Miracle-dz/UniFAR-FTUs huggingface dataset:
Cooperative Breeding in the Ambrosia Beetle Xyleborus affinis and Management of Its Fungal Symbionts

Fungus-farming is known from attine ants, macrotermites, and ambrosia beetles (Scolytinae, Platypodinae). Farming ant and termite societies are superorganismal and grow fungal cultivars in monocultures. Social organization of ambrosia beetle groups and their farming systems are poorly studied, because of their enigmatic life within tunnel systems inside of wood. Ambrosia beetle-fungus symbioses evolved many times independently in both the beetles and their fungal cultivars. Observations suggest that there is evolutionary convergence between these lineages, but also a high variation in the degree of sociality and the modes of fungiculture. Using a laboratory observation technique, I here tried to give insights into the social system and fungus symbiosis of the sugar-cane borer, Xyleborus affinis Eichhoff (Scolytinae: Curculionidae), a currently poorly studied ambrosia beetle. The study revealed a cooperatively breeding system characterized by delayed dispersal of adult daughters, alloparental brood care by larvae and adults, and about half of the totipotent adult daughters laying eggs within the natal nest. Most interesting, there was a tendency of egg-laying females to engage more commonly in mutually beneficial behaviors than non-egg-layers. Fungus gardens covering gallery walls composed of five different filamentous fungi. A Raffaelea isolate was predominant and together with an unidentified fungus likely served as the main food for adults and larvae. Three isolates, a Mucor, a Fusarium and a Phaeoacremonium isolate were most abundant in the oldest gallery part close to the entrance; Mucor, Fusarium and the Raffaelea isolate in diseased individuals. Additionally, there was correlative evidence for some fungal isoaltes influencing beetle feeding and hygienic behaviors. Overall, X. affinis is now the second ambrosia beetle that can be classified as a cooperative breeder with division of labor among and between adults and larvae.
Hydrological Conditions Influence Soil and Methane-Cycling Microbial Populations in Seasonally Saturated Wetlands

Seasonally saturated wetlands are hydrologically dynamic ecosystems that provide various ecosystem services; however, their variable hydrologic conditions may promote greenhouse gas emissions. The extent to which wetlands produce and emit greenhouse gases is intimately tied to the underlying microbial community. We established a linear transect spanning a hydrologic gradient on the margins adjacent to five seasonally saturated wetlands to characterize how water level, saturation duration, and frequency of wet-dry cycles influenced the soil and methane-cycling microbial community composition. We found that the soil and methane-cycling microbial community diversity and structure were strongly related to water level changes and saturation duration but not saturation frequency. Soils that experienced inundation or saturation for more than 50% of the year harbored a soil microbial composition distinct from soils that were rarely saturated or inundated. While soils closer to the wetland basin supported a higher relative abundance of methanogens, methane-oxidizing microbes were present across the entire topographic gradient; however, the composition shifted from a primarily anaerobic to aerobic methanotroph community. Findings suggest that soil and methane cycling microorganisms are influenced by the soil’s hydrologic conditions, with methane producers being restricted to specific ranges and methane oxidizers able to occur under a variety of hydrologic conditions. The mechanisms driving methane oxidation by the latter, however, may depend on the hydrologic conditions.
Systems Biology Methods Applied to Blood and Tissue for a Comprehensive Analysis of Immune Response to Hepatitis B Vaccine in Adults

Conventional vaccine design has been based on trial-and-error approaches, which have been generally successful. However, there have been some major failures in vaccine development and we still do not have highly effective licensed vaccines for tuberculosis, HIV, respiratory syncytial virus, and other major infections of global significance. Approaches at rational vaccine design have been limited by our understanding of the immune response to vaccination at the molecular level. Tools now exist to undertake in-depth analysis using systems biology approaches, but to be fully realized, studies are required in humans with intensive blood and tissue sampling. Methods that support this intensive sampling need to be developed and validated as feasible. To this end, we describe here a detailed approach that was applied in a study of 15 healthy adults, who were immunized with hepatitis B vaccine. Sampling included ~350 mL of blood, 12 microbiome samples, and lymph node fine needle aspirates obtained over a ~7-month period, enabling comprehensive analysis of the immune response at the molecular level, including single cell and tissue sample analysis. Samples were collected for analysis of immune phenotyping, whole blood and single cell gene expression, proteomics, lipidomics, epigenetics, whole blood response to key immune stimuli, cytokine responses, in vitro T cell responses, antibody repertoire analysis and the microbiome. Data integration was undertaken using different approaches—NetworkAnalyst and DIABLO. Our results demonstrate that such intensive sampling studies are feasible in healthy adults, and data integration tools exist to analyze the vast amount of data generated from a multi-omics systems biology approach. This will provide the basis for a better understanding of vaccine-induced immunity and accelerate future rational vaccine design.
Two-Dimensional Metal Organic Framework Nanosheets as Bifunctional Catalyst for Electrochemical and Photoelectrochemical Water Oxidation

