bluuebunny/crossref_metadata_2025_split huggingface dataset:
De haalbaarheid van een gecombineerde leefstijlinterventie (GLI-GGZ) bij patiënten met stemmings- en/of angststoornissen
Authors: Booij Sanne H. , Wester Christianne D. , Gurvits Vita , Nederhof Esther , Steffek Esther , Hoenders H. J. Rogier 
Published: January 2022

Samenvatting
Inleiding
Werken aan leefstijlveranderingen krijgt binnen de geestelijke gezondheidszorg (ggz) steeds meer aandacht. Toch worden leefstijlinterventies nog maar weinig toegepast en richten deze zich vaak op één leefstijlaspect, terwijl een gecombineerd programma de effectiviteit mogelijk vergroot. Wij ontwikkelden een twaalf weken durende, gecombineerde leefstijlinterventie (GLI-GGZ), met aandacht voor voeding, beweging en ontspanning. We hebben onderzoek gedaan naar de haalbaarheid van GLI-GGZ bij patiënten met een stemmings- en/of angststoornis, en naar gedragsverandering en voorlopige effecten op symptomen, welzijn en geluk.

Methode
Negen deelnemers werkten in twaalf wekelijkse begeleide bijeenkomsten van 2,5 uur door middel van informatie, registratie en toepassing aan leefstijlveranderingen op het gebied van voeding, beweging en ontspanning. Gedurende GLI-GGZ vulden ze viermaal vragenlijsten in over psychische klachten, welzijn, voeding, beweging en ontspanning.

Resultaten
GLI-GGZ lijkt haalbaar en deelnemers lijken hun gedrag daadwerkelijk te veranderen. Er is gedurende de interventie ook een afname van angst-, stemmings- en stressklachten, en een toename van geluks- en welzijnsgevoel.

Conclusie
Deze positieve resultaten geven aanleiding om verder onderzoek te doen naar GLI-GGZ als nieuwe behandelmethode.
Landelijk gezondheidsonderzoek bij rampen
Authors: Bosmans Mark , Marra Elske , Tak Nannah , Jansen Noortje , de Zwart Femke , Dückers Michel 
Published: January 2022

SamenvattingDe coronacrisis en de gevolgen die deze op de gezondheid van de Nederlandse bevolking heeft gaan de normale regionale onderzoeksaanpak te boven. Daarom heeft het Netwerk GOR-COVID-19 – dat bestaat uit GGD GHOR Nederland (namens de GGD’en), RIVM, Nivel en ARQ Nationaal Psychotrauma Centrum – het initiatief genomen voor een landelijk onderzoeksprogramma om de impact van de coronapandemie op de mentale en fysieke gezondheid van de Nederlandse bevolking op lange termijn te monitoren: de integrale Gezondheidsmonitor COVID-19. In dit artikel beschrijven we de achtergrond en opzet van deze gezondheidsmonitor, die erop is gericht onderzoeksbevindingen toepasbaar te maken voor praktijk en beleid, zowel lokaal als nationaal.
Patiëntbetrokkenheid tijdens crisisbesluitvorming
Authors: Schmidt Eduard , van der Pas Suzan , Schalk Jelmer , Wieringa Sietse , Kraaijeveld Britt , Groeneveld Sandra , Bussemaker Jet 
Published: January 2022

SamenvattingPatiëntbetrokkenheid is een belangrijk thema in de zorg. Cliëntenraden vervullen hierin een belangrijke rol. Tegelijkertijd is er nog weinig bekend over hun rol bij de besluitvorming tijdens crises, en wat we van deze ervaringen kunnen leren. De coronacrisis liet echter zien dat wanneer besluitvorming plaatsvindt in interorganisationele netwerken, en sterk top-down en gecentraliseerd is, er weinig ruimte overblijft voor patiëntbetrokkenheid. Wij stellen dat zowel bestuurders als cliëntenraden meer werk moeten maken van patiëntbetrokkenheid in interorganisationele netwerken en tijdens crisissituaties.
Decisions and reasonings of top-class handball referees under physical load
Authors: Bloß Nicolas , Schorer Jörg , Loffing Florian , Büsch Dirk 
Published: January 2022

