bluuebunny/crossref_metadata_2025_split huggingface dataset:
Study and development of diagnostic systems to characterise the extraction region in SPIDER
Authors: Segalini B. , Candeloro V. , Poggi C. , Berton G. , Fasolo D. , Franchin L. , Laterza B. , Magagna M. , Trevisan L. , Pasqualotto R. , Serianni G. , Tollin M. , Sartori E. 
Published: January 2024

Abstract
SPIDER, an RF-driven negative ion source in the Neutral Beam Test Facility (NBTF), serves as the prototype for ITER's neutral beam injector (NBI). It is composed of 8 drivers powered by 4 RF generators, aiming to accelerate 50 A of negative hydrogen ions to 100 KeV with a beam uniformity target of 10%. The experiment, launched in 2018, tested negative ion production using caesium. Results match those of similar facilities, but SPIDER faces challenges due to its size, multiple drivers, and non-uniform plasma expansion. These issues impact beam uniformity, preventing the machine from reaching expected performance. To address this, SPIDER initiated a significant shutdown at the end of 2021 for improvements.

One the most important aspects studied during the first experimental campaign is source uniformity, addressed both in terms of plasma and of caesium distribution. The latter is particularly relevant since its quality is directly related to the beam uniformity and divergence. To have more insight about these issues, monitoring the plasma properties in the extraction region is crucial, hence in the present contribution, the design and development of two new diagnostic systems are described: a movable Langmuir probe and a Retarding Field Energy Analyser (RFEA).

The first can provide a vertical scan of the main plasma parameters close to the plasma grid. The spatial resolution would improve with respect to the already installed set of fixed Langmuir probes embedded in the grid system, and the newly installed diagnostic could interact with other sensors to produce complementary measurements (namely, electron photo-detachment).
The latter, instead, allows the monitoring of the positive ion energy distribution: positive ions, in fact, can be precursors of the negative ones produced at the caesiated surface, but also influence the energy of negative ion and their extraction probability and thus collecting information about their energy distribution allows inferring details about the extracted negative ion beam.

The two diagnostics are designed focusing on the experimental constraint of integrating the diagnostics in a harsh and complex environment such as SPIDER plasma: a preliminary study of the placement inside the source is carried out, then the electrode of the movable probe and the RFEA sensor are sized according to the spatial and energy resolution requested by the system.
Effect of sampling bias on global estimates of ocean carbon export
Authors: Henson Stephanie , Bisson Kelsey , Hammond Matthew L , Martin Adrian , Mouw Colleen , Yool Andrew 
Published: January 2024

Abstract
Shipboard sampling of ocean biogeochemical properties is necessarily limited by logistical and practical constraints. As a result, the majority of observations are obtained for the spring/summer period and in regions relatively accessible from a major port. This limitation may bias the conceptual understanding we have of the spatial and seasonal variability in important components of the Earth system. Here we examine the influence of sampling bias on global estimates of carbon export flux by sub-sampling a biogeochemical model to simulate real, realistic and random sampling. We find that both the sparseness and the ‘clumpy’ character of shipboard flux observations generate errors in estimates of globally extrapolated export flux of up to ∼ ± 20%. The use of autonomous technologies, such as the Biogeochemical-Argo network, will reduce the uncertainty in global flux estimates to ∼ ± 3% by both increasing the sample size and reducing clumpiness in the spatial distribution of observations. Nevertheless, determining the climate change-driven trend in global export flux may be hampered due to the uncertainty introduced by interannual variability in sampling patterns.
Brain connectivity for subtypes of parkinson’s disease using structural MRI
Authors: Samantaray Tanmayee , Saini Jitender , Pal Pramod Kumar , Gupta Cota Navin 
Published: January 2024

