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opencc-by-4.0Aug 2017View details →

ROS-Specific Huntingtin Interactions: IP Optimization in Patient-Derived Cells

Optimization step in the lead up to mass spec identification of ROS-specific huntingtin protein-protein interactions.

opencc-by-4.0Apr 2023View details →

BFM17 Optimization Data and Plotting Scripts

This is the data and plotting scripts for the optimization of a high-dimensional biophysical model by coupling it to DAKOTA. The biophysical model used is this work was the Biogeochemical Flux Model with 17 state variables coupled to a 1D implementation of the Princeton Ocean Model, referred to as BFM17+POM1D.  Included is data from a one-at-a-time sensitivity analysis, random sampling of the model's parameter space, and optimization runs. The data is accompanied by plotting scripts. This material is published to accompany a forthcoming paper describing the parameter estimation methodology applied here.

opencc-zeroOct 2023View details →

Reaching the limit in autonomous racing: Optimal control versus reinforcement learning

A central question in robotics is how to design a control system for an agile mobile robot. This paper studies this question systematically, focusing on a challenging setting: autonomous drone racing. We show that a neural network controller trained with reinforcement learning (RL) outperformed optimal control (OC) methods in this setting. We then investigated which fundamental factors have contributed to the success of RL or have limited OC. Our study indicates that the fundamental advantage of RL over OC is not that it optimizes its objective better but that it optimizes a better objective. OC decomposes the problem into planning and control with an explicit intermediate representation, such as a trajectory, that serves as an interface. This decomposition limits the range of behaviors that can be expressed by the controller, leading to inferior control performance when facing unmodeled effects. In contrast, RL can directly optimize a task-level objective and can leverage domain randomization to cope with model uncertainty, allowing the discovery of more robust control responses. Our findings allowed us to push an agile drone to its maximum performance, achieving a peak acceleration greater than 12 times the gravitational acceleration and a peak velocity of 108 kilometers per hour. Our policy achieved superhuman control within minutes of training on a standard workstation. This work presents a milestone in agile robotics and sheds light on the role of RL and OC in robot control.

opencc-zeroOct 2023View details →

Study on optimization effect of south roof curve of Chinese solar greenhouse

To maximize the use of solar energy and increase the utilization area of Chinese solar greenhouse (CSG) land, a solar greenhouse radiation model was established based on previous studies, combining with the law of solar movement, meteorological data, and optical properties of materials. The model is verified by selecting the measured data of typical sunny days. The results show that the optimization of the south roof curve of the greenhouse can not only increase the land-use area in the greenhouse but also increase the solar energy captured by the solar greenhouse. And the CSG in the greenhouse, the land-use area in the greenhouse increased to 42 m2, the lighting rate of the south roof, and the lighting rate of the ground increased 15.2% and 0.78% respectively. The amount of interception in the back wall of the greenhouse was reduced by 0.67%, and that in the greenhouse was increased by 2.22%. The optimal structure CSG in Shenyang (span 9 m) is the optimal shoulder height position, obtain the best shoulder height position of the greenhouse, the level of front and bottom Angle is 0.7 m, and the height is 2 m. Combined with the actual greenhouse construction demand, the optimal curve function Y1 and Y2 of the south roof of the greenhouse are calculated. This paper verifies the structural safety of the front roof of the greenhouse after the renovation, and shows that increasing the shoulder height can also increase the structural stability of the greenhouse and strengthen the structural safety of the greenhouse.

opencc-zeroOct 2023View details →

Optimal mechanical interactions direct multicellular network formation on elastic substrates

Cells self-organize into functional, ordered structures during tissue morphogenesis, a process that is evocative of colloidal self-assembly into engineered soft materials. Understanding how intercellular mechanical interactions may drive the formation of ordered and functional multicellular structures is important in developmental biology and tissue engineering. Here, by combining an agent-based model for contractile cells on elastic substrates with endothelial cell culture experiments, we show that substrate deformation–mediated mechanical interactions between cells can cluster and align them into branched networks. Motivated by the structure and function of vasculogenic networks, we predict how measures of network connectivity like percolation probability and fractal dimension as well as local morphological features including junctions, branches, and rings depend on cell contractility and density and on substrate elastic properties including stiffness and compressibility. We predict and confirm with experiments that cell network formation is substrate stiffness dependent, being optimal at intermediate stiffness. We also show the agreement between experimental data and predicted cell cluster types by mapping a combined phase diagram in cell density substrate stiffness. Overall, we show that long-range, mechanical interactions provide an optimal and general strategy for multicellular self-organization, leading to more robust and efficient realizations of space-spanning networks than through just local intercellular interactions.

opencc-zeroNov 2023View details →

Robust evidence for bats as reservoir hosts is lacking in most African virus studies – a review and call to optimize sampling and conserve bats

