Miracle-dz/UniFAR-FTUs huggingface dataset:
In vivo voltage-sensitive dye imaging of mouse cortical activity with mesoscopic optical tomography

Abstract. Significance: Cellular layering is a hallmark of the mammalian neocortex with layer and cell type-specific connections within the cortical mantle and subcortical connections. A key challenge in studying circuit function within the neocortex is to understand the spatial and temporal patterns of information flow between different columns and layers. Aim: We aimed to investigate the three-dimensional (3D) layer- and area-specific interactions in mouse cortex in vivo. Approach: We applied a new promising neuroimaging method—fluorescence laminar optical tomography in combination with voltage-sensitive dye imaging (VSDi). VSDi is a powerful technique for interrogating membrane potential dynamics in assemblies of cortical neurons, but it is traditionally used for two-dimensional (2D) imaging. Our mesoscopic technique allows visualization of neuronal activity in a 3D manner with high temporal resolution. Results: We first demonstrated the depth-resolved capability of 3D mesoscopic imaging technology in Thy1-ChR2-YFP transgenic mice. Next, we recorded the long-range functional projections between sensory cortex (S1) and motor cortex (M1) in mice, in vivo, following single whisker deflection. Conclusions: The results show that mesoscopic imaging technique has the potential to investigate the layer-specific neural connectivity in the mouse cortex in vivo. Combination of mesoscopic imaging technique with optogenetic control strategy is a promising platform for determining depth-resolved interactions between cortical circuit elements.
A mutation in DOP1B identified as a probable cause for autosomal recessive Peters anomaly in a consanguineous family

Purpose Peters anomaly (PA) is a heterogeneous developmental disorder characterized by central corneal opacity and iridocorneal or corneolenticular adhesions. Although many causative genes have been identified, most screened patients do not have mutations in the known genes. We aimed to identify the genetic cause of Peters anomaly in a pedigree with three affected individuals. Methods Slit-lamp biomicroscopy and ultrasound biomicroscopy were performed for definitive diagnosis. Exome sequencing was conducted on the DNA of all three patients. After identification of a candidate causative gene, expression of the gene was assessed with real-time PCR in various ocular tissues of three human embryos and three adults. Results The patients were affected with isolated PA. The parents of the patients were related to one another. Inheritance of PA was autosomal recessive. After appropriate filtering of the exome data, a homozygous variation in DOP1B remained as the only candidate genetic cause of PA in the pedigree. The variant segregated with disease status in the pedigree and was absent among 800 control Iranians. The variant has been reported in various databases at frequencies of 0.006 or less only in the heterozygous state in some cohorts of African origin. The p.Val1660 amino acid affected by the mutation is completely conserved in mammals and birds during evolution. Expression of DOP1B was shown in all adult and embryonic lens, iris, cornea, sclera, and retina tissues that were tested. Conclusions DOP1B that encodes DOP1 leucine zipper like protein B was identified as the putative PA-causing gene in pedigree PA-101. As DOP1B is positioned within the Down syndrome chromosomal region on chromosome 21, until now this gene has mostly been studied with respect to brain functions. However, members of the Dopey gene family have been shown to have roles in development in other organisms. Evidence of the expression of DOP1B in various PA-relevant eye tissues, which, to the best of our knowledge, is shown here for the first time, is to be noted. However, this finding does not necessarily implicate a specific role for DOP1B in eye development as the gene is expressed in many tissues. Ultimately, definitive assessment of the contribution of DOP1B to PA pathology awaits identification of mutations in the gene in unrelated patients with PA and functional studies.
Full loss analysis for a multicrystalline silicon wafer solar cell PV module at short‐circuit conditions

