Intratumoral and extratumoral synapses are required for glioblastoma progression in Drosophila

Glioblastoma (GB) is the most aggressive, lethal and frequent primary brain tumor. It originates from glial cells and is characterized by rapid expansion through infiltration. GB cells interact with the microenvironment and healthy surrounding tissues, mostly neurons and vessels. GB cells project tumor microtubes (TMs) that contact with neurons and exchange signaling molecules related to Wingless/WNT, JNK, Insulin or Neuroligin-3 pathways. This cell to cell communication promotes GB expansion and neurodegeneration. Moreover, healthy neurons form glutamatergic functional synapses with GB cells which facilitate GB expansion and premature death in mouse GB xerograph models. Targeting signaling and synaptic components of GB progression may become a suitable strategy against glioblastoma. In a Drosophila GB model, we have determined the post-synaptic nature of GB cells with respect to neurons, and the contribution of post-synaptic genes expressed in GB cells to tumor progression. In addition, we document the presence of intratumoral synapses between GB cells, and the functional contribution of pre-synaptic genes to GB calcium dependent activity and expansion. Finally, we explore the relevance of synaptic genes in GB cells to the lifespan reduction caused by GB advance. Our results indicate that both presynaptic and postsynaptic proteins play a role in GB progression and lethality.

Timing of the solar wind propagation delay between L1 and Earth based on machine learning

Erroneous GNSS positioning, failures in spacecraft operations and power outages due to geomagnetically induced currents are severe threats originating from space weather. Having knowledge of potential impacts on modern society in advance is key for many end-user applications. This covers not only the timing of severe geomagnetic storms but also predictions of substorm onsets at polar latitudes. In this study we aim at contributing to the timing problem of space weather impacts and propose a new method to predict the solar wind propagation delay between Lagrangian point L1 and the Earth based on machine learning, specifically decision tree models. The propagation delay is measured from the identification of interplanetary discontinuities detected by the Advanced Composition Explorer (ACE) and their subsequent sudden commencements in the magnetosphere recorded by ground-based magnetometers. A database of the propagation delay has been constructed on this principle including 380 interplanetary shocks with data ranging from 1998 to 2018. The feature set of the machine learning approach consists of six features, namely the three components of each the solar wind speed and position of ACE around L1. The performance assessment of the machine learning model is examined on the basis of a 10-fold cross-validation. The machine learning results are compared to physics-based models, i.e., the flat propagation delay and the more sophisticated method based on the normal vector of solar wind discontinuities (vector delay). After hyperparameter optimization, the trained gradient boosting (GB) model is the best machine learning model among the tested ones. The GB model achieves an RMSE of 4.5 min with respect to the measured solar wind propagation delay and also outperforms the physical flat and vector delay models by 50 % and 15 % respectively. To increase the confidence in the predictions of the trained GB model, we perform a operational validation, provide drop-column feature importance and analyse the feature impact on the model output with Shapley values. The major advantage of the machine learning approach is its simplicity when it comes to its application. After training, values for the solar wind speed and spacecraft position from only one datapoint have to be fed into the algorithm for a good prediction.

Development, Calibration and Validation of an Internal Air Temperature Model for a Naturally Ventilated Nearly Zero Energy Building: Comparison of Model Types and Calibration Methods

In this study, a grey box (GB) model for simulating internal air temperatures in a naturally ventilated nearly zero energy building (nZEB) was developed and calibrated, using multiple data configurations for model parameter selection and an automatic calibration algorithm. The GB model was compared to a white box (WB) model for the same application using identical calibration and validation datasets. Calibrating the GB model using only one week of data produced very accurate results for the calibration periods but led to inconsistent and typically inaccurate results for the validation periods (root mean squared error (RMSE) in validation periods was 229% larger than the RMSE in calibration periods). Using three weeks of data from varying seasons for calibration reduced the model accuracy in the calibration period but substantially increased the model accuracy and generalisation abilities for the validation period, reducing the mean RMSE by over 160%. The use of one week of data increased the standard deviation in parameter selections by over 40% when compared with the three-week calibration datasets. Utilising data from multiple seasons for calibration purposes was found to substantially improve generalisation abilities. When compared to the WB model, the GB model produced slightly less accurate results (mean RMSE of the GB model was 1.5% higher). However, the authors found that employing GB modelling with an automatic model calibration technique reduced the human labour input for simulating internal air temperature of a naturally ventilated nZEB by approximately 90%, relative to WB modelling using a manually calibrated approach.

