bluuebunny/crossref_metadata_2025_split huggingface dataset:
Fast or Slow? A Comparison Between Two Transcranial Electrical Stimulation Techniques for Eliciting Motor-Evoked Potentials During Supratentorial Surgery
Authors: Lettieri Christian , Pauletto Giada , Valiante Gabriele , Ius Tamara , Verriello Lorenzo , Valente Mariarosaria , Skrap Miran , Gigli Gian L. , Budai Riccardo 
Published: November 2021

Purpose:
During intraoperative neurophysiological monitoring of motor pathways, two types of transcranial electrical stimulation are available, i.e., constant-current and constant-voltage stimulation. Few previous studies, performed only during spinal surgery, analyzed and compared them during intraoperative neurophysiological monitoring. The aim of our study was to compare these two stimulation techniques for eliciting motor-evoked potentials during intraoperative neurophysiological monitoring in a group of patients affected by supratentorial lesions.

Methods:
Supratentorial lesions from 16 patients were retrospectively collected and analyzed. Motor-evoked potentials were performed only from transcranial electrical stimulation because the inability to place the subdural strip electrodes correctly did not permit to perform direct cortical stimulation. At the beginning of surgery, in each patient, motor-evoked potentials were monitored by using both “fast-charge” constant-voltage and “slow-charge” constant-current stimulation. Several neurophysiological parameters were collected and compared between the two stimulation techniques by means of statistical analysis.

Results:
“Fast-charge” constant-voltage stimulation allowed statistically higher efficiency rates for eliciting motor-evoked potentials compared with “slow-charge” constant-current stimulation, both for upper and lower limbs. We also found that threshold and maximal charge as well as charge density were significantly lower during constant-voltage stimulation, thus lowering the potential tissue damage.

Conclusions:
“Fast-charge” constant-voltage transcranial electrical stimulation is feasible and safe during intraoperative neurophysiological monitoring for supratentorial surgery and may be preferable to “slow-charge” constant-current stimulation.
Genotyping of structural variation using PacBio high-fidelity sequencing
Authors: Zhang Zhiliang , Zhang Jijin , Kang Lipeng , Qiu Xuebing , Niu Beirui , Bi Aoyue , Zhao Xuebo , Xu Daxing , Wang Jing , Yin Changbin , Fu Xiangdong , Lu Fei 
Published: November 2021

AbstractBackgroundStructural variations (SVs) pervade the genome and contribute substantially to the phenotypic diversity of species. However, most SVs were ineffectively assayed because of the complexity of plant genomes and the limitations of sequencing technologies. Recent advancement of third-generation sequencing technologies, particularly the PacBio high-fidelity (HiFi) sequencing, which generates both long and highly accurate reads, offers an unprecedented opportunity to characterize SVs and reveal their functionality. Since HiFi sequencing is new, it is crucial to evaluate HiFi reads in SV detection before applying the technology at scale.ResultsWe sequenced wheat genomes using HiFi, then conducted a comprehensive evaluation of SV detection using mainstream long-read aligners and SV callers. The results showed the accuracy of SV discovery depends more on aligners rather than callers. For aligners, pbmm2 and NGMLR provided the most accurate results while detecting deletion and insertion, respectively. Likewise, cuteSV and SVIM achieved the best performance across all SV callers. We demonstrated that the combination of the aligners and callers mentioned above is optimal for SV detection. Furthermore, we evaluated the impact of sequencing depth on the accuracy of SV detection. The results showed that low-coverage HiFi sequencing is capable of generating high-quality SV genotyping.ConclusionsThis study provides a robust benchmark of SV discovery with HiFi reads, showing the remarkable potential of long-read sequencing to investigate structural variations in plant genomes. The high accuracy SV discovery from low-coverage HiFi sequencing indicates that skim HiFi sequencing is an ideal approach to study structural variations at the population level.
Identifying Lethal Dependencies with HUGE Predictive Power from Large-Scale Functional Genomic Screens
Authors: Carazo Fernando , José-Enériz Edurne San , Gimeno Marian , Garate Leire , Miranda Estíbaliz , Castilla Carlos , Agirre Xabier , Rubio Ángel , Prósper Felipe 
Published: November 2021

