bluuebunny/crossref_metadata_2025_split huggingface dataset:
Inducing upwards band bending by surface stripping ZnO nanowires with argon bombardment
Authors: Barnett Chris J , Navarro-Torres Jorge , McGettrick James D , Maffeis Thierry G G , Barron Andrew R 
Published: September 2020

Abstract
Metal oxide semiconductors such as ZnO have attracted much scientific attention due their material and electrical properties and their ability to form nanostructures that can be used in numerous devices. However, ZnO is naturally n-type and tailoring its electrical properties towards intrinsic or p-type in order to optimise device operation have proved difficult. Here, we present an x-ray photon-electron spectroscopy and photoluminescence study of ZnO nanowires that have been treated with different argon bombardment treatments including with monoatomic beams and cluster beams of 500 atoms and 2000 atoms with acceleration volte of 0.5 keV–20 keV. We observed that argon bombardment can remove surface contamination which will improve contact resistance and consistency. We also observed that using higher intensity argon bombardment stripped the surface for nanowires causing a reduction in defects and surface OH– groups both of which are possible causes of the n-type nature and observed a shift in the valance band edge suggest a shift to a more p-type nature. These results indicate a simple method for tailoring the electrical characteristic of ZnO.
Direct electron attachment to fast hydrogen in 10−9 contrast 1018 W cm−2 intense laser solid target interaction
Authors: Tata Sheroy , Mondal Angana , Sarkar Soubhik , Lad Amit D , Colgan James , Pasley John , Krishnamurthy M 
Published: September 2020

Abstract
The interaction of an ultra-short (<30 fs), high-contrast (<10−9), high-intensity (>1018 W cm−2) laser pulse with a solid target is not generally known to produce and accelerate negative ions. The transient accelerating electrostatic-fields are so strong that they ionize any atom or negative ion at the target surface. In spite of what may appear to be unfavourable conditions, here it is reported that H− ions extending up to 80 keV are measured from such an interaction. The H− ion flux is about 0.1% that of the H+ ions at 20 keV. These measurements employ a recently developed temporally-gated Thomson parabola ion spectrometry diagnostic which significantly improves signal-to-noise ratios. Electrons that co-propagate with the fast protons cause a two-step charge-reduction reaction. The gas phase three-body attachment of electrons to fast neutral hydrogen atoms accounts for the measured H− yield. It is intriguing that such a fundamental gas-phase reaction, involving the attachment of an electron to a hydrogen atom, has not been observed in laboratory experiments previously. Laser-produced plasma offers an alternative environment to the conventional charged particle beam experiments, in which such atomic physics processes can be investigated.
A review of shaped colloidal particles in fluids: anisotropy and chirality
Authors: Witten Thomas A , Diamant Haim 
Published: September 2020

Abstract
This review treats asymmetric colloidal particles moving through their host fluid under the action of some form of propulsion. The propulsion can come from an external body force or from external shear flow. It may also come from externally-induced stresses at the surface, arising from imposed chemical, thermal or electrical gradients. The resulting motion arises jointly from the driven particle and the displaced fluid. If the objects are asymmetric, every aspect of their motion and interaction depends on the orientation of the objects. This orientation in turn changes in response to the driving. The objects’ shape can thus lead to a range of emergent anisotropic and chiral motion not possible with isotropic spherical particles. We first consider what aspects of a body’s asymmetry can affect its drift through a fluid, especially chiral motion. We next discuss driving by injecting external force or torque into the particles. Then we consider driving without injecting force or torque. This includes driving by shear flow and driving by surface stresses, such as electrophoresis. We consider how time-dependent driving can induce collective orientational order and coherent motion. We show how a given particle shape can be represented using an assembly of point forces called a Stokeslet object. We next consider the interactions between anisotropic propelled particles, the symmetries governing the interactions, and the possibility of bound pairs of particles. Finally we show how the collective hydrodynamics of a suspension can be qualitatively altered by the particles’ shapes. The asymmetric responses discussed here are broadly relevant also for swimming propulsion of active micron-scale objects such as microorganisms.
What happens to an acellular scar matrix after implantation in vivo? A histological and related molecular biology study
Authors: Lai Chenzhi , Song Guodong , Pan Bo , Zhao Bo , Wang Hongquan , Tian Dan , Zhao Jingyi , Du Le , Guo Xiaoshuang , Jin Xiaolei , Zong Xianlei 
Published: September 2020