We present an extended analysis to quantify losses in a module under short‐circuit conditions. The presented method includes an analysis of the area‐related losses in a full‐sized module and introduces the concept of “effective area coverage. ” The effective area coverage accounts for the amount of light lost on a certain type of area and is different from the actual area coverage, if light trapping area occurs. An example for this effect is light reflected from the backsheet, trapped in the glass cover and reaching the solar cell. Based on our analysis, the effective solar cell area is increased by 5% absolute by this effect. Furthermore, we used an extension of Basore's model for modules in the loss analysis and used it to quantify losses in one exemplary, encapsulated multicrystalline silicon wafer solar cell. These losses are absorption in the rear reflector (corresponding to a loss of 3.5 mA/cm2 on the active solar cell area), collection losses (3.4 mA/cm2) and free carrier absorption (0.18 mA/cm2). Finally, the presented analysis allowed us to calculate a breakdown of all losses in a full‐sized module under short‐circuit conditions. A list of all considered losses sorted by the corresponding current is presented at the end. Copyright © 2015 John Wiley & Sons, Ltd.
The machine learning model as a guide: pointing users to interesting instances for labeling through visual cues

The labeling of datasets is an important task for supervised and semi-supervised machine learning that can be addressed with visual analytics. With model-based active learning and user-based interactive labeling, there are two complementary strategies for this task. We present an approach that combines the strengths of both areas and aims to guide users through model-based recommendations and highlighting in one interface when selecting and labeling instances. For this purpose, an active learning strategy is used to recommend useful instances in addition to the user-based selection of instances. We have implemented the approach and conducted a user survey to research the effects guidance by visual cues has on the users' selection strategies. The proposed approach combines both perspectives in a single interactive visualization to support the user with different degrees of guidance in the selection of instances. Our results of the user survey suggest that user guidance has a positive influence on the users' perceived confidence and difficulty in selecting instances, on their orientation, and on their perceived impression of the models' performance. A video of the approach is available: https://youtu.be/TYPWG85Akn0.
Cluster-clean-label: an interactive machine learning approach for labeling high-dimensional data

One of the major problems of applying supervised machine learning methods in real-world problems is the absence of labeled data. Labeling huge amounts of data is time consuming and cost intensive. Moreover, in many cases, labels can only be assigned by domain experts like medical doctors or engineers, who have little time and do not necessarily have profound machine learning knowledge. In this paper, we propose an efficient interactive cluster-clean-label approach. First, to visualize the potentially huge amount of data, principal component analysis followed by t-SNE projection is applied. On the 2-dimensional representation of the data, HDBSCAN clustering is utilized to identify groups of potentially similar class membership. Subsequently, anomaly detection in form of an autoencoder is applied on each cluster, and instances that are likely to belong to different classes are suggested to the user. The user decides which of these suggested instances to include and restarts the anomaly detection process with the remaining subset of instances. This iterative process is repeated until the user is satisfied with the clusters' purity. Eventually, labels are assigned to the clusters. The approach is evaluated by a user study with 25 participants using the initially unlabeled MNIST data set, where on average users were able to label 91.59% of the data set, with an accuracy of 98.99%. A video showing the approach is available: https://youtu.be/RsLI0dg90qE.
A new possibility degree measure for interval‐valued 
q ‐rung orthopair fuzzy sets in decision‐making

As a generalization of the interval‐valued intuitionistic fuzzy sets, a consciousness of interval‐valued q ‐rung orthopair fuzzy sets (IV q ‐ROFSs) is a robust and trustworthy tool to fulfill the imprecise information with an adaptation of the manageable parameter q≥1 . However, the ranking of any interval‐valued numbers is very valuable for interval‐valued decision‐making problems. Possibility degree measure is a worthy tool to manage the degree of possibility of one object over the other. Driven by these requisite characteristics, it is fascinating to manifest the possibility degree of comparison between two IV q ‐ROFSs, and an innovative method is then encouraged to rank the given numbers. Few properties are checked to explain their features and exhibited the advantages of it over the existing possibility measures with some counterintuitive examples. Later on, we consider the multiattribute group decision making (MAGDM) method and embellish it with numerical examples, to rank the alternatives. Several numerical examples are implemented to test the superiority of the stated MAGDM method and to confer its more manageable and adaptable nature.
Tissue chemistry and morphology affect root decomposition of perennial bioenergy grasses on sandy soil in a sub‐tropical environment

