Miracle-dz/UniFAR-FTUs huggingface dataset:
Understanding the Human Brain using Brain Organoids and a Structure-Function Theory

A basic neurobiology-clinical trial paradigm motivates our use of constrained mathematical models and analysis of personalized human-derived brain organoids toward predicting clinical outcomes and safely developing new therapeutics. Physical constraints imposed on the brain can guide the analyses an interpretation of experimental data and the construction of mathematical models that attempt to make sense of how the brain works and how cognitive functions emerge. Development of these mathematical models for human-derived brain organoids offer an opportunity for testing new hypotheses about the human brain. When it comes to testing ideas about the brain that require a careful balance between experimental accessibility, manipulation, and complexity, in order to connect neurobiological details with higher level cognitive properties and clinical considerations, we argue that fundamental structure-function constraints applied to models of brain organoids offer a path forward. Moreover, we show these constraints appear in canonical and novel math models of neural activity and learning, and we make the case that constraint-based modeling and use of representations can bridge to machine learning for powerful mutual benefit.
Consequences of two common techniques for sampling vagile macrofauna associated with the seagrass Zostera capricorni

Fish and decapod crustaceans associated with the seagrass Zostera capricorni Aschers. were sampled by beam trawling during the day, beam trawling at night and poisoning during the day. There were significant differences in the numbers of species and individuals sampled by each method. Beam trawling dur~ng the day captured 30 % fewer species of fish than did either night trawling or poisoning. However, day trawls caught 85 % fewer and night trawls caught 60 % fewer indiv~duals of fish than poisoning. Poisoning and day trawling, respectively, caught 46 and 40 % fewer species, and 82 and 48 % fewer individuals, of decapods than beam trawling at night. Consequently, species con~position of samples also differed between methods O u r data suggest that more serious consideration should be given to the rel~ability of d~fferent techn~ques for sampling the vagile macrofauna of seagrass meadows.
Disturbance of information in superior parietal lobe during dual-task interference in a simulated driving task

Performing a secondary task while driving causes a decline in driving performance. This phenomenon, called dual-task interference, can have lethal consequences. Previous fMRI studies have looked at the changes in the average brain activity to uncover the neural correlates of dual-task interference. From these results, it is unclear whether the overall modulations in brain activity result from general effects such as task difficulty, attentional modulations, and mental effort or whether it is caused by a change in the responses specific to each condition due to dual-task interference. To overcome this limitation, here, we used multi-voxel pattern analysis (MVPA) to interrogate the change in the information content in multiple brain regions during dual-task interference in simulated driving. Participants performed a lane change task in a simulated driving environment, along with a tone discrimination task with either short or long-time onset difference (Stimulus Onset Asynchrony, SOA) between the two tasks. Behavioral results indicated a robust dual-task effect on driving RT. MVPA revealed regions that carry information about the driving direction, including the superior parietal lobe (SPL), visual, and motor regions. Comparing the decoding accuracies across short and long SOA conditions, we showed lower accuracies in the SPL region in short than long SPA conditions. This change in accuracy was not observed in the visual and motor regions. In addition, the classification accuracy in the SPL was inversely correlated with participants’ reaction time in the driving task. These findings suggest that the dual-task interference in driving may be related to the disturbance of information processing in the SPL region. Significance Statement During real-world driving, when a driver wants to make a turn at an intersection and simultaneously respond to a cell phone call, his reaction time slows down. This effect is called dual-task interference. Here, we aimed to examine its neural mechanisms using a paradigm that consisted of a driving turn task and a tone discrimination task in a simulated environment. Results showed that the information for the driving turn was disturbed in the superior parietal lobe (SPL) during dual-task interference. We suggest that the driving performance decline in the presence of the secondary task might be related to the disturbance of information in the SPL.
CyAn: A MATLAB toolbox for image and data analysis of cyanobacteria

Studies of photosynthetic cyanobacteria are often carried out by growing the bacteria in large volume cultures. However, this results in an uneven amount of light received by the cells due to cell-cell shading. Fluorescence microscopy solves this issue as cells can be grown in a single layer, eliminating shading issues. However, microscopy brings its own challenge as hundreds of cells are imaged at the same time, making it difficult to analyze the resulting data by hand. Here we present CyAn, a toolbox to process and analyze microscope images of cyanobacteria. The toolbox automates cyanobacteria identification in microscope images, tracks moving and dividing cells, and computes relevant biological data such as growth rates. As proof-of-principle, we demonstrate that wild type and mutants can be grown on the same slide and identified using the software, allowing a much more accurate probe into photosynthetic processes that are often masked due to cellular heterogeneity or those requiring precise light control to prevent cell-cell shading.
Facilitating Students’ Interaction in MOOCs through Timeline-Anchored Discussion

