bluuebunny/crossref_metadata_2025_split huggingface dataset:
Controlled electron injection into beam driven plasma wakefield accelerators employing a co-propagating laser pulse
Authors: Barzegar S , Sedaghat M , Niknam A R 
Published: October 2021

Abstract
A novel technique for generating high current electron bunches in electron beam driven plasma wakefield accelerators (PWFAs) is suggested based on co-propagation of an electron beam and a laser pulse. It is observed that propagation of a laser pulse in front of an electron beam driver leads to bubble expansion and consequently electron injection into a PWFA. The acceleration structure is extensively studied in this scheme and the bubble evolution process is discussed. The difference in propagation velocity of the laser pulse and the beam driver in the plasma and variation of electron beam driver density in presence of the laser pulse cause the bubble radius grows. Using a laser pulse in a PWFA leads to the generation of an ultra short (10 fs) electron bunch with charge three times larger than the electron beam driver total charge. It is shown by altering the initial electron beam driver density and the laser pulse intensity, the external control of the amount of loaded charge is possible. The number of self-injected electrons is enhanced by increasing the laser pulse intensity and the density of the electron beam driver. The results represent that the accelerator operates in a highly loaded regime. Therefore, by raising the density of the electron beam driver and the laser pulse intensity, the final energy spread of the generated electron bunch increases. An interpretive approach to find the appropriate parameters for the laser pulse and the electron beam is proposed in this scheme.
Influence of paper surface characteristics on fully inkjet printed PEDOT:PSS-based electrochemical transistors
Authors: Morais Rogério , Vieira Douglas Henrique , Gaspar Cristina , Pereira Luis , Martins Rodrigo , Alves E Neri 
Published: October 2021

Abstract
Paper electronics has emerged as an ecofriendly, light, low-cost, and recyclable material for the fabrication of flexible and printed transistors. In this study, we present fully printed organic electrochemical transistors using an active layer of PEDOT:PSS, carbon electrodes, cellulose-based electrolyte, and three different papers as substrates: bond, vegetal, and Lumi Silk, relating the electrical properties to the different morphologies of the paper surfaces. Each paper presents different regularity, diffusion capabilities, and roughness, with significant influence on the transistor performance. The more organized and smooth the surface, the better the electrical characteristics, the best of these being the Lumi Silk, with higher I
on/I
off ratio of 46, on-current of 8.3 × 10−5 A, V
on of 1.3 V, and power gain of 43.5 dB associated with ultra-low hysteresis of 0.1 V, high transconductance of −57.3 μS, and suitablity for flexible electronics and sensors applications.
Mem-fractive properties of mushrooms
Authors: Beasley Alexander E , Abdelouahab Mohammed-Salah , Lozi René , Tsompanas Michail-Antisthenis , Powell Anna L , Adamatzky Andrew 
Published: October 2021

Abstract
Memristors close the loop for I–V characteristics of the traditional, passive, semi-conductor devices. A memristor is a physical realisation of the material implication and thus is a universal logical element. Memristors are getting particular interest in the field of bioelectronics. Electrical properties of living substrates are not binary and there is nearly a continuous transitions from being non-memristive to mem-fractive (exhibiting a combination of passive memory) to ideally memristive. In laboratory experiments we show that living oyster mushrooms Pleurotus ostreatus exhibit mem-fractive properties. We offer a piece-wise polynomial approximation of the I–V behaviour of the oyster mushrooms. We also report spiking activity, oscillations in conduced current of the oyster mushrooms.
From a biological template model to gait assistance with an exosuit
Authors: Firouzi Vahid , Davoodi Ayoob , Bahrami Fariba , Sharbafi Maziar A 
Published: October 2021

Abstract
The invention of soft wearable assistive devices, known as exosuits, introduced a new aspect in assisting unimpaired subjects. In this study, we designed and developed an exosuit with compliant biarticular thigh actuators called BATEX. Unlike the conventional method of using rigid actuators in exosuits, the BATEX is made of serial elastic actuators (SEA) resembling artificial muscles. This bioinspired design is complemented by the novel control concept of using the ground reaction force to adjust the artificial muscles’ stiffness in the stance phase. By locking the motors in the swing phase, the SEAs will be simplified to passive biarticular springs, which is sufficient for leg swinging. The key concept in our design and control approach is to synthesize human locomotion to develop an assistive device instead of copying human motor control outputs. Analyzing human walking assistance using experiment-based OpenSim simulations demonstrates the advantages of the proposed design and control of BATEX, such as 9.4% reduction in metabolic cost during normal walking condition. This metabolic reduction increases to 10.4% when the subjects carry a 38 kg load. The adaptability of our proposed model-based control to such an unknown condition outperforms the assistance level of the model-free optimal controller. Moreover, increasing the assistive system’s efficiency by adjusting the actuator compliance with the force feedback supports our previous findings on the LOPES II exoskeleton.
Increasing fire and logging disturbances in Siberian boreal forests: a case study of the Angara region
Authors: Shvetsov Evgeny G , Kukavskaya Elena A , Shestakova Tatiana A , Laflamme Jocelyne , Rogers Brendan M 
Published: October 2021