AbstractRecent functional genomic screens -such as CRISPR-Cas9 or RNAi screening-have fostered a new wave of targeted treatments based on the concept of synthetic lethality. These approaches identified LEthal Dependencies (LEDs) by estimating the effect of genetic events on cell viability. The multiple-hypothesis problem related to the large number of gene knockouts limits the statistical power of these studies. Here, we show that predictions of LEDs from functional screens can be dramatically improved by incorporating the “HUb effect in Genetic Essentiality” (HUGE) of gene alterations. We analyze three recent genome-wide loss-of-function screens - Project Score, CERES score and DEMETER score-identifying LEDs with 75 times larger statistical power than using state-of-the-art methods. HUGE shows an increased enrichment in a recent harmonized knowledgebase of clinical interpretations of somatic genomic variants in cancer (with an AUROC up to 0.87). Our approach is effective even in tumors with large genetic heterogeneity such as acute myeloid leukemia, where we identified LEDs not recalled by previous pipelines, including FLT3-mutant genotypes sensitive to FLT3 inhibitors. Interestingly, in-vitro validations confirm lethal dependencies of either NRAS or PTPN11 depending on the NRAS mutational status. HUGE will hopefully help discover novel genetic dependencies amenable for precision-targeted therapies in cancer.
Essential elements of radical pair magnetosensitivity inDrosophila
Authors: Bradlaugh Adam A , Fedele Giorgio , Munro Anna L , Hansen Celia Napier , Patel Sanjai , Kyriacou Charalambos P. , Jones Alex R. , Rosato Ezio , Baines Richard A. 
Published: November 2021

SummaryMany animals use the Earth’s magnetic field (geoMF) for navigation1. The favored mechanism for magnetosensitivity involves a blue-light (BL) activated electron transfer reaction between flavin adenine dinucleotide (FAD) and a chain of tryptophan (Trp) residues within the photoreceptor protein, CRYPTOCHROME (CRY). The spin-state of the resultant radical pair (RP), and hence the concentration of CRY in its active state, is influenced by the geoMF2. The canonical CRY-centric radical pair mechanism (RPM) does not, however, explain many physiological and behavioural observations2–8. Here, using electrophysiology and behavioural analyses, we assay magnetic field (MF) responses at single neuron and organismal level. We show that the 52 C-terminal (CT) amino acids of CRY, which are missing the canonical FAD binding domain and Trp chain, are sufficient to facilitate magnetoreception. We also show that increasing intracellular FAD potentiates both BL-induced and MF-dependent effects on the activity mediated by the CT. Additionally, high levels of FAD alone are sufficient to cause BL neuronal sensitivity and, remarkably, potentiation of this response in the co-presence of a MF. These unexpected results reveal the essential components of a primary magnetoreceptor in flies, providing strong evidence that non-canonical(i.e.,non-CRY-dependent) RPs can elicit MF responses in cells.
Substrate rigidity modulates traction forces and stoichiometry of cell matrix adhesions
Authors: Balcioglu Hayri E , Harkes Rolf , Danen Erik HJ , Schmidt Thomas 
Published: November 2021

