bluuebunny/crossref_metadata_2025_split huggingface dataset:
Resurrecting extinct cephalopods with biomimetic robots to explore hydrodynamic stability, maneuverability, and physical constraints on life habits
Authors: Peterman David J. , Ritterbush Kathleen A. 
Published: July 2022

AbstractExternally shelled cephalopods with coiled, planispiral conchs were ecologically successful for hundreds of millions of years. These animals displayed remarkable morphological disparity, reflecting comparable differences in physical properties that would have constrained their life habits and ecological roles. To investigate these constraints, self-propelling, neutrally buoyant, biomimetic robots were 3D-printed for four disparate morphologies. These robots were engineered to assume orientations computed from virtual hydrostatic simulations while producingNautilus-like thrusts. Compressed morphotypes had improved hydrodynamic stability (coasting efficiency) and experienced lower drag while jetting backwards. However, inflated morphotypes had improved maneuverability while rotating about the vertical axis. These differences highlight an inescapable physical tradeoff between hydrodynamic stability and yaw maneuverability, illuminating different functional advantages and life-habit constraints across the cephalopod morphospace. This tradeoff reveals there is no single optimum conch morphology, and elucidates the success and iterative evolution of disparate morphologies through deep time, including non-streamlined forms.
Stable isotope analysis confirms substantial changes in the fatty acid composition of bacteria treated with antimicrobial random peptide mixtures (RPMs)
Authors: Wiedmaier-Czerny Nina , Schroth Dorothee , Krauß Stephanie , Topman-Rakover Shiri , Brill Aya , Burdman Saul , Hayouka Zvi , Vetter Walter 
Published: July 2022

AbstractResistance of plant-pathogenic bacteria to classic antibiotics has prompted the search for suitable alternative antimicrobial substances. One promising strategy could be the use of purposely synthesized random peptide mixtures (RPMs). Six plant-pathogenic bacteria were cultivated and treated with two RPMs previously found to show antimicrobial activity mainly by bacterial membrane disruption. Here, we show that bacteria treated with RPMs showed partly remarkable changes in the fatty acid pattern while those unaffected did not. Quantitative changes could be verified by compound specific isotope analysis of δ13C values (‰). This technique was employed due to the characteristic feature of stronger bonds between heavier isotopes in (bio)chemical reactions. As a proof of concept, the increase in abundance of a fatty acid group after RPM treatment was accompanied with a decrease in the 13C content and vice versa. We propose that our findings will help designing and synthesizing more selective antimicrobial peptides.
Suitability of the global forest cover change map to assess climatic megadisturbance impacts on remote tropical forests
Authors: Geler Roffe Tatiana , Couturier Stéphane , García-Romero Arturo 
Published: July 2022

AbstractThe occurrence and combination of extreme meteorological events may lead to severe (megadisturbance) impacts on conserved forests and protected areas all over the world. Recent research has shown that megadisturbance impacts (after the events of hurricanes and prolonged drought) may be detected in subtropical forest cover using changes in spectral indices derived from satellite imagery. The objective of this study is to assess the impact of megadisturbance on forest types of the Alejandro de Humboldt National Park, Republic of Cuba in the 2001–2017 time-period. The Global Forest Cover Change (GFCC, available on the Global Forest Watch website) product was validated and indicated the prominence of megadisturbance for year 2016 (85% of the total disturbed area), largely associated with the Hurricane Matthew event. A robust estimator of the disturbed forest area, based on GFCC-stratified sampled verification sites, suggests that 11,110 ± 1,771 hectares of forest (~ 16% of the Park’s total area) was affected by megadisturbance between 2001 and 2017. In 2017, about 1276 hectares of forests were impacted, presumably related to a long-lasting effect of megadisturbance due to Hurricane Matthew and prolonged droughts in previous years. Four types of tropical rainforests (especially lowland rainforest and submountainous sclerophyllous rainforest on serpentinite), that cover 43% of the National Park, accounted for about 85% of the impacts by megadisturbance. The Easternmost portion of these forests should be prioritized for conservation monitoring and possibly for forest restoration strategies. This study contributes to establishing methodological guidelines for rapid environmental assessment of remote, tropical protected areas facing the impacts of extreme meteorological events and climate change.
Counting generations in birth and death processes with competing Erlang and exponential waiting times
Authors: Belluccini Giulia , López-García Martín , Lythe Grant , Molina-París Carmen 
Published: July 2022