Africa experiences frequent emerging disease outbreaks among humans, with bats often proposed as zoonotic pathogen hosts. We comprehensively reviewed virus-bat findings from papers published between 1978 and 2020 to evaluate the evidence that African bats are reservoirs and/or bridging hosts for viruses that cause human disease. We present data from 162 papers (of 1322) with original findings on (1) numbers and species of bats sampled across bat families and the continent, (2) how bats were selected for study inclusion, (3) if bats were terminally sampled, (4) what types of ecological data, if any, were recorded, and (5) which viruses were detected and with what methodology. We propose a scheme for evaluating presumed virus-host relationships by evidence type and quality, using the contrasting available evidence for Orthoebolavirus (formerly Ebolavirus) versus Orthomarburgvirus (formerly Marburgvirus) as an example. We review the wording in abstracts and discussions of all 162 papers, identifying key framing terms, how these refer to findings, and how they might contribute to people's beliefs about bats. We discuss the impact of scientific research communication on public perception and emphasize the need for strategies that minimize human-bat conflict and support bat conservation. Finally, we make recommendations for best practices that will improve virological study metadata.

opencc-by-4.0Oct 2023View details →

Optimized protocols for RNA interference in Macrostomum lignano.

Microscopy raw data from figures and supplemental figures and videos from "Optimized protocols for RNA interference in Macrostomum lignano" article. Zeiss LSM format (.lsm or czi) and tiff exportation (.tif). Videos converted to AVI format (.avi).

opencc-by-4.0Aug 2023View details →

Molecular dynamics trajectories, GROMACS input files, and analysis code from "Rational optimization of a transcription factor activation domain inhibitor" by Basu et. al, Nature Structural & Molecular Biology, 2023

Molecular dynamics trajectories, GROMACS input files, and analysis code from "Rational optimization of a transcription factor activation domain inhibitor" by Basu et. al, Nature Structural & Molecular Biology,  2023

opencc-by-4.0May 2022View details →

Designing Optimal Convolutional Neural Network Architecture Using Differential Evolution Algorithm

Convolutional Neural Networks (CNNs) are widely used deep learning models for solving various tasks such as computer vision, speech recognition, among others. However, CNNs are developed manually based on problem-specific domain knowledge and tricky settings, which are laborious, time-consuming and challenging. To address these issues, this study proposes an Improved Differential Evolution of Convolutional Neural Network algorithm, namely IDECNN, to design CNN layer architectures for image classification task.

Data supporting "Lessons learned from multi-objective automatic optimizations of classical three-site rigid water models using microscopic and macroscopic target experimental observables"

This repository contains the set of data and the code to reproduce the results shown in "Lessons learned from multi-objective automatic optimizations of classical three-site rigid water models using microscopic and macroscopic target experimental observables" published on Journal of Chemical Engineering and Data (DOI: 10.1021/acs.jced.3c00538).

Research data supporting ""Lessons learned from multi-objective automatic optimizations of classical three-site rigid water models using microscopic and macroscopic target experimental observables""

opencc-by-4.0Jan 2024View details →

Passive Non-Cooperative Intruder State Estimation and Optimal-Feedback Avoidance System for UAVs

In recent years, numerous applications for unmanned aircraft systems (UAS) have emerged, such as manufacturing inspections and reconnaissance. Ensuring safety is crucial for integrating UAS into the National Airspace System (NAS); this integration is being conducted on the basis of a century of experience that has made manned aircraft operations incredibly safe.A key challenge for unmanned flight is the inability to "detect-and-avoid" (DAA) obstacles. Various DAA systems have been proposed in recent years, each employing different sensor modalities. Cooperative systems enable air vehicles to exchange state information, while devices like the Automatic Dependent Surveillance-Broadcast (ADS-B) and Traffic Collision Avoidance System (TCAS) use satellite navigation sensors and transponders, respectively, to broadcast position data. Additionally, the Airborne Collision Avoidance System (ACAS) led to the creation of the ACAS-XU standard for unmanned aircraft.The DAA capability for UAS must be extended to address non-cooperative intruders. This paper introduces an integrated vision-based passive collision alert system (PCAS) and guidance system that is designed to detect and optimally avoid collision with non-cooperative intruders. The system can adhere to recently-introduced regulations for safety zones and can be customized pre-flight. Hardware-in-the-loop (HITL) simulation demonstrates the feasibility for deployment on UAS in a plug-and-play fashion.

