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General General

Hourly 5-km surface total and diffuse solar radiation in China, 2007-2018.

In Scientific data

Surface solar radiation is an indispensable parameter for numerical models, and the diffuse component contributes to the carbon uptake in ecosystems. We generated a 12-year (2007-2018) hourly dataset from Multi-functional Transport Satellite (MTSAT) satellite observations, including surface total solar radiation (Rs) and diffuse radiation (Rdif), with 5-km spatial resolution through deep learning techniques. The used deep network tacks the integration of spatial pattern and the simulation of complex radiation transfer by combining convolutional neural network and multi-layer perceptron. Validation against ground measurements shows the correlation coefficient, mean bias error and root mean square error are 0.94, 2.48 W/m2 and 89.75 W/m2 for hourly Rs and 0.85, 8.63 W/m2 and 66.14 W/m2 for hourly Rdif, respectively. The correlation coefficient of Rs and Rdif increases to 0.94 (0.96) and 0.89 (0.92) at daily (monthly) scales, respectively. The spatially continuous hourly maps accurately reflect regional differences and restore the diurnal cycles of solar radiation at fine resolution. This dataset can be valuable for studies on regional climate changes, terrestrial ecosystem simulations and photovoltaic applications.

Jiang Hou, Lu Ning, Qin Jun, Yao Ling

2020-09-23

General General

Preparing to adapt is key for Olympic curling robots.

In Science robotics

Continued advances in machine learning could enable robots to solve tasks on a human level and adapt to changing conditions.

Stork Johannes A

2020-Sep-23

General General

Pandemic number five - Latest insights into the COVID-19 crisis.

In Biomedical journal

About nine months after the emergence of SARS-CoV-2, this special issue of the Biomedical Journal takes stock of its evolution into a pandemic. We acquire an elaborate overview of the history and virology of SARS-CoV-2, the epidemiology of COVID-19, and the development of therapies and vaccines, based on useful tools such as a pseudovirus system, artificial intelligence, and repurposing of existing drugs. Moreover, we learn about a potential link between COVID-19 and oral health, and some of the strategies that allowed Taiwan to handle the outbreak exceptionally well, including a COVID-19 biobank establishment, online tools for contact tracing, and the efficient management of emergency departments.

Häfner Sophia Julia

2020-Aug-27

COVID-19, Contact tracing, Pseudovirus system, Repurposing drugs, SARS-CoV-2

General General

Deep learning-enabled analysis reveals distinct neuronal phenotypes induced by aging and cold-shock.

In BMC biology

BACKGROUND : Access to quantitative information is crucial to obtain a deeper understanding of biological systems. In addition to being low-throughput, traditional image-based analysis is mostly limited to error-prone qualitative or semi-quantitative assessment of phenotypes, particularly for complex subcellular morphologies. The PVD neuron in Caenorhabditis elegans, which is responsible for harsh touch and thermosensation, undergoes structural degeneration as nematodes age characterized by the appearance of dendritic protrusions. Analysis of these neurodegenerative patterns is labor-intensive and limited to qualitative assessment.

RESULTS : In this work, we apply deep learning to perform quantitative image-based analysis of complex neurodegeneration patterns exhibited by the PVD neuron in C. elegans. We apply a convolutional neural network algorithm (Mask R-CNN) to identify neurodegenerative subcellular protrusions that appear after cold-shock or as a result of aging. A multiparametric phenotypic profile captures the unique morphological changes induced by each perturbation. We identify that acute cold-shock-induced neurodegeneration is reversible and depends on rearing temperature and, importantly, that aging and cold-shock induce distinct neuronal beading patterns.

CONCLUSION : The results of this work indicate that implementing deep learning for challenging image segmentation of PVD neurodegeneration enables quantitatively tracking subtle morphological changes in an unbiased manner. This analysis revealed that distinct patterns of morphological alteration are induced by aging and cold-shock, suggesting different mechanisms at play. This approach can be used to identify the molecular components involved in orchestrating neurodegeneration and to characterize the effect of other stressors on PVD degeneration.

Saberi-Bosari Sahand, Flores Kevin B, San-Miguel Adriana

2020-Sep-23

Aging, C. elegans, Convolutional neural networks, Deep learning, Machine learning, Neurodegeneration, Neuronal beading, Phenotyping

Radiology Radiology

Differential diagnosis and mutation stratification of desmoid-type fibromatosis on MRI using radiomics.

In European journal of radiology ; h5-index 47.0

PURPOSE : Diagnosing desmoid-type fibromatosis (DTF) requires an invasive tissue biopsy with β-catenin staining and CTNNB1 mutational analysis, and is challenging due to its rarity. The aim of this study was to evaluate radiomics for distinguishing DTF from soft tissue sarcomas (STS), and in DTF, for predicting the CTNNB1 mutation types.

METHODS : Patients with histologically confirmed extremity STS (non-DTF) or DTF and at least a pretreatment T1-weighted (T1w) MRI scan were retrospectively included. Tumors were semi-automatically annotated on the T1w scans, from which 411 features were extracted. Prediction models were created using a combination of various machine learning approaches. Evaluation was performed through a 100x random-split cross-validation. The model for DTF vs. non-DTF was compared to classification by two radiologists on a location matched subset.

RESULTS : The data included 203 patients (72 DTF, 131 STS). The T1w radiomics model showed a mean AUC of 0.79 on the full dataset. Addition of T2w or T1w post-contrast scans did not improve the performance. On the location matched cohort, the T1w model had a mean AUC of 0.88 while the radiologists had an AUC of 0.80 and 0.88, respectively. For the prediction of the CTNNB1 mutation types (S45 F, T41A and wild-type), the T1w model showed an AUC of 0.61, 0.56, and 0.74.

CONCLUSIONS : Our radiomics model was able to distinguish DTF from STS with high accuracy similar to two radiologists, but was not able to predict the CTNNB1 mutation status.

Timbergen Milea J M, Starmans Martijn P A, Padmos Guillaume A, Grünhagen Dirk J, van Leenders Geert J L H, Hanff D F, Verhoef Cornelis, Niessen Wiro J, Sleijfer Stefan, Klein Stefan, Visser Jacob J

2020-Sep-08

Aggressive, Beta catenin, Fibromatosis, Machine learning, Magnetic resonance imaging, Radiomics

General General

Causal conflicts produce domino effects.

In Quarterly journal of experimental psychology (2006)

Inconsistent beliefs call for revision-but which of them should individuals revise? A long-standing view is that they should make minimal changes that restore consistency. An alternative view is that their primary task is to explain how the inconsistency arose. Hence, they are likely to violate minimalism in two ways: they should infer more information than is strictly necessary to establish consistency and they should reject more information than is strictly necessary to establish consistency. Previous studies corroborated the first effect: reasoners use causal simulations to build explanations that resolve inconsistencies. Here, we show that the second effect is true too: they use causal simulations to reject more information than is strictly necessary to establish consistency. When they abandon a cause, the effects of the cause topple like dominos: Reasoners tend to deny the occurrence of each subsequent event in the chain. Four studies corroborated this prediction.

Khemlani Sangeet, Johnson-Laird P N

2020-Sep-23

Inconsistency, bridging inferences, causal reasoning, domino effects, mental models, minimalism