bluuebunny/crossref_metadata_2025_split huggingface dataset:
An in-depth Exploration of LAMOST Unknown Spectra Based on Density Clustering
Authors: Yang Hai-Feng , Yin Xiao-Na , Cai Jiang-Hui , Yang Yu-Qing , Luo A-Li , Bai Zhong-Rui , Zhou Li-Chan , Zhao Xu-Jun , Xun Ya-Ling 
Published: March 2023

Abstract
Large sky Area Multi-Object fiber Spectroscopic Telescope (LAMOST) has completed the observation of nearly 20 million celestial objects, including a class of spectra labeled “Unknown.” Besides low signal-to-noise ratio, these spectra often show some anomalous features that do not work well with current templates. In this paper, a total of 637,889 “Unknown” spectra from LAMOST DR5 are selected, and an unsupervised-based analytical framework of “Unknown” spectra named SA-Frame (Spectra Analysis-Frame) is provided to explore their origins from different perspectives. The SA-Frame is composed of three parts: NAPC-Spec clustering, characterization and origin analysis. First, NAPC-Spec (Nonparametric density clustering algorithm for spectra) characterizes different features in the “unknown” spectrum by adjusting the influence space and divergence distance to minimize the effects of noise and high dimensionality, resulting in 13 types. Second, characteristic extraction and representation of clustering results are carried out based on spectral lines and continuum, where these 13 types are characterized as regular spectra with low S/Ns, splicing problems, suspected galactic emission signals, contamination from city light and un-gregarious type respectively. Third, a preliminary analysis of their origins is made from the characteristics of the observational targets, contamination from the sky, and the working status of the instruments. These results would be valuable for improving the overall data quality of large-scale spectral surveys.
Delaying feedback during pre-device training facilitates the retention of novel myoelectric skills: a laboratory and home-based study
Authors: Stuttaford Simon A , Dupan Sigrid S G , Nazarpour Kianoush , Dyson Matthew 
Published: March 2023

Abstract

Objective. The objective of this study was to assess the impact of delayed feedback training on the retention of novel myoelectric skills, and to demonstrate the use of this training approach in the home environment. Approach. We trained limb-intact participants to use a motor learning-based upper-limb prosthesis control scheme called abstract decoding. A delayed feedback paradigm intended to prevent within-trial adaptation and to facilitate motor learning was used. We conducted two multi-day experiments. Experiment 1 was a laboratory-based study consisting of two groups trained over a 4 day period with concurrent or delayed feedback. An additional follow-up session took place after 18 days to assess the retention of motor skills. Experiment 2 was a home-based pilot study that took place over five consecutive days to investigate delayed feedback performance when using bespoke training structures. Main Results. Approximately 35 000 trials were collected across both experiments. Experiment 1 found that the retention of motor skills for the delayed feedback group was significantly better than that of their concurrent feedback counterparts. In addition, the delayed feedback group improved their retention of motor skills across days, whereas the concurrent feedback group did not. Experiment 2 demonstrated that by using a bespoke training protocol in an environment that is more conducive to learning, it is possible for participants to become highly accurate in the absence of feedback. Significance. These results show that with delayed feedback training, it is possible to retain novel myoelectric skills. Using abstract decoding participants can activate four distinct muscle patterns without using complex algorithms. The accuracy achieved in the pilot study supports the feasibility of motor learning-based upper-limb prosthesis control after home-based myoelectric training.
Pretty good state transfer in discrete-time quantum walks
Authors: Chan Ada , Zhan Hanmeng 
Published: March 2023

Abstract
We establish the theory for pretty good state transfer in discrete-time quantum walks. For a class of walks, we show that pretty good state transfer is characterized by the spectrum of certain Hermitian adjacency matrix of the graph; more specifically, the vertices involved in pretty good state transfer must be strongly cospectral relative to this matrix, and the arccosines of its eigenvalues must satisfy some number theoretic conditions. Using normalized adjacency matrices, cyclic covers, and the theory on linear relations between geodetic angles, we construct several infinite families of walks that exhibits this phenomenon.
Hazard-selfsimilarity of diffusions’ first passage times
Authors: Eliazar Iddo 
Published: March 2023

