Two karrooite crystals, one with a disorder parameter (X = Ti content in the M1 site) of 0.070(5) and the other with X = 0.485(5), were mounted together in one diamond-anvil cell and studied by single-crystal X-ray diffraction at several pressures up to 7.51 GPa. The most noticeable effect of increasing cation disorder on the high-pressure behavior of the structure is to increase the compressibilities of the mean < M12-O > bond length from 0.00148(2) GPa(-1) in the ordered sample to 0.00163(7) GPa(-1) in the disordered one and decrease those of the mean < M1-O > bond length from 0.00243(5) to 0.00193(12) GPa These changes are responsible for the compressibility difference between the two phases observed by Hazen and Yang (1997). Both compressibilities of the mean < M-O > bond lengths and the octahedral volumes in two phases decrease linearly with increasing the Ti contents in the octahedral sites. All octahedra in two samples become less distorted as pressure increases, but those in the more disordered structure exhibit larger decreases in terms of the octahedral angle variance than the corresponding ones in the more ordered structure. The influence of pressure on the interatomic angles is small compared to the interatomic distances, suggesting that compression of the karrooite structure is controlled primarily by the bond-length shortening, rather than by bend-angle bending. The strong compressional anisotropy of the structure is a consequence of the differential compressibilities of the weaker Mg2+-O and stronger Ti4+-O bonds and the complex edge-sharing linkage involving the M1 and M2 octahedra.

Accreting protoplanets are windows into planet formation processes, and high-contrast differential imaging is an effective way to identify them. We report results from the Giant Accreting Protoplanet Survey (GAPlanetS), which collected Ha differential imagery of 14 transitional disk host stars with the Magellan Adaptive Optics System. To address the twin challenges of morphological complexity and point-spread function instability, GAPlanetS required novel approaches for frame selection and optimization of the Karhounen-Loeve Image Processing algorithm pyKLIP. We detect one new candidate, CS Cha "c," at a separation of 68 mas and a modest Delta mag of 2.3. We recover the HD 142527 B and HD 100453 B accreting stellar companions in several epochs, and the protoplanet PDS 70 c in 2017 imagery, extending its astrometric record by nine months. Though we cannot rule out scattered light structure, we also recover LkCa 15 "b," at Ha; its presence inside the disk cavity, absence in Continuum imagery, and consistency with a forward-modeled point source suggest that it remains a viable protoplanet candidate. Through targeted optimization, we tentatively recover PDS 70 c at two additional epochs and PDS 70 b in one epoch. Despite numerous previously reported companion candidates around GAplanetS targets, we recover no additional point sources. Our moderate Ha contrasts do not preclude most protoplanets, and we report limiting Ha contrasts at unrecovered candidate locations. We find an overall detection rate of similar to 36 -22+26%

MOTIVATION: Computational inference of genome organization based on Hi-C sequencing has greatly aided the understanding of chromatin and nuclear organization in three dimensions (3D). However, existing computational methods fail to address the cell population heterogeneity. Here we describe a probabilistic-modeling-based method called CscoreTool-M that infers multiple 3D genome sub-compartments from Hi-C data.

BACKGROUND: Genetic variation in regulatory sequences that alter transcription factor (TF) binding is a major cause of phenotypic diversity. Brassinosteroid is a growth hormone that has major effects on plant phenotypes. Genetic variation in brassinosteroid-responsive cis-elements likely contributes to trait variation. Pinpointing such regulatory variations and quantitative genomic analysis of the variation in TF-target binding, however, remains challenging. How variation in transcriptional targets of signaling pathways such as the brassinosteroid pathway contributes to phenotypic variation is an important question to be investigated with innovative approaches.

Pressing environmental research questions demand the integration of increasingly diverse and large-scale ecological datasets as well as complex analytical methods, which require specialized tools and resources. Computational training for ecological and evolutionary sciences has become more abundant and accessible over the past decade, but tool development has outpaced the availability of specialized training. Most training for scripted analyses focuses on individual analysis steps in one script rather than creating a scripted pipeline, where modular functions comprise an ecosystem of interdependent steps. Although current computational training creates an excellent starting place, linear styles of scripting can risk becoming labor- and time-intensive and less reproducible by often requiring manual execution. Pipelines, however, can be easily automated or tracked by software to increase efficiency and reduce potential errors. Ecology and evolution would benefit from techniques that reduce these risks by managing analytical pipelines in a modular, readily parallelizable format with clear documentation of dependencies. Workflow management software (WMS) can aid in the reproducibility, intelligibility and computational efficiency of complex pipelines. To date, WMS adoption in ecology and evolutionary research has been slow. We discuss the benefits and challenges of implementing WMS and illustrate its use through a case study with the targets r package to further highlight WMS benefits through workflow automation, dependency tracking and improved clarity for reviewers. Although WMS requires familiarity with function-oriented programming and careful planning for more advanced applications and pipeline sharing, investment in training will enable access to the benefits of WMS and impart transferable computing skills that can facilitate ecological and evolutionary data science at large scales.