Metacommunity Concepts Provide New Insights in Explaining Zooplankton Spatial Patterns within Large Floodplain Systems

Flood pulse related physical variables (FLOOD) can affect zooplankton community structure through local factors directly and can also influence through regional dispersal factors of metacommunity concepts indirectly. Therefore, we infer that spatial patterns of zooplankton communities could be related to metacommunity concepts and their importance may depend on the size of the aquatic/terrestrial transition zone (ATTZ). Herein, we explored the relative importance of limnological (LIMNO) and FLOOD variables in zooplankton community by analyzing data from 272 sites across three floodplain lakes in the middle reaches of the Yangtze River. Our results showed that the variation in the zooplankton community can be well explained by the LIMNO and FLOOD variables in all of the lakes under the low water level season. However, during the high water level season, neither LIMNO nor FLOOD can explain the spatial variances of zooplankton. Therefore, our results indicated that testing biogeographical theories and macroecological laws using zooplankton should consider temporal aspects of flood pulse. Furthermore, we noted that the number of explained variance by local variables is negatively correlated with the size of the ATTZ. Metacommunity concepts provide complementary insights in explaining zooplankton spatial patterns within large floodplain systems, which also provide a theoretical basis for ATTZ protection in floodplain management.

Co-Occurrence of Violence on the Severity of Abuse in Intimate Relationships

Several empirical studies have shown that women who experience violence in intimate personal relationships (IPV) commonly experience more than one form of violence. While it is recognized that individual trajectories of IPV vary over time, little is known about the temporal dynamics of this co-occurrence or its consequences. This study describes the different forms of violence experienced by women and looks at whether it is possible to predict when severe violence (physical and sexual) is most likely to occur. Data in the study comes from interviews with 70 women who had been victims of intimate partner violence. The life history calendar method was used to facilitate identifying kinds and levels of violence and the month in which violence took place. Individual victimization trajectories were found to be heterogenous and likely to change in the short term. The women in our sample experienced more than one form of intimate partner violence and co-occurrence of different forms of violence was common in individual trajectories. The characteristics of the kind of violence experienced were important in understanding the temporal aspects of acts of severe violence. The increased knowledge about patterns of violence provided by these results should help to develop better ways to intervene to prevent such events.

Determining Immunoglobulin Content of Bovine Colostrum and Factors Affecting the Outcome: A Review

The immunoglobulin concentration in bovine colostrum should be measured to ensure feeding with sufficient immunoglobulins (≥50 mg immunoglobulin G mL−1). Adequate feeding prevents diseases, promotes development, and has a positive influence on the adult animal. Indirect and direct measurement methods are available for this purpose. Direct measurement methods cannot be easily used in practice; therefore, farmers use indirect methods such as a colostrometer and a refractometer. Many factors influence the immunoglobulin concentration of colostrum; some of them have already been intensively researched. In particular, lactation and temporal aspects play an essential role. Newer aspects such as dry period, seasonal influences, and genetics are gaining importance, but their impact on immunoglobulin content has not been sufficiently investigated. Developments are still needed, especially in data management. This review analyzes the outcome of different studies on the indirect and direct measurement methods and discusses different factors influencing the immunoglobulin concentration of bovine colostrum.

Intrinsic and environmental factors modulating autonomous robotic search under high uncertainty

Autonomous robotic search problems deal with different levels of uncertainty. When uncertainty is low, deterministic strategies employing available knowledge result in most effective searches. However, there are domains where uncertainty is always high since information about robot location, environment boundaries or precise reference points is unattainable, e.g., in cave, deep ocean, planetary exploration, or upon sensor or communications impairment. Furthermore, latency regarding when search targets move, appear or disappear add to uncertainty sources. Here we study intrinsic and environmental factors that affect low-informed robotic search based on diffusive Brownian, naive ballistic, and superdiffusive strategies (Lévy walks), and in particular, the effectiveness of their random exploration. Representative strategies were evaluated considering both intrinsic (motion drift, energy or memory limitations) and extrinsic factors (obstacles and search boundaries). Our results point towards minimum-knowledge based modulation approaches that can adjust distinct spatial and temporal aspects of random exploration to lead to effective autonomous search under uncertainty.

Health Status Prediction with Local-Global Heterogeneous Behavior Graph

Health management is getting increasing attention all over the world. However, existing health management mainly relies on hospital examination and treatment, which are complicated and untimely. The emergence of mobile devices provides the possibility to manage people’s health status in a convenient and instant way. Estimation of health status can be achieved with various kinds of data streams continuously collected from wearable sensors. However, these data streams are multi-source and heterogeneous, containing complex temporal structures with local contextual and global temporal aspects, which makes the feature learning and data joint utilization challenging. We propose to model the behavior-related multi-source data streams with a local-global graph, which contains multiple local context sub-graphs to learn short-term local context information with heterogeneous graph neural networks and a global temporal sub-graph to learn long-term dependency with self-attention networks. Then health status is predicted based on the structure-aware representation learned from the local-global behavior graph. We take experiments on the StudentLife dataset, and extensive results demonstrate the effectiveness of our proposed model.

Isolation-Induced Ultrasonic Vocalization in Environmental and Genetic Mice Models of Autism

Studies in rodent models suggest that calls emitted by isolated pups serve as an early behavioral manifestation of communication deficits and autistic like behavior. Previous studies in our labs showed that gestational exposure to the pesticide chlorpyrifos (CPF) and the Mthfr-knock-out mice are associated with impaired social preference and restricted or repetitive behavior. To extend these studies, we examine how pup communication via ultrasonic vocalizations is altered in these ASD models. We implemented an unsupervised hierarchical clustering method based on the spectral properties of the syllables in order to exploit syllable classification to homogeneous categories while avoiding over-categorization. Comparative exploration of the spectral and temporal aspects of syllables emitted by pups in two ASD models point to the following: (1) Most clusters showed a significant effect of the ASD factor on the start and end frequencies and bandwidth and (2) The highest percent change due to the ASD factor was on the bandwidth and duration. In addition, we found sex differences in the spectral and temporal properties of the calls in both control groups as well as an interaction between sex and the gene/environment factor. Considering the basal differences in the characteristics of syllables emitted by pups of the C57Bl/6 and Balb/c strains used as a background in the two models, we suggest that the above spectral-temporal parameters start frequency, bandwidth, and duration are the most sensitive USV features that may represent developmental changes in ASD models.

Modelling Cyberspace to Determine Cybersecurity Training Requirements

Cyberspace is a constantly evolving and expanding environment that is being used for an ever-increasing range of purposes. As such, it attracts numerous threat actors seeking to identify and exploit its vulnerabilities. In order to be able to fully mitigate the risk of compromise, it is necessary to first understand the nature and composition of cyberspace and how it is used. This chapter seeks to address this issue by presenting a method to model cyberspace in three dimensions with humans included as an integral part. Expanding beyond describing cyberspace purely in terms of technology and its uses, it explores geographic, political, and temporal aspects to reflect its dynamic nature. The first component of the model examines the varied attributes of cyberspace ranging from the landscape in which its components are located to how they are used. The second dimension investigates the path of data in all its forms from its source to destination, emphasising that cyberspace is fundamentally a communications medium and is not borderless. Thirdly, it focuses on the security dimension and the motivations of those with malicious intent, demonstrating the multidisciplinary and essentially human nature of cybersecurity in countering their activities.