Abstract
A recent study introduced a novel approach to the exploration of diffusions’ first-passage times (FPTs): selfsimilarity. Specifically, consider a general diffusion process that runs over the non-negative half-line; initiating the diffusion at fixed positive levels, further consider the diffusion’s FPTs to the origin. Selfsimilarity means that the FPTs are spanned by an intrinsic scaling of their initial levels. The recent study addressed two types of selfsimilarity: stochastic, scaling the FPTs in ‘real space’; and Laplace, scaling the FPTs in ‘Laplace space’. The Laplace selfsimilarity manifests an underlying sum-like structure. Shifting from the sum-like structure to a max-like structure—a-la the shift from the Central Limit Theorem to Extreme Value Theory—this study addresses a third type of selfsimilarity: hazard, scaling the FPTs in ‘hazard space’. A comprehensive analysis of hazard-selfsimilarity is established here, including: the universal distribution of the FPTs; the dramatically different statistical behaviors that the universal distribution exhibits, and the statistical phase transition between the different behaviors; the characterization of the generative diffusion dynamics, and their universal Langevin representation; and the universal Poissonian statistics that emerge when the initial levels are scattered according to the statistical steady-state of the generative diffusion dynamics. The analysis unveils the following linkages: of the universal distribution to the Gumbel, Gompertz, and Frechet laws; of the universal Langevin representation to diffusion in quadratic and logarithmic potentials; and of the universal Poissonian statistics to non-normalizable densities, to the maxima of the exponential law, and to the harmonic Poisson process.
Construction of a three-dimensional urothelium on-chip with barrier function based on urinary flow microenvironment
Authors: Hou Changhao , Gu Yubo , Yuan Wei , Zhang Wukai , Xiu Xianjie , Lin Jiahao , Gao Yue , Liu Peichuan , Chen Xiang , Song Lujie 
Published: March 2023

Abstract
The urothelium covers the inner surface of the urinary tract, forming a urinary tract barrier. Impairment of the integrity and dysfunction of the urinary tract barrier is associated with the occurrence and development of various diseases. The development of a three-dimensional model of the urothelium is critical for pathophysiological studies of this site, especially under physiological fluid shear stress stimulated by the urinary flow. In this study, a urothelium on-chip is fabricated with micromilling and replica molding techniques, which contains a microfluidic chip for cell culture and a pump-based fluid perfusion system. The mechanical properties of the human urinary tract are simulated by adjusting the concentration and degree of amino substitution of the gelatin methacrylate hydrogel. The matrix stiffness is similar to the natural urinary tract. Pulsatile flow and periodic flow are provided to simulate the fluid environment of the upper and lower urinary tracts, respectively. The results show that the physiological fluid shear stress could promote the differentiation and maturation of urothelial cells. The model could simulate the three-dimensional structure of urothelium and urinary flow microenvironment, showing morphological structure close to the natural urothelium, specific differentiation and maturation markers (uroplakin 2, cytokeratin 20), and urothelial barrier function.
Supervised learning of random quantum circuits via scalable neural networks
Authors: Cantori Simone , Vitali David , Pilati Sebastiano 
Published: March 2023

Abstract
Predicting the output of quantum circuits is a hard computational task that plays a pivotal role in the development of universal quantum computers. Here we investigate the supervised learning of output expectation values of random quantum circuits. Deep convolutional neural networks (CNNs) are trained to predict single-qubit and two-qubit expectation values using databases of classically simulated circuits. These circuits are built using either a universal gate set or a continuous set of rotations plus an entangling gate, and they are represented via properly designed encodings of these gates. The prediction accuracy for previously unseen circuits is analyzed, also making comparisons with small-scale quantum computers available from the free IBM Quantum program. The CNNs often outperform these quantum devices, depending on the circuit depth, on the network depth, and on the training set size. Notably, our CNNs are designed to be scalable. This allows us exploiting transfer learning and performing extrapolations to circuits larger than those included in the training set. These CNNs also demonstrate remarkable resilience against noise, namely, they remain accurate even when trained on (simulated) expectation values averaged over very few measurements.
Quantum mixed state compiling
Authors: Ezzell Nic , Ball Elliott M , Siddiqui Aliza U , Wilde Mark M , Sornborger Andrew T , Coles Patrick J , Holmes Zoë 
Published: March 2023