AbstractCorrect decision-making under physical load is a central challenge for referees in sports games. Handball referees are assumed to make both a decision (calling a foul or no foul) and to call its reasonings (type of foul, punishments). However, the impact of physical load on these two aspects has not been differentiated so far. Here, in two studies, we aimed to investigate the hypothesised impact of physical load on both referees’ decisions and reasonings. To this end, N = 66 (study 1) and N = 73 (study 2) top-class handball referees performed the Yo-Yo Intermittent Recovery Test combined with a video-based decision-making test. Referees’ decisions improved from initial to medium physical load and they deteriorated under maximal physical load in study 1, whereas in study 2 the quality of the decisions was constant across physical load conditions. The percent of correct reasonings decreased from initial to medium physical load in study 1, whereas the opposite pattern was found in study 2. In both studies, reasoning performance did not change from medium to maximal physical load. Moreover, referees demonstrated better endurance performance in study 2 than in study 1. Despite some methodological limitations (e.g. familiarisation with the experimental setup in referees who participated in both studies), the present findings tentatively indicate that a well-trained endurance capacity may support referees’ decision-making, i.e. to make correct decisions and reasonings. Specifically, enhanced endurance capacity may lead to lower subjectively perceived fatigue, resulting in larger cognitive capacities that may facilitate referees’ decision-making.
Path planning of a manipulator based on an improved P_RRT* algorithm
Authors: Yi Junhui , Yuan Qingni , Sun Ruitong , Bai Huan 
Published: January 2022

AbstractAiming to build upon the slow convergence speed and low search efficiency of the potential function-based rapidly exploring random tree star (RRT*) algorithm (P_RRT*), this paper proposes a path planning method for manipulators with an improved P_RRT* algorithm (defined as improved P_RRT*), which is used to solve the path planning problem for manipulators in three-dimensional space. This method first adopts a random sampling method based on a potential function. Second, based on a probability value, the nearest neighbour node is selected by the nearest Euclidean distance to the random sampling point and the minimum cost function, and in the expansion of new nodes, twice expansion methods are used to accelerate the search efficiency of the algorithm. The first expansion adopts the goal-biased expansion strategy, and the second expansion adopts the strategy of random sampling in a rectangular area. Then, the parent node of the new node is reselected, and the path is rerouted to obtain a clear path from the initial point to the target point. Redundant node deletion and the maximum curvature constraint are used to remove redundant nodes and minimize the curvature on the generated path to reduce the tortuosity of the path. The Bezier curve is used to fit the processed path and obtain the trajectory planning curve for the manipulator. Finally, the improved P_RRT* algorithm is verified experimentally in Python and the Robot Operating System (ROS) and compared with other algorithms. The experimental results verify the effectiveness and superiority of the improved algorithm.
Safe-Nav: learning to prevent PointGoal navigation failure in unknown environments
Authors: Jin Sheng , Meng Qinghao , Dai Xuyang , Hou Huirang 
Published: January 2022

AbstractTraining robots to safely navigate (Safe-Nav) in uncertain complex environments using the RGB-D sensor is quite challenging as it involves the performance of different tasks such as obstacle avoidance, optimal path planning, and control. Traditional navigation approaches cannot generate suitable paths which guarantee enough visible features. Recent learning-based methods are still not mature enough due to their proneness to collisions and prohibitive computational cost. This paper focuses on generating safe trajectories to the desired goal while avoiding collisions and tracking failure in unknown complex environments. We present Safe-Nav, a hierarchical framework composed of the visual simultaneous localization and mapping (SLAM) module, the global planner module and the local planner module. The visual SLAM module generates the navigation map and the robot pose. The global planner module plans a local waypoint on the real-time navigation map. In the local planner module, a deep-reinforcement-learning-based (DRL-based) policy is presented for taking safe actions towards local waypoints. Our DRL-based policy can learn different navigation skills (e.g., avoiding collisions and avoiding tracking failure) through specialized modes without any supervisory signals when the PointGoal-navigation-specied reward is provided. We have demonstrated the performance of our proposed Safe-Nav in the Habitat simulation environment. Our approach outperforms the recent learning-based method and conventional navigation approach with relative improvements of over 205% (0.55 vs. 0.18) and 139% (0.55 vs. 0.23) in the success rate, respectively.
The Classic Form of Progressive Multifocal Leukoencephalopathy in Advanced Prostate Cancer: a Case Report
Authors: Switlyk Marta D. , Lilleby Wolfgang 
Published: January 2022