Modeling Diameter Distributions of Loblolly Pine Plantations in Western Gulf Coastal Plain

Diameter distribution models are useful tools for forest management planning, in particular for even-aged plantations of important commercial species such as loblolly pine. Using data collected from loblolly pine plantations across East Texas, two diameter distribution model systems were developed, with the first being a conventional, Weibull-form statistical model system and the second being developed using gradient boosting (GB) technique. Both models were tested using an independent data set and compared with the regional model currently being used, which was developed by Lee and Coble (2006). Compared with Lee and Coble (2006), the Weibull-form model of this study had 66.7% smaller prediction bias, 27.2% lower mean absolute error (MAE), and 18.9% smaller root-mean-square error (RMSE). Compared with the Weibull-form model of this study, the GB model had 33.9% lower MAE, 39.5% smaller RMSE, and greater R2. Thus, the GB model greatly outperformed the Weibull-form model, which, in turn, was greatly improved over the Lee and Coble (2006) in prediction accuracy. By combining a regional volume or weight equation, both proposed diameter distribution models can be used to predict stand wood volume or weight by diameter class. Both models, in particular the GB model, are recommended for use in predicting stand structures and developing stand and stock tables for loblolly pine plantations in the region. Management and Policy Implications: Knowing future stand tree size distributions is important for forest management planning. This study developed two quantitative tools to predict diameter distributions for loblolly pine (Pinus taeda) plantations in the Western Gulf Coastal Plain, with one based on the Weibull function (Weibull-form model) and the other developed using the gradient boosting technique (GB model). For the Weibull-form model, using current stand information, readers can manually calculate future stand trees per acre by diameter class. Importantly, the Weibull-form model provides more accurate (less bias and more precise) prediction than any currently available models for loblolly pine in the region. The GB model, which uses the same predictors as the Weibull-form model, can achieve even better (similar bias but more precise) prediction than the Weibull-form model. However, no equations and model coefficients for the GB model were provided, and use of the GB model relies on computer programming. A computer program was developed to implement the GB model. We recommend use of both models, in particular of the GB model, in managing loblolly pine in the region. The results aid our understanding in loblolly pine stand structure development and management in the region.

A Gradient Boosting Crash Prediction Approach for Highway-Rail Grade Crossing Crash Analysis

Highway-rail grade crossing (HRGC) crashes continue to be the major contributors to rail causalities in the United States and have been intensively researched in the past. Data-mining models focus on prediction while dominant general linear models focus on model and data fitness. Decision makers and traffic engineers rely on prediction models to examine at-grade crash frequency and make safety improvement. The gradient boosting (GB) model has gained popularity in many research areas. In this study, to fully understand the model performance on HRGC accident prediction performance, the GB model with functional gradient descent algorithm is selected to analyze crashes at highway-rail grade crossings (HRGCs) and to identify contributor factors. Moreover, contributors’ importance and partial-dependent relations are generated to further understand the relationship of identified contributors and HRGC crash likelihood to concur “black box” issues that most machine learning methods face. Furthermore, to fully demonstrate the model’s prediction performance, a comprehensive model prediction power assessment based on six measurements is conducted, and the prediction performance of the GB model is verified and compared with a decision tree model as a reference due to their popularity and comparable data availability. It is demonstrated that the GB model produces better prediction accuracy and reveals nonlinear relationships among contributors and crash likelihood. In general, HRGC crash likelihood is significantly impacted by several traffic exposure factors: highway traffic volume, railway traffic volume, and train travel speed and others.

