Tree seedling shade tolerance arises from interactions with microbes and is mediated by functional traits

Shade tolerance is a central concept in forest ecology and strongly influences forest community dynamics. However, the plant traits and conditions conferring shade tolerance are yet to be resolved. We propose that shade tolerance is shaped not only by responses to light but also by a species’ defense and recovery functional traits, soil microbial communities, and interactions of these factors with light availability. We conducted a greenhouse experiment for three temperate species in the genus Acer that vary in shade tolerance. We grew newly germinated seedlings in two light levels (2% and 30% sun) and controlled additions of microbial filtrates using a wet-sieving technique. Microbial filtrate treatments included: <20 µm, likely dominated by pathogenic microbes; 40-250 µm, containing arbuscular mycorrhizal fungi (AMF); combination, including both filtrate sizes; and sterilized combination. We monitored survival for nine weeks and measured fine root AMF colonization, hypocotyl phenolics, stem lignin, and stem+root nonstructural carbohydrates (NSC) at three-week intervals. We found that differences in seedling survival between low and high light only occurred when microbes were present. AMF colonization, phenolics, and NSC generally increased with light. Phenolics were greater with <20 µm microbial filtrate, suggesting that soil-borne pathogens may induce phenolic production; and NSC was greater with 40-250 µm filtrate, suggesting that mycorrhizal fungi may induce NSC production. Across species, microbe treatments, and light availability, survival increased as phenolics and NSC increased. Therefore, shade tolerance may be explained by interactions among soil-borne microbes, seedling traits, and light availability, providing a more mechanistic and trait-based explanation of shade tolerance and thus forest community dynamics

Interactive effects of vascular risk burden and advanced age on cerebral blood flow.

Vascular risk factors and cerebral blood flow (CBF) reduction have been linked to increased risk of cognitive impairment and Alzheimer's disease (AD); however the possible moderating effects of age and vascular risk burden on CBF in late life remain understudied. We examined the relationships among elevated vascular risk burden, age, CBF, and cognition. Seventy-one non-demented older adults completed an arterial spin labeling MR scan, neuropsychological assessment, and medical history interview. Relationships among vascular risk burden, age, and CBF were examined in a priori regions of interest (ROIs) previously implicated in aging and AD. Interaction effects indicated that, among older adults with elevated vascular risk burden (i.e., multiple vascular risk factors), advancing age was significantly associated with reduced cortical CBF whereas there was no such relationship for those with low vascular risk burden (i.e., no or one vascular risk factor). This pattern was observed in cortical ROIs including medial temporal (hippocampus, parahippocampal gyrus, uncus), inferior parietal (supramarginal gyrus, inferior parietal lobule, angular gyrus), and frontal (anterior cingulate, middle frontal gyrus, medial frontal gyrus) cortices. Furthermore, among those with elevated vascular risk, reduced CBF was associated with poorer cognitive performance. Such findings suggest that older adults with elevated vascular risk burden may be particularly vulnerable to cognitive change as a function of CBF reductions. Findings support the use of CBF as a potential biomarker in preclinical AD and suggest that vascular risk burden and regionally-specific CBF changes may contribute to differential age-related cognitive declines