Abstract
The task of learning a quantum circuit to prepare a given mixed state is a fundamental quantum subroutine. We present a variational quantum algorithm (VQA) to learn mixed states which is suitable for near-term hardware. Our algorithm represents a generalization of previous VQAs that aimed at learning preparation circuits for pure states. We consider two different ansätze for compiling the target state; the first is based on learning a purification of the state and the second on representing it as a convex combination of pure states. In both cases, the resources required to store and manipulate the compiled state grow with the rank of the approximation. Thus, by learning a lower rank approximation of the target state, our algorithm provides a means of compressing a state for more efficient processing. As a byproduct of our algorithm, one effectively learns the principal components of the target state, and hence our algorithm further provides a new method for principal component analysis. We investigate the efficacy of our algorithm through extensive numerical implementations, showing that typical random states and thermal states of many body systems may be learnt this way. Additionally, we demonstrate on quantum hardware how our algorithm can be used to study hardware noise-induced states.
Selection of optimal hyperparameter for detecting multiple contacts from large-area tactile sensors based on electrical impedance tomography
Authors: Chen Ying , Jin Leizhi , Wang Peng , Liu Haibin 
Published: March 2023

Abstract
Large-area tactile sensors based on the technique of electrical impedance tomography (EIT) has drawn considerable interest in human-robot interactions. However, due to the ill-posed condition, it is challenging to differentiate between the real contacts and the artifacts from the reconstructed image. To address this issue, a new method to select an optimal hyperparameter that tunes the amount of regularization is developed in the context of tactile sensing. The optimal hyperparameter is determined to be the minimum value to obtain a stabilized number of sub-regions in the reconstructed image. The proposed method not only guarantees a correct detection on the number of multiple contacts at the minimum amount of regularization, but also provides a proper range of hyperparameters. The optimal hyperparameter is found in a chair-shape relation with the boundary signal-to-noise ratio (SNR), by varying the noise level of the hardware in simulation. The optimal hyperparameter decreases significantly when the boundary SNR increases between 5 ∼ 10 dB and 25 ∼ 35 dB, and keeps almost unchanged when SNR is between 10 ∼ 25 dB. The chair-shape relation also holds for contact conditions with varied intensities and sizes. Experimental validations on the proposed method are conducted on a compliant piezoresistive tactile sensor made of exfoliated graphite polymer composites. By varying the number of contacts in experiments, the relation between the optimal hyperparameter and the boundary SNR is consistent with the chair-shape curve. The investigation made in this work helps improve the performance of identifying multiple contacts from tactile sensors based on electrical impedance tomography.
Life-history characteristics and historical factors are important to explain regional variation in reproductive traits and genetic diversity in perennial mosses
Authors: Bisang Irene , Ehrlén Johan , Hedenäs Lars 
Published: March 2023

Abstract

Background and Aims
Plants have evolved an unrivalled diversity of reproductive strategies, including variation in the degree of sexual vs. clonal reproduction. This variation has important effects on the dynamics and genetic structure of populations. We examined the association between large-scale variation in reproductive patterns and intraspecific genetic diversity in two moss species where sex is manifested in the dominant haploid generation and sex expression is irregular. We predicted that in regions with more frequent realized sexual reproduction, populations should display less skewed sex ratios, should more often express sex and should have higher genetic diversity than in regions with largely clonal reproduction.

Methods
We assessed reproductive status and phenotypic sex in the dioicous long-lived Drepanocladus trifarius and D. turgescens, in 248 and 438 samples across two regions in Scandinavia with frequent or rare realized sexual reproduction, respectively. In subsets of the samples, we analysed genetic diversity using nuclear and plastid sequence information and identified sex with a sex-specific molecular marker in non-reproductive samples.