Second‐generation biofuels and bio‐based products derived from lignocellulosic biomass are likely to replace current fuels derived from simple sugars and starch because of greater yield potential and less competition with food production. Besides the high aboveground biomass production, these bioenergy grasses also exhibit extensive root systems. The decomposition of root biomass greatly influences nutrient cycling and microbial activity and subsequent accumulation of carbon (C) in the soil. The objective of this research was thus to characterize root morphological and chemical differences in six perennial grass species in order to better understand root decomposition and belowground C cycling of these bioenergy cropping systems. Giant reed (Arundo donax), elephantgrass (Pennisetum purpureum), energycane (Saccharum spp.), sugarcane (Saccharum spp.), sweetcane (Saccharum arundinaceum), and giant miscanthus (Miscanthus × giganteus) were established in Fall 2008 in research plots near Gainesville, Florida. Root decomposition rates were measured in situ from root decomposition bags over 12 months along with initial and final root tissue composition. Root potential decomposition rate constant (K) was higher in elephantgrass (3.64 g kg−1 day−1) and sweetcane (2.77 g kg−1 day−1) than in sugarcane (1.62 g kg−1 day−1) and energycane (1.48 g kg−1 day−1). Notably, K was positively related to initial root tissue total C (Total C), total fiber glucose (TFG), total fiber xylose (TFX), and total fiber carbohydrate (TFC) concentrations, but negatively related to total fiber arabinose (TFA) and lignin (TL) concentrations and specific root volume (SRV). Among the six species, elephantgrass exhibited root traits most favorable for fast decomposition: high TFG, high TFX, high TFC, high specific root length (SRL), and a low SRV, whereas giant reed, sugarcane, and energycane exhibited slow decomposition rates and the corresponding root traits. Thus, despite similar aboveground biomass yields in many cases, these species are likely to differentially affect soil C accumulation.
Regime Shifts in an Early Triassic Subtropical Ecosystem

The Early Triassic was one of the most remarkable time intervals in Earth History. To begin with, life on Earth had to face one of the largest subaerial volcanic degassing, the Siberian Traps, followed by a plethora of accompanying environmental hazards with pronounced and repeated climatic changes. These changes not only led to repeated and, for several marine nektonic clades, intense extinction events but also to significant changes in terrestrial ecosystems. The Early Triassic terrestrial ecosystems of the southern subtropical region (Pakistan) are not necessarily marked by abrupt extinction events but by extreme shifts in composition. Modern ecological theories describe such shifts as catastrophic regime shifts. Here, the applicability of modern ecological theories to these past events is tested. Abrupt shifts in ecosystems can occur when protracted changing abiotic drivers (e.g. climate) reach critical points (thresholds or tipping points) sometimes accentuated by stochastic events. Early Triassic terrestrial plant ecosystem changes stand out from the longer term paleobotanical records because changes of similar magnitude have not been observed for many millions of years before and after the Early Triassic. To date, these changes have been attributed to repeated severe environmental perturbations, but here an alternative explanation is tested: the initial environmental perturbations around the Permian–Triassic boundary interval are regarded here as a main cause for a massive loss in terrestrial ecosystem resilience with the effect that comparatively small-scale perturbations in the following ∼5 Ma lead to abrupt regime shifts in terrestrial ecosystems.
Larch Cellulose Shows Significantly Depleted Hydrogen Isotope Values With Respect to Evergreen Conifers in Contrast to Oxygen and Carbon Isotopes