ABSTRACTIn cell matrix adhesions, integrin receptors and associated proteins provide a dynamic coupling of the extracellular matrix (ECM) to the cytoskeleton. This allows bidirectional transmission of forces between the ECM and the cytoskeleton, which tunes intracellular signaling cascades that control survival, proliferation, differentiation, and motility. The quantitative relationships between recruitment of distinct cell matrix adhesion proteins and local cellular traction forces are not known. Here, we applied quantitative superresolution microscopy to cell matrix adhesions formed on fibronectin-stamped elastomeric pillars and developed an approach to relate the number of talin, vinculin, paxillin, and focal adhesion kinase (FAK) molecules to the local cellular traction force. We find that FAK recruitment does not show an association with traction-force application whereas a ~60 pN force increase is associated with the recruitment of one talin, two vinculin, and two paxillin molecules on a substrate of effective stiffness of 47 kPa. On a substrate with a four-fold lower effective stiffness the stoichiometry of talin:vinculin:paxillin changes to 2:12:6 for the same ~60 pN traction force. The relative change in force-related vinculin recruitment indicates a stiffness-dependent switch in vinculin function in cell matrix adhesions. Our results reveal a substrate-stiffness-dependent modulation of the relation between cellular traction-force and the molecular stoichiometry of cell-matrix adhesions.
Modelling COVID-19 Pandemic Dynamics Using Transparent, Interpretable, Parsimonious and Simulatable (TIPS) Machine Learning Models: A Case Study from Systems Thinking and System Identification Perspectives
Authors: Wei Hua-Liang , Billings Stephen A. 
Published: November 2021

AbstractSince the outbreak of COVID-19, an astronomical number of publications on the pandemic dynamics appeared in the literature, of which many use the susceptible infected removed (SIR) and susceptible exposed infected removed (SEIR) models, or their variants, to simulate and study the spread of the coronavirus. SIR and SEIR are continuous-time models which are a class of initial value problems (IVPs) of ordinary differential equations (ODEs). Discrete-time models such as regression and machine learning have also been applied to analyze COVID-19 pandemic data (e.g. predicting infection cases), but most of these methods use simplified models involving a small number of input variables pre-selected based on a priori knowledge, or use very complicated models (e.g. deep learning), purely focusing on certain prediction purposes and paying little attention to the model interpretability. There have been relatively fewer studies focusing on the investigations of the inherent time-lagged or time-delayed relationships e.g. between the reproduction number (R number), infection cases, and deaths, analyzing the pandemic spread from a systems thinking and dynamic perspective. The present study, for the first time, proposes using systems engineering and system identification approach to build transparent, interpretable, parsimonious and simulatable (TIPS) dynamic machine learning models, establishing links between the R number, the infection cases and deaths caused by COVID-19. The TIPS models are developed based on the well-known NARMAX (Nonlinear AutoRegressive Moving Average with eXogenous inputs) model, which can help better understand the COVID-19 pandemic dynamics. A case study on the UK COVID-19 data is carried out, and new findings are detailed. The proposed method and the associated new findings are useful for better understanding the spread dynamics of the COVID-19 pandemic.
Deep learning-based surrogate model for fast multi-material topology optimization of IPM motor
Authors: Sato Hayaho , Igarashi Hajime 
Published: November 2021

Purpose
This paper aims to present a deep learning–based surrogate model for fast multi-material topology optimization of an interior permanent magnet (IPM) motor. The multi-material topology optimization based on genetic algorithm needs large computational burden because of execution of finite element (FE) analysis for many times. To overcome this difficulty, a convolutional neural network (CNN) is adopted to predict the motor performance from the cross-sectional motor image and reduce the number of FE analysis.

Design/methodology/approach
To predict the average torque of an IPM motor, CNN is used as a surrogate model. From the input cross-sectional motor image, CNN infers dq-inductance and magnet flux to compute the average torque. It is shown that the average torque for any current phase angle can be predicted by this approach, which allows the maximization of the average torque by changing the current phase angle. The individuals in the multi-material topology optimization are evaluated by the trained CNN, and the limited individuals with higher potentials are evaluated by finite element method.

Findings
It is shown that the proposed method doubles the computing speed of the multi-material topology optimization without loss of search ability. In addition, the optimized motor obtained by the proposed method followed by simplification for manufacturing is shown to have higher average torque than a reference model.