Reactive Element Effects in High-Temperature Alloys Disentangled

Reactive elements—REs—are decisive for the longevity of high-temperature alloys. This work joins several previous efforts to disentangle various RE effects in order to explain apparently contradicting experimental observations in alumina forming alloys. At 800–1000 °C, “messy” aluminum oxy-hydroxy-hydride transients initially formed due to oxidation by H2O which in turn undergo secondary oxidation by O2. The formation of the transient oxide becomes supported by dispersed RE oxide particles acting as water equivalents. At higher temperatures, electron conductivity in impurity states owing to oxygen vacancies in grain boundaries (GBs) becomes increasingly relevant. These channels are subsequently closed by REs pinning the said vacancies. The universality of the emerging understanding is supported by a comparative first-principles study by means of density functional theory addressing RE(III): Sc2O3, Y2O3, and La2O3, and RE(IV): TiO2, ZrO2, and HfO2, that upon reaction with water, co-decorate a generic GB model by hydroxide and RE ions. At 100% RE coverage, the GB model becomes relevant at both temperature regimes. Based on reaction enthalpy ΔHr considerations, “messy” aluminum oxy-hydroxy-hydride transients are accessed in both classes. Larger variations in ΔHr are found for RE(III)-decorated alumina GBs as compared to RE(IV). For RE(III), correlation with GB width is found, increasing with increased ionic radius. Similarly, upon varying RE(IV), minor changes in stability correlate with minor structural variations. GB decorations by Ce(III) and Ce(IV) further consolidate the emerging understanding. The findings are used to discuss experimental observations that include impact of co-doping by RE(III) and RE(IV).

Gradient Crystal Plasticity: A Grain Boundary Model for Slip Transmission

Interaction between dislocations and grain boundaries (GBs) in the forms of dislocation absorption, emission, and slip transmission at GBs significantly affects size-dependent plasticity in fine-grained polycrystals. Thus, it is vital to consider those GB mechanisms in continuum plasticity theories. In the present paper, a new GB model is proposed by considering slip transmission at GBs within the framework of gradient polycrystal plasticity. The GB model consists of the GB kinematic relations and governing equations for slip transmission, by which the influence of geometric factors including the misorientation between the incoming and outgoing slip systems and GB orientation, GB defects, and stress state at GBs are captured. The model is numerically implemented to study a benchmark problem of a bicrystal thin film under plane constrained shear. It is found that GB parameters, grain size, grain misorientation, and GB orientation significantly affect slip transmission and plastic behaviors in fine-grained polycrystals. Model prediction qualitatively agrees with experimental observations and results of discrete dislocation dynamics simulations.

A Simplistic Preliminary Assessment of Ginstling-Brounstein Model for Solid Spherical Particles in the Context of a Diffusion-Controlled Synthesis

Wet precipitation (WP) is a diffusion-controlled synthesis method, which is often used for synthesizing such compounds as hydroxyapatite (HAp). Since the process is limited by diffusion, the choice of a diffusion model becomes a critical aspect. In this simplistic assessment for a preliminary evaluation of the diffusion model applicability, the Ginstling-Brounstein (GB) equation is chosen and analyzed for the case of spherical particles. The nominal kinetic constant K is a parameter in GB model which describes diffusion and is related to the effective molecular diffusivity. When the value of K is known, it becomes possible to predict the required time to achieve desired conversion and design the synthesis accordingly. The GB model is assessed mathematically using simulations, a parametric study and Yates analysis (2n factorial design). Parameters chosen for a preliminary study are in the range of characteristic values for a laboratory-scale WP synthesis of HAp and are thus representative for the application of the model to practice. It should be noted that the analysis is simplistic and is meant to provide only preliminary information for future research, requiring experimental validation.

Nested-Clique Network Model of Neural Associative Memory

Clique-based neural associative memories introduced by Gripon and Berrou (GB), have been shown to have good performance, and in our previous work we improved the learning capacity and retrieval rate by local coding and precoding in the presence of partial erasures. We now take a step forward and consider nested-clique graph structures for the network. The GB model stores patterns as small cliques, and we here replace these by nested cliques. Simulation results show that the nested-clique structure enhances the clique-based model.