Electrochemical (EC) and photoelectrochemical (PEC) water splitting represent promising strategies for renewable energy conversion and fuel production and require design of efficient catalysts for the oxygen evolution reaction (OER). Herein, we report the synthesis of two-dimensional (2D) Co-based metal organic framework (Co-MOF) nanosheets and their bifunctional catalytic properties for both EC and PEC OER. Benefiting from the large surface area and abundant isolated metal active sites, the Co-MOF nanosheets exhibited excellent OER activity and stability. The efficient electron–hole generation and separation of the nanosheets, owing to dimensional confinement, contributed to an improved visible light response in PEC OER. This study presents a new strategy to design EC/PEC bifunctional catalyst utilizing unique structural and electronic features of 2D MOF.
Longitudinal Macro/Microstructural Alterations of Different Callosal Subsections in Parkinson’s Disease Using Connectivity-Based Parcellation

Background The corpus callosum (CC) is an important feature of Parkinson’s disease (PD) not only in motor but also in non-motor functions. However, CC is not a homogeneous component, and the damage of specific subsection may contribute to corresponding clinical deficit. Objective The objective of the study is to investigate the structural alterations of different callosal subsections cross-sectionally and longitudinally in PD and evaluate their relationships to clinical performance. Methods Thirty-nine PD patients who had been longitudinally reexamined and 82 normal controls (NC) were employed. According to their specific callosal–cortical connectivity, 3D CC was divided into five subsections (including prefrontal, premotor, motor, somatosensory, and temporal–parietal–occipital subsection). The fractional anisotropy (FA), mean diffusivity (MD), and volume of whole CC and its subsections were computed and compared between groups. Regression model was constructed to explore the relationships between callosal structure and clinical performance. Results At baseline, PD did not show any significant macro/microstructural difference compared with NC. During disease course, there was a decreased FA and increased MD of whole CC as well as its subsections (except temporal–parietal–occipital subsection), and the volume of motor subsection was decreased. Moreover, the FA of temporal–parietal–occipital subsection and volume of motor subsection were correlated with the mood domain at baseline, and the MD of somatosensory subsection was associated with the motor domain at follow-up. Conclusion The structure of CC and its connectivity-specific subsections remain preserved at a relatively early stage in PD and are progressively disrupted during disease course. Besides, different callosal subsections possess specific associations with clinical performance in PD.
Significant lagrangian linear hotspot discovery

Given a collection of multi-attribute trajectories, an event definition, and a spatial network, the Significant Lagrangian Linear Hotspot Discovery (SLLHD) problem finds the paths where records in the trajectories tend to be events in the Lagrangian perspective. The SLLHD problem is of significant societal importance because of its applications in transportation planning, vehicle design, and environmental protection. Its main challenges include the potentially large number of candidate hotspots caused by the tremendous volume of trajectories as well as the non-monotonicity of the statistic measuring event concentration. The related work on the linear hotspot discovery problem is designed in the Eulerian perspective and focuses on point datasets, which ignores the dependence of event occurrence on trajectories and the paths where trajectories are. To solve this problem, we introduce an algorithm in the Lagrangian perspective, as well as five refinements that improve its computational scalability. Two case studies on real-world datasets and experiments on synthetic data show that the proposed approach finds hotspots which are not detectable by existing techniques. Cost analysis and experimental results on synthetic data show that the proposed approach yields substantial computational savings.
You can’t hide you can’t run: a performance assessment of managed applications on a NUMA machine

The ever-growing demand for more memory capacity from applications has always been a challenging factor in computer architecture. The advent of the Non Unified Memory Access (NUMA) architecture has achieved to work around the physical constraints of a single processor by providing more system memory using pools of processors, each with their own memory elements, but with variable access times. However, the efficient exploitation of such computing systems is a non-trivial task for software engineers. We have observed that the performance of more than half of the applications picked from two distinct benchmark suites is negatively affected when running on a NUMA machine, in the absence of manual tuning. This finding motivated us to develop a new profiling tool, so called PerfUtil, to study, characterize and better understand why those benchmarks have sub-optimal performance on NUMA machines. PerfUtil's effectiveness is based on its ability to track numerous events throughout the system at the managed runtime system level, that, ultimately, assists in demystifying NUMA peculiarities and accurately characterize managed applications profiles.
MAC-Layer Spoofing Detection and Prevention in IoT Systems: Randomized Moving Target Approach