Abstract
Forest disturbances are a critical environmental issue globally and within the boreal biome, yet detailed attribution and trends in disturbances are lacking for many Siberian regions. The Angara region located in the southern taiga of Central Siberia has experienced significant disturbances during the past several decades and is a hotspot of change in Eurasia. Here we estimated fire and logging disturbances using MODIS and Landsat data for the period 2002–2020 across the Angara region and analyzed the resulting trends. Average annual burned and logged area was about 220 and 31 thousand ha or 2 and 0.3% of the study area, respectively. In total, about 4.1 million ha (38% of the region) and 0.6 million ha (6% of the region) were disturbed by fires and logging, respectively. Spatial analysis showed that almost 50% of fires were ignited within 2 km of anthropogenic features such as settlements, roads and logged areas. Almost 5% of the Angara region was burned two or more times during the 19 years of observations. Improved and strictly-enforced conservation and management policies are required to halt continued forest degradation in the Angara region and similarly-affected boreal forests in Siberia.
Growth optimization of TaN for superconducting spintronics
Authors: Müller M , Hoepfl R , Liensberger L , Geprägs S , Huebl H , Weiler M , Gross R , Althammer M 
Published: October 2021

Abstract
We have optimized the growth of superconducting TaN thin films on SiO2 substrates via dc magnetron sputtering and extract a maximum superconducting transition temperature of T
c = 5 K as well as a maximum critical field μ
0
H
c2 = (13.8 ± 0.1) T. This material is of interest for both different fields of quantum technology and superconducting spintronics as it represents a magnetic field-robust superconductor with strong spin–orbit interaction (SOI). After presenting the results of the growth optimization, we investigate in the second part the impact of the strong SOI in TaN on superconductor/ferromagnet heterostructures. To this end, we analyze the magnetization dynamics of both normal state and superconducting TaN/Ni80Fe20 (permalloy, Py)-bilayers as a function of temperature using broadband ferromagnetic resonance spectroscopy. In particular, we quantify the inverse current-induced torques of the bilayers and compare these results to NbN/Py-bilayers. In the normal state of TaN, we detect a positive damping-like current-induced torque σ
d from the inverse spin Hall effect and a small field-like torque σ
f attributed to the inverse Rashba–Edelstein effect at the TaN/Py-interface. In the superconducting state of TaN, we detect a negative σ
d attributed to the quasiparticle mediated inverse spin Hall effect (QMiSHE) and the unexpected manifestation of a large positive field-like σ
f of unknown origin matching our previous results for NbN/Py-bilayers. The QMiSHE can be used to probe spin currents in emergent quantum materials.
Integrative COVID-19 biological network inference with probabilistic core decomposition
Authors: Guo Yang , Esfahani Fatemeh , Shao Xiaojian , Srinivasan Venkatesh , Thomo Alex , Xing Li , Zhang Xuekui 
Published: October 2021

AbstractThe severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for millions of deaths around the world. To help contribute to the understanding of crucial knowledge and to further generate new hypotheses relevant to SARS-CoV-2 and human protein interactions, we make use of the information abundant Biomine probabilistic database and extend the experimentally identified SARS-CoV-2-human protein–protein interaction (PPI) network in silico. We generate an extended network by integrating information from the Biomine database, the PPI network and other experimentally validated results. To generate novel hypotheses, we focus on the high-connectivity sub-communities that overlap most with the integrated experimentally validated results in the extended network. Therefore, we propose a new data analysis pipeline that can efficiently compute core decomposition on the extended network and identify dense subgraphs. We then evaluate the identified dense subgraph and the generated hypotheses in three contexts: literature validation for uncovered virus targeting genes and proteins, gene function enrichment analysis on subgraphs and literature support on drug repurposing for identified tissues and diseases related to COVID-19. The major types of the generated hypotheses are proteins with their encoding genes and we rank them by sorting their connections to the integrated experimentally validated nodes. In addition, we compile a comprehensive list of novel genes, and proteins potentially related to COVID-19, as well as novel diseases which might be comorbidities. Together with the generated hypotheses, our results provide novel knowledge relevant to COVID-19 for further validation.
A network-based method for brain disease gene prediction by integrating brain connectome and molecular network
Authors: Wang Wei , Han Ruijiang , Zhang Menghan , Wang Yuxian , Wang Tao , Wang Yongtian , Shang Xuequn , Peng Jiajie 
Published: October 2021