ABSTRACT Interaction is central to any learning experience. Currently, Massive Open Online Courses (MOOCs) rely on discussion forums as a primary means of interaction among learners and instructors. Threaded discussions of such forums help learners to hold an ongoing and recursive discussion over an extended period, but the lack of temporal and spatial contiguity makes it insufficient to deliver a smooth interaction experience. To facilitate such interaction, this study designed DanMOOC, a commenting tool that facilitates timeline-anchored discussion among MOOC learners and instructors. DanMOOC was the first to combine threaded discussions with Danmaku commenting, a video commenting feature that allows viewers of the same video to share comments at the top of the video screen. The design process followed an iterative process of user-centered design. The impact of the new design on learning was assessed empirically through a laboratory experiment comparing DanMOOC with the current MOOC system (video-based learning + forum). The results show that DanMOOC increases learners’ perceptions of social, teaching, and cognitive presence, engages learners more in discussion, and improves learners’ satisfaction with both the course and the platform.
Agricultural land use and the sustainability of social-ecological systems

Agricultural land expansion and intensification, driven by human consumption of agricultural goods, are among the major threats to environmental degradation and biodiversity conservation. Land degradation can ultimately hamper agricultural production through a decrease in ecosystem services. Thus, designing viable land use strategies is a key sustainability challenge. We develop a model describing the coupled dynamics of human demography and landscape composition, while imposing a trade-off between agricultural expansion and intensification. We model land use strategies spanning from low-intensity agriculture and high land conversion rates per person to high-intensity agriculture and low land conversion rates per person; and explore their consequences on the long-term dynamics of the coupled human-land system. We seek to characterise the strategies’ viability in the long run; and understand the mechanisms that potentially lead to large-scale land degradation and population collapse due to resource scarcity. We show that the viability of land use strategies strongly depends on the land’s intrinsic recovery rate. We also find that socio-ecological collapses occur when agricultural intensification is not accompanied by a sufficient decrease in land conversion. Based on these findings we stress the dangers of naive land use planning and the importance of precautionary behaviour for land use management.
Synergy of High-Resolution Radar and Optical Images Satellite for Identification and Mapping of Wetland Macrophytes on the Danube Delta

In wetland environments, vegetation has an important role in ecological functioning. The main goal of this work was to identify an optimal combination of Sentinel-1 (S1), Sentinel-2 (S2), and Pleiades data using ground-reference data to accurately map wetland macrophytes in the Danube Delta. We tested several combinations of optical and Synthetic Aperture Radar (SAR) data rigorously at two levels. First, in order to reduce the confusion between reed (Phragmites australis (Cav.) Trin. ex Steud. ) and other macrophyte communities, a time series analysis of S1 data was performed. The potential of S1 for detection of compact reed on plaur, compact reed on plaur/reed cut, open reed on plaur, pure reed, and reed on salinized soil was evaluated through time series of backscatter coefficient and coherence ratio images, calculated mainly according to the phenology of the reed. The analysis of backscattering coefficients allowed separation of reed classes that strongly overlapped. The coherence coefficient showed that C-band SAR repeat pass interferometric coherence for cut reed detection is feasible. In the second section, random forest (RF) classification was applied to the S2, Pleiades, and S1 data and in situ observations to discriminate and map reed against other aquatic macrophytes (submerged aquatic vegetation (SAV), emergent macrophytes, some floating broad-leaved and floating vegetation of delta lakes). In addition, different optical indices were included in the RF. A total of 67 classification models were made in several sensor combinations with two series of validation samples (with the reed and without reed) using both a simple and more detailed classification schema. The results showed that reed is completely discriminable compared to other macrophyte communities with all sensor combinations. In all combinations, the model-based producer’s accuracy (PA) and user’s accuracy (UA) for reed with both nomenclatures were over 90%. The diverse combinations of sensors were valuable for improving the overall classification accuracy of all of the communities of aquatic macrophytes except Myriophyllum spicatum L.
A novel ultrasound-guided mouse model of sudden cardiac arrest