Abstract
It has been established that scar acellular matrices (AMs), which allow cell proliferation, have similar characteristics. The aim of this study was to investigate the repair effect of scar AMs on animals, thus providing a reference for clinical application. Selected mature and immature scar AMs were implanted into animals, and then a negative control group was set for comparison. The effect of scar AMs on wound healing was observed through tissue staining, RT-qPCR, and immunohistochemistry. The materials showed milder inflammation and faster extracellular matrix (ECM) deposition than the negative control group. The ECM deposition and new vessels increased over time. However, the arrangement of ECM in mature scar AM was more regular than in immature scar AM and the negative control group, and more new vessels grew in the mature scar AM group than in the immature scar AM group and negative control group over the same period. The transforming growth factor-β level was elevated at one month, two months, and six months. COLA1 and vimentin levels all peaked at six months. Matrix metalloproteinase and TIMP1 were also elevated at different months. Collectively, scar AMs can effectively promote wound healing and vascularization. Mature scar AMs have a better regeneration effect.
Hydrothermal oxidation method to synthesize nitrogen containing carbon dots from compost humic acid as selective Fe(III) sensor
Authors: Zhu Yan , Bao Ying , Wang Liping , Bi Jingtao , Liu Yong , Xie Chuang 
Published: September 2020

Abstract
Residual sludge which is generated as a by-product during wastewater treatment needs efficient and environmentally friendly disposal. In this work, nitrogen containing carbon dots (N-CDs) was synthesized via one-step hydrothermal oxidation of humic acid for the first time, which was alkalinely extracted from the compost of sludge. The successful doping of the functional groups (hydroxyl, carboxyl and amino) on the surface of N-CDs were characterized by x-ray photoelectron spectroscopy (XPS) and Fourier transform infrared spectroscopy (FT-IR). The optical properties of the synthetic N-CDs were obtained from Ultraviolet-visible spectroscopy and Photoluminescence spectra. The N-CDs had an average diameter at 1.88 nm and exhibited a well-defined spherical shape. The aqueous solution of N-CDs with excitation-dependent properties showed a strong blue emission with 8.8% quantum yield (QY) under an excitation wavelength of 340 nm, which indicate the synthesized N-CDs a promising sensing probes for iron ions in aquatic system. Moreover, the mechanism of the N-CDs sensing technique was investigated in detail, which can be explained by the fluorescence static quenching effect and a ground-state complex formed in this process. This research demonstrates an extremely sensitive method for detecting heavy metal Fe3+ at a lower 1.9 nM level by using N-CDs as fluorescent probes without a complicated procedure.
The sequential effects of a multifactorial detergent based decellularization process on bovine pericardium
Authors: Laker L , Dohmen P M , Smit F E 
Published: September 2020

Abstract
Decellularization is a promising method for obtaining extracellular matrix scaffolds (ECM) to be used as replacement material in reconstructive procedures. The effectiveness of decellularization and the alterations to the ECM vary, depending on several factors, including the tissue source, composition and density. With an optimized decellularization process, decellularized scaffolds can preserve the spatial and temporal ECM microenvironment, which play an integral role in modulating cell migration, proliferation and differentiation. The exploration of a variety of decellularization protocols has led to mixed outcomes and comparisons between decellularization protocols could not attribute these differences to any single step in a multiple-step process. This study aimed to characterize the effects of each step of a multifactorial decellularization method on the scaffold structure and mechanical integrity of bovine pericardium. Each step of the decellularization process and the effect on the tissue was assessed using hematoxylin and eosin staining, electron microscopy, total protein, ECM protein and triglyceride quantification. The biomechanical properties were assessed using uniaxial tensile strength testing. Cell lysis occurred mainly during the detergent and alcohol steps. Collagen structural damage occurred during the detergent and alcohol steps, with no significant decreased in collagen concentration. No significant damage to elastin could be shown throughout the process, however glycosaminoglycans were significantly removed by detergent treatment. Triglycerides were removed mostly by the alcohol treatment. The strength of the pericardium decreased somewhat after each step of the protocol. It is important to characterize each decellularization protocol with regards to the decellularization efficiency and the effect on the ECM proteins structure and function to accurately evaluate in vivo outcomes.
Femtosecond laser shockwave peening ablation in liquids for hierarchical micro/nanostructuring of brittle silicon and its biological application
Authors: Zhang Dongshi , Wu Liang-Chun , Ueki Masashi , Ito Yoshihiro , Sugioka Koji 
Published: September 2020