AbstractBrain disease gene identification is critical for revealing the biological mechanism and developing drugs for brain diseases. To enhance the identification of brain disease genes, similarity-based computational methods, especially network-based methods, have been adopted for narrowing down the searching space. However, these network-based methods only use molecular networks, ignoring brain connectome data, which have been widely used in many brain-related studies. In our study, we propose a novel framework, named brainMI, for integrating brain connectome data and molecular-based gene association networks to predict brain disease genes. For the consistent representation of molecular-based network data and brain connectome data, brainMI first constructs a novel gene network, called brain functional connectivity (BFC)-based gene network, based on resting-state functional magnetic resonance imaging data and brain region-specific gene expression data. Then, a multiple network integration method is proposed to learn low-dimensional features of genes by integrating the BFC-based gene network and existing protein–protein interaction networks. Finally, these features are utilized to predict brain disease genes based on a support vector machine-based model. We evaluate brainMI on four brain diseases, including Alzheimer’s disease, Parkinson’s disease, major depressive disorder and autism. brainMI achieves of 0.761, 0.729, 0.728 and 0.744 using the BFC-based gene network alone and enhances the molecular network-based performance by 6.3% on average. In addition, the results show that brainMI achieves higher performance in predicting brain disease genes compared to the existing three state-of-the-art methods.
Positive-unlabeled learning in bioinformatics and computational biology: a brief review
Authors: Li Fuyi , Dong Shuangyu , Leier André , Han Meiya , Guo Xudong , Xu Jing , Wang Xiaoyu , Pan Shirui , Jia Cangzhi , Zhang Yang , Webb Geoffrey I , Coin Lachlan J M , Li Chen , Song Jiangning 
Published: October 2021

AbstractConventional supervised binary classification algorithms have been widely applied to address significant research questions using biological and biomedical data. This classification scheme requires two fully labeled classes of data (e.g. positive and negative samples) to train a classification model. However, in many bioinformatics applications, labeling data is laborious, and the negative samples might be potentially mislabeled due to the limited sensitivity of the experimental equipment. The positive unlabeled (PU) learning scheme was therefore proposed to enable the classifier to learn directly from limited positive samples and a large number of unlabeled samples (i.e. a mixture of positive or negative samples). To date, several PU learning algorithms have been developed to address various biological questions, such as sequence identification, functional site characterization and interaction prediction. In this paper, we revisit a collection of 29 state-of-the-art PU learning bioinformatic applications to address various biological questions. Various important aspects are extensively discussed, including PU learning methodology, biological application, classifier design and evaluation strategy. We also comment on the existing issues of PU learning and offer our perspectives for the future development of PU learning applications. We anticipate that our work serves as an instrumental guideline for a better understanding of the PU learning framework in bioinformatics and further developing next-generation PU learning frameworks for critical biological applications.
ImmuCellAI-mouse: a tool for comprehensive prediction of mouse immune cell abundance and immune microenvironment depiction
Authors: Miao Ya-Ru , Xia Mengxuan , Luo Mei , Luo Tao , Yang Mei , Guo An-Yuan 
Published: October 2021

Abstract

Motivation
Immune cells are important components of the immune system and are crucial for disease initiation, progression, prognosis and survival. Although several computational methods have been designed for predicting the abundance of immune cells, very few tools are applicable to mouse. Given that, mouse is the most widely used animal model in biomedical research, there is an urgent need to develop a precise algorithm for predicting mouse immune cells.

Results
We developed a tool named Immune Cell Abundance Identifier for mouse (ImmuCellAI-mouse), for estimating the abundance of 36 immune cell (sub)types from gene expression data in a hierarchical strategy of three layers. Reference expression profiles and robust marker gene sets of immune cell types were curated. The abundance of cells in three layers was predicted separately by calculating the ssGSEA enrichment score of the expression deviation profile per cell type. Benchmark results showed high accuracy of ImmuCellAI-mouse in predicting most immune cell types, with correlation coefficients between predicted value and real cell proportion of most cell types being larger than 0.8. We applied ImmuCellAI-mouse to a mouse breast tumor dataset and revealed the dynamic change of immune cell infiltration during treatment, which is consistent with the findings of the original study but with more details. We also constructed an online server for ImmuCellAI-mouse, on which users can upload expression matrices for analysis. ImmuCellAI-mouse will be a useful tool for studying the immune microenvironment, cancer immunology and immunotherapy in mouse models, providing an indispensable supplement for human disease studies.

Availability and implementation
Software is available at http://bioinfo.life.hust.edu.cn/ImmuCellAI-mouse/.