The project data sphere initiative: accelerating cancer research by sharing data

Background. In this paper, we provide background and context regarding the potential for a new data-sharing platform, the Project Data Sphere (PDS) initiative, funded by financial and in-kind contributions from the CEO Roundtable on Cancer, to transform cancer research and improve patient outcomes. Given the relatively modest decline in cancer death rates over the past several years, a new research paradigm is needed to accelerate therapeutic approaches for oncologic diseases. Phase III clinical trials generate large volumes of potentially usable information, often on hundreds of patients, including patients treated with standard of care therapies (i.e., controls). Both nationally and internationally, a variety of stakeholders have pursued data-sharing efforts to make individual patient-level clinical trial data available to the scientific research community. Potential Benefits and Risks of Data Sharing. For researchers, shared data have the potential to foster a more collaborative environment, to answer research questions in a shorter time frame than traditional randomized control trials, to reduce duplication of effort, and to improve efficiency. For industry participants, use of trial data to answer additional clinical questions could increase research and development efficiency and guide future projects through validation of surrogate end points, development of prognostic or predictive models, selection of patients for phase II trials, stratification in phase III studies, and identification of patient subgroups for development of novel therapies. Data transparency also helps promote a public image of collaboration and altruism among industry participants. For patient participants, data sharing maximizes their contribution to public health and increases access to information that may be used to develop better treatments. Concerns about data-sharing efforts include protection of patient privacy and confidentiality. To alleviate these concerns, data sets are deidentified to maintain anonymity. To address industry concerns about protection of intellectual property and competitiveness, we illustrate several models for data sharing with varying levels of access to the data and varying relationships between trial sponsors and data access sponsors. The Project Data Sphere Initiative. PDS is an independent initiative of the CEO Roundtable on Cancer Life Sciences Consortium, built to voluntarily share, integrate, and analyze comparator arms of historical cancer clinical trial data sets to advance future cancer research. The aim is to provide a neutral, broad-access platform for industry and academia to share raw, deidentified data from late-phase oncology clinical trials using comparator-arm data sets. These data are likely to be hypothesis generating or hypothesis confirming but, notably, do not take the place of performing a well-designed trial to address a specific hypothesis. Prospective providers of data to PDS complete and sign a data sharing agreement that includes a description of the data they propose to upload, and then they follow easy instructions on the website for uploading their deidentified data. The SAS Institute has also collaborated with the initiative to provide intrinsic analytic tools accessible within the website itself. As of October 2014, the PDS website has available data from 14 cancer clinical trials covering 9,000 subjects, with hopes to further expand the database to include more than 25,000 subject accruals within the next year. PDS differentiates itself from other data-sharing initiatives by its degree of openness, requiring submission of only a brief application with background information of the individual requesting access and agreement to terms of use. Data from several different sponsors may be pooled to develop a comprehensive cohort for analysis. In order to protect patient privacy, data providers in the U.S. are responsible for deidentifying data according to standards set forth by the Privacy Rule of the U.S. Health Insurance Portability and Accountability Act of 1996. Using Data Sharing to Improve Outcomes in Cancer: The “Prostate Cancer Challenge.” Control-arm data of several studies among patients with metastatic castration-resistant prostate cancer (mCRPC) are currently available through PDS. These data sets have multiple potential uses. The “Prostate Cancer Challenge” will ask the cancer research community to use clinical trial data deposited in the PDS website to address key research questions regarding mCRPC. General themes that could be explored by the cancer community are described in this article: prognostic models evaluating the influence of pretreatment factors on survival and patient-reported outcomes; comparative effectiveness research evaluating the efficacy of standard of care therapies, as illustrated in our companion article comparing mitoxantrone plus prednisone with prednisone alone; effects of practice variation in dose, frequency, and duration of therapy; level of patient adherence to elements of trial protocols to inform the design of future clinical trials; and age of subjects, regional differences in health care, and other confounding factors that might affect outcomes. Potential Limitations and Methodological Challenges. The number of data sets available and the lack of experimental arm data limit the potential scope of research using the current PDS. The number of trials is expected to grow exponentially over the next year and may include multiple cancer settings, such as breast, colorectal, lung, hematologic malignancy, and bone marrow transplantation. Other potential limitations include the retrospective nature of the data analyses performed using PDS and its generalizability, given that clinical trials are often conducted among younger, healthier, and less racially diverse patient populations. Methodological challenges exist when combining individual patient data from multiple clinical trials; however, advancements in statistical methods for secondary database analysis offer many tools for reanalyzing data arising from disparate trials, such as propensity score matching. Despite these concerns, few if any comparable data sets include this level of detail across multiple clinical trials and populations. Conclusion. Access to large, late-phase, cancer-trial data sets has the potential to transform cancer research by optimizing research efficiency and accelerating progress toward meaningful improvements in cancer care. This type of platform provides opportunities for unique research projects that can examine relatively neglected areas and that can construct models necessitating large amounts of detailed data.The full potential of PDS will be realized only when multiple tumor types and larger numbers of data sets are available through the website

Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond

A fundamental goal of scientific research is to learn about causal relationships. However, despite its critical role in the life and social sciences, causality has not had the same importance in Natural Language Processing (NLP), which has traditionally placed more emphasis on predictive tasks. This distinction is beginning to fade, with an emerging area of interdisciplinary research at the convergence of causal inference and language processing. Still, research on causality in NLP remains scattered across domains without unified definitions, benchmark datasets and clear articulations of the challenges and opportunities in the application of causal inference to the textual domain, with its unique properties. In this survey, we consolidate research across academic areas and situate it in the broader NLP landscape. We introduce the statistical challenge of estimating causal effects with text, encompassing settings where text is used as an outcome, treatment, or to address confounding. In addition, we explore potential uses of causal inference to improve the robustness, fairness, and interpretability of NLP models. We thus provide a unified overview of causal inference for the NLP community.Comment: Accepted to Transactions of the Association for Computational Linguistics (TACL

Measuring “waiting” impulsivity in substance addictions and binge eating disorder in a novel analogue of rodent serial reaction time task

Background Premature responding is a form of motor impulsivity that preclinical evidence has shown to predict compulsive drug seeking but has not yet been studied in humans. We developed a novel translation of the task, based on the rodent 5-choice serial reaction time task, testing premature responding in disorders of drug and natural food rewards. Methods Abstinent alcohol- (n = 30) and methamphetamine-dependent (n = 23) subjects, recreational cannabis users (n = 30), and obese subjects with (n = 30) and without (n = 30) binge eating disorder (BED) were compared with matched healthy volunteers and tested on the premature responding task. Results Compared with healthy volunteers, alcohol- and methamphetamine-dependent subjects and cannabis users showed greater premature responding with no differences observed in obese subjects with or without BED. Current smokers exhibited greater premature responding versus ex-smokers and nonsmokers. Alcohol-dependent subjects also had lower motivation for explicit monetary incentives. A Motivation Index correlated negatively with alcohol use and binge eating severity. Conclusions Premature responding on a novel translation of a serial reaction time task was more evident in substance use disorders but not in obese subjects with or without BED. Lower motivation for monetary incentives linked alcohol use and binge eating severity. Our findings add to understanding the relationship between drug and natural food rewards

Does having a cat in your house increase your risk of catching COVID-19?