Abstract
This paper presents a new technique, termed femtosecond laser shock peening ablation in liquids (fs-LSPAL), which can realize simultaneous crack micro/nanomanufacturing and hierarchical micro/nanolaser ablation, giving rise to the formation of diverse multiscale hierarchical structures, such as macroporous ratcheted structures and en échelon microfringes decorated with parabolic nanoripples. Through analysis of surface morphologies, many phenomena have been confirmed to take place during fs-LSPAL, including en échelon cracks, nanostriation, ripple densification, crack branching, and selective formation of high spatial frequency laser-induced periodic surface structures of 100–200 nm in period. At a high laser power of 700 mW, fs-LSPAL at scanning speeds of 0.2 mm s−1 and 1 mm s−1 enables the generation of height-fluctuated and height-homogeneous hierarchical structures, respectively. The height-fluctuated structures can be used to induce ‘colony’ aggregates of embryonic EB3 stem cells. At 200 mW, fs-LSPAL at 1 mm s−1 is capable of producing homogeneous tilt macroporous structures with cracked structures interleaved among them, which are the synergistic effects of bubble-induced light refraction/reflection ablation and cracks. As shown in this paper, the conventional laser ablation technique integrated with its self-driven unconventional cracking under extreme conditions expands the horizons of extreme manufacturing and offers more opportunities for complex surface structuring, which can potentially be used for biological applications.
Predation risk increases intraspecific heterogeneity in white-tailed deer diel activity patterns
Authors: Crawford Daniel A , Conner L Mike , Morris Gail , Cherry Michael J 
Published: September 2020

Abstract
Prey species often mitigate predation risk through alteration of spatiotemporal diel activity patterns whereby prey access high-quality resources in risky areas during predator downtimes. However, dominance hierarchies exist in some prey species, and temporal partitioning is a mechanism thought to reduce aggressive intraspecific interactions. How demographic-specific responses to predation risk influence intraspecific temporal partitioning in prey are largely unknown and could be key to understanding the effects of predators on intraspecific interactions in prey. To assess the effects of predation risk on intraspecific interactions in white-tailed deer (Odocoileus virginianus), we monitored deer diel activity during the fawning season in four pairs of predator exclusion and control plots (~40 ha) from 2015 to 2018 using 16 camera traps. We examined the effect of predation risk on diel activity of males, females, and nursery groups by comparing the within-group coefficient of activity overlap (d̂) across predator exclusion and control plots. We then examined within-treatment activity overlap between groups in the predator exclosure and control plots. All groups maintained different diel activity patterns in safe and risky areas. Unconstrained by predation risk, all groups behaved more similarly, and interspecific group overlap was greater in the predator exclusion plots than control plots. Male-nursery group overlap exhibited the strongest treatment effect, increasing 24% in predator exclusion plots (d̂ = 0.91, confidence interval [CI]: 0.87–0.95) relative to control plots (d̂ = 0.67, CI: 0.57–0.76). Our results suggest predators increase heterogeneity in prey behavior and may be important drivers of behavioral processes, such as temporal partitioning, that minimize antagonistic intraspecific interactions of prey.
Joint epitope selection and spacer design for string-of-beads vaccines
Authors: Dorigatti Emilio , Schubert Benjamin 
Published: September 2020

Abstract

Motivation
Conceptually, epitope-based vaccine design poses two distinct problems: (i) selecting the best epitopes to elicit the strongest possible immune response and (ii) arranging and linking them through short spacer sequences to string-of-beads vaccines, so that their recovery likelihood during antigen processing is maximized. Current state-of-the-art approaches solve this design problem sequentially. Consequently, such approaches are unable to capture the inter-dependencies between the two design steps, usually emphasizing theoretical immunogenicity over correct vaccine processing, thus resulting in vaccines with less effective immunogenicity in vivo.