Originality/value
This paper proposes a novel method based on deep learning for fast multi-material topology optimization considering the current phase angle.
A smart tech lever to augment caregivers' touch and foster vulnerable patient engagement and well-being
Authors: Mele Cristina , Marzullo Marialuisa , Di Bernardo Irene , Russo-Spena Tiziana , Massi Roberta , La Salandra Alessandra , Cialabrini Stefania 
Published: November 2021

PurposeSome population groups face precarious health, reflecting their vulnerability, in terms of lack of agency or control. Smart technologies promise to transform people's lives from the enhanced connectedness, greater computational processing and more complex decision-making they can achieve. This study aims to investigate how smart technology can mitigate vulnerability and improve well-being.Design/methodology/approachThe research group, of three scholars and three managers, pursued an action research methodology with an iterative process of planning, action and learning. The authors conducted three related action studies: (1) adopting smart technologies, (2) fostering patient engagement and (3) assessing well-being.FindingsThe adoption of sensors and wearable devices had positive impacts for both patients and caregivers. Technologies highlighted their meaning as resources to support actors' (caregivers' and vulnerable patients') activities. Smart devices as resources get integrated, stimulate change and enable new practices. For caregivers, such innovative solutions help improve their knowledge of patients and their ability to act efficiently; for vulnerable patients, they fostered engagement in daily activities to improve well-being.Originality/valueThe paper delineates an overall model (SEVP) that describes how the integration of high-tech and high touch enables patient engagement to mitigate vulnerability and improve well-being.
Preparation and application of water-based nano-silver conductive ink in paper-based 3D printing
Authors: Zhao Chenfei , Wang Jun , Lu Lini 
Published: October 2021

Purpose
In flexible electronics applications, organic inks are mostly used for inkjet printing. Three-dimensional (3 D) printing technology has the advantages of low cost, high speed and good precision in modern electronic printing. The purpose of this study is to solve the high cost of traditional printing and the pollution emissions of organic ink. It is necessary to develop a water-based conductive ink that is easily degradable and can be 3 D printed. A nano-silver ink printed circuit pattern with high precision, high conductivity and good mechanical properties is a promising strategy.

Design/methodology/approach
The researched nano-silver conductive ink is mainly composed of silver nanoparticles and resin. The effect of adding methyl cellulose on the ink was also explored. A simple 3 D circuit pattern was printed on photographic paper. The line width, line length, line thickness and conductivity of the printed circuit were tested. The influence of sintering temperature and sintering time on pattern resistivity was studied. The relationship between circuit pattern bending performance and electrical conductivity is analyzed.

Findings
The experimental results show that the ink has the characteristics of low silver content and good environmental protection effect. The printing feasibility of 3 D printing circuit patterns on paper substrates was confirmed. The best printing temperature is 160°C–180°C, and the best sintering time is 30 min. The circuit pattern can be folded 120°, and the cycle is folded more than 60 times. The minimum resistivity of the circuit pattern is 6.07 µΩ·cm. Methyl cellulose can control the viscosity of the ink. The mechanical properties of the pattern have been improved. The printing method of 3 D printing can significantly reduce the sintering time and temperature of the conductive ink. These findings may provide innovation for the flexible electronics industry and pave the way for alternatives to cost-effective solutions.

Originality/value
In this study, direct ink writing technology was used to print circuit patterns on paper substrates. This process is simple and convenient and can control the thickness of the ink layer. The ink material is nonpolluting to the environment. Nano-silver ink has suitable viscosity and pH value. It can meet the requirements of pneumatic 3 D printers. The method has the characteristics of simple process, fast forming, low cost and high environmental friendliness.
Time series prediction using machine learning: a case of Bitcoin returns
Authors: Shakri Irfan Haider 
Published: November 2021

Purpose
The purpose of this study is to compare five data-driven-based ML techniques to predict the time series data of Bitcoin returns, namely, alternating model tree, random forest (RF), multiple linear regression, multi-layer perceptron regression and M5 Tree algorithms.