AbstractLymphocyte populations, stimulated in vitro or in vivo, grow as cells divide. Stochastic models are appropriate because some cells undergo multiple rounds of division, some die, and others of the same type in the same conditions do not divide at all. If individual cells behave independently, then each cell can be imagined as sampling from a probability density of times to division and death. The exponential density is the most mathematically and computationally convenient choice. It has the advantage of satisfying the memoryless property, consistent with a Markov process, but it overestimates the probability of short division times. With the aim of preserving the advantages of a Markovian framework while improving the representation of experimentally-observed division times, we consider a multi-stage model of cellular division and death. We use Erlang-distributed (or, more generally, phase-type distributed) times to division, and exponentially distributed times to death. We classify cells into generations, using the rule that the daughters of cells in generation n are in generation $$n+1$$

n
+
1

. In some circumstances, our representation is equivalent to established models of lymphocyte dynamics. We find the growth rate of the cell population by calculating the proportions of cells by stage and generation. The exponent describing the late-time cell population growth, and the criterion for extinction of the population, differs from what would be expected if N steps with rate $$\lambda$$
λ
were equivalent to a single step of rate $$\lambda /N$$

λ
/
N

. We link with a published experimental dataset, where cell counts were reported after T cells were transferred to lymphopenic mice, using Approximate Bayesian Computation. In the comparison, the death rate is assumed to be proportional to the generation and the Erlang time to division for generation 0 is allowed to differ from that of subsequent generations. The multi-stage representation is preferred to a simple exponential in posterior distributions, and the mean time to first division is estimated to be longer than the mean time to subsequent divisions.
Object based classification of a riparian environment using ultra-high resolution imagery, hierarchical landcover structures, and image texture
Authors: Kutz Kain , Cook Zachary , Linderman Marc 
Published: July 2022

AbstractLand cover mapping is an important part of resource management, planning, and economic predictions. Improvements in remote sensing, machine learning, image processing, and object based image analysis (OBIA) has made the process of identifying land cover types increasingly faster and reliable but these advances have not been able to utilize all of the information encompassed within ultra-high (sub-meter) resolution imagery. There have been few known attempts to try and maximize this detailed information in high resolution imagery using advanced textural components. Hierarchical land classes are also rarely used as an attribute within the machine learning step of object-based image analysis. In this study we try to circumnavigate the inherent problems associated with high resolution imagery by combining well researched data transformations that aid the OBIA process with a seldom used texture transformation in Geographic Object Based Image Analyses (GEOBIA/OBIA) known as the Gabor Transform and the hierarchal organization of landscapes. We will observe the difference made in segmentation and classification accuracy of a random forest classifier when we fuse a Gabor transformed image to a Normalized Difference Vegetation Index (NDVI), high resolution multi-spectral imagery (RGB and NIR) and Light Detection and Ranging (LiDAR) derived canopy height model (CHM) within a riparian area in Southeast Iowa, United States. Additionally, we will observe the effects on classification accuracy when adding multi-scale land cover data to objects. Both, the addition of hierarchical information and Gabor textural information, could aid the GEOBIA process in delineating and classifying the same objects that human experts would delineate within this riparian landscape.
Motivations underlying self-infliction of pain during thinking for pleasure
Authors: Eder Andreas B. , Maas Franzisca , Schubmann Alexander , Krishna Anand , Erle Thorsten M. 
Published: July 2022

AbstractPrevious research suggested that people prefer to administer unpleasant electric shocks to themselves rather than being left alone with their thoughts because engagement in thinking is an unpleasant activity. The present research examined this negative reinforcement hypothesis by giving participants a choice of distracting themselves with the generation of electric shock causing no to intense pain. Four experiments (N = 254) replicated the result that a large proportion of participants opted to administer painful shocks to themselves during the thinking period. However, they administered strong electric shocks to themselves even when an innocuous response option generating no or a mild shock was available. Furthermore, participants inflicted pain to themselves when they were assisted in the generation of pleasant thoughts during the waiting period, with no difference between pleasant versus unpleasant thought conditions. Overall, these results question that the primary motivation for the self-administration of painful shocks is avoidance of thinking. Instead, it seems that the self-infliction of pain was attractive for many participants, because they were curious about the shocks, their intensities, and the effects they would have on them.
Laser cluster interaction in ambient magnetic fields for accelerating electrons in two stages without external injection
Authors: Swain Kalyani , Mahalik Sagar Sekhar , Kundu Mrityunjay 
Published: July 2022

