joineR: Joint modelling of repeated measurements and time-to-event data

The joineR package implements methods for analysing data from longitudinal studies in which the response from each subject consists of a time-sequence of repeated measurements and a possibly censored time-toevent outcome. The modelling framework for the repeated measurements is the linear model with random effects and/or correlated error structure. The model for the time-to-event outcome is a Cox proportional hazards model with log-Gaussian frailty. Stochastic dependence is captured by allowing the Gaussian random effects of the linear model to be correlated with the frailty term of the Cox proportional hazards model

The Structure of a BamA-BamD Fusion Illuminates the Architecture of the β-Barrel Assembly Machine Core

SummaryThe β-barrel assembly machine (BAM) mediates folding and insertion of integral β-barrel outer membrane proteins (OMPs) in Gram-negative bacteria. Of the five BAM subunits, only BamA and BamD are essential for cell viability. Here we present the crystal structure of a fusion between BamA POTRA4-5 and BamD from Rhodothermus marinus. The POTRA5 domain binds BamD between its tetratricopeptide repeats 3 and 4. The interface structural elements are conserved in the Escherichia coli proteins, which allowed structure validation by mutagenesis and disulfide crosslinking in E. coli. Furthermore, the interface is consistent with previously reported mutations that impair BamA-BamD binding. The structure serves as a linchpin to generate a BAM model where POTRA domains and BamD form an elongated periplasmic ring adjacent to the membrane with a central cavity approximately 30 × 60 Å wide. We propose that nascent OMPs bind this periplasmic ring prior to insertion and folding by BAM

Improving the Nutritional, Structural, and Sensory Properties of Gluten-Free Bread with Different Species of Microalgae

Microalgae are an enormous source of nutrients that can be utilized to enrich common food of inherently low nutritional value, such as gluten-free (GF) bread. Addition of the algae species: Tetraselmis chuii (Tc), Chlorella vulgaris (Cv), and Nannochloropsis gaditana (Ng) biomass led to a significant increase in proteins, lipids, minerals (Ca, Mg, K, P, S, Fe, Cu, Zn, Mn), and antioxidant activity. Although, a compromise on dough rheology and consequential sensory properties was observed. To address this, ethanol treatment of the biomass was necessary to eliminate pigments and odor compounds, which resulted in the bread receiving a similar score as the control during sensory trials. Ethanol treatment also resulted in increased dough strength depicted by creep/recovery tests. Due to the stronger dough structure, more air bubbles were trapped in the dough resulting in softer breads (23–65%) of high volume (12–27%) vs. the native algae biomass bread. Breads baked with Ng and Cv resulted in higher protein-enrichment than the Tc, while Tc enrichment led to an elevated mineral content, especially the Ca, which was six times higher than the other algae species. Overall, Ng, in combination with ethanol treatment, yielded a highly nutritious bread of improved technological and sensory properties, indicating that this species might be a candidate for functional GF bread development.publishedVersio

REDUCTION OF CLUSTER ITERATION MAPS

We study iteration maps of difference equations arising from mutation periodic quivers of arbitrary period. Combining tools from cluster algebra theory and presymplectic geometry, we show that these cluster iteration maps can be reduced to symplectic maps on a lower dimensional submanifold, provided the matrix representing the quiver is singular. The reduced iteration map is explicitly computed for several periodic quivers using either the presymplectic reduction or a Poisson reduction via log-canonical Poisson structures

ESTIMASI SUMBERDAYA MARMER MENGGUNAKAN METODE PENAMPANG TEGAK (CROSS SECTION) DAN PENAMPANG MENDATAR (CONTOUR) DI ALDEIA MARMER,SUCO UMA KADUAK, SUB DISTRITO LACLO, DISTRITO MANATUTO, TIMOR - LESTE