AbstractProgressive multifocal leukoencephalopathy (PML) is a serious and often lethal demyelinating disease of the brain that develops almost exclusively in patients with severe immunosuppression. The disease is caused by the reactivation of latent polyoma JC virus (JCV). PML occurs rarely in non-hematologic malignancies, and in this report, the authors present an uncommon case of rapidly progressing, fatal PML in a patient with advanced prostate cancer. Although uncommon, PML should be included in the differential diagnosis of white matter lesions in the oncologic population.
Epistemic and aleatoric uncertainties reduction with rotation variation for medical image segmentation with ConvNets
Authors: Zhang Ge , Dang Hao , Xu Yulong 
Published: January 2022

AbstractThe deep convolutional neural network (ConvNet) achieves significant segmentation performance on medical images of various modalities. However, the isolated errors in a large testing set with various tumor conditions are not acceptable in clinical practice. This is usually caused in inadequate training and noise inherent during data collection, which are recognized as epistemic and aleatoric uncertainties in deep learning-based approaches. In this paper, we analyze the two types of uncertainties in medical image segmentation tasks and propose a reduction method by training models with data augmentation. The shelter zones in images are reduced with 2D imaging on surfaces of different angles from 3D organs. Rotation transformation and noise are estimated by Monte Carlo simulation with prior parameter distributions, and the aleatoric uncertainty is quantized in this process. Experiments on segmentation of computed tomography images demonstrate that overconfident incorrect predictions are reduced through uncertainty reduction and that our method outperforms prediction baselines based on epistemic and aleatoric estimation.
Simplicial and Minimal-Variance Distances in Multivariate Data Analysis
Authors: Gillard Jonathan , O’Riordan Emily , Zhigljavsky Anatoly 
Published: January 2022

AbstractIn this paper, we study the behaviour of the so-calledk-simplicial distances andk-minimal-variance distances between a point and a sample. The family ofk-simplicial distances includes the Euclidean distance, the Mahalanobis distance, Oja’s simplex distance and many others. We give recommendations about the choice of parameters used to calculate the distances, including the size of the sub-sample of simplices used to improve computation time, if needed. We introduce a new family of distances which we callk-minimal-variance distances. Each of these distances is constructed using polynomials in the sample covariance matrix, with the aim of providing an alternative to the inverse covariance matrix, that is applicable when data is degenerate. We explore some applications of the considered distances, including outlier detection and clustering, and compare how the behaviour of the distances is affected for different parameter choices.
The DOE E3SM v1.2 Cryosphere Configuration: Description and Simulated Antarctic Ice‐Shelf Basal Melting
Authors: Comeau Darin , Asay‐Davis Xylar S. , Begeman Carolyn Branecky , Hoffman Matthew J. , Lin Wuyin , Petersen Mark R. , Price Stephen F. , Roberts Andrew F. , Van Roekel Luke P. , Veneziani Milena , Wolfe Jonathan D. , Fyke Jeremy G. , Ringler Todd D. , Turner Adrian K. 
Published: January 2022