MAC-layer spoofing, also known as identity spoofing, is recognized as a serious problem in many practical wireless systems. IoT systems are particularly vulnerable to this type of attack, as IoT devices (due to their various limitations) are often incapable of deploying advanced MAC-layer spoofing prevention and detection techniques - such as cryptographic authentication. Signal-level device fingerprinting is an approach to identity spoofing detection that is highly suitable for sensor-based IoT networks, but can be also utilized in many other types of wireless system. Unfortunately, the previous research works on signal-level device fingerprinting have been based on rather simplistic assumptions about both - the adversary's behavior as well as the operation of the defense system. The goal of our work was to examine the effectiveness of a novel system that combines signal-level device fingerprinting with the principles of Randomized Moving Target Defense (RMTD) when dealing with a very advanced adversary. The obtained results show that our RMTD-enhanced signal-level device fingerprinting technique exhibits far superior defense performance over the non-RMTD techniques previously discussed in the literature, and as such could be of great value for practical wireless systems subjected to identity spoofing attacks.
Disambiguating Preferences for Gamification Strategies to Motivate Pro-Environmental Behaviour

Much topical gamification research has focused on the application of personality trait models and the development of player typologies. These models are often applied under the assumption that personality or gamification user type reside as a stable construct within the individual. However, the preference for gamification strategies as well as their motivational impact, may vary in different contexts and situations. Therefore, our study aimed to disambiguate the different levels of motivation (global, contextual and situational) underlying preferences for gamification strategies, in the specific domain of pro-environmental behaviour. To this end, we developed a gamified mobile app to promote pro-environmental behaviour. Next, 56 participants interacted with the app during on average 22 days, while logs with various game elements were collected. Additionally, participants filled out the Hexad questionnaire to establish gamification user type, a survey polling for intrinsic versus extrinsic motivation towards the environment, and two questionnaires to assess pro-environmental behaviour before and after app usage. Our findings suggest that particularly intrinsic motivation towards the environment predicts preferences for gamification strategies whereas gamification user types fall short. In sum, our study lends support to measuring at different levels of motivation to understand and tailor gamification strategies for pro-environmental behaviour.
Research on an Improved Degree Influence Maximization Algorithm

The problem of influence maximization is an important research topic in social networks. The problem is described as follows: under a specific propagation model, how to select a specified number of seed-node sets for message propagation, that the number of influence nodes reaches a maximum at the end of propagation. There are some imperfections in the existing algorithms. It may not estimate the influence of the node accurately, when the selected seed-nodes have overlapping influences and the edges in the network have different propagation probabilities. To solve this problem, an improved influence maximization algorithm APCA (Active Probability Coverage Algorithm) is proposed. Firstly, calculating influence of the node with the improved k-shell algorithm to select the first seed-node; Secondly, the activated-easily node is marked as covered status by the coverage threshold &thgr;, and the influence of the node is updated until a specified number of seed-nodes are selected; Finally, the CCA (Core Covering Algorithm), MaxDegree (Maximum Degree Algorithm), IRIE algorithm (Impact Ranking Impact Estimation) and APCA were compared, and analyzed with three real data sets from the website. The experimental results show that under the IC model (Independent Cascade Model) and the WC model (Weight Cascade Model), the influence of APCA is better than the other three algorithms.
Creating cloud-free satellite imagery from image time series with deep learning

Optical satellite images are important for environmental monitoring. Unfortunately, such images are often affected by distortions, such as clouds, shadows, or missing data. This work proposes a deep learning approach for cleaning and imputing satellite images, which could serve as a reliable preprocessing step for spatial and spatio-temporal analyzes. More specifically, a coherent and cloud-free image for a specific target date and region is created based on a sequence of images of that region obtained at previous dates. Our model first extracts information from the previous time steps via a special gating function and then resorts to a modified version of the well-known U-Net architecture to obtain the desired output image. The model uses supplementary data, namely the approximate cloud coverage of input images, the temporal distance to the target time, and a missing data mask for each input time step. During the training phase we condition our model with the targets cloud coverage and missing values (disabled in production), which allows us to use data afflicted by distortion during training and thus does not require pre-selection of distortion-free data. Our experimental evaluation, conducted on data of the Landsat missions, shows that our approach outperforms the commonly utilized approach that resorts to taking the median of cloud-free pixels for a given position. This is especially the case when the quality of the data for the considered period is poor (e.g., lack of cloud free-images during the winter/fall periods). Our deep learning approach allows to improve the utility of the entire Landsat archive, the only existing global medium-resolution free-access satellite archive dating back to the 1970s. It therefore holds scientific and societal potential for future analyses conducted on data from this and other satellite imagery repositories.
Green infrastructures and their impact on resilience: spatial interactions in centralized sewer systems