Key Results
Contrary to our predictions, sex ratios did not differ between regions; genetic diversity did not differ in D. trifarius and it was higher in the region with rare sexual reproduction in D. turgescens. Supporting our predictions, relatively more samples expressed sex in D. trifarius in the region with frequent sexual reproduction. Overall, samples were mostly female. The degree of sex expression and genetic diversity differed between sexes.

Conclusions
Sex expression levels, regional sex ratios and genetic diversity were not directly associated with the regional frequency of realized sexual reproduction, and relationships and variation patterns differed between species. We conclude that a combination of species-specific life histories, such as longevity, overall degree of successful sexual reproduction and recruitment, and historical factors are important to explain this variation. Our data on haploid-dominated plants significantly complement plant reproductive biology.
Homeostatic normalization of alpha brain rhythms within the default-mode network and reduced symptoms in post-traumatic stress disorder following a randomized controlled trial of electroencephalogram neurofeedback
Authors: Nicholson Andrew A , Densmore Maria , Frewen Paul A , Neufeld Richard W J , Théberge Jean , Jetly Rakesh , Lanius Ruth A , Ros Tomas 
Published: March 2023

AbstractCollective research has identified a key electroencephalogram signature in patients with post-traumatic stress disorder, consisting of abnormally reduced alpha (8–12 Hz) rhythms. We conducted a 20-session, double-blind, randomized controlled trial of alpha desynchronizing neurofeedback in patients with post-traumatic stress disorder over 20 weeks. Our objective was to provide mechanistic evidence underlying potential clinical improvements by examining changes in aberrant post-traumatic stress disorder brain rhythms (namely, alpha oscillations) as a function of neurofeedback treatment. We randomly assigned participants with a primary diagnosis of post-traumatic stress disorder (n = 38) to either an experimental group (n = 20) or a sham-control group (n = 18). A multichannel electroencephalogram cap was used to record whole-scalp resting-state activity pre- and post-neurofeedback treatment, for both the experimental and sham-control post-traumatic stress disorder groups. We first observed significantly reduced relative alpha source power at baseline in patients with post-traumatic stress disorder as compared to an age/sex-matched group of neurotypical healthy controls (n = 32), primarily within regions of the anterior default mode network. Post-treatment, we found that only post-traumatic stress disorder patients in the experimental neurofeedback group demonstrated significant alpha resynchronization within areas that displayed abnormally low alpha power at baseline. In parallel, we observed significantly decreased post-traumatic stress disorder severity scores in the experimental neurofeedback group only, when comparing baseline to post-treatment (Cohen’s d = 0.77) and three-month follow-up scores (Cohen’s d = 0.75), with a remission rate of 60.0% at the three-month follow-up. Overall, our results indicate that neurofeedback training can rescue pathologically reduced alpha rhythmicity, a functional biomarker that has repeatedly been linked to symptoms of hyperarousal and cortical disinhibition in post-traumatic stress disorder. This randomized controlled trial provides long-term evidence suggesting that the ‘alpha rebound effect’ (i.e. homeostatic alpha resynchronization) occurs within key regions of the default mode network previously implicated in post-traumatic stress disorder.
A vertigo network derived from human brain lesions and brain stimulation
Authors: Li Yanran , Qi Lei , Schaper Frédéric L W V J , Wu Di , Friedrich Maximilian , Du Jialin , Yu Tao , Wang Qiao , Wang Xiaopeng , Wang Di , Jin Guangyuan , Liu Aihua , Fan Chunqiu , Wang Yuping , Fox Michael D , Ren Liankun 
Published: March 2023