AbstractIn the few-cycle pulse regime of laser-cluster interaction (intensity $$>10^{16}\,\text{ W/cm}^{2}$$

>

10
16

W/cm
2

, wavelength $$> 780$$

>
780

nm), laser absorption is mostly collisionless and may happen via anharmonic resonance (AHR) process in the overdense (cluster) plasma potential. Many experiments, theory and simulation show average absorbed energy per cluster-electron ($${\mathcal {E}_A}$$

E
A

) close to the electron’s ponderomotive energy ($$U_\mathrm {p}$$

U
p

) in the collisionless regime. In this work, by simple rigid sphere model (RSM) and detailed particle-in-cell (PIC) simulation, we show enhanced $${\mathcal {E}_A}\approx$$

E
A

≈

30–70$$U_\mathrm {p}$$

U
p

—a 15–30 fold increase—with an external (crossed) magnetic field near the electron-cyclotron resonance (ECR). Due to relativistic mass increase, electrons quickly deviate from the standard (non-relativistic) ECR, but time-dependent relativistic-ECR (RECR) happens which also contributes to enhanced $${\mathcal {E}_A}$$

E
A

. Here laser is coupled to electrons in two stages, i.e, AHR and ECR/RECR. To probe further we retrieve the phase-difference $$\Delta \psi$$

Δ
ψ

between the driving electric field and corresponding velocity component for each electron (in PIC and RSM). We find absorption by electron via AHR happens in a very short interval $$\Delta \tau$$

Δ
τ

for less than half a laser period where $$\Delta \psi$$

Δ
ψ

remains close to $$\pi$$
π
(necessary condition for maximum laser absorption) and then $$\Delta \psi$$

Δ
ψ

drops to its initial $$\pi /2$$

π
/
2

(meaning no absorption) after such short-lived AHR. On the contrary, auxiliary magnetic field near the ECR modifies AHR scenario inside the cluster and also helps maintaining the required phase $$\Delta \psi \approx \pi$$

Δ
ψ
≈
π

for the liberated cluster-electron accompanied by frequency matching for ECR/RECR for a prolonged $$\Delta \tau$$

Δ
τ

(which covers 50–60% of the laser pulse through pulse maxima) even after AHR—leading to jump in $${\mathcal {E}_A}\approx$$

E
A

≈

30–70$$U_\mathrm {p}$$

U
p

. We note that to realize the second stage of enhanced energy coupling via ECR/RECR, the first stage via AHR is necessary.
Pathogenicity of missense variants affecting the collagen IV α5 carboxy non-collagenous domain in X-linked Alport syndrome
Authors: Gibson Joel T. , Sadeghi-Alavijeh Omid , Gale Daniel P. , Rothe Hansjörg ,   Genomics England Research Consortium, Sadeghi-Alavijeh Omid , Gale Daniel P. , Savige Judy , Ambrose J. C. , Arumugam P. , Baple E. L. , Bleda M. , Boardman-Pretty F. , Boissiere J. M. , Boustred C. R. , Brittain H. , Caulfield M. J. , Chan G. C. , Craig C. E. H. , Daugherty L. C. , de Burca A. , Devereau A. , Elgar G. , Foulger R. E. , Fowler T. , Furió-Tarí P. , Giess A. , Hackett J. M. , Halai D. , Hamblin A. , Henderson S. , Holman J. E. , Hubbard T. J. P. , Ibáñez K. , Jackson R. , Jones L. J. , Kasperaviciute D. , Kayikci M. , Kousathanas A. , Lahnstein L. , Lawson K. , Leigh S. E. A. , Leong I. U. S. , Lopez F. J. , Maleady-Crowe F. , Mason J. , McDonagh E. M. , Moutsianas L. , Mueller M. , Murugaesu N. , Need A. C. , Odhams C. A. , Orioli A. , Patch C. , Perez-Gil D. , Pereira M. B. , Polychronopoulos D. , Pullinger J. , Rahim T. , Rendon A. , Riesgo-Ferreiro P. , Rogers T. , Ryten M. , Savage K. , Sawant K. , Scott R. H. , Siddiq A. , Sieghart A. , Smedley D. , Smith K. R. , Smith S. C. , Sosinsky A. , Spooner W. , Stevens H. E. , Stuckey A. , Sultana R. , Tanguy M. , Thomas E. R. A. , Thompson S. R. , Tregidgo C. , Tucci A. , Walsh E. , Watters S. A. , Welland M. J. , Williams E. , Witkowska K. , Wood S. M. , Zarowiecki M. , Savige Judy 
Published: July 2022