Design/methodology/approach
The data used to forecast time series data of Bitcoin returns ranges from 8 July 2010 to 30 Aug 2020. This study used several predictors to predict bitcoin returns including economic policy uncertainty, equity market volatility index, S&P returns, USD/EURO exchange rates, oil and gold prices, volatilities and returns. Five statistical indexes, namely, correlation coefficient, mean absolute error, root mean square error, relative absolute error and root relative squared error are determined. The results of these metrices are used to develop colour intensity ranking.

Findings
Among the machine learning (ML) techniques used in this study, RF models has shown superior predictive ability for estimating the Bitcoin returns.

Originality/value
This study is first of its kind to use and compare ML models in the prediction of Bitcoins. More studies can be carried out by using further cryptocurrencies and other ML data-driven models in future.
Utilising a capability maturity model to leverage inclusion and diversity in public sector organisations
Authors: Lundy Judy , Keast Robyn , Farr‐Wharton Ben , Omari Maryam , Teo Stephen , Bentley Tim 
Published: October 2021

AbstractDiversity and inclusion (D&I) bring many benefits to society, particularly in public sector organisations servicing increasingly diverse communities. To deliver public value, government agencies at all levels must more intentionally direct public sector knowledge, skills, and experiences to shape the current and future capabilities of a more diverse and inclusive workforce. Fully optimising workplace D&I has proven elusive. An evolving array of new and residual policy and practice elements has led to a mismatch of goals and outcomes. Moreover, many accepted ‘best’ practice approaches are out‐of‐step with rapidly shifting societal and workforce compositions and mechanisms and societal expectations for organisations to reflect and embrace D&I. Accommodating these shifts demands a bolder, more agile ‘next’ practice approach that is fit‐for‐purpose in creating and maintaining a modern, diverse, inclusive workplace. This practice‐focused article constructs a Capability Maturity Model to guide D&I decision‐making and support continuous improvement.
Total opioid‐free general anaesthesia can improve postoperative outcomes after surgery, without evidence of adverse effects on patient safety and pain management: A systematic review and meta‐analysis
Authors: Olausson Alexander , Svensson Carl Johan , Andréll Paulin , Jildenstål Pether , Thörn Sven‐Egron , Wolf Axel 
Published: November 2021

AbstractBackgroundOpioid‐based treatment is used to manage stress responses during surgery and postoperative pain. However, opioids have both acute and long‐term side effects, calling for opioid‐free anaesthetic strategies. This meta‐analysis compares adverse events, postoperative recovery, discharge time from post‐anaesthesia care unit, and postoperative pain, nausea, vomiting, and opioid consumption between strict opioid‐free and opioid‐based general anaesthesia.MethodsWe conducted a systematic review and meta‐analysis. We searched PubMed, Embase, Cinahl, Cochrane Library, selected reference lists, and Google Scholar. We included randomised controlled trials (RCTs) published between January 2000 and February 2021 with at least one opioid‐free study arm, i.e. no opioids administered preoperatively, during anaesthesia induction, before skin closure, or before emergence from anaesthesia.ResultsThe study comprised 1934 patients from 26 RCTs. Common interventions included laparoscopic gynaecological surgery, upper gastrointestinal surgery, and breast surgery. There is firm evidence that opioid‐free anaesthesia significantly reduced adverse postoperative events (OR 0.32, 95% CI 0.22 to 0.46, I2 = 56%, p < 0.00001), mainly driven by decreased nausea (OR 0.27, (0.17 to 0.42), p < 0.00001) and vomiting (OR 0.22 (0.11 to 0.41), p < 0.00001). Postoperative opioid consumption was significantly lower in the opioid‐free group (−6.00 mg (−8.52 to −3.48), p < 0.00001). There was no significant difference in length of post‐anaesthesia care unit stay and overall postoperative pain between groups.ConclusionsOpioid‐free anaesthesia can improve postoperative outcomes in several surgical settings without evidence of adverse effects on patient safety and pain management. There is a need for more evidence‐based non‐opioid anaesthetic protocols for different types of surgery as well as postoperative phases.