A Paleoseismic Record Spanning 2-Myr Reveals Episodic Late Pliocene Deformation in the Western Qaidam Basin, NE Tibet

Acknowledgments This research was inspired by Prof. Lin Ding's comments on the doctoral thesis proposal of Yin Lu in May 2014 at the Institute of Tibetan Plateau Research, China. We thank Profs. Todd Ehlers, Erwin Appel, and Oliver Friedrich for fruitful discussions in the early stage of this research. We appreciate the editor Germán Prieto for handling our manuscript, Jérôme Nomade and one anonymous reviewer for constructive reviews. We thank Werner Fielitz for comments, A. Koutsodendris, K. S. Nakajima, and H. Campos for help with lab work, and W. Rösler and H. Schulz for help with core sampling. Financial support was provided by the German Research Foundation (# FR2544/13-1 to O. Friedrich) and the University of Liege under Special Funds for Research, IPD-STEMA Program (R.DIVE.0899-JF-G to Y. Lu).Peer reviewedPublisher PD

Orbital- and Millennial-Scale Changes in Lake-Levels Facilitate Earthquake-Triggered Mass Failures in the Dead Sea Basin

Acknowledgments The authors appreciate the editor L. Flesch for handling our manuscript, Ed Pope and Sebastian Cardona for constructive reviews. This research was supported by the Austrian Science Fund (FWF): M 2817 to Y. Lu and P30285-N34 to J. Moernaut, the University of Liege under Special Funds for Research, IPD-STEMA Program (R.DIVE.0899-J-F-G to Y. Lu), the Israel Science Foundation (#1645/19 to S. Marco and #1093/10 to R. Bookman), and the ICDP. A.A. is indebted to the Helmholtz Virtual Institute DESERVE for support. The authors thank C. Daxer for help modeling the Kernel Density and Nadav Wetzler for discussion.Peer reviewedPublisher PD

A New Approach to Constrain the Seismic Origin for Prehistoric Turbidites as Applied to the Dead Sea Basin

Acknowledgments The authors appreciate the editor Lucy Flesch for handling our manuscript, Stefano Vitale and Alina Polonia for constructive reviews. This research was supported by the University of Liege under Special Funds for Research, IPD‐STEMA Program (R.DIVE.0899‐J‐F‐G to Y. Lu), Austrian Science Fund (FWF: M 2817 to Y. Lu), the DESERVE Virtual Institute of the Helmholtz Association (to A. Agnon), the Israel Science Foundation (#1093/10 to R.Bookman and #1645/19 to S.Marco), and the ICDP.Peer reviewedPublisher PD

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Effects of eight neuropsychiatric copy number variants on human brain structure

Many copy number variants (CNVs) confer risk for the same range of neurodevelopmental symptoms and psychiatric conditions including autism and schizophrenia. Yet, to date neuroimaging studies have typically been carried out one mutation at a time, showing that CNVs have large effects on brain anatomy. Here, we aimed to characterize and quantify the distinct brain morphometry effects and latent dimensions across 8 neuropsychiatric CNVs. We analyzed T1-weighted MRI data from clinically and non-clinically ascertained CNV carriers (deletion/duplication) at the 1q21.1 (n = 39/28), 16p11.2 (n = 87/78), 22q11.2 (n = 75/30), and 15q11.2 (n = 72/76) loci as well as 1296 non-carriers (controls). Case-control contrasts of all examined genomic loci demonstrated effects on brain anatomy, with deletions and duplications showing mirror effects at the global and regional levels. Although CNVs mainly showed distinct brain patterns, principal component analysis (PCA) loaded subsets of CNVs on two latent brain dimensions, which explained 32 and 29% of the variance of the 8 Cohen’s d maps. The cingulate gyrus, insula, supplementary motor cortex, and cerebellum were identified by PCA and multi-view pattern learning as top regions contributing to latent dimension shared across subsets of CNVs. The large proportion of distinct CNV effects on brain morphology may explain the small neuroimaging effect sizes reported in polygenic psychiatric conditions. Nevertheless, latent gene brain morphology dimensions will help subgroup the rapidly expanding landscape of neuropsychiatric variants and dissect the heterogeneity of idiopathic conditions

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost