Current Standing of Longleaf Pine Trees under Climate Change

Climate change poses many risks to economically and ecologically crucial species. Longleaf pine (Pinus palustris Mill.) trees are keystone species that were once dominant across the southeastern United States, but now occupy less than 5% of their historic range and are thus classified as endangered. Here we review the current status and challenges facing longleaf pine trees, what is known on how changing climate will impact longleaf growth and reproduction, and gaps in the literature that are important to address. We found that many fundamental aspects of longleaf pine growth and reproduction are understood. However, these systems are complex, and not all is known about each factor that influences the relationship between climate, growth, and reproductive output. Additionally, long-term data sets capable of examining all relevant factors in these relationships do not currently exist. To fill necessary gaps, we recommend a joint approach between using readily available data sets and establishing new long-term monitoring plots targeted to collect data on missing or poorly understood conditions. This review provides a clue from an ecological complexity perspective to understand and manage longleaf pine forests under climate change

A representation learning model based on variational inference and graph autoencoder for predicting lncRNA‑disease associations

Background: Numerous studies have demonstrated that long non-coding RNAs are related to plenty of human diseases. Therefore, it is crucial to predict potential lncRNAdisease associations for disease prognosis, diagnosis and therapy. Dozens of machine learning and deep learning algorithms have been adopted to this problem, yet it is still challenging to learn efficient low-dimensional representations from high-dimensional features of lncRNAs and diseases to predict unknown lncRNA-disease associations accurately. Results: We proposed an end-to-end model, VGAELDA, which integrates variational inference and graph autoencoders for lncRNA-disease associations prediction. VGAELDA contains two kinds of graph autoencoders. Variational graph autoencoders (VGAE) infer representations from features of lncRNAs and diseases respectively, while graph autoencoders propagate labels via known lncRNA-disease associations. These two kinds of autoencoders are trained alternately by adopting variational expectation maximization algorithm. The integration of both the VGAE for graph representation learning, and the alternate training via variational inference, strengthens the capability of VGAELDA to capture efficient low-dimensional representations from high-dimensional features, and hence promotes the robustness and preciseness for predicting unknown lncRNA-disease associations. Further analysis illuminates that the designed co-training framework of lncRNA and disease for VGAELDA solves a geometric matrix completion problem for capturing efficient low-dimensional representations via a deep learning approach. Conclusion: Cross validations and numerical experiments illustrate that VGAELDA outperforms the current state-of-the-art methods in lncRNA-disease association prediction. Case studies indicate that VGAELDA is capable of detecting potential lncRNAdisease associations. The source code and data are available at https:// github. com/ zhang labNKU/ VGAEL DA

Power law relationships in the branches of loblolly pine, red maple and sugar maple trees

Abstract: Power laws are interesting patterns that exist over wide ranges. Power laws may be used to determine the organization of countless networks in nature. The purpose of this study was to test whether the distribution patterns in shoot lengths of three popular tree species follow a power law. This study not only adds to the general knowledge base for these species but also may be used to make predictions about other species. Three common tree species were included in this study: loblolly pine (Pinus taeda), red maple (Acer rubrum), and the sugar maple (Acer saccharum). The height and all shoot lengths of five individuals of each tree species were measured, recorded and sorted. Loblolly pines and sugar maples followed the same power law at individual and species level. Most of the red maple individuals did not follow a power law although they followed a power law at the species level. One possible reason might be that the red maple trees measured were too young and were in strong competition for resources with other tree species

Spin-dependent transport for armchair-edge graphene nanoribbons between ferromagnetic leads

We theoretically investigate the spin-dependent transport for the system of an armchair-edge graphene nanoribbon (AGNR) between two ferromagnetic (FM) leads with arbitrary polarization directions at low temperatures, where a magnetic insulator is deposited on the AGNR to induce an exchange splitting between spin-up and -down carriers. By using the standard nonequilibrium Green's function (NGF) technique, it is demonstrated that, the spin-resolved transport property for the system depends sensitively on both the width of AGNR and the polarization strength of FM leads. The tunneling magnetoresistance (TMR) around zero bias voltage possesses a pronounced plateau structure for system with semiconducting 7-AGNR or metallic 8-AGNR in the absence of exchange splitting, but this plateau structure for 8-AGNR system is remarkably broader than that for 7-AGNR one. Interestingly, the increase of exchange splitting $\Delta$ suppresses the amplitude of the structure for 7-AGNR system. However, the TMR is enhanced much for 8-AGNR system under the bias amplitude comparable to splitting strength. Further, the current-induced spin transfer torque (STT) for 7-AGNR system is systematically larger than that for 8-AGNR one. The findings here suggest the design of GNR-based spintronic devices by using a metallic AGNR, but it is more favorable to fabricate a current-controlled magnetic memory element by using a semiconducting AGNR.Comment: 8 pages, 8 figure

Insulin inhibits cardiac contractility by inducing a Gi-biased β2-adrenergic signaling in hearts.

Insulin and adrenergic stimulation are two divergent regulatory systems that may interact under certain pathophysiological circumstances. Here, we characterized a complex consisting of insulin receptor (IR) and β2-adrenergic receptor (β2AR) in the heart. The IR/β2AR complex undergoes dynamic dissociation under diverse conditions such as Langendorff perfusions of hearts with insulin or after euglycemic-hyperinsulinemic clamps in vivo. Activation of IR with insulin induces protein kinase A (PKA) and G-protein receptor kinase 2 (GRK2) phosphorylation of the β2AR, which promotes β2AR coupling to the inhibitory G-protein, Gi. The insulin-induced phosphorylation of β2AR is dependent on IRS1 and IRS2. After insulin pretreatment, the activated β2AR-Gi signaling effectively attenuates cAMP/PKA activity after β-adrenergic stimulation in cardiomyocytes and consequently inhibits PKA phosphorylation of phospholamban and contractile responses in myocytes in vitro and in Langendorff perfused hearts. These data indicate that increased IR signaling, as occurs in hyperinsulinemic states, may directly impair βAR-regulated cardiac contractility. This β2AR-dependent IR and βAR signaling cross-talk offers a molecular basis for the broad interaction between these signaling cascades in the heart and other tissues or organs that may contribute to the pathophysiology of metabolic and cardiovascular dysfunction in insulin-resistant states