Lokasi penelitian terletak di Aldeia Marmer, Suco Uma Kaduak, Sub Distrito Laclo, Distrito Manatuto, Timor – Leste.luas lokasi penelitian sekitar 100.000 m2 atau 10 Ha. Batas kedalaman perhitungan Sumberdaya Marmer mengikuti permukaan rata – rata tanah datar di sebelah Utara daerah penelitian yang terletak pada ketinggian 50 meter di atas permukaan air laut, sedangkan puncak tertinggi daerah penelitian adalah 350 meter di atas permukaan air laut. Marmer yang terdapat di daerah penelitian terbentuk massif dengan topografi yang berbentuk bukit.perhitungan sumberdaya yang dilakukan di daerah penelitian menggunakan metode Cross Section dan metode Contour. Hasil perhitungan dengan menggunakan metode Cross Section diperoleh sumberdaya marmer sebesar 126.764.035,0 BCM, dimana sayatan yang dihitung luasnya berjumlah 21 buah sayatan dengan jarak antar sayatan 50 meter. Dari 21 sayatan tersebut terbagi dalam 20 blok yang akan dihitung volumenya, dimana semua blok dihitung dengan menggunakan rumus Mean Area.dan dengan metode Contour diperoleh Sumberdaya Marmer sebesar 121.067.265,9 BCM. perhitungan dilakukan dari elevasi 350 mdpal sampai 50 mdpal yang merupakan permukaan rata – rata tanah datar disebelah Utara daerah penelitian. Adanya pengaruh tanah penutup setebal 2 meter dan menjadi faktor koreksinya sebesar 200.000 BCM. faktor koreksi pada metode Cross Section sebesar 126.763,835 BCM, dan pada metode Contour sebesar 121.067.065,9 BCM. Berdasarkan pada klasifikasi Standar Nasional Indonesia (SNI) Amandemen 1 – SNI – 13 – 4726 – 1998 ICS 73.028, maka marmer pada daerah penyelidikan dapat diklasifikasikan sebagai Sumberdaya Terukur (Measured Mineral Resource), volume total sumberdaya setelah dikurangi dari volume total factor koreksi dengan metode Cross Section sebesar 126.763,835 BCM dan dengan metode Contour sebesar 121.067.065,9 BCM

What's in Lisbon? Art Museums, Art Dealers, and Refugees in Portugal between 1933 and 1945

This thesis focuses on the transactions of works of art that took place in Portugal during the period of the Third Reich (1933-1945), the institutions and individuals involved in these transactions, and the works of art that were the object of those transactions. Drawing on American, Austrian, British, Dutch, French, German, Portuguese, and Swiss primary sources, the thesis sets out to determine the extent of the influence of the refugee flux into the country and of the international circulation of Nazi-looted art on the Portuguese art market. It does so by identifying and studying the actions of specific groups identified as having the higher chances of benefiting from these circumstances: importers and exporters of non-contemporary works of art; national art museums in Lisbon and Porto, cities with international communications and transport networks, which hosted the highest number of refugees; public museums in seaside and spa resorts, where thousands of refugees resided during the war years; and the foreign art dealers who opened businesses in Lisbon. Conceptually, it begins with a large universe of analysis, narrowing its scope as the chapters progress, culminating in the study of the actions of one single figure, and clarifying the provenance of one single painting, in a case study that brings together various areas of research examined previously. While findings confirm the supposition that the Portuguese State and public institutions did not knowingly engage in the acquisition of Nazi-looted art during this period, they reveal that some of their acquisitions require further provenance research, and that the actions of specific individuals in bringing works of art into the country demand further scrutiny

Robustness Assessment with a Data-Centric Machine Learning Pipeline

Publisher Copyright: AuthorAs long as the COVID-19 pandemic is still active in most countries worldwide, rapid diagnostic continues to be crucial to mitigate the impact of seasonal infection waves. Commercialized rapid antigen self-tests proved they cannot handle the most demanding periods, lacking availability and leading to cost rises. Thus, developing a non-invasive, costless, and more decentralized technology capable of giving people feedback about the COVID-19 infection probability would fill these gaps. This paper explores a sound-based analysis of vocal and respiratory audio data to achieve that objective. This work presents a modular data-centric Machine Learning pipeline for COVID-19 identification from voice and respiratory audio samples. Signals are processed to extract and classify relevant segments that contain informative events, such as coughing or breathing. Temporal, amplitude, spectral, cepstral, and phonetic features are extracted from audio along with available metadata for COVID-19 identification. Audio augmentation and data balancing techniques are used to mitigate class disproportionality. The open-access Coswara and COVID-19 Sounds datasets were used to test the performance of the proposed architecture. Obtained sensitivity scores ranged from 60.00% to 80.00% in Coswara and from 51.43% to 77.14% in COVID-19 Sounds. Although previous works report higher accuracy on COVID-19 detection, this research focused on a data-centric approach by validating the quality of the samples, segmenting the speech events, and exploring interpretable features with physiological meaning. As the pandemic evolves, its lessons must endure, and pipelines such as the proposed one will help prepare new stages where quick and easy disease identification is essential.publishersversionepub_ahead_of_prin