Abstract

Objective. Delineating Parkinson’s disease (PD) into distinct subtypes is a major challenge. Most studies use clinical symptoms to label PD subtypes while our work uses an imaging-based data-mining approach to subtype PD. Our study comprises two major objectives - firstly, subtyping Parkinson’s patients based on grey matter information from structural magnetic resonance imaging scans of human brains; secondly, comparative structural brain connectivity analysis of PD subtypes derived from the former step. Approach. Source-based-morphometry decomposition was performed on 131 Parkinson’s patients and 78 healthy controls from PPMI dataset, to derive at components (regions) with significance in disease and high effect size. The loading coefficients of significant components were thresholded for arriving at subtypes. Further, regional grey matter maps of subtype-specific subjects were separately parcellated and employed for construction of subtype-specific association matrices using Pearson correlation. These association matrices were binarized using sparsity threshold and leveraged for structural brain connectivity analysis using network metrics. Main results. Two distinct Parkinson’s subtypes (namely A and B) were detected employing loadings of two components satisfying the selection criteria, and a third subtype (AB) was detected, common to these two components. Subtype A subjects were highly weighted in inferior, middle and superior frontal gyri while subtype B subjects in inferior, middle and superior temporal gyri. Network metrics analyses through permutation test revealed significant inter-subtype differences (p < 0.05) in clustering coefficient, local efficiency, participation coefficient and betweenness centrality. Moreover, hubs were obtained using betweenness centrality and mean network degree. Significance. MRI-based data-driven subtypes show frontal and temporal lobes playing a key role in PD. Graph theory-driven brain network analyses could untangle subtype-specific differences in structural brain connections showing differential network architecture. Replication of these initial results in other Parkinson’s datasets may be explored in future. Clinical Relevance- Investigating structural brain connections in Parkinson’s disease may provide subtype-specific treatment.
Optimizing for periodicity: a model-independent approach to flux crosstalk calibration for superconducting circuits
Authors: Dai X , Trappen R , Yang R , Disseler S M , Basham J I , Gibson J , Melville A J , Niedzielski B M , Das R , Kim D K , Yoder J L , Weber S J , Hirjibehedin C F , Lidar D A , Lupascu A 
Published: January 2024

Abstract
Flux tunability is an important engineering resource for superconducting circuits. Large-scale quantum computers based on flux-tunable superconducting circuits face the problem of flux crosstalk, which needs to be accurately calibrated to realize high-fidelity quantum operations. Typical calibration methods either assume that circuit elements can be effectively decoupled and simple models can be applied, or require a large amount of data. Such methods become ineffective as the system size increases and circuit interactions become stronger. Here we propose a new method for calibrating flux crosstalk, which is independent of the underlying circuit model. Using the fundamental property that superconducting circuits respond periodically to external fluxes, crosstalk calibration of N flux channels can be treated as N independent optimization problems, with the objective functions being the periodicity of a measured signal depending on the compensation parameters. We demonstrate this method on a small-scale quantum annealing circuit based on superconducting flux qubits, achieving comparable accuracy with previous methods. We also show that the objective function usually has a nearly convex landscape, allowing efficient optimization.
Diagnostic Prediction of portal vein thrombosis in chronic cirrhosis patients using data-driven precision medicine model
Authors: Li Ying , Gao Jing , Zheng Xubin , Nie Guole , Qin Jican , Wang Haiping , He Tao , Wheelock Åsa , Li Chuan-Xing , Cheng Lixin , Li Xun 
Published: January 2024

Abstract

Background
Portal vein thrombosis (PVT) is a significant issue in cirrhotic patients, necessitating early detection. This study aims to develop a data-driven predictive model for PVT diagnosis in chronic hepatitis liver cirrhosis patients.

Methods
We employed data from a total of 816 chronic cirrhosis patients with PVT, divided into the Lanzhou cohort (n = 468) for training and the Jilin cohort (n = 348) for validation. This dataset encompassed a wide range of variables, including general characteristics, blood parameters, ultrasonography findings and cirrhosis grading. To build our predictive model, we employed a sophisticated stacking approach, which included Support Vector Machine (SVM), Naïve Bayes and Quadratic Discriminant Analysis (QDA).

