Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time, and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space. While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes, vast areas of the tropics remain understudied. In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity, but it remains among the least known forests in America and is often underrepresented in biodiversity databases. To worsen this situation, human-induced modifications 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, 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

The emergence of drug resistance to targeted cancer therapies: Clinical evidence

For many decades classical anti-tumor therapies included chemotherapy, radiation and surgery; however, in the last two decades, following the identification of the genomic drivers and main hallmarks of cancer, the introduction of therapies that target specific tumor-promoting oncogenic or non-oncogenic pathways, has revolutionized cancer therapeutics. Despite the significant progress in cancer therapy, clinical oncologists are often facing the primary impediment of anticancer drug resistance, as many cancer patients display either intrinsic chemoresistance from the very beginning of the therapy or after initial responses and upon repeated drug treatment cycles, acquired drug resistance develops and thus relapse emerges, resulting in increased mortality. Our attempts to understand the molecular basis underlying these drug resistance phenotypes in pre-clinical models and patient specimens revealed the extreme plasticity and adaptive pathways employed by tumor cells, being under sustained stress and extensive genomic/proteomic instability due to the applied therapeutic regimens. Subsequent efforts have yielded more effective inhibitors and combinatorial approaches (e.g. the use of specific pharmacologic inhibitors with immunotherapy) that exhibit synergistic effects against tumor cells, hence enhancing therapeutic indices. Furthermore, new advanced methodologies that allow for the early detection of genetic/epigenetic alterations that lead to drug chemoresistance and prospective validation of biomarkers which identify patients that will benefit from certain drug classes, have started to improve the clinical outcome. This review discusses emerging principles of drug resistance to cancer therapies targeting a wide array of oncogenic kinases, along with hedgehog pathway and the proteasome and apoptotic inducers, as well as epigenetic and metabolic modulators. We further discuss mechanisms of resistance to monoclonal antibodies, immunomodulators and immune checkpoint inhibitors, potential biomarkers of drug response/drug resistance, along with possible new therapeutic avenues for the clinicians to combat devastating drug resistant malignancies. It is foreseen that these topics will be major areas of focused multidisciplinary translational research in the years to come. © 201

Multi-messenger Observations of a Binary Neutron Star Merger

International audienceOn 2017 August 17 a binary neutron star coalescence candidate (later designated GW170817) with merger time 12:41:04 UTC was observed through gravitational waves by the Advanced LIGO and Advanced Virgo detectors. The Fermi Gamma-ray Burst Monitor independently detected a gamma-ray burst (GRB 170817A) with a time delay of $\sim 1.7\,{\rm{s}}$ with respect to the merger time. From the gravitational-wave signal, the source was initially localized to a sky region of 31 deg(2) at a luminosity distance of ${40}_{-8}^{+8}$ Mpc and with component masses consistent with neutron stars. The component masses were later measured to be in the range 0.86 to 2.26 $\,{M}_{\odot }$. An extensive observing campaign was launched across the electromagnetic spectrum leading to the discovery of a bright optical transient (SSS17a, now with the IAU identification of AT 2017gfo) in NGC 4993 (at $\sim 40\,{\rm{Mpc}}$) less than 11 hours after the merger by the One-Meter, Two Hemisphere (1M2H) team using the 1 m Swope Telescope. The optical transient was independently detected by multiple teams within an hour. Subsequent observations targeted the object and its environment. Early ultraviolet observations revealed a blue transient that faded within 48 hours. Optical and infrared observations showed a redward evolution over ∼10 days. Following early non-detections, X-ray and radio emission were discovered at the transient’s position $\sim 9$ and $\sim 16$ days, respectively, after the merger. Both the X-ray and radio emission likely arise from a physical process that is distinct from the one that generates the UV/optical/near-infrared emission. No ultra-high-energy gamma-rays and no neutrino candidates consistent with the source were found in follow-up searches. These observations support the hypothesis that GW170817 was produced by the merger of two neutron stars in NGC 4993 followed by a short gamma-ray burst (GRB 170817A) and a kilonova/macronova powered by the radioactive decay of r-process nuclei synthesized in the ejecta