Aim Mouse models of sudden cardiac arrest are limited by challenges with surgical technique and reliable venous access. To overcome this limitation, we sought to develop a simplified method in the mouse that uses ultrasound-guided injection of potassium chloride directly into the heart. Methods Potassium chloride was delivered directly into the left ventricular cavity under ultrasound guidance in intubated mice, resulting in immediate asystole. Mice were resuscitated with injection of epinephrine and manual chest compressions and evaluated for survival, body temperature, cardiac function, kidney damage, and diffuse tissue injury. Results The direct injection sudden cardiac arrest model causes rapid asystole with high surgical survival rates and low surgical duration. Sudden cardiac arrest mice with 8-min of asystole have significant cardiac dysfunction at 24 hours and high lethality within the first seven days, where after cardiac function begins to improve. Sudden cardiac arrest mice have secondary organ damage, including significant kidney injury, but no clear evidence of neurologic dysfunction. Conclusions Ultrasound-guided direct injection of potassium chloride allows for rapid and reliable cardiac arrest in the mouse that mirrors human pathology. This technique lowers the barriers to entry for adoption of the mouse model of sudden cardiac arrest, which will improve investigators’ ability to study the mechanisms underlying post-arrest changes.
Establishing Live-Cell Single-Molecule Localization Microscopy Imaging and Single-Particle Tracking in the Archaeon Haloferax volcanii

In recent years, fluorescence microscopy techniques for the localization and tracking of single molecules in living cells have become well-established and indispensable tools for the investigation of cellular biology and in vivo biochemistry of many bacterial and eukaryotic organisms. Nevertheless, these techniques are still not established for imaging archaea. Their establishment as a standard tool for the study of archaea will be a decisive milestone for the exploration of this branch of life and its unique biology. Here we have developed a reliable protocol for the study of the archaeon Haloferax volcanii. We have generated an autofluorescence-free H. volcanii strain, evaluated several fluorescent proteins for their suitability to serve as single-molecule fluorescence markers and codon-optimized them to work under optimal H. volcanii cultivation conditions. We found that two of them, Dendra2Hfx and PAmCherry1Hfx, provide state-of-the-art single-molecule imaging. Our strategy is quantitative and allows dual-color imaging of two targets in the same field of view as well as DNA co-staining. We present the first single-molecule localization microscopy (SMLM) images of the subcellular organization and dynamics of two crucial intracellular proteins in living H. volcanii cells, FtsZ1, which shows complex structures in the cell division ring, and RNA polymerase, which localizes around the periphery of the cellular DNA. This work should provide incentive to develop SMLM strategies for other archaeal organisms in the near future.
Editorial for the Special Issue "Ecosystem Services with Remote Sensing"

Ecosystem services refer to the environmental conditions and utilities provided and maintained by ecosystems, which are the basis for the survival and development of human society. The studies on ecosystem services in quantitative assessments, driving mechanisms, and correlation with human well-being, based on remote sensing, have increased in recent years. Various applications of remote sensing in ecosystem services are reported in six papers published in this Special Issue. The major research topics covered by this Special Issue include the multi-method analysis (e.g., linear regression, geographical detector, and geographically weighted regression methodology) of the normalized difference vegetation index (NDVI) to reflect ecosystem structure, the dynamic changing process of ecosystem services, and the determinants, which include a new image-analysis method based on a time series of a biophysical variable and the application of fractional vegetation cover (FVC) to analyze the spatiotemporal relationship between ecosystem structure and function and the comprehensive study on ecosystem function and service based on multi-source remote sensing data. The application of remote sensing data to ecosystem services research has the advantage of monitoring ecological structure and functions at multi-scales. Furthermore, the quantitative calculation of ecosystem services, based on remote sensing, can provide a scientific basis for enhancing land use optimization and sustainable development.
Prediction Method of Periodic Limb Movements Based on Deep Learning Using ECG Signal

In this study, we demonstrated a novel method to predict a patient with periodic limb movements (PLMs) based on a deep learning model using an electrocardiogram (ECG) signal. A convolutional neural network (CNN) model was used to distinguish between the PLM and control subjects through morphological analysis of an ECG signal. The constructed CNN model consisted of convolutional, pooling, and fully connected layers. For this study, polysomnography (PSG) data that were measured from 14 subjects at the Samsung Medical Center were used. The subjects were divided into control group (4 males, 3 females) and PLM group (4 males, 3 females). To train and evaluate the CNN model, the ECG dataset was collected during the PSG study, and it was normalized and segmented at a duration of 10 s. The training and test sets consisted of 30,324 and 7,582 segments, respectively. The CNN model presented a prediction performance with an F1-score of 100.0% for the test sets. We obtained robust results that demonstrated the possibility of the automatic screening of PLM patients using the CNN model with an ECG signal.
Basic Classes in Conceptual Modeling: Theory and Practical Guidelines