[EN]Due to the zoonotic origin of SARS-Coronavirus 2 (SARS-CoV-2), the potential for its transmission from humans back to animals and the possibility that it might establish ongoing infection pathways in other animal species has been discussed. Cats are highly susceptible to SARS-CoV-2 and were shown experimentally to transmit the virus to other cats. Infection of cats has been widely reported. Domestic cats in COVID-19-positive households could therefore be a part of a human to animal to human transmission pathway. Here, we report the results of a qualitative risk assessment focusing on the potential of cat to human transmission in such settings. The assessment was based on evidence available by October 2021. After the introduction of SARS-CoV-2 to a household by a human, cats may become infected and infected cats may pose an additional infection risk for other members of the household. In order to assess this additional risk qualitatively, expert opinion was elicited within the framework of a modified Delphi procedure. The conclusion was that the additional risk of infection of an additional person in a household associated with keeping a domestic cat is very low to negligible, depending on the intensity of cat-to-human interactions. The separation of cats from humans suffering from SARS-CoV-2 infection should contribute to preventing further transmission.SIThis work was funded by the German Federal Ministry of Education and Research within the COVMon Project, being part of the InfectControl2020 Initiative (BMBF grant no. 03COV16D)

Personalized recurrence risk assessment following the birth of a child with a pathogenic de novo mutation

Following the diagnosis of a paediatric disorder caused by an apparently de novo mutation, a recurrence risk of 1–2% is frequently quoted due to the possibility of parental germline mosaicism; but for any specific couple, this figure is usually incorrect. We present a systematic approach to providing individualized recurrence risk. By combining locus-specific sequencing of multiple tissues to detect occult mosaicism with long-read sequencing to determine the parent-of-origin of the mutation, we show that we can stratify the majority of couples into one of seven discrete categories associated with substantially different risks to future offspring. Among 58 families with a single affected offspring (representing 59 de novo mutations in 49 genes), the recurrence risk for 35 (59%) was decreased below 0.1%, but increased owing to parental mixed mosaicism for 5 (9%)—that could be quantified in semen for paternal cases (recurrence risks of 5.6–12.1%). Implementation of this strategy offers the prospect of driving a major transformation in the practice of genetic counselling

Conditioning with Treosulfan and Fludarabine followed by Allogeneic Hematopoietic Cell Transplantation for High-Risk Hematologic Malignancies

In this prospective study 60 patients of median age 46 (range: 5-60 years), with acute myelogenous leukemia (AML; n = 44), acute lymphoblastic leukemia (ALL; n = 3), or myelodysplastic syndrome (MDS; n = 13) were conditioned for allogeneic hematopoietic cell transplantation with a treosulfan/fludarabine (Flu) combination. Most patients were considered at high risk for relapse or nonrelapse mortality (NRM). Patients received intravenous treosulfan, 12 g/m2/day (n = 5) or 14 g/m2/day (n = 55) on days −6 to −4, and Flu (30 mg/m2/day) on days −6 to −2, followed by infusion of marrow (n = 7) or peripheral blood stem cells (n = 53) from HLA-identical siblings (n = 30) or unrelated donors (n = 30). All patients engrafted. NRM was 5% at day 100, and 8% at 2 years. With a median follow-up of 22 months, the 2-year relapse-free survival (RFS) for all patients was 58% and 88% for patients without high-risk cytogenetics. The 2-year cumulative incidence of relapse was 33% (15% for patients with MDS, 34% for AML in first remission, 50% for AML or ALL beyond first remission and 63% for AML in refractory relapse). Thus, a treosulfan/Flu regimen was well tolerated and yielded encouraging survival and disease control with minimal NRM. Further trials are warranted to compare treosulfan/Flu to other widely used regimens, and to study the impact of using this regimen in more narrowly defined groups of patients

Longitudinal data reveal strong genetic and weak non-genetic components of ethnicity-dependent blood DNA methylation levels

Epigenetic architecture is influenced by genetic and environmental factors, but little is known about their relative contributions or longitudinal dynamics. Here, we studied DNA methylation (DNAm) at over 750,000 CpG sites in mononuclear blood cells collected at birth and age 7 from 196 children of primarily self-reported Black and Hispanic ethnicities to study race-associated DNAm patterns. We developed a novel Bayesian method for high-dimensional longitudinal data and showed that race-associated DNAm patterns at birth and age 7 are nearly identical. Additionally, we estimated that up to 51% of all self-reported race-associated CpGs had race-dependent DNAm levels that were mediated through local genotype and, quite surprisingly, found that genetic factors explained an overwhelming majority of the variation in DNAm levels at other, previously identified, environmentally-associated CpGs. These results indicate that race-associated blood DNAm patterns in particular, and blood DNAm levels in general, are primarily driven by genetic factors, and are not as sensitive to environmental exposures as previously suggested, at least during the first 7 years of life