AbstractThe processes responsible for freshwater flux from the Antarctic Ice Sheet (AIS), ice‐shelf basal melting and iceberg calving, are generally poorly represented in current Earth System Models (ESMs). Here we document the cryosphere configuration of the U.S. Department of Energy's Energy Exascale Earth System Model (E3SM) v1.2. This includes simulating Antarctic ice‐shelf basal melting, which has been implemented through simulating the ocean circulation within static Antarctic ice‐shelf cavities, allowing for the ability to calculate ice‐shelf basal melt rates from the associated heat and freshwater fluxes. In addition, we added the capability to prescribe forcing from iceberg melt, allowing for realistic representation of the other dominant mass loss process from the AIS. In standard resolution simulations (using a noneddying ocean) under preindustrial climate forcing, we find high sensitivity of modeled ocean/ice shelf interactions to the ocean state, which can result in a transition to a high basal melt regime under the Filchner‐Ronne Ice Shelf (FRIS), presenting a significant challenge to representing the ocean/ice shelf system in a coupled ESM. We show that inclusion of a spatially dependent parameterization of eddy‐induced transport reduces biases in water mass properties on the Antarctic continental shelf. With these improvements, E3SM produces realistic ice‐shelf basal melt rates across the continent that are generally within the range inferred from observations. The accurate representation of ice‐shelf basal melting within a coupled ESM is an important step toward reducing uncertainties in projections of the Antarctic response to climate change and Antarctica's contribution to global sea‐level rise.
Correcting Coarse‐Grid Weather and Climate Models by Machine Learning From Global Storm‐Resolving Simulations
Authors: Bretherton Christopher S. , Henn Brian , Kwa Anna , Brenowitz Noah D. , Watt‐Meyer Oliver , McGibbon Jeremy , Perkins W. Andre , Clark Spencer K. , Harris Lucas 
Published: January 2022

AbstractGlobal atmospheric “storm‐resolving” models with horizontal grid spacing of less than 5 km resolve deep cumulus convection and flow in complex terrain. They promise to be reference models that could be used to improve computationally affordable coarse‐grid global climate models across a range of climates, reducing uncertainties in regional precipitation and temperature trends. Here, machine learning of nudging tendencies as functions of column state is used to correct the physical parameterization tendencies of temperature, humidity, and optionally winds, in a real‐geography coarse‐grid model (FV3GFS with a 200 km grid) to be closer to those of a 40‐day reference simulation using X‐SHiELD, a modified version of FV3GFS with a 3 km grid. Both simulations specify the same historical sea‐surface temperature fields. This methodology builds on a prior study using a global observational analysis as the reference. The coarse‐grid model without machine learning corrections has too few clouds, causing too much daytime heating of land surfaces that creates excessive surface latent heat flux and rainfall. This bias is avoided by learning downwelling radiative flux from the fine‐grid model. The best configuration uses learned nudging tendencies for temperature and humidity but not winds. Neural nets slightly outperform random forests. Forecasts of 850 hPa temperature gain 18 hr of skill at 3–7 days leads and time‐mean precipitation patterns are improved 30% by applying the ML correction. Adding machine‐learned wind tendencies improves 500 hPa height skill for the first five days of forecasts but degrades time‐mean upper tropospheric temperature and zonal wind patterns thereafter.
Massively parallel characterization of engineered transcript isoforms using direct RNA sequencing
Authors: Tarnowski Matthew J. , Gorochowski Thomas E. 
Published: January 2022

AbstractTranscriptional terminators signal where transcribing RNA polymerases (RNAPs) should halt and disassociate from DNA. However, because termination is stochastic, two different forms of transcript could be produced: one ending at the terminator and the other reading through. An ability to control the abundance of these transcript isoforms would offer bioengineers a mechanism to regulate multi-gene constructs at the level of transcription. Here, we explore this possibility by repurposing terminators as ‘transcriptional valves’ that can tune the proportion of RNAP read-through. Using one-pot combinatorial DNA assembly, we iteratively construct 1780 transcriptional valves for T7 RNAP and show how nanopore-based direct RNA sequencing (dRNA-seq) can be used to characterize entire libraries of valves simultaneously at a nucleotide resolution in vitro and unravel genetic design principles to tune and insulate termination. Finally, we engineer valves for multiplexed regulation of CRISPR guide RNAs. This work provides new avenues for controlling transcription and demonstrates the benefits of long-read sequencing for exploring complex sequence-function landscapes.
The combined DNA and RNA synthetic capabilities of archaeal DNA primase facilitate primer hand-off to the replicative DNA polymerase
Authors: Greci Mark D. , Dooher Joseph D. , Bell Stephen D. 
Published: January 2022