Since the 1970s, many approaches to representing domains have been suggested. Each approach maintains the assumption that the information about the objects represented in the information system (IS) is specified and verified by domain experts and potential users. Yet, as more IS are developed to support a larger diversity of users such as customers, suppliers, and members of the general public (e.g., in the case of many multiuser online systems), analysts can no longer rely on a stable single group of people for the complete specification of domains; therefore, prior research has questioned the efficacy of conceptual modeling in these heterogeneous settings. This paper aims to address this problem by providing theoretical foundations rooted in psychology research supporting the existence and importance of special classes that are termed basic-level categories. Based on this research, we formulate principles for identifying basic classes in a domain. These classes can guide conceptual modeling, database design, and user interface development in a wide variety of traditional and emergent domains.
Effectiveness of emission control to reduce PM2.5 pollution of Central China during winter haze episodes under various potential synoptic controls

Currently, mitigating severe particle pollution in autumn and winter is the key to further improving the air quality of China. The source contributions and transboundary transport of fine particles (PM2.5) in pollution episodes are closely related to large-scale or synoptic-scale atmospheric circulation. How to effectively reduce emissions to control haze pollution under different synoptic conditions is rarely reported. In this study, we classify the synoptic conditions over central China from 2013 to 2018 by using Lamb–Jenkinson method and the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) Final (FNL) operational global analysis data. The effectiveness of emission control to reduce PM2.5 pollution during winter haze episodes under potential synoptic controls is simulated by GEOS-Chem model. Among the 10 identified synoptic patterns, four types account for 87 % of the total pollution days. Two typical synoptic modes are characterized by low surface wind speed and stable weather conditions or high relative humidity (A or C type) over central China due to a high-pressure system or a southwest trough and low-pressure system, blocking pollutants dispersion. Sensitivity simulations show that these two heavy pollution processes are mainly contributed by local emission sources with ∼ 82 % for A type and ∼ 85 % for C type, respectively. The other two patterns lead to pollution of transport characteristics affected by northerly or southerly winds (NW or SW type), carrying air pollution from northern or southern China to central China. The contribution of pollution transmission from northern and southern China is 36.9 % and 7.6 %, respectively of PM2.5, and local emission sources contribute 41 % and 69 %, respectively. We also estimate the effectiveness of emission reduction in these four typical severe pollution synoptic processes. By only reducing SO2 and NOx emission and not controlling NH3, the enhanced nitrate counteracts the effect of sulfate reduction on PM2.5 mitigation, with a less than 4 % decrease in PM2.5. In addition, to effectively mitigate haze pollution of NWand SW-type synoptic-controlled episodes, local emission control actions should be in coordination with regional collaborative actions. Published by Copernicus Publications on behalf of the European Geosciences Union. 3144 Y. Yan et al.: Effectiveness of emission control in reducing PM2.5 pollution
Perceptive Packet Scheduling for Carrier Aggregation in Satellite Communication Systems

Carrier Aggregation is one of the essential approaches to achieve several orders of magnitude increase in peak data rates. While carrier aggregation benefits have been extensively studied in terrestrial wireless systems, its application to satellite has not been substantially explored. Carrier aggregation can be a prominent solution to address the issue of the spatially-heterogeneous satellite data traffic demand. This paper studies introducing carrier aggregation into satellite systems from a link layer perspective. The proposed modifications at the link layer have been carefully designed to make carrier aggregation transparent to the other layers. However, deployment of carrier aggregation in satellite systems with the combination of multiple carriers that have different characteristics requires effective scheduling schemes for reliable communications. Since channel awareness is indispensable for any efficient resource allocation schemes, we have proposed a perceptive scheduling algorithm that takes into account channel properties along with the instantaneous available resources to ensure that the received data packets are delivered without perturbing the original transmission order. Simulation results are given to validate our analysis and demonstrate the design tradeoffs, and thus, our results provide useful insights to practical scheduler design.
A Survey on Secure Data Analytics in Edge Computing

Internet of Things (IoT) is gaining increasing popularity. Overwhelming volumes of data are generated by IoT devices. Those data after analytics provide significant information that could greatly benefit IoT applications. Different from traditional applications, IoT applications, such as environmental monitoring, smart navigation, and smart healthcare come with new requirements, such as mobility, real-time response, and location awareness. However, traditional cloud computing paradigm cannot satisfy these demands due to centralized processing and being far away from local devices. Hence, edge computing was introduced to perform data processing and storage in the edge of networks, which is closer to data sources than cloud computing, thus efficient and location-aware. Unfortunately, edge computing brings new security and privacy challenges when applied to data analytics. The literature still lacks a thorough review on the recent advances in secure data analytics in edge computing. In this paper, we first introduce the concept and features of edge computing, and then propose a number of requirements for its secure data analytics by analyzing potential security threats in edge computing. Furthermore, we give a comprehensive review on the pros and cons of the existing works on data analytics in edge computing based on our proposed requirements. Based on our literature survey, we highlight current open issues and propose future research directions.