Effect of intrahippocampal administration of anti-melanin-concentrating hormone on spatial food-seeking behavior in rats

Melanin-concentrating hormone (MCH) is a hypothalamic peptide that plays a critical role in the regulation of food intake and energy metabolism. In this study, we investigated the potential role of dense hippocampal MCH innervation in the spatially oriented food-seeking component of feeding behavior. Rats were trained for eight sessions to seek food buried in an arena using the working memory version of the food-seeking behavior (FSB) task. The testing day involved a bilateral anti-MCH injection into the hippocampal formation followed by two trials. The anti-MCH injection did not interfere with the performance during the first trial on the testing day, which was similar to the training trials. However, during the second testing trial, when no food was presented in the arena, the control subjects exhibited a dramatic increase in the latency to initiate digging. Treatment with an anti-MCH antibody did not interfere with either the food-seeking behavior or the spatial orientation of the subjects, but the increase in the latency to start digging observed in the control subjects was prevented. These results are discussed in terms of a potential MCH-mediated hippocampal role in the integration of the sensory information necessary for decision-making in the pre-ingestive component of feeding behavior.     

Comparative regional-scale soil salinity assessment with near-ground apparent electrical conductivity and remote sensing canopy reflectance

Soil salinity is recognized worldwide as a major threat to agriculture, particularly in arid and semi-arid regions. Producers and decision makers need updated and accurate maps of salinity in agronomically and environmentally relevant ranges (i.e., <20 dS m⁻¹, when salinity is measured as electrical conductivity of the saturation extract, EC_e). State-of-the-art approaches for creating accurate EC_e maps beyond field scale (i.e., 1 km²) include: (i) Analysis Of Covariance (ANOCOVA) of near-ground measurements of apparent soil electrical conductivity (EC_a) and (ii) regression modeling of multi-year remote sensing canopy reflectance and other co-variates (e.g., crop type, annual rainfall). This study presents a comparison of the two approaches to establish their viability and utility. The approaches were tested using 22 fields (total 542 ha) located in California’s western San Joaquin Valley. In 2013 EC_a-directed soil sampling resulted in the collection of 267 soil samples across the 22 fields, which were analyzed for EC_e, ranging from 0 to 38.6 dS m⁻¹. The ANOCOVA EC_a-EC_e model returned a coefficient of determination (R²) of 0.87 and root mean square prediction error (RMSPE) of 3.05 dS m⁻¹. For the remote sensing approach seven years (2007–2013) of Landsat 7 reflectance were considered. The remote sensing salinity model had R² = 0.73 and RMSPE = 3.63 dS m⁻¹. The robustness of the models was tested with a leave-one-field-out (lofo) cross-validation to assure maximum independence between training and validation datasets. For the ANOCOVA model, lofo cross-validation provided a range of scenarios in terms of RMSPE. The worst, median, and best fit scenarios provided global cross-validation R² of 0.52, 0.80, and 0.81, respectively. The lofo cross-validation for the remote sensing approach returned a R² of 0.65. The ANOCOVA approach performs particularly well at EC_e values <10 dS m⁻¹, but requires extensive field work. Field work is reduced considerably with the remote sensing approach, but due to the larger errors at low EC_e values, the methodology is less suitable for crop selection, and other practices that require accurate knowledge of salinity variation within a field, making it more useful for assessing trends in salinity across a regional scale. The two models proved to be viable solutions at large spatial scales, with the ANOCOVA approach more appropriate for multiple-field to landscape scales (1–10 km²) and the remote sensing approach best for landscape to regional scales (>10 km²).     

Long-term monitoring data meet freshwater species distribution models: Lessons from an LTER-site

Long-term monitoring datasets provide a solid framework for ecological research. Such a dataset from the German long-term ecological research (LTER) site Rhine-Main-Observatory was used to set up a species distribution model (SDM) for the Kinzig catchment. The extensive knowledge on the monitoring data provided by the LTER-site framework allowed to calibrate a robust model for 175 taxa of stream macroinvertebrates and to project their distributions on the Kinzig River stream network using bioclimatic, topographical, hydrological, land use and geological predictors. On average, model performance was good, with a TSS of 0.83 (±0.09 SD) and a ROC of 0.95 (±0.03 SD). The model delivered valuable insights on three sources of bias that plague SDMs in general: (a) level of taxonomic identification of the modeled organisms, (b) the spatial arrangement of sampling sites, and (c) the sampling intensity at each sampling site. Taxonomic resolution did not affect SDM performance. The distribution of high predicted