Abstract
Determining protein levels in each tissue and how they compare with RNA levels is important for understanding human biology and disease as well as regulatory processes that control protein levels. We quantified the relative protein levels from over 12,000 genes across 32 normal human tissues. Tissue-specific or tissue-enriched proteins were identified and compared to transcriptome data. Many ubiquitous transcripts are found to encode tissue-specific proteins. Discordance of RNA and protein enrichment revealed potential sites of synthesis and action of secreted proteins. The tissue-specific distribution of proteins also provides an in-depth view of complex biological events that require the interplay of multiple tissues. Most importantly, our study demonstrated that protein tissue-enrichment information can explain phenotypes of genetic diseases, which cannot be obtained by transcript information alone. Overall, our results demonstrate how understanding protein levels can provide insights into regulation, secretome, metabolism, and human diseases.

Abstract
AbstractBackgroundHigh-quality data is crucial for guiding decision making and practicing evidence-based healthcare, especially if previous knowledge is lacking. Nevertheless, data quality frailties have been exposed worldwide during the current COVID-19 pandemic. Focusing on a major Portuguese surveillance dataset, our study aims to assess data quality issues and suggest possible solutions.MethodsOn April 27th 2020, the Portuguese Directorate-General of Health (DGS) made available a dataset (DGSApril) for researchers, upon request. On August 4th, an updated dataset (DGSAugust) was also obtained. The quality of data was assessed through analysis of data completeness and consistency between both datasets.ResultsDGSAugust has not followed the data format and variables as DGSApril and a significant number of missing data and inconsistencies were found (e.g. 4,075 cases from the DGSApril were apparently not included in DGSAugust). Several variables also showed a low degree of completeness and/or changed their values from one dataset to another (e.g. the variable ‘underlying conditions’ had more than half of cases showing different information between datasets). There were also significant inconsistencies between the number of cases and deaths due to COVID-19 shown in DGSAugust and by the DGS reports publicly provided daily.ConclusionsThe low quality of COVID-19 surveillance datasets limits its usability to inform good decisions and perform useful research. Major improvements in surveillance datasets are therefore urgently needed - e.g. simplification of data entry processes, constant monitoring of data, and increased training and awareness of health care providers - as low data quality may lead to a deficient pandemic control.

Abstract
This paper compares the export promotion system of advanced and emerging economies in fifty countries. Results show that advanced economies offer, on average, more complete export promotion system, i.e. a greater variety of Export Promotion Programs (EPPs) than emerging economies. Advanced countries offer more financial support, informational services, facilitating activities and education and training services. The specific services that contribute most to these differences are also identified, which is important for national export promotion agencies and policy makers to upgrade their offer to firms in order for them to be better prepared for international trade interactions, especially emerging economies.

Abstract
This paper introduces Yet Another Programming Exercises Interoperability Language (YAPExIL), a JSON format that aims to: (1) support several kinds of programming exercises behind traditional blank sheet activities; (2) capitalize on expressiveness and interoperability to constitute a strong candidate to standard open programming exercises format. To this end, it builds upon an existing open format named PExIL, by mitigating its weaknesses and extending its support for a handful of exercise types. YAPExIL is published as an open format, independent from any commercial vendor, and supported with dedicated open-source software.

Abstract
The investigation of a D-shaped photonic crystal fiber sensor with a bimetallic layer for operation at the visible and infrared spectra is presented. The bimetallic layer is composed by silver and gold slabs deposited adjacently on the flat face of the fiber. It is shown that this architecture allows the excitation of two sharply distinguished plasmon resonance, which suggest potential applications for multiparameter sensing.

Abstract
Programming by demonstration allows non-experts in robot programming to train the robots in an intuitive manner. However, this learning paradigm requires multiple demonstrations of the same task, which can be time-consuming and annoying for the human tutor. To overcome this limitation, we propose a fast learning system - based on neural dynamics - that permits collaborative robots to memorize sequential information from single task demonstrations by a human-tutor. Important, the learning system allows not only to memorize long sequences of sub-goals in a task but also the time interval between them. We implement this learning system in Sawyer (a collaborative robot from Rethink Robotics) and test it in a construction task, where the robot observes several human-tutors with different preferences on the sequential order to perform the task and different behavioral time scales. After learning, memory recall (of what and when to do a sub-task) allows the robot to instruct inexperienced human workers, in a particular human-centered task scenario.