/II) DCIS based on mammographic features. We explored whether the CNN could be used as a decision support tool, from excluding high-risk patients for active surveillance. Methods: In this single centre retrospective study, 464 patients diagnosed with DCIS based on pre-surgery biopsy between 2000 and 2014 were included. The collection of mammography images was partitioned on a patient-level into two subsets, one for training containing 80% of cases (371 cases, 681 images) and 20% (93 cases, 173 images) for testing. A deep learning model based on the U-Net CNN was trained and validated on 681 two-dimensional mammograms. Classification performance was assessed with the Area Under the Curve (AUC) receiver operating characteristic and predictive values on the test set for predicting high risk DCIS-and high-risk DCIS and/ or IBC from low-risk DCIS. Results: When classifying DCIS as high-risk, the deep learning network achieved a Positive Predictive Value (PPV) of 0.40, Negative Predictive Value (NPV) of 0.91 and an AUC of 0.72 on the test dataset. For distinguishing high-risk and/or upstaged DCIS (occult invasive breast cancer) from low-risk DCIS a PPV of 0.80, a NPV of 0.84 and an AUC of 0.76 were achieved. Conclusion: For both scenarios (DCIS grade I/II vs. III, DCIS grade I/II vs. III and/or IBC) AUCs were high, 0.72 and 0.76, respectively, concluding that our convolutional neural network can discriminate low-grade from high-grade DCIS.</p

Biallelic mutations in valyl-tRNA synthetase gene VARS are associated with a progressive neurodevelopmental epileptic encephalopathy.

Aminoacyl-tRNA synthetases (ARSs) function to transfer amino acids to cognate tRNA molecules, which are required for protein translation. To date, biallelic mutations in 31 ARS genes are known to cause recessive, early-onset severe multi-organ diseases. VARS encodes the only known valine cytoplasmic-localized aminoacyl-tRNA synthetase. Here, we report seven patients from five unrelated families with five different biallelic missense variants in VARS. Subjects present with a range of global developmental delay, epileptic encephalopathy and primary or progressive microcephaly. Longitudinal assessment demonstrates progressive cortical atrophy and white matter volume loss. Variants map to the VARS tRNA binding domain and adjacent to the anticodon domain, and disrupt highly conserved residues. Patient primary cells show intact VARS protein but reduced enzymatic activity, suggesting partial loss of function. The implication of VARS in pediatric neurodegeneration broadens the spectrum of human diseases due to mutations in tRNA synthetase genes

Quantum fluctuations can promote or inhibit glass formation

The very nature of glass is somewhat mysterious: while relaxation times in glasses are of sufficient magnitude that large-scale motion on the atomic level is essentially as slow as it is in the crystalline state, the structure of glass appears barely different than that of the liquid that produced it. Quantum mechanical systems ranging from electron liquids to superfluid helium appear to form glasses, but as yet no unifying framework exists connecting classical and quantum regimes of vitrification. Here we develop new insights from theory and simulation into the quantum glass transition that surprisingly reveal distinct regions where quantum fluctuations can either promote or inhibit glass formation.Comment: Accepted for publication in Nature Physics. 22 pages, 3 figures, 1 Tabl

Association of pretreatment hippocampal volume with neurocognitive function in patients treated with hippocampal avoidance whole brain radiation therapy for brain metastases: Secondary analysis of NRG Oncology/RTOG 0933

PURPOSE: Hippocampal volume (HV) is an established predicting factor for neurocognitive function (NCF) in neurodegenerative disease. Whether the same phenomenon exists with hippocampal-avoidant whole brain radiation therapy is not known; therefore, we assessed the association of baseline HV with NCF among patients enrolled on RTOG 0933. METHODS AND MATERIALS: Hippocampal volume and total brain volume were calculated from the radiation therapy plan. Hippocampal volume was correlated with baseline and 4-month NCF scores (Hopkins Verbal Learning Test-Revised [HVLT-R] Total Recall [TR], Immediate Recognition, and Delayed Recall [DR]) using Pearson correlation. Deterioration in NCF was defined per the primary endpoint of RTOG 0933(mean 4-month relative decline in HVLT-R DR). Comparisons between patients with deteriorated and nondeteriorated NCF were made using the Wilcoxon test. RESULTS: Forty-two patients were evaluable. The median age was 56.5 years (range, 28-83 years), and 81% had a class II recursive partitioning analysis. The median total, right, and left HVs were 5.4 cm CONCLUSIONS: Larger HV was positively associated with improved performance on baseline and 4-month HVLT-R TR and DR scores in patients with brain metastases undergoing hippocampal-avoidant whole brain radiation therapy but was not associated with a change in NCF

Radiation dosimetry in digital breast tomosynthesis: Report of AAPM Tomosynthesis Subcommittee Task Group 223

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134828/1/mp2600.pd

CD98hc facilitates B cell proliferation and adaptive humoral immunity.

The proliferation of antigen-specific lymphocytes and resulting clonal expansion are essential for adaptive immunity. We report here that B cell-specific deletion of the heavy chain of CD98 (CD98hc) resulted in lower antibody responses due to total suppression of B cell proliferation and subsequent plasma cell formation. Deletion of CD98hc did not impair early B cell activation but did inhibit later activation of the mitogen-activated protein kinase Erk1/2 and downregulation of the cell cycle inhibitor p27. Reconstitution of CD98hc-deficient B cells with CD98hc mutants showed that the integrin-binding domain of CD98hc was required for B cell proliferation but that the amino acid-transport function of CD98hc was dispensable for this. Thus, CD98hc supports integrin-dependent rapid proliferation of B cells. We propose that the advantage of adaptive immunity favored the appearance of CD98hc in vertebrates