Resilience in urban drainage infrastructure management has gained traction in the last few years, where systems need to adapt and recover from failure in face of deep uncertain threats. Green infrastructures, on-site nature-based stormwater strategies, are a promising concept that has proven to be effective in increasing the overall resilience performance in sewer systems. However, the improvement is not always significant or guaranteed. There is a lack of understanding of the local effects of these infrastructures and the spatial components of the impact on resilience in the network. In this work, the spatial interactions between GI placement and improvements in the centralized sewer networks resilience were studied, whilst considering a wide range of design storms. Resilience is assessed using two metrics: flood volume and flood duration. The scenarios simulated were baseline scenarios with no green infrastructure for each rainfall (scenarios type 1) and a placement scheme using critical component analysis (scenarios type 2). The spatial interactions were analysed through three main points, the magnitude of the impact, the number of affected nodes and the location of the impact in the network. This analysis was applied in a case-study in the United Kingdom. Regarding the magnitude of the impact, even though at a system level the impact is not high, at a node level the impact can be significant. Also, the impact is higher in shorter duration and lower return period storms. Regarding the number of affected nodes, most of the nodes remain unchanged. When all the scenarios are considered, there are as many nodes with an increase, as there are with a decrease in flooding volume and duration. Regarding the location of the impact, the nearest nodes to the outlet show the highest reduction in flood volume and flood duration. Subcatchments upstream the network and with highest areas seem to be the most impactful in the flood volume change. For flood duration, the subcatchments with smaller areas and generally in a middle region in the network cause the highest changes. This study is a first approximation to understand spatial considerations regarding the impact on resilience based on different green infrastructure location in the network.
Novel Strategies for Efficient Production and Delivery of Live Biotherapeutics and Biotechnological Uses of Lactococcus lactis: The Lactic Acid Bacterium Model

Lactic acid bacteria (LAB) are traditionally used in fermentation and food preservation processes and are recognized as safe for consumption. Recently, they have attracted attention due to their health-promoting properties; many species are already widely used as probiotics for treatment or prevention of various medical conditions, including inflammatory bowel diseases, infections, and autoimmune disorders. Some LAB, especially Lactococcus lactis, have been engineered as live vehicles for delivery of DNA vaccines and for production of therapeutic biomolecules. Here, we summarize work on engineering of LAB, with emphasis on the model LAB, L. lactis. We review the various expression systems for the production of heterologous proteins in Lactococcus spp. and its use as a live delivery system of DNA vaccines and for expression of biotherapeutics using the eukaryotic cell machinery. We have included examples of molecules produced by these expression platforms and their application in clinical disorders. We also present the CRISPR-Cas approach as a novel methodology for the development and optimization of food-grade expression of useful substances, and detail methods to improve DNA delivery by LAB to the gastrointestinal tract. Finally, we discuss perspectives for the development of medical applications of recombinant LABs involving animal model studies and human clinical trials, and we touch on the main safety issues that need to be taken into account so that bioengineered versions of these generally recognized as safe organisms will be considered acceptable for medical use.
Performance Analysis of Real-Time Scheduling Algorithms

Real-time systems are intensively used nowadays. Scheduling algorithms are very important to manage the scheduling of tasks in real-time systems. In this paper we give an overview on the real-time scheduling techniques for uniprocessors and multiprocessors, then we present a comparison between the multiprocessor scheduling algorithms which are classified into partitioning and global scheduling algorithms. The results achieved from the comparison have showed that the parameters such as makespan, waiting time, missed deadlines and task preemptions are better in performance when using partitioned scheduling algorithms than that for global scheduling algorithms when the number of tasks is small. While when the number of tasks increased, it gives more better performance when using global scheduling algorithms than partitioned scheduling algorithms using all parameters except for some of the algorithms gave low performance in missed deadlines, they give a high number of missed deadlines. But in total when the number of tasks increased the global scheduling algorithms gives better performance than partitioned scheduling algorithms.
A Data-Driven, Player-Centric Approach to Evaluating Spatial Skill Training Games

Certain commercial games train spatial skills, a subset of cognitive skills used in school, the workplace, and everyday life. However, it is difficult to design spatial skill training games without knowing why some games are effective. In addition, existing game training studies do not analyze motivational factors for critical target populations. We conducted a study comparing spatial skill training effects and enjoyment for low spatial skill students on three training interventions: the computer game Homeworld Bound: Redux, digital spatial workbook exercises, and an active control group. We found no training effects for any intervention, but performance in certain game levels predicted spatial skill, and low spatial skill students enjoyed the game more than the workbook exercises. We provide three design recommendations for spatial skill training games based on our findings: use asymmetry for object manipulation tasks, require explicit 2D to 3D representation translation, and employ time pressure for navigation tasks.