Calcium binding to the photosystem II subunit CP29.

We have identified a Ca(2+)-binding site of the 29-kDa chlorophyll a/b-binding protein CP29, a light harvesting protein of photosystem II most likely involved in photoregulation. (45)Ca(2+) binding studies and dot blot analyses of CP29 demonstrate that CP29 is a Ca(2+)-binding protein. The primary sequence of CP29 does not exhibit an obvious Ca(2+)-binding site therefore we have used Yb(3+) replacement to analyze this site. Near-infrared Yb(3+) vibronic side band fluorescence spectroscopy (Roselli, C., Boussac, A., and Mattioli, T. A. (1994) Proc. Natl. Acad. Sci. U. S. A. 91, 12897-12901) of Yb(3+)-reconstituted CP29 indicated a single population of Yb(3+)-binding sites rich in carboxylic acids, characteristic of Ca(2+)-binding sites. A structural model of CP29 presents two purported extra-membranar loops which are relatively rich in carboxylic acids, one on the stromae side and one on the lumenal side. The loop on the lumenal side is adjacent to glutamic acid 166 in helix C of CP29, which is known to be the binding site for dicyclohexylcarbodiimide (Pesaresi, P., Sandonà, D., Giuffra, E. , and Bassi, R. (1997) FEBS Lett. 402, 151-156). Dicyclohexylcarbodiimide binding prevented Ca(2+) binding, therefore we propose that the Ca(2+) in CP29 is bound in the domain including the lumenal loop between helices B and C

A Critical Appraisal of the UK’s Regulatory Regime for Combustible Façades

The Grenfell Tower fire has brought the regulatory system that permitted combustible materials on high-rise buildings in England into question. At the heart of that system is the BS 8414 test, and the BR 135 criteria used to demonstrate compliance with the Building Regulations. The test is empirical and the criteria arbitrary: there is no scientific link between test performance and how a building will perform in the event of a fire; nor any detailed analysis of why fires spread through façade systems which have passed the test. Following the Grenfell tragedy, the UK government commissioned a series of tests on Grenfell Tower-type facades, using BS 8414. This paper critically analyses BS 8414, the BR 135 criteria and the government tests. It shows that important aspects of the standard are poorly defined: the heat flux imposed on the façade is not measured and the fire load can vary by at least a factor of 2; the ambient ventilation has a significant impact on the thermal attack but is not adequately controlled; judicious location of the cavity barriers can confer compliance or failure on a façade system. As the vehicle for allowing combustible products on tall buildings, the test does not specify the extent of cavity barrier deployment, while ignoring features present in real buildings, such as windows, vents or other openings, despite a test rig height of more than 8 m. There is no restriction on debris, or molten or burning droplets falling from the façade during the test. The BR 135 criteria only specify that the test must run for the full 60 min duration without flames reaching the top, and the temperature rise at thermocouples 5 m above the fire chamber must only remain below 600°C for the first 15 min. It is unclear how the fire safety of the occupants behind the façade system can be ensured, when the criteria specify such a high temperature for such a short period, so early in the test. There is no direct connection between the façade system in the test and the actual façade system the results deem compliant. Worse, “desktop studies”, using large-scale test data, have been allowed to confer compliance on systems which have never been subject to the test. The UK government tests used heavy-duty welded aluminium “window pods”, preventing flames from entering the cavity within the façade. They also used a disproportionately large number of vertical and horizontal cavity barriers of a higher specification than required by statutory guidance. These aids to meeting the criteria are not proscribed by BS 8414-1 but are not commonly found in actual rainscreen system designs

Semi-natural test methods to evaluate fire safety of wall claddings: Update

There are a number of test methods worldwide to evaluate fire safety of facades. An overview of available test methods implemented in fire safety codes was presented at the 1st Conference of Fire Safety of Facades in 2013. [1] Since then, a number of changes and developments occurred. The purpose of this paper is to present the updated global overview of facade fire spread test methods made for building regulations

Semi-natural test methods to evaluate fire safety of wall claddings: Update

There are a number of test methods worldwide to evaluate fire safety of facades. An overview of available test methods implemented in fire safety codes was presented at the 1st Conference of Fire Safety of Facades in 2013. [1] Since then, a number of changes and developments occurred. The purpose of this paper is to present the updated global overview of facade fire spread test methods made for building regulations

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset