World Trade Flows: 1962-2000

We document a set of bilateral trade data by commodity for 1962-2000, which is available from www.nber.org/data (International Trade Data, NBER-UN world trade data). Users must agree not to resell or distribute the data for 1984-2000. The data are organized by the 4-digit Standard International Trade Classification, revision 2, with country codes similar to the United Nations classification. This dataset updates the Statistics Canada World Trade Database as described in Feenstra, Lipsey, and Bowen (1997), which was available for years 1970-1992. In that database, Statistics Canada had revised the United Nations trade data, mostly derived from the export side, to fit the Canadian trade classification and in some cases to add data not available from the export reports. In contrast, in the new NBER-UN dataset we give primacy to the trade flows reported by the importing country, whenever they are available, assuming that these are more accurate than reports by the exporters. If the importer report is not available for a country-pair, however, then the corresponding exporter report is used instead. Corrections and additions are made to the United Nations data for trade flows to and from the United States, exports from Hong Kong and China, and imports into many other countries.

Parameter Selection and Uncertainty Measurement for Variable Precision Probabilistic Rough Set

In this paper, we consider the problem of parameter selection and uncertainty measurement for a variable precision probabilistic rough set. Firstly, within the framework of the variable precision probabilistic rough set model, the relative discernibility of a variable precision rough set in probabilistic approximation space is discussed, and the conditions that make precision parameters α discernible in a variable precision probabilistic rough set are put forward. Concurrently, we consider the lack of predictability of precision parameters in a variable precision probabilistic rough set, and we propose a systematic threshold selection method based on relative discernibility of sets, using the concept of relative discernibility in probabilistic approximation space. Furthermore, a numerical example is applied to test the validity of the proposed method in this paper. Secondly, we discuss the problem of uncertainty measurement for the variable precision probabilistic rough set. The concept of classical fuzzy entropy is introduced into probabilistic approximation space, and the uncertain information that comes from approximation space and the approximated objects is fully considered. Then, an axiomatic approach is established for uncertainty measurement in a variable precision probabilistic rough set, and several related interesting properties are also discussed. Thirdly, we study the attribute reduction for the variable precision probabilistic rough set. The definition of reduction and its characteristic theorems are given for the variable precision probabilistic rough set. The main contribution of this paper is twofold. One is to propose a method of parameter selection for a variable precision probabilistic rough set. Another is to present a new approach to measurement uncertainty and the method of attribute reduction for a variable precision probabilistic rough set

Enhancing lysosome biogenesis attenuates BNIP3-induced cardiomyocyte death

Hypoxia-inducible pro-death protein BNIP3 (BCL-2/adenovirus E1B 19-kDa interacting protein 3), provokes mitochondrial permeabilization causing cardiomyocyte death in ischemia-reperfusion injury. Inhibition of autophagy accelerates BNIP3-induced cell death, by preventing removal of damaged mitochondria. We tested the hypothesis that stimulating autophagy will attenuate BNIP3-induced cardiomyocyte death. Neonatal rat cardiac myocytes (NRCMs) were adenovirally transduced with BNIP3 (or LacZ as control; at multiplicity of infection = 100); and autophagy was stimulated with rapamycin (100 nM). Cell death was assessed at 48 h. BNIP3 expression increased autophagosome abundance 8-fold and caused a 3.6-fold increase in cardiomyocyte death as compared with control. Rapamycin treatment of BNIP3-expressing cells led to further increase in autophagosome number without affecting cell death. BNIP3 expression led to accumulation of autophagosome-bound LC3-II and p62, and an increase in autophagosomes, but not autolysosomes (assessed with dual fluorescent mCherry-GFP-LC3 expression). BNIP3, but not the transmembrane deletion variant, interacted with LC3 and colocalized with mitochondria and lysosomes. However, BNIP3 did not target to lysosomes by subcellular fractionation, provoke lysosome permeabilization or alter lysosome pH. Rather, BNIP3-induced autophagy caused a decline in lysosome numbers with decreased expression of the lysosomal protein LAMP-1, indicating lysosome consumption and consequent autophagosome accumulation. Forced expression of transcription factor EB (TFEB) in BNIP3-expressing cells increased lysosome numbers, decreased autophagosomes and increased autolysosomes, prevented p62 accumulation, removed depolarized mitochondria and attenuated BNIP3-induced death. We conclude that BNIP3 expression induced autophagosome accumulation with lysosome consumption in cardiomyocytes. Forced expression of TFEB, a lysosomal biogenesis factor, restored autophagosome processing and attenuated BNIP3-induced cell death

PST: Plant segmentation transformer for 3D point clouds of rapeseed plants at the podding stage

Segmentation of plant point clouds to obtain high-precise morphological traits is essential for plant phenotyping. Although the fast development of deep learning has boosted much research on segmentation of plant point clouds, previous studies mainly focus on the hard voxelization-based or down-sampling-based methods, which are limited to segmenting simple plant organs. Segmentation of complex plant point clouds with a high spatial resolution still remains challenging. In this study, we proposed a deep learning network plant segmentation transformer (PST) to achieve the semantic and instance segmentation of rapeseed plants point clouds acquired by handheld laser scanning (HLS) with the high spatial resolution, which can characterize the tiny siliques as the main traits targeted. PST is composed of: (i) a dynamic voxel feature encoder (DVFE) to aggregate the point features with the raw spatial resolution; (ii) the dual window sets attention blocks to capture the contextual information; and (iii) a dense feature propagation module to obtain the final dense point feature map. The results proved that PST and PST-PointGroup (PG) achieved superior performance in semantic and instance segmentation tasks. For the semantic segmentation, the mean IoU, mean Precision, mean Recall, mean F1-score, and overall accuracy of PST were 93.96%, 97.29%, 96.52%, 96.88%, and 97.07%, achieving an improvement of 7.62%, 3.28%, 4.8%, 4.25%, and 3.88% compared to the second-best state-of-the-art network PAConv. For instance segmentation, PST-PG reached 89.51%, 89.85%, 88.83% and 82.53% in mCov, mWCov, mPerc90, and mRec90, achieving an improvement of 2.93%, 2.21%, 1.99%, and 5.9% compared to the original PG. This study proves that the deep-learning-based point cloud segmentation method has a great potential for resolving dense plant point clouds with complex morphological traits.Comment: 46 pages, 10 figure