The analysis of the stable isotope of the tree-ring cellulose is an important tool for paleo climatic investigations. Long tree-ring chronologies consist predominantly of oaks and conifers in Europe, including larch trees (Larix decidua) and cembran pines (Pinus cembra) that form very long tree ring chronologies in the Alps and grow at the treeline, where tree growth is mainly determined by temperature variations. We analyzed δ13C, δ18O and δ2H isotopes in the cellulose extracted from tree-rings of wood samples collected at high altitude in the Swiss and Tyrol Alps, covering the whole Holocene period. We found that larch cellulose was remarkably more depleted in deuterium than that of cembran pine, with mean δ2H values of −113.4 ± 9.7‰ for larch and of −65.4 ± 11.3‰ for cembran pine. To verify if these depleted values were specific to larch or a property of the deciduous conifers, we extended the analysis to samples from various living conifer species collected at the Bern Botanical Garden. The results showed that not only the larch, but also all the samples of the deciduous larch family had a cellulose composition that was highly depleted in δ2H with regard to the other evergreen conifers including cembran pine, a difference that we attribute to a faster metabolism of the deciduous conifers. The δ18O values were not statistically different among the species, in agreement with the hypothesis that they are primary signals of the source water. While the δ13C values were slightly more depleted for larch than for cembran pine, likely due to metabolic differences of the two species. We conclude that the deciduous larch conifers have specific metabolic hydrogen fractionations and that the larch unique signature of δ2H is useful to recognize it from other conifers in subfossil wood samples collected for paleoclimatic studies. For climate information the absolute δ2H values of larch should be considered carefully and separate from other species.
Body Localization of ACE-2: On the Trail of the Keyhole of SARS-CoV-2

The explosion of the new coronavirus (SARS-CoV-2) pandemic has brought the role of the angiotensin converting enzyme 2 (ACE2) back into the scientific limelight. Since SARS-CoV-2 must bind the ACE2 for entering the host cells in humans, its expression and body localization are critical to track the potential target organ of this infection and to outline disease progression and clinical outcomes. Here, we mapped the physiological body distribution, expression, and activities of ACE2 and discussed its potential correlations and mutal interactions with the disparate symptoms present in SARS-CoV-2 patients at the level of different organs. We highlighted that despite during SARS-CoV-2 infection ACE2-expressing organs may become direct targets, leading to severe pathological manifestations, and subsequent multiple organ failures, the exact mechanism and the potential interactions through which ACE2 acts in these organs is still heavily debated. Further scientific efforts, also considering a personalized approach aimed to consider specific patient differences in the mutual interactions ACE2-SARS-CoV-2 and the long-term health effects associated with COVID-19 are currently mandatory.
The Possession of Coccoliths Fails to Deter Microzooplankton Grazers

Phytoplankton play a central role in the regulation of global carbon and nutrient cycles, forming the basis of the marine food webs. A group of biogeochemically important phytoplankton, the coccolithophores, produce calcium carbonate scales that have been hypothesized to deter or reduce grazing by microzooplankton. Here, a meta-analysis of mesocosm-based experiments demonstrates that calcification of the cosmopolitan coccolithophore, Emiliania huxleyi, fails to deter microzooplankton grazing. The median grazing to growth ratio for E. huxleyi (0.56 ± 0.40) was not significantly different among non-calcified nano- or picoeukaryotes (0.71 ± 0.31 and 0.55 ± 0.34, respectively). Additionally, the environmental concentration of E. huxleyi did not drive preferential grazing of non-calcified groups. These results strongly suggest that the possession of coccoliths does not provide E. huxleyi effective protection from microzooplankton grazing. Such indiscriminate consumption has implications for the dissolution and fate of CaCO3 in the ocean, and the evolution of coccoliths.
Picking Your Brains: Where and How Neuroscience Tools Can Enhance Marketing Research

The use of neuroscience tools to study consumer behavior and the decision making process in marketing has improved our understanding of cognitive, neuronal, and emotional mechanisms related to marketing-relevant behavior. However, knowledge about neuroscience tools that are used in consumer neuroscience research is scattered. In this article, we present the results of a literature review that aims to provide an overview of the available consumer neuroscience tools and classifies them according to their characteristics. We analyse a total of 219 full-texts in the area of consumer neuroscience. Our findings suggest that there are seven tools that are currently used in consumer neuroscience research. In particular, electroencephalography (EEG) and eye tracking (ET) are the most commonly used tools in the field. We also find that consumer neuroscience tools are used to study consumer preferences and behaviors in different marketing domains such as advertising, branding, online experience, pricing, product development and product experience. Finally, we identify two ready-to-use platforms, namely iMotions and GRAIL that can help in integrating the measurements of different consumer neuroscience tools simultaneously. Measuring brain activity and physiological responses on a common platform could help by (1) reducing time and costs for experiments and (2) linking cognitive and emotional aspects with neuronal processes. Overall, this article provides relevant input in setting directions for future research and for business applications in consumer neuroscience. We hope that this study will provide help to researchers and practitioners in identifying available, non-invasive and useful tools to study consumer behavior.
A scalable "exploranation" technique for hierarchically indexed table data