AbstractReplicative DNA polymerases cannot initiate DNA synthesis de novo and rely on dedicated RNA polymerases, primases, to generate a short primer. This primer is then extended by the DNA polymerase. In diverse archaeal species, the primase has long been known to have the ability to synthesize both RNA and DNA. However, the relevance of these dual nucleic acid synthetic modes for productive primer synthesis has remained enigmatic. In the current work, we reveal that the ability of primase to polymerize DNA serves dual roles in promoting the hand-off of the primer to the replicative DNA polymerase holoenzyme. First, it creates a 5′-RNA-DNA-3′ hybrid primer which serves as an optimal substrate for elongation by the replicative DNA polymerase. Second, it promotes primer release by primase. Furthermore, modeling and experimental data indicate that primase incorporates a deoxyribonucleotide stochastically during elongation and that this switches the primase into a dedicated DNA synthetic mode polymerase.
Stochastic many-body calculations of moiré states in twisted bilayer graphene at high pressures
Authors: Romanova Mariya , Vlček Vojtěch 
Published: January 2022

AbstractWe introduce three developments within the stochastic many-body perturbation theory: efficient evaluation of off-diagonal self-energy terms, construction of Dyson orbitals, and stochastic constrained random phase approximation. The stochastic approaches readily handle systems with thousands of atoms. We use them to explore the electronic states of twisted bilayer graphene (tBLG) characterized by giant unit cells and correlated electronic states. We document the formation of electron localization under compression; weakly correlated states are merely shifted in energy. We demonstrate how to efficiently downfold the correlated subspace on a model Hamiltonian with a screened frequency-dependent two-body interaction. For the 6° tBLG system, the onsite interactions are between 200 and 300 meV under compression. The Dyson orbitals exhibit spatial distribution similar to the mean-field single-particle states. Under pressure, the electron-electron interactions increase in the localized states; however, the dynamical screening does not fully balance the dominant bare Coulomb interaction.
Trained laser-patterned carbon as high-performance mechanical sensors
Authors: Hepp Marco , Wang Huize , Derr Katharina , Delacroix Simon , Ronneberger Sebastian , Loeffler Felix F. , Butz Benjamin , Strauss Volker 
Published: January 2022

AbstractWe describe the mechanical properties of turbostratically graphitized carbon films obtained by carbon laser-patterning (CLaP) and their application as bending or mechanical pressure sensors. Stable conductive carbonized films were imprinted on a flexible polyethylene terephthalate (PET) substrate by laser-induced carbonization. After initial gentle bending, i.e. training, these sponge-like porous films show a quantitative and reversible change in resistance upon bending or application of pressure in normal loading direction. Maximum response values of ΔR/R0 = 388% upon positive bending (tensile stress) and −22.9% upon negative bending (compression) are implicit for their high sensitivity towards mechanical deformation. Normal mechanical loading in a range between 0 and 500 kPa causes a response between ΔR/R0 = 0 and −15%. The reversible increase or decrease in resistance is attributed to compression or tension of the turbostratically graphitized domains, respectively. This mechanism is supported by a detailed microstructural and chemical high-resolution transmission electron microscopic analysis of the cross-section of the laser-patterned carbon.
Rate-compatible multi-edge type low-density parity-check code ensembles for continuous-variable quantum key distribution systems
Authors: Jeong Suhwang , Jung Hyunwoo , Ha Jeongseok 
Published: January 2022

AbstractIn this paper, we propose a design rule of rate-compatible punctured multi-edge type low-density parity-check (MET-LDPC) code ensembles with degree-one variable nodes for the information reconciliation (IR) of continuous-variable quantum key distribution (CV-QKD) systems. In addition to the rate compatibility, the design rule effectively resolves the high error-floor issue which has been known as a technical challenge of MET-LDPC codes at low rates. Thus, the proposed design rule allows one to implement rate-compatible MET-LDPC codes with good performances both in the threshold and low-error-rate regions. The rate compatibility and the improved error-rate performances significantly enhance the efficiency of IR for CV-QKD systems. The performance improvements are confirmed by comparing complexities and secret key rates of IR schemes with MET-LDPC codes whose ensembles are optimized with the proposed and existing design rules. In particular, the SNR range of positive secrecy rate increases by 1.44 times, and the maximum secret key rate improves by 2.10 times as compared to the existing design rules. The comparisons clearly show that an IR scheme can achieve drastic performance improvements in terms of both the complexity and secret key rate by employing rate-compatible MET-LDPC codes constructed with code ensembles optimized with the proposed design rule.