Supplementary information
Supplementary data are available at Bioinformatics online.
Biogeography and ecological drivers of evolution in the Andes: resolving the phylogenetic backbone forCalceolaria(Calceolariaceae)
Authors: Frankel Lauren , Murúa Maureen , Espíndola Anahí 
Published: October 2021

AbstractCalceolaria (Calceolariaceae) is an emblematic and diverse genus in the Americas. Despite being one of the most easily recognized genera in the region and a system with great potential to improve our understanding of different drivers of species diversification in the Andes, its intrageneric evolutionary relationships are still poorly understood. Responding to the need for additional molecular markers to resolve the phylogenetic relationships of the group, we perform plastome analyses and resolve the backbone of the genus. Specifically, using low-coverage genomes for 14 species, we assembled plastomes, estimated and dated phylogenetic hypotheses and evaluated evolutionary trends in the group. Our approach allowed us to resolve the backbone of the genus, identify two main clades and estimate a timing of diversification contemporaneous to major climatic and orogenic events. Our biogeographic reconstructions suggest an independent colonization of the whole range of the genus by both clades. Finally, our evaluations of floral morphology reveal future avenues for investigating the relationship between the pollination biology and diversification of the group.
Patchily distributed but not necessarily isolated populations ofDeuterocohnia meziana: a threatened bromeliad from rock outcrops
Authors: Vicente-Silva Luciana , De Russo Godoy Fernanda Maria , Faggioni Gabriel Paganini , Lorenz Aline Pedroso , Paggi Gecele Matos 
Published: October 2021

AbstractThe movement of organisms or genes among resource patches is fundamental to the persistence of natural populations over time. Species occurring in rock outcrops may have difficulty maintaining genetic connectivity due to the natural disjunct distribution of these formations, which may lead to genetic differentiation. In this study, we tested the hypothesis of genetic isolation among populations of Deuterocohnia meziana on rock outcrops in Brazil. We evaluated genetic diversity and structure, number of migrants, and the contribution of pollen and seed to dispersal. Additionally, we used niche modelling to estimate the potential distribution of D. meziana and combined this with genetic data to identify potential dispersal routes under different climatic scenarios from the Quaternary. Deuterocohnia meziana showed high diversity and moderate genetic structure, low number of migrants and more efficient dispersal by pollen. Only populations separated by &gt; 70 km showed genetic differentiation. Dispersal routes varied mainly in the intensity of the environmental suitability and indicated the present scenario as the most favourable for gene flow in D. meziana. Our findings contribute to the understanding of processes related to the connectivity and isolation of naturally fragmented populations, highlighting the role of biotic and abiotic drivers for genetic differentiation.
The multidimensional nature of aphasia recovery post-stroke
Authors: Stefaniak James D , Geranmayeh Fatemeh , Lambon Ralph Matthew A 
Published: October 2021

Abstract
Language is not a single function, but instead results from interactions between neural representations and computations that can be damaged independently of each other. Although there is now clear evidence that the language profile in post-stroke aphasia reflects graded variations along multiple underlying dimensions (‘components’), it is still entirely unknown if these distinct language components have different recovery trajectories and rely on the same, or different, neural regions during aphasia recovery. Accordingly, this study examined whether language components in the subacute stage: (i) mirror those observed in the chronic stage; (ii) recover together in a homogeneous manner; and (iii) have recovery trajectories that relate to changing activation in distinct or overlapping underlying brain regions.
We analysed longitudinal data from 26 individuals with mild–moderate aphasia following left hemispheric infarct who underwent functional MRI and behavioural testing at ∼2 weeks and ∼4 months post-stroke. The language profiles in early post-stroke aphasia reflected three orthogonal principal components consisting of fluency, semantic/executive function and phonology. These components did not recover in a singular, homogeneous manner; rather, their longitudinal trajectories were uncorrelated, suggesting that aphasia recovery is heterogeneous and multidimensional. Mean regional brain activation during overt speech production in unlesioned areas was compared with patient scores on the three principal components of language at both the early and late time points. In addition, the change in brain activation over time was compared with the change on each of the principal component scores, both before and after controlling for baseline scores. We found that different language components were associated with changing activation in multiple, non-overlapping bilateral brain regions during aphasia recovery. Specifically, fluency recovery was associated with increasing activation in bilateral middle frontal gyri and right temporo-occipital middle temporal gyrus; semantic/executive recovery was associated with reducing activation in bilateral anterior temporal lobes; while phonology recovery was associated with reducing activation in bilateral precentral gyri, dorso-medial frontal poles and the precuneus. Overlapping clusters in the ventromedial prefrontal cortex were positively associated with fluency recovery but negatively associated with semantic/executive and phonology recovery.
This combination of detailed behavioural and functional MRI data provides novel insights into the neural basis of aphasia recovery. Because different aspects of language seem to rely on different neural regions for recovery, treatment strategies that target the same neural region in all stroke survivors with aphasia might be entirely ineffective or even impair recovery, depending on the specific language profile of each individual patient.