bluuebunny/crossref_metadata_2025_split huggingface dataset:
Direct Observation of the Depth of Active Groundwater Circulation in an Alpine Watershed
Authors: Manning Andrew H. , Ball Lyndsay B. , Wanty Richard B. , Williams Kenneth H. 
Published: December 2020

AbstractThe depth of active groundwater circulation is a fundamental control on stream flows and chemistry in mountain watersheds, yet it remains challenging to characterize and is rarely well constrained. We collected hydraulic conductivity, hydraulic head, temperature, chemical, noble gas, and 3H/3He groundwater age data from discrete levels in two boreholes 46 and 81 m deep in an alpine watershed, in combination with chemical and age data from shallow groundwater discharge, to discern groundwater flow rates at different depths and directly observe active and inactive groundwater. Vertical head gradients are steep (average of 0.4) and thermal profiles are consistent with typical linear conductive continental geotherms. Groundwater deeper than ∼20 m is distinct from shallow groundwater and creek water in its chemistry, noble gas signature, and age (dominantly >65 years compared to <9 years). Together these results suggest low vertical groundwater flow velocities and a relatively shallow active circulation depth of ∼20 m. This hypothesis is tested with a simple 2‐D numerical fluid flow and heat transport model representing a hillslope transect through the two boreholes. The modeling indicates that the subhorizontally bedded sedimentary rocks underlying the basin are highly anisotropic with low vertical hydraulic conductivity, and at most ∼10% of bedrock recharge (equivalent to <2% of stream baseflow) flows below a depth of 20 m. The study demonstrates the considerable value of discrete‐depth hydrogeologic, chemical, and age data for determining active circulation depth, and illustrates an approach for maximizing the utility of individual boreholes drilled for mountain bedrock aquifer characterization.
Harmonic information transitions of spatiotemporal metasurfaces
Authors: Wu Haotian , Gao Xin Xin , Zhang Lei , Bai Guo Dong , Cheng Qiang , Li Lianlin , Cui Tie Jun 
Published: December 2020

AbstractFacilitated by ultrafast dynamic modulations, spatiotemporal metasurfaces have been identified as a pivotal platform for manipulating electromagnetic waves and creating exotic physical phenomena, such as dispersion cancellation, Lorentz reciprocity breakage, and Doppler illusions. Motivated by emerging information-oriented technologies, we hereby probe the information transition mechanisms induced by spatiotemporal variations and present a general model to characterize the information processing capabilities of the spatiotemporal metasurface. Group theory and abstract number theory are adopted through this investigation, by which the group extension and independent controls of multiple harmonics are proposed and demonstrated as two major tools for information transitions from the spatiotemporal domain to the spectra-wavevector domain. By incorporating Shannon’s entropy theory into the proposed model, we further discover the corresponding information transition efficiencies and the upper bound of the channel capacity of the spatiotemporal metasurface. The results of harmonic information transitions show great potential in achieving high-capacity versatile information processing systems with spatiotemporal metasurfaces.
How the harm of drugs and their availability affect brain reactions to drug cues: a meta-analysis of 64 neuroimaging activation studies
Authors: Devoto F. , Zapparoli L. , Spinelli G. , Scotti G. , Paulesu E. 
Published: December 2020

AbstractVisual drug cues are powerful triggers of craving in drug abusers contributing to enduring addiction. According to previous qualitative reviews, the response of the orbitofrontal cortex to such cues is sensitive to whether subjects are seeking treatment. Here we re-evaluate this proposal and assessed whether the nature of the drug matters. To this end, we performed a quantitative meta-analysis of 64 neuroimaging studies on drug-cue reactivity across legal (nicotine, alcohol) or illegal substances (cocaine, heroin). We used the ALE algorithm and a hierarchical clustering analysis followed by a cluster composition statistical analysis to assess the association of brain clusters with the nature of the substance, treatment status, and their interaction. Visual drug cues activate the mesocorticolimbic system and more so in abusers of illegal substances, suggesting that the illegal substances considered induce a deeper sensitization of the reward circuitry. Treatment status had a different modulatory role for legal and illegal substance abusers in anterior cingulate and orbitofrontal areas involved in inter-temporal decision making. The class of the substance and the treatment status are crucial and interacting factors that modulate the neural reactivity to drug cues. The orbitofrontal cortex is not sensitive to the treatment status per se, rather to the interaction of these factors. We discuss that these varying effects might be mediated by internal predispositions such as the intention to quit from drugs and external contingencies such as the daily life environmental availability of the drugs, the ease of getting them and the time frame of potential reward through drug consumption.
Identifying mental health status using deep neural network trained by visual metrics
Authors: Shafiei Somayeh B. , Lone Zaeem , Elsayed Ahmed S. , Hussein Ahmed A. , Guru Khurshid A. 
Published: December 2020