MATLAB scripts and raw experimental data for the paper "Optimizing measurements of linear changes of NMR signal parameters" by Javier Agustin Romero, Krzysztof Kazimierczuk and Paweł Kasprzak

Classical_fit.m   -    Comparison of simulation results and theoretical predictions for the linear fit of the resonance frequencies.  Radon_transform - the same, but using Radon transform to determine linear coefficients. Amplitude.m - Comparison of errors of the linear coefficients for varying amplitude fit in simulations and theory. process_measurements.m - script to process experimental data (caffeine peak at 7.90 ppm). The data are stored in real.mat and imag.mat For the details of theoretical formulas, see the paper "Optimizing measurements of linear changes of NMR signal parameters" by Javier Agustin Romero, Krzysztof Kazimierczuk, and Paweł Kasprzak. The scripts were used to generate Figures in the paper.

dataset- Tuberculosis detection using Squid Game Optimization with Deep Learning Model on Chest X-Ray Images

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Data set: Al-Biruni Earth Radius Optimization with Deep Transfer Learning based Scene Image Classification on Remote Sensing Imagery

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opencc-bySep 2017View details →

Bodhisattva Optimized

Maps are Big and can be lowered.... Thank'You to the Original Scan of Geoffrey Marchal Source: Objaverse 1.0 / Sketchfab

Data set for Earthquakes Trigger Rapid Flash Boiling Front at Optimal Geologic Conditions

Data set for manuscript "Earthquakes Trigger Rapid Flash Boiling Front at Optimal Geologic Conditions" submitted to Geophysical Research Letters.

opencc-zeroJan 2024View details →

Data from: Optimizing detectability of the endangered fan mussel using eDNA and ddPCR

Spatial and temporal monitoring of species threatened with extinction is of critical importance for conservation and ecosystem management. On the Mediterranean coast, the fan mussel (Pinna nobilis) is listed as critically endangered after suffering from a mass mortality event since 2016, leading to 100% mortality in most marine populations. Conventional monitoring for this macroinvertebrate is done using scuba, which is challenging in dense meadows or with low visibility. Here we developed an environmental DNA assay targeting the fan mussel and assessed the influence of several environmental parameters on the species detectability in situ. We developed and tested an eDNA molecular marker and collected 48 water samples in two sites at the Thau lagoon (France) with distinct fan mussel density, depths, and during two seasons (summer and autumn). Our marker can amplify fan mussel DNA but lacks specificity since it also amplifies a conspecific species (Pinna rudis). We successfully amplified fan mussel DNA from in situ samples with 46 positive samples (out of 48) using ddPCR, although the DNA concentrations measured were low over almost all samples. Deeper sampling depth slightly increased DNA concentrations, but no seasonal effect was found. We highlight a putative spawning event on a single summer day with much higher DNA concentration compared to all other samples. We present an eDNA molecular assay able to detect the endangered fan mussel and provide guidelines to optimize the sampling protocol to maximize detectability. Effective and non-invasive monitoring tools for endangered species are promising to monitor remaining populations and have the potential for ecological restoration or habitat recolonisation following a mass mortality event.

opencc-zeroJan 2024View details →

Human access constrains optimal foraging and habitat availability in an avian generalist

Animals balance costs of anti-predator behaviors with resource acquisition to minimize hunting and other mortality risks and maximize their physiological condition. This inherent trade-off between forage abundance and quality, and mortality risk is intensified in human-dominated landscapes because fragmentation, habitat loss, and degradation of natural vegetation communities is often coupled with artificially-enhanced vegetation (i.e., food plots) creating high-risk high-reward resource selection decisions. Our goal was to evaluate autumn–winter resource selection trade-offs for an intensively hunted avian generalist. We hypothesized human access was a reliable cue for hunting predation risk and thus predicted resource selection patterns would be spatiotemporally dependent upon levels of access and their perceived risk. Specifically, we evaluated resource selection of local-scale flights between diel periods of 426 mallards (Anas platyrhynchos) relative to wetland type, forage quality, and differing levels of human access across hunting and non-hunting seasons. Mallards selected areas that prohibited human access and generally avoided areas that allowed access diurnally, especially during hunting season. Mallards compensated by selecting for high-energy and greater quality foraging patches on allowable human access areas nocturnally when they were devoid of hunters. Post-season selection across human access gradients did not return to pre-hunting levels immediately, perhaps suggesting a delayed response to reacclimate to non-hunted activities and thus agreeing with the assessment mismatch hypothesis. Last, wetland availability and human access constrained selection for optimal natural forage quality (i.e., seed biomass and forage productivity) diurnally during pre-season and hunting season, respectively; however, mallards were freed from these constraints nocturnally during hunting season and during post-season. Our results suggest risk-avoidance of human accessible (i.e., hunted) areas is a primary driver of resource selection behaviors by mallards and could be a local to landscape-level process influencing distributions, instead of forage abundance and quality, which has long-been assumed by waterfowl conservation planners in North America. Broadly, even an avian generalist, well-adapted to anthropogenic landscapes, avoids areas where hunting and human access is allowed. Future conservation planning and implementation must consider management for recreational access (i.e., people) equally important as foraging habitat management for wintering waterfowl.