AbstractX-linked Alport syndrome is a genetic kidney disease caused by pathogenic COL4A5 variants, but little is known of the consequences of missense variants affecting the NC1 domain of the corresponding collagen IV α5 chain. This study examined these variants in a normal (gnomAD) and other databases (LOVD, Clin Var and 100,000 Genomes Project) to determine their pathogenicity and clinical significance. Males with Cys substitutions in the collagen IV α5 NC1 domain reported in LOVD (n = 25) were examined for typical Alport features, including age at kidney failure. All NC1 variants in LOVD (n = 86) were then assessed for structural damage using an online computational tool, Missense3D. Variants in the ClinVar, gnomAD and 100,000 Genomes Project databases were also examined for structural effects. Predicted damage associated with NC1 substitutions was then correlated with the level of conservation of the affected residues. Cys substitutions in males were associated with the typical features of X-linked Alport syndrome, with a median age at kidney failure of 31 years. NC1 substitutions predicted to cause structural damage were overrepresented in LOVD (p < 0.001), and those affecting Cys residues or ‘buried’ Gly residues were more common than expected (both p < 0.001). Most NC1 substitutions in gnomAD (88%) were predicted to be structurally-neutral. Substitutions affecting conserved residues resulted in more structural damage than those affecting non-conserved residues (p < 0.001). Many pathogenic missense variants affecting the collagen IV α5 NC1 domain have their effect through molecular structural damage and 3D modelling is a useful tool in their assessment.
Role of copper and alumina for heat transfer in hybrid nanofluid by using Fourier sine transform
Authors: Souayeh Basma , Abro Kashif Ali , Siyal Ambreen , Hdhiri Najib , Hammami Faycal , Al-Shaeli Muayad , Alnaim Nisrin , Raju S. Suresh Kumar , Alam Mir Waqas , Alsheddi Tarfa 
Published: July 2022

AbstractThe convection, thermal conductivity, and heat transfer of hybrid nanofluid through nanoparticles has become integral part of several natural and industrial processes. In this manuscript, a new fractionalized model based on hybrid nanofluid is proposed and investigated by employing singular verses and non-singular kernels. The mathematical modeling of hybrid nanofluid is handled via modern fractional definitions of differentiations. The combined Laplace and Fourier Sine transforms have been configurated on the governing equations of hybrid nanofluid. The analytical expression of the governing temperature and velocity equations of hybrid nanofluid have been solved via special functions. For the sake of thermal performance, dimensional analysis of governing equations and suitable boundary conditions based on Mittage-Leffler function have been invoked for the first time in literature. The comparative analysis of heat transfer from hybrid nanofluid has been observed through Caputo-Fabrizio and Atangana-Baleanu differential operators. Finally, our results suggest that volume fraction has the decelerated and accelerated trends of temperature distribution and inclined and declined profile of heat transfer is observed copper and alumina nanoparticles.
Machine learning predicts clinically significant health related quality of life improvement after sensorimotor rehabilitation interventions in chronic stroke
Authors: Liao Wan-Wen , Hsieh Yu-Wei , Lee Tsong-Hai , Chen Chia-ling , Wu Ching-yi 
Published: July 2022