Results
In this work, we present a computational approach based on linear programming, called JessEV, that solves both design steps simultaneously, allowing to weigh the selection of a set of epitopes that have great immunogenic potential against their assembly into a string-of-beads construct that provides a high chance of recovery. We conducted Monte Carlo cleavage simulations to show that a fixed set of epitopes often cannot be assembled adequately, whereas selecting epitopes to accommodate proper cleavage requirements substantially improves their recovery probability and thus the effective immunogenicity, pathogen and population coverage of the resulting vaccines by at least 2-fold.

Availability and implementation
The software and the data analyzed are available at https://github.com/SchubertLab/JessEV.

Supplementary information
Supplementary data are available at Bioinformatics online.
CLPred: a sequence-based protein crystallization predictor using BLSTM neural network
Authors: Xuan Wenjing , Liu Ning , Huang Neng , Li Yaohang , Wang Jianxin 
Published: September 2020

Abstract

Motivation
Determining the structures of proteins is a critical step to understand their biological functions. Crystallography-based X-ray diffraction technique is the main method for experimental protein structure determination. However, the underlying crystallization process, which needs multiple time-consuming and costly experimental steps, has a high attrition rate. To overcome this issue, a series of in silico methods have been developed with the primary aim of selecting the protein sequences that are promising to be crystallized. However, the predictive performance of the current methods is modest.

Results
We propose a deep learning model, so-called CLPred, which uses a bidirectional recurrent neural network with long short-term memory (BLSTM) to capture the long-range interaction patterns between k-mers amino acids to predict protein crystallizability. Using sequence only information, CLPred outperforms the existing deep-learning predictors and a vast majority of sequence-based diffraction-quality crystals predictors on three independent test sets. The results highlight the effectiveness of BLSTM in capturing non-local, long-range inter-peptide interaction patterns to distinguish proteins that can result in diffraction-quality crystals from those that cannot. CLPred has been steadily improved over the previous window-based neural networks, which is able to predict crystallization propensity with high accuracy. CLPred can also be improved significantly if it incorporates additional features from pre-extracted evolutional, structural and physicochemical characteristics. The correctness of CLPred predictions is further validated by the case studies of Sox transcription factor family member proteins and Zika virus non-structural proteins.

Availability and implementation
https://github.com/xuanwenjing/CLPred.
RAINFOREST: a random forest approach to predict treatment benefit in data from (failed) clinical drug trials
Authors: Ubels Joske , Schaefers Tilman , Punt Cornelis , Guchelaar Henk-Jan , de Ridder Jeroen 
Published: September 2020

Abstract

Motivation
When phase III clinical drug trials fail their endpoint, enormous resources are wasted. Moreover, even if a clinical trial demonstrates a significant benefit, the observed effects are often small and may not outweigh the side effects of the drug. Therefore, there is a great clinical need for methods to identify genetic markers that can identify subgroups of patients which are likely to benefit from treatment as this may (i) rescue failed clinical trials and/or (ii) identify subgroups of patients which benefit more than the population as a whole. When single genetic biomarkers cannot be found, machine learning approaches that find multivariate signatures are required. For single nucleotide polymorphism (SNP) profiles, this is extremely challenging owing to the high dimensionality of the data. Here, we introduce RAINFOREST (tReAtment benefIt prediction using raNdom FOREST), which can predict treatment benefit from patient SNP profiles obtained in a clinical trial setting.