Results
In the Lanzhou cohort, SVM and Naïve Bayes classifiers effectively classified PVT cases from non-PVT cases, among the top features of which seven were shared: Portal Velocity (PV), Prothrombin Time (PT), Portal Vein Diameter (PVD), Prothrombin Time Activity (PTA), Activated Partial Thromboplastin Time (APTT), age and Child–Pugh score (CPS). The QDA model, trained based on the seven shared features on the Lanzhou cohort and validated on the Jilin cohort, demonstrated significant differentiation between PVT and non-PVT cases (AUROC = 0.73 and AUROC = 0.86, respectively). Subsequently, comparative analysis showed that our QDA model outperformed several other machine learning methods.

Conclusion
Our study presents a comprehensive data-driven model for PVT diagnosis in cirrhotic patients, enhancing clinical decision-making. The SVM–Naïve Bayes–QDA model offers a precise approach to managing PVT in this population.
scFed: federated learning for cell type classification with scRNA-seq
Authors: Wang Shuang , Shen Bochen , Guo Lanting , Shang Mengqi , Liu Jinze , Sun Qi , Shen Bairong 
Published: January 2024

Abstract
The advent of single-cell RNA sequencing (scRNA-seq) has revolutionized our understanding of cellular heterogeneity and complexity in biological tissues. However, the nature of large, sparse scRNA-seq datasets and privacy regulations present challenges for efficient cell identification. Federated learning provides a solution, allowing efficient and private data use. Here, we introduce scFed, a unified federated learning framework that allows for benchmarking of four classification algorithms without violating data privacy, including single-cell-specific and general-purpose classifiers. We evaluated scFed using eight publicly available scRNA-seq datasets with diverse sizes, species and technologies, assessing its performance via intra-dataset and inter-dataset experimental setups. We find that scFed performs well on a variety of datasets with competitive accuracy to centralized models. Though Transformer-based model excels in centralized training, its performance slightly lags behind single-cell-specific model within the scFed framework, coupled with a notable time complexity concern. Our study not only helps select suitable cell identification methods but also highlights federated learning’s potential for privacy-preserving, collaborative biomedical research.
Genotype imputation accuracy and the quality metrics of the minor ancestry in multi-ancestry reference panels
Authors: Shi Mingyang , Tanikawa Chizu , Munter Hans Markus , Akiyama Masato , Koyama Satoshi , Tomizuka Kohei , Matsuda Koichi , Lathrop Gregory Mark , Terao Chikashi , Koido Masaru , Kamatani Yoichiro 
Published: January 2024

Abstract
Large-scale imputation reference panels are currently available and have contributed to efficient genome-wide association studies through genotype imputation. However, whether large-size multi-ancestry or small-size population-specific reference panels are the optimal choices for under-represented populations continues to be debated. We imputed genotypes of East Asian (180k Japanese) subjects using the Trans-Omics for Precision Medicine reference panel and found that the standard imputation quality metric (Rsq) overestimated dosage r2 (squared correlation between imputed dosage and true genotype) particularly in marginal-quality bins. Variance component analysis of Rsq revealed that the increased imputed-genotype certainty (dosages closer to 0, 1 or 2) caused upward bias, indicating some systemic bias in the imputation. Through systematic simulations using different template switching rates (θ value) in the hidden Markov model, we revealed that the lower θ value increased the imputed-genotype certainty and Rsq; however, dosage r2 was insensitive to the θ value, thereby causing a deviation. In simulated reference panels with different sizes and ancestral diversities, the θ value estimates from Minimac decreased with the size of a single ancestry and increased with the ancestral diversity. Thus, Rsq could be deviated from dosage r2 for a subpopulation in the multi-ancestry panel, and the deviation represents different imputed-dosage distributions. Finally, despite the impact of the θ value, distant ancestries in the reference panel contributed only a few additional variants passing a predefined Rsq threshold. We conclude that the θ value substantially impacts the imputed dosage and the imputation quality metric value.
Influenza: cause or excuse? An analysis of flu’s influence on worsening mortality trends in England and Wales, 2010–19
Authors: Hiam Lucinda , McKee Martin , Dorling Danny 
Published: January 2024