AbstractHealth related quality of life (HRQOL) reflects individuals perceived of wellness in health domains and is often deteriorated after stroke. Precise prediction of HRQOL changes after rehabilitation interventions is critical for optimizing stroke rehabilitation efficiency and efficacy. Machine learning (ML) has become a promising outcome prediction approach because of its high accuracy and easiness to use. Incorporating ML models into rehabilitation practice may facilitate efficient and accurate clinical decision making. Therefore, this study aimed to determine if ML algorithms could accurately predict clinically significant HRQOL improvements after stroke sensorimotor rehabilitation interventions and identify important predictors. Five ML algorithms including the random forest (RF), k-nearest neighbors (KNN), artificial neural network, support vector machine and logistic regression were used. Datasets from 132 people with chronic stroke were included. The Stroke Impact Scale was used for assessing multi-dimensional and global self-perceived HRQOL. Potential predictors included personal characteristics and baseline cognitive/motor/sensory/functional/HRQOL attributes. Data were divided into training and test sets. Tenfold cross-validation procedure with the training data set was used for developing models. The test set was used for determining model performance. Results revealed that RF was effective at predicting multidimensional HRQOL (accuracy: 85%; area under the receiver operating characteristic curve, AUC-ROC: 0.86) and global perceived recovery (accuracy: 80%; AUC-ROC: 0.75), and KNN was effective at predicting global perceived recovery (accuracy: 82.5%; AUC-ROC: 0.76). Age/gender, baseline HRQOL, wrist/hand muscle function, arm movement efficiency and sensory function were identified as crucial predictors. Our study indicated that RF and KNN outperformed the other three models on predicting HRQOL recovery after sensorimotor rehabilitation in stroke patients and could be considered for future clinical application.
Development and implementation of patient-level prediction models of end-stage renal disease for type 2 diabetes patients using fast healthcare interoperability resources
Authors: Wang San , Han Jieun , Jung Se Young , Oh Tae Jung , Yao Sen , Lim Sanghee , Hwang Hee , Lee Ho-Young , Lee Haeun 
Published: July 2022

AbstractThis study aimed to develop a model to predict the 5-year risk of developing end-stage renal disease (ESRD) in patients with type 2 diabetes mellitus (T2DM) using machine learning (ML). It also aimed to implement the developed algorithms into electronic medical records (EMR) system using Health Level Seven (HL7) Fast Healthcare Interoperability Resources (FHIR). The final dataset used for modeling included 19,159 patients. The medical data were engineered to generate various types of features that were input into the various ML classifiers. The classifier with the best performance was XGBoost, with an area under the receiver operator characteristics curve (AUROC) of 0.95 and area under the precision recall curve (AUPRC) of 0.79 using three-fold cross-validation, compared to other models such as logistic regression, random forest, and support vector machine (AUROC range, 0.929–0.943; AUPRC 0.765–0.792). Serum creatinine, serum albumin, the urine albumin-to-creatinine ratio, Charlson comorbidity index, estimated GFR, and medication days of insulin were features that were ranked high for the ESRD risk prediction. The algorithm was implemented in the EMR system using HL7 FHIR through an ML-dedicated server that preprocessed unstructured data and trained updated data.
In-vivo data-driven parcellation of Heschl’s gyrus using structural connectivity
Authors: Lee Hyebin , Byeon Kyoungseob , Park Bo-yong , Lee Sean H. , Park Hyunjin 
Published: July 2022

AbstractThe human auditory cortex around Heschl’s gyrus (HG) exhibits diverging patterns across individuals owing to the heterogeneity of its substructures. In this study, we investigated the subregions of the human auditory cortex using data-driven machine-learning techniques at the individual level and assessed their structural and functional profiles. We studied an openly accessible large dataset of the Human Connectome Project and identified the subregions of the HG in humans using data-driven clustering techniques with individually calculated imaging features of cortical folding and structural connectivity information obtained via diffusion magnetic resonance imaging tractography. We characterized the structural and functional profiles of each HG subregion according to the cortical morphology, microstructure, and functional connectivity at rest. We found three subregions. The first subregion (HG1) occupied the central portion of HG, the second subregion (HG2) occupied the medial-posterior-superior part of HG, and the third subregion (HG3) occupied the lateral-anterior-inferior part of HG. The HG3 exhibited strong structural and functional connectivity to the association and paralimbic areas, and the HG1 exhibited a higher myelin density and larger cortical thickness than other subregions. A functional gradient analysis revealed a gradual axis expanding from the HG2 to the HG3. Our findings clarify the individually varying structural and functional organization of human HG subregions and provide insights into the substructures of the human auditory cortex.