AbstractVertigo is a common neurological complaint, which can result in significant morbidity and decreased quality of life. While pathology to peripheral and subtentorial brain structures is a well-established cause of vertigo, cortical lesions have also been linked to vertigo and may lend insight into relevant neuroanatomy. Here, we investigate the supratentorial lesion locations associated with vertigo and test whether they map to a common brain network. We performed a systematic literature search and identified 23 cases of supratentorial brain lesions associated with vertigo. We mapped the lesion locations to a standard brain template and computed the network of brain regions functionally connected to each lesion location, using a ‘wiring diagram’ of the human brain termed the human connectome (n = 1000). Sensitivity was assessed by identifying the most common connection to lesion locations associated with vertigo, and specificity was assessed through comparison with control lesions associated with symptoms other than vertigo (n = 68). We found that functional connectivity between lesion locations and the bilateral ventral posterior insula was both sensitive (22/23 lesions) and specific (voxel-wise family-wise error-corrected P &lt; 0.05) for lesion-induced vertigo. We computed connectivity with this hub region to define a lesion-based vertigo network, which included regions in the bilateral insula, somatosensory cortex, higher-level visual areas, cingulate sulcus, thalamus and multiple cerebellar regions in the territory of the posterior inferior cerebellar artery. Next, we used stereo-electroencephalography (80 stimulation sites across 17 patients) to test whether stimulation sites associated with vertigo mapped to this same network. We found that 36/42 (86%) of stimulation sites eliciting vertigo fell within the lesion-based vertigo network in contrast to 16/39 (41%) of stimulation sites that did not elicit vertigo. Connectivity between stimulation sites and our lesion-based hub in the ventral posterior insula was also significantly associated with vertigo (P &lt; 0.0001). We conclude that cortical lesions and direct electrical stimulation sites associated with vertigo map to a common brain network, offering insights into the causal neuroanatomical substrate of vertigo.
FungiProteomeDB: a database for the molecular weight and isoelectric points of the fungal proteomes
Authors: Rashid Muhammad , Omar Muhammad , Mohanta Tapan Kumar 
Published: March 2023

AbstractProteins’ molecular weight (MW) and isoelectric point (pI) are crucial for their subcellular localization and subsequent function. These are also useful in 2D gel electrophoresis, liquid chromatography–mass spectrometry and X-ray protein crystallography. Moreover, visualizations like a virtual 2D proteome map of pI vs. MW are worthwhile to discuss the proteome diversity among different species. Although the genome sequence data of the fungi kingdom improved enormously, the proteomic details have been poorly elaborated. Therefore, we have calculated the MW and pI of the fungi proteins and reported them in, FungiProteomeDB, an online database (DB) https://vision4research.com/fungidb/. We analyzed the proteome of 685 fungal species that contain 7 127 141 protein sequences. The DB provides an easy-to-use and efficient interface for various search options, summary statistics and virtual 2D proteome map visualizations. The MW and pI of a protein can be obtained by searching the name of a protein, a keyword or a list of accession numbers. It also allows querying protein sequences. The DB will be helpful in hypothesis formulation and in various biotechnological applications.Database URL https://vision4research.com/fungidb/
Multiorgan evaluation of perfusion and congestion using ultrasound in patients with shock
Authors: Tavazzi Guido , Spiegel Rory , Rola Philippe , Price Susanna , Corradi Francesco , Hockstein Maxwell 
Published: March 2023

Abstract
There is increasing evidence on the role of ultrasound in the evaluation of multiorgan hypoperfusion and congestion in patients with cardiocirculatory shock both to identify the underlying pathophysiological mechanism and to drive and monitor the treatment. The cardiac and lung ultrasound is included as an integrated multiparametric approach to the very early phase of patients with haemodynamic instability/cardiogenic shock. Splanchnic ultrasound has been mainly applied in heart failure and predominant circulatory failure. Although poorly validated in the critically ill, many ultrasound parameters have a strong physiological background to support their use in the acute setting those that apply either for heart/lung and for splanchnic organ evaluation. This review summarizes the ultrasonographic parameters that have shown evidence in literature in the diagnostic/therapeutic pathway to define the congestion/perfusion profile of the organs that are involved in the pathophysiological cascade of cardiocirculatory shock.