AbstractMental health is an integral part of the quality of life of cancer patients. It has been found that mental health issues, such as depression and anxiety, are more common in cancer patients. They may result in catastrophic consequences, including suicide. Therefore, monitoring mental health metrics (such as hope, anxiety, and mental well-being) is recommended. Currently, there is lack of objective method for mental health evaluation, and most of the available methods are limited to subjective face-to-face discussions between the patient and psychotherapist. In this study we introduced an objective method for mental health evaluation using a combination of convolutional neural network and long short-term memory (CNN-LSTM) algorithms learned and validated by visual metrics time-series. Data were recorded by the TobiiPro eyeglasses from 16 patients with cancer after major oncologic surgery and nine individuals without cancer while viewing18 artworks in an in-house art gallery. Pre-study and post-study questionnaires of Herth Hope Index (HHI; for evaluation of hope), anxiety State-Trait Anxiety Inventory for Adults (STAI; for evaluation of anxiety) and Warwick-Edinburgh Mental Wellbeing Scale (WEMWBS; for evaluation of mental well-being) were completed by participants. Clinical psychotherapy and statistical suggestions for cutoff scores were used to assign an individual’s mental health metrics level during each session into low (class 0), intermediate (class 1), and high (class 2) levels. Our proposed model was used to objectify evaluation and categorize HHI, STAI, and WEMWBS status of individuals. Classification accuracy of the model was 93.81%, 94.76%, and 95.00% for HHI, STAI, and WEMWBS metrics, respectively. The proposed model can be integrated into applications for home-based mental health monitoring to be used by patients after oncologic surgery to identify patients at risk.
Tumour-free distance: a novel prognostic marker in patients with early-stage cervical cancer treated by primary surgery
Authors: Cibula David , Slama Jiri , Dostálek Lukáš , Fischerová Daniela , Germanova Anna , Frühauf Filip , Dundr Pavel , Nemejcova Kristyna , Jarkovsky Jiri , Sebestova Silvie , Burgetová Andrea , Borčinová Martina , Kocián Roman 
Published: December 2020

Abstract
Background
Models predicting recurrence risk (RR) of cervical cancer are used to tailor adjuvant treatment after radical surgery. The goal of our study was to compare available prognostic factors and to develop a prognostic model that would be easy to standardise and use in routine clinical practice.

Methods
All consecutive patients with early-stage cervical cancer treated by primary surgery in a single referral centre (01/2007–12/2016) were eligible if assessed by standardised protocols for pre-operative imaging and pathology. Fifteen prognostic markers were evaluated in 379 patients, out of which 320 lymph node (LN)-negative.

Results
The best predictive model for the whole cohort entailed a combination of tumour-free distance (TFD) ≤ 3.5 mm and LN positivity, which separated two subgroups with a substantially distinct RR 36% and 6.5%, respectively. In LN-negative patients, a combination of TFD ≤ 3.5 mm and adenosquamous tumour type separated a group of nine patients with RR 33% from the rest of the group with 6% RR.