Data analytics tools that combine automated text generation and visualization techniques suffer from scalability problems. The amount of the generated text explodes with the increase of items and attributes. This study addresses this problem for table data, whose attributes and data items are hierarchically organized. In our approach, the user's point of view is modeled by the dual focalization axis, which consists of the attribute-based and the data-item-based focal points. The user can refine the two-dimensional focal points to obtain the chart and the text that explain the data facts found in a more focused portion of the dataset. The proposal's efficacy was assessed through a quantitative and qualitative evaluation using a prototype visual analytics tool that employs the idea.
Physiological Synchrony in EEG, Electrodermal Activity and Heart Rate Detects Attentionally Relevant Events in Time

Interpersonal physiological synchrony (PS), or the similarity of physiological signals between individuals over time, may be used to detect attentionally engaging moments in time. We here investigated whether PS in the electroencephalogram (EEG), electrodermal activity (EDA), heart rate and a multimodal metric signals the occurrence of attentionally relevant events in time in two groups of participants. Both groups were presented with the same auditory stimulus, but were instructed to attend either to the narrative of an audiobook (audiobook-attending: AA group) or to interspersed emotional sounds and beeps (stimulus-attending: SA group). We hypothesized that emotional sounds could be detected in both groups as they are expected to draw attention involuntarily, in a bottom-up fashion. Indeed, we found this to be the case for PS in EDA or the multimodal metric. Beeps, that are expected to be only relevant due to specific “top-down” attentional instructions, could indeed only be detected using PS among SA participants, for EDA, EEG and the multimodal metric. We further hypothesized that moments in the audiobook accompanied by high PS in either EEG, EDA, heart rate or the multimodal metric for AA participants would be rated as more engaging by an independent group of participants compared to moments corresponding to low PS. This hypothesis was not supported. Our results show that PS can support the detection of attentionally engaging events over time. Currently, the relation between PS and engagement is only established for well-defined, interspersed stimuli, whereas the relation between PS and a more abstract self-reported metric of engagement over time has not been established. As the relation between PS and engagement is dependent on event type and physiological measure, we suggest to choose a measure matching with the stimulus of interest. When the stimulus type is unknown, a multimodal metric is most robust.
Multimodality Imaging for Discordant Low-Gradient Aortic Stenosis: Assessing the Valve and the Myocardium

Aortic stenosis (AS) is a disease of the valve and the myocardium. A correct assessment of the valve disease severity is key to define the need for aortic valve replacement (AVR), but a better understanding of the myocardial consequences of the increased afterload is paramount to optimize the timing of the intervention. Transthoracic echocardiography remains the cornerstone of AS assessment, as it is universally available, and it allows a comprehensive structural and hemodynamic evaluation of both the aortic valve and the rest of the heart. However, it may not be sufficient as a significant proportion of patients with severe AS presents with discordant grading (i.e., an AVA ≤ 1 cm2 and a mean gradient <40 mmHg) which raises uncertainty about the true severity of AS and the need for AVR. Several imaging modalities (transesophageal or stress echocardiography, computed tomography, cardiovascular magnetic resonance, positron emission tomography) exist that allow a detailed assessment of the stenotic aortic valve and the myocardial remodeling response. This review aims to provide an updated overview of these multimodality imaging techniques and seeks to highlight a practical approach to help clinical decision making in the challenging group of patients with discordant low-gradient AS.