AbstractBackgroundEngland and Wales experienced a stagnation of previously improving life expectancy during the 2010s. Public bodies cited influenza as an important cause.Sources of dataWe used data from the Office for National Statistics to examine mortality attributed directly to influenza and to all influenza-like diseases for the total population of England and Wales 2010–19. Several combinations of ICD-10 codes were used to address the possibility of under-counting influenza deaths.Areas of agreementDeaths from influenza and influenza-like diseases declined between 2010 and 2019, while earlier improvements in mortality from all causes of death were stalling and, with some causes, worsening. Our findings support existing research showing that influenza is not an important cause of the stalling of mortality rates 2010–19.Areas of controversyInfluenza was accepted by many as an important cause of stalling life expectancy for much of the 2010s, while few in public office have accepted austerity as a key factor in the changes seen during that time.Growing pointsThis adds to the mounting evidence that austerity damaged health prior to COVID-19 and left the population more vulnerable when it arrived.Areas for developing timely researchFuture research should explore why so many in public office were quick to attribute the change in trends in overall mortality in the UK in this period to influenza, and why many continue to do so through to 2023 and to deny the key role of austerity in harming population health.
A novel combination treatment for fragile X syndrome predicted using computational methods
Authors: Chadwick Wayne , Angulo-Herrera Ivan , Cogram Patricia , Deacon Robert J M , Mason Daniel J , Brown David , Roberts Ian , O’Donovan Daniel J , Tranfaglia Michael R , Guilliams Tim , Thompson Neil T 
Published: January 2024

Abstract
Fragile X syndrome is a neurodevelopmental disorder caused by silencing of the fragile X messenger ribonucleotide gene. Patients display a wide spectrum of symptoms ranging from intellectual and learning disabilities to behavioural challenges including autism spectrum disorder. In addition to this, patients also display a diversity of symptoms due to mosaicism. These factors make fragile X syndrome a difficult syndrome to manage and suggest that a single targeted therapeutic approach cannot address all the symptoms. To this end, we utilized Healx’s data-driven drug discovery platform to identify a treatment strategy to address the wide range of diverse symptoms among patients. Computational methods identified the combination of ibudilast and gaboxadol as a treatment for several pathophysiological targets that could potentially reverse multiple symptoms associated with fragile X syndrome. Ibudilast is an approved broad-spectrum phosphodiesterase inhibitor, selective against both phosphodiesterase 4 and phosphodiesterase 10, and has demonstrated to have several beneficial effects in the brain. Gaboxadol is a GABAA receptor agonist, selective against the delta subunit, which has previously displayed encouraging results in a fragile X syndrome clinical trial. Alterations in GABA and cyclic adenosine monophosphate metabolism have long since been associated with the pathophysiology of fragile X syndrome; however, targeting both pathways simultaneously has never been investigated. Both drugs have a good safety and tolerability profile in the clinic making them attractive candidates for repurposing. We set out to explore whether the combination of ibudilast and gaboxadol could demonstrate therapeutic efficacy in a fragile X syndrome mouse model. We found that daily treatment with ibudilast significantly enhanced the ability of fragile X syndrome mice to perform a number of different cognitive assays while gaboxadol treatment improved behaviours such as hyperactivity, aggression, stereotypy and anxiety. Importantly, when ibudilast and gaboxadol were co-administered, the cognitive deficits as well as the aforementioned behaviours were rescued. Moreover, this combination treatment showed no evidence of tolerance, and no adverse effects were reported following chronic dosing. This work demonstrates for the first time that by targeting multiple pathways, with a combination treatment, we were able to rescue more phenotypes in a fragile X syndrome mouse model than either ibudilast or gaboxadol could achieve as monotherapies. This combination treatment approach holds promise for addressing the wide spectrum of diverse symptoms in this heterogeneous patient population and may have therapeutic potential for idiopathic autism.
Modulations in high-density EEG during the suppression of phantom-limb pain with neurostimulation in upper limb amputees
Authors: Kleeva Daria , Soghoyan Gurgen , Biktimirov Artur , Piliugin Nikita , Matvienko Yury , Sintsov Mikhail , Lebedev Mikhail 
Published: January 2024