Conclusions
A newly identified prognostic marker, TFD, surpassed all traditional tumour-related markers in the RR assessment. Predictive models combining TFD, which can be easily accessed on pre-operative imaging, with LN status or tumour type can be used in daily practice and can help to identify patients with the highest RR.
Effectiveness of transfer learning for enhancing tumor classification with a convolutional neural network on frozen sections
Authors: Kim Young-Gon , Kim Sungchul , Cho Cristina Eunbee , Song In Hye , Lee Hee Jin , Ahn Soomin , Park So Yeon , Gong Gyungyub , Kim Namkug 
Published: December 2020

AbstractFast and accurate confirmation of metastasis on the frozen tissue section of intraoperative sentinel lymph node biopsy is an essential tool for critical surgical decisions. However, accurate diagnosis by pathologists is difficult within the time limitations. Training a robust and accurate deep learning model is also difficult owing to the limited number of frozen datasets with high quality labels. To overcome these issues, we validated the effectiveness of transfer learning from CAMELYON16 to improve performance of the convolutional neural network (CNN)-based classification model on our frozen dataset (N = 297) from Asan Medical Center (AMC). Among the 297 whole slide images (WSIs), 157 and 40 WSIs were used to train deep learning models with different dataset ratios at 2, 4, 8, 20, 40, and 100%. The remaining, i.e., 100 WSIs, were used to validate model performance in terms of patch- and slide-level classification. An additional 228 WSIs from Seoul National University Bundang Hospital (SNUBH) were used as an external validation. Three initial weights, i.e., scratch-based (random initialization), ImageNet-based, and CAMELYON16-based models were used to validate their effectiveness in external validation. In the patch-level classification results on the AMC dataset, CAMELYON16-based models trained with a small dataset (up to 40%, i.e., 62 WSIs) showed a significantly higher area under the curve (AUC) of 0.929 than those of the scratch- and ImageNet-based models at 0.897 and 0.919, respectively, while CAMELYON16-based and ImageNet-based models trained with 100% of the training dataset showed comparable AUCs at 0.944 and 0.943, respectively. For the external validation, CAMELYON16-based models showed higher AUCs than those of the scratch- and ImageNet-based models. Model performance for slide feasibility of the transfer learning to enhance model performance was validated in the case of frozen section datasets with limited numbers.
A missing color area extraction approach from high-resolution statue images for cultural heritage documentation
Authors: Nasri Adel , Huang XianFeng 
Published: December 2020

AbstractAncient statues are usually fragile and have a tendency to deteriorate over time, developing cracks, corrosion, and losing color. Before any intervention on the object of art, a conservator must map degradation and take measurements. Deterioration mapping is an extremely long process, as the conservator or restorer must locate and digitize the damages manually and collect physical measurements from the artwork. Extracting and measuring the deterioration automatically from images is less expensive and aids the digital documentation process, thus reducing the time cost of manual deterioration mapping. In this paper, we propose an effective approach named Missing Color Area Extraction in order to extract and measure missing color areas from high-resolution imagery statues, using a thresholding technique. The conversion from RGB color space to HSV color space is applied, in addition to morphological operations to remove the dust and small objects.
Unequal chances: ex ante fairness and individual control
Authors: Micheli Leticia , Gagnon Nickolas 
Published: December 2020

AbstractUnequal financial outcomes often originate from unequal chances. Yet, compared to outcomes, little is known about how individuals perceive unequal distributions of chances. We investigate empirically the role of different sources of unequal chances in shaping inequality perceptions. Importantly, we do so from an ex ante perspective—i.e., before the chances are realized—which has rarely been explored. In an online survey, we asked uninvolved respondents to evaluate ex ante the fairness of unequal allocations of chances. We varied the source of inequality of chances, using a comprehensive range of factors which resemble several real world situations. Respondents also evaluated how much control individuals hold over the distribution of chances. Results show that different sources generate different ex ante perception of fairness. That is, unequal chances based on socioeconomic and biological factors, such as gender, family income and ethnicity, are evaluated to be unfair relative to the same chances based on effort, knowledge, and benevolence. Results also show that, for most individuals, there is a positive correlation between perceived control of a factor and fairness of unequal chances based on that factor. Luck appears to be an exception to this correlation, ranking as high in fairness as effort, knowledge, and benevolence, but similarly low in individual control as ethnicity, family income, and gender.
Effects of intensity, attention and medication on auditory-evoked potentials in patients with fibromyalgia
Authors: Samartin-Veiga N. , González-Villar A. J. , Triñanes Y. , Gómez-Perretta C. , Carrillo-de-la-Peña M. T. 
Published: December 2020