Results
We demonstrate the performance of RAINFOREST on the CAIRO2 dataset, a phase III clinical trial which tested the addition of cetuximab treatment for metastatic colorectal cancer and concluded there was no benefit. However, we find that RAINFOREST is able to identify a subgroup comprising 27.7% of the patients that do benefit, with a hazard ratio of 0.69 (P = 0.04) in favor of cetuximab. The method is not specific to colorectal cancer and could aid in reanalysis of clinical trial data and provide a more personalized approach to cancer treatment, also when there is no clear link between a single variant and treatment benefit.

Availability and implementation
The R code used to produce the results in this paper can be found at github.com/jubels/RAINFOREST. A more configurable, user-friendly Python implementation of RAINFOREST is also provided. Due to restrictions based on privacy regulations and informed consent of participants, phenotype and genotype data of the CAIRO2 trial cannot be made freely available in a public repository. Data from this study can be obtained upon request. Requests should be directed toward Prof. Dr. H.J. Guchelaar (h.j.guchelaar@lumc.nl).

Supplementary information
Supplementary data are available at Bioinformatics online.
CoLiDe: Combinatorial Library Design tool for probing protein sequence space
Authors: Tretyachenko Vyacheslav , Voráček Václav , Souček Radko , Fujishima Kosuke , Hlouchová Klára 
Published: September 2020

Abstract

Motivation
Current techniques of protein engineering focus mostly on re-designing small targeted regions or defined structural scaffolds rather than constructing combinatorial libraries of versatile compositions and lengths. This is a missed opportunity because combinatorial libraries are emerging as a vital source of novel functional proteins and are of interest in diverse research areas.

Results
Here, we present a computational tool for Combinatorial Library Design (CoLiDe) offering precise control over protein sequence composition, length and diversity. The algorithm uses evolutionary approach to provide solutions to combinatorial libraries of degenerate DNA templates. We demonstrate its performance and precision using four different input alphabet distribution on different sequence lengths. In addition, a model design and experimental pipeline for protein library expression and purification is presented, providing a proof-of-concept that our protocol can be used to prepare purified protein library samples of up to 1011–1012 unique sequences. CoLiDe presents a composition-centric approach to protein design towards different functional phenomena.

Availabilityand implementation
CoLiDe is implemented in Python and freely available at https://github.com/voracva1/CoLiDe.

Supplementary information
Supplementary data are available at Bioinformatics online.
Using a GTR+Γ substitution model for dating sequence divergence when stationarity and time-reversibility assumptions are violated
Authors: Barba-Montoya Jose , Tao Qiqing , Kumar Sudhir 
Published: September 2020

AbstractMotivationAs the number and diversity of species and genes grow in contemporary datasets, two common assumptions made in all molecular dating methods, namely the time-reversibility and stationarity of the substitution process, become untenable. No software tools for molecular dating allow researchers to relax these two assumptions in their data analyses. Frequently the same General Time Reversible (GTR) model across lineages along with a gamma (+Γ) distributed rates across sites is used in relaxed clock analyses, which assumes time-reversibility and stationarity of the substitution process. Many reports have quantified the impact of violations of these underlying assumptions on molecular phylogeny, but none have systematically analyzed their impact on divergence time estimates.ResultsWe quantified the bias on time estimates that resulted from using the GTR + Γ model for the analysis of computer-simulated nucleotide sequence alignments that were evolved with non-stationary (NS) and non-reversible (NR) substitution models. We tested Bayesian and RelTime approaches that do not require a molecular clock for estimating divergence times. Divergence times obtained using a GTR + Γ model differed only slightly (∼3% on average) from the expected times for NR datasets, but the difference was larger for NS datasets (∼10% on average). The use of only a few calibrations reduced these biases considerably (∼5%). Confidence and credibility intervals from GTR + Γ analysis usually contained correct times. Therefore, the bias introduced by the use of the GTR + Γ model to analyze datasets, in which the time-reversibility and stationarity assumptions are violated, is likely not large and can be reduced by applying multiple calibrations.Availability and implementationAll datasets are deposited in Figshare: https://doi.org/10.6084/m9.figshare.12594638.