Abstract
Phantom limb pain (PLP) is a distressing and persistent sensation that occurs after the amputation of a limb. While medication-based treatments have limitations and adverse effects, neurostimulation is a promising alternative approach whose mechanism of action needs research, including electroencephalographic (EEG) recordings for the assessment of cortical manifestation of PLP relieving effects. Here we collected and analyzed high-density EEG data in 3 patients (P01, P02, and P03). Peripheral nerve stimulation suppressed PLP in P01 but was ineffective in P02. In contrast, transcutaneous electrical nerve stimulation was effective in P02. In P03, spinal cord stimulation was used to suppress PLP. Changes in EEG oscillatory components were analyzed using spectral analysis and Petrosian fractal dimension. With these methods, changes in EEG spatio-spectral components were found in the theta, alpha, and beta bands in all patients, with these effects being specific to each individual. The changes in the EEG patterns were found for both the periods when PLP level was stationary and the periods when PLP was gradually changing after neurostimulation was turned on or off. Overall, our findings align with the proposed roles of brain rhythms in thalamocortical dysrhythmia or disruption of cortical excitation and inhibition which has been linked to neuropathic pain. The individual differences in the observed effects could be related to the specifics of each patient’s treatment and the unique spectral characteristics in each of them. These findings pave the way to the closed-loop systems for PLP management where neurostimulation parameters are adjusted based on EEG-derived markers.
Strengthening of alpha synchronization is a neural correlate of cognitive transfer
Authors: Ericson Julia , Palva Satu , Palva Matias , Klingberg Torkel 
Published: January 2024

Abstract
Cognitive training can lead to improvements in both task-specific strategies and general capacities, such as visuo-spatial working memory (VSWM). The latter emerge slowly and linearly throughout training, in contrast to strategy where changes typically occur within the first days of training. Changes in strategy and capacity have not been separated in prior neuroimaging studies. Here, we used a within-participants design with dense temporal sampling to capture the time dynamics of neural mechanisms associated with change in capacity. In four participants, neural activity was recorded with magnetoencephalography on seven occasions over two months of visuo-spatial working memory training. During scanning, the participants performed a trained visuo-spatial working memory task, a transfer task, and a control task. First, we extracted an individual visuo-spatial working memory-load-dependent synchronization network for each participant. Next, we identified linear changes over time in the network, congruent with the temporal dynamics of capacity change. Three out of four participants showed a gradual strengthening of alpha synchronization. Strengthening of the same connections was also found in the transfer task but not in the control task. This suggests that cognitive transfer occurs through slow, gradual strengthening of alpha synchronization between cortical regions that are vital for both the trained task and the transfer task.
Parenting links to parent–child interbrain synchrony: a real-time fNIRS hyperscanning study
Authors: Liu Sihan , Han Zhuo Rachel , Xu Jianjie , Wang Qiandong , Gao Mengyu (Miranda) , Weng Xiaofang , Qin Shaozheng , Rubin Kenneth H 
Published: January 2024

Abstract
Parent–child interaction is crucial for children’s cognitive and affective development. While bio-synchrony models propose that parenting influences interbrain synchrony during interpersonal interaction, the brain-to-brain mechanisms underlying real-time parent–child interactions remain largely understudied. Using functional near-infrared spectroscopy, we investigated interbrain synchrony in 88 parent–child dyads (Mage children = 8.07, 42.0% girls) during a collaborative task (the Etch-a-Sketch, a joint drawing task). Our findings revealed increased interbrain synchrony in the dorsolateral prefrontal cortex and temporo-parietal areas during interactive, collaborative sessions compared to non-interactive, resting sessions. Linear regression analysis demonstrated that interbrain synchrony in the left temporoparietal junction was associated with enhanced dyadic collaboration, shared positive affect, parental autonomy support, and parental emotional warmth. These associations remained significant after controlling for demographic variables including child age, child gender, and parent gender. Additionally, differences between fathers and mothers were observed. These results highlight the significant association between brain-to-brain synchrony in parent–child dyads, the quality of the parent–child relationship, and supportive parenting behaviors. Interbrain synchrony may serve as a neurobiological marker of real-time parent–child interaction, potentially underscoring the pivotal role of supportive parenting in shaping these interbrain synchrony mechanisms.