AbstractFibromyalgia (FM) has been associated to an increased processing of somatosensory stimuli, but its generalization to other sensory modalities is under discussion. To clarify this, we studied auditory event-related potentials (AEPs) to stimuli of different intensity in patients with FM and healthy controls (HCs), considering the effects of attention mechanisms and medication. We performed two experiments: In study 1 (n = 50 FM, 60 HCs), the stimuli were presented randomly within the sequence; in study 2 (n = 28 FM, 30 HCs), they were presented in blocks of the same intensity. We analyzed intensity and group effects on N1-P2 amplitude and, only for the FM group, the effect of medication and the correlation between AEPs and clinical variables. Contrary to the expectation, the patients showed a trend of reduced AEPs to the loudest tones (study 1) or no significant differences with the HCs (study 2). Medication with central effects significantly reduced AEPs, while no significant relationships between the N1-P2 amplitude/intensity function and patients’ symptoms were observed. The findings do not provide evidence of augmented auditory processing in FM. Nevertheless, given the observed effect of medication, the role of sensory amplification as an underlying pathophysiological mechanism in fibromyalgia cannot be discarded.
Emergence of disconnected clusters in heterogeneous complex systems
Authors: Kovács István A. , Juhász Róbert 
Published: December 2020

AbstractPercolation theory dictates an intuitive picture depicting correlated regions in complex systems as densely connected clusters. While this picture might be adequate at small scales and apart from criticality, we show that highly correlated sites in complex systems can be inherently disconnected. This finding indicates a counter-intuitive organization of dynamical correlations, where functional similarity decouples from physical connectivity. We illustrate the phenomenon on the example of the disordered contact process (DCP) of infection spreading in heterogeneous systems. We apply numerical simulations and an asymptotically exact renormalization group technique (SDRG) in 1, 2 and 3 dimensional systems as well as in two-dimensional lattices with long-ranged interactions. We conclude that the critical dynamics is well captured by mostly one, highly correlated, but spatially disconnected cluster. Our findings indicate that at criticality the relevant, simultaneously infected sites typically do not directly interact with each other. Due to the similarity of the SDRG equations, our results hold also for the critical behavior of the disordered quantum Ising model, leading to quantum correlated, yet spatially disconnected, magnetic domains.
Fully automatic wound segmentation with deep convolutional neural networks
Authors: Wang Chuanbo , Anisuzzaman D. M. , Williamson Victor , Dhar Mrinal Kanti , Rostami Behrouz , Niezgoda Jeffrey , Gopalakrishnan Sandeep , Yu Zeyun 
Published: December 2020

AbstractAcute and chronic wounds have varying etiologies and are an economic burden to healthcare systems around the world. The advanced wound care market is expected to exceed $22 billion by 2024. Wound care professionals rely heavily on images and image documentation for proper diagnosis and treatment. Unfortunately lack of expertise can lead to improper diagnosis of wound etiology and inaccurate wound management and documentation. Fully automatic segmentation of wound areas in natural images is an important part of the diagnosis and care protocol since it is crucial to measure the area of the wound and provide quantitative parameters in the treatment. Various deep learning models have gained success in image analysis including semantic segmentation. This manuscript proposes a novel convolutional framework based on MobileNetV2 and connected component labelling to segment wound regions from natural images. The advantage of this model is its lightweight and less compute-intensive architecture. The performance is not compromised and is comparable to deeper neural networks. We build an annotated wound image dataset consisting of 1109 foot ulcer images from 889 patients to train and test the deep learning models. We demonstrate the effectiveness and mobility of our method by conducting comprehensive experiments and analyses on various segmentation neural networks. The full implementation is available at https://github.com/uwm-bigdata/wound-segmentation.
Autonomous control of ventilation through closed-loop adaptive respiratory pacing
Authors: Siu Ricardo , Abbas James J. , Fuller David D. , Gomes Jefferson , Renaud Sylvie , Jung Ranu 
Published: December 2020

AbstractMechanical ventilation is the standard treatment when volitional breathing is insufficient, but drawbacks include muscle atrophy, alveolar damage, and reduced mobility. Respiratory pacing is an alternative approach using electrical stimulation-induced diaphragm contraction to ventilate the lung. Oxygenation and acid–base homeostasis are maintained by matching ventilation to metabolic needs; however, current pacing technology requires manual tuning and does not respond to dynamic user-specific metabolic demand, thus requiring re-tuning of stimulation parameters as physiological changes occur. Here, we describe respiratory pacing using a closed-loop adaptive controller that can self-adjust in real-time to meet metabolic needs. The controller uses an adaptive Pattern Generator Pattern Shaper (PG/PS) architecture that autonomously generates a desired ventilatory pattern in response to dynamic changes in arterial CO2levels and, based on a learning algorithm, modulates stimulation intensity and respiratory cycle duration to evoke this ventilatory pattern. In vivo experiments in rats with respiratory depression and in those with a paralyzed hemidiaphragm confirmed that the controller can adapt and control ventilation to ameliorate hypoventilation and restore normocapnia regardless of the cause of respiratory dysfunction. This novel closed-loop bioelectronic controller advances the state-of-art in respiratory pacing by demonstrating the ability to automatically personalize stimulation patterns and adapt to achieve adequate ventilation.
Genome-wide correlation analysis to identify amplitude regulators of circadian transcriptome output
Authors: Littleton Evan S. , Childress Madison L. , Gosting Michaela L. , Jackson Ayana N. , Kojima Shihoko 
Published: December 2020

AbstractCell-autonomous circadian system, consisting of core clock genes, generates near 24-h rhythms and regulates the downstream rhythmic gene expression. While it has become clear that the percentage of rhythmic genes varies among mouse tissues, it remains unclear how this variation can be generated, particularly when the clock machinery is nearly identical in all tissues. In this study, we sought to characterize circadian transcriptome datasets that are publicly available and identify the critical component(s) involved in creating this variation. We found that the relative amplitude of 13 genes and the average level of 197 genes correlated with the percentage of cycling genes. Of those, the correlation of Rorc in both relative amplitude and the average level was one of the strongest. In addition, the level of Per2AS, a novel non-coding transcript that is expressed at the Period 2 locus, was also linearly correlated, although with a much lesser degree compared to Rorc. Overall, our study provides insight into how the variation in the percentage of clock-controlled genes can be generated in mouse tissues and suggests that Rorc and potentially Per2AS are involved in regulating the amplitude of circadian transcriptome output.
Reconfiguration of human evolving large-scale epileptic brain networks prior to seizures: an evaluation with node centralities
Authors: Fruengel Rieke , Bröhl Timo , Rings Thorsten , Lehnertz Klaus 
Published: December 2020

AbstractPrevious research has indicated that temporal changes of centrality of specific nodes in human evolving large-scale epileptic brain networks carry information predictive of impending seizures. Centrality is a fundamental network-theoretical concept that allows one to assess the role a node plays in a network. This concept allows for various interpretations, which is reflected in a number of centrality indices. Here we aim to achieve a more general understanding of local and global network reconfigurations during the pre-seizure period as indicated by changes of different node centrality indices. To this end, we investigate—in a time-resolved manner—evolving large-scale epileptic brain networks that we derived from multi-day, multi-electrode intracranial electroencephalograpic recordings from a large but inhomogeneous group of subjects with pharmacoresistant epilepsies with different anatomical origins. We estimate multiple centrality indices to assess the various roles the nodes play while the networks transit from the seizure-free to the pre-seizure period. Our findings allow us to formulate several major scenarios for the reconfiguration of an evolving epileptic brain network prior to seizures, which indicate that there is likely not a single network mechanism underlying seizure generation. Rather, local and global aspects of the pre-seizure network reconfiguration affect virtually all network constituents, from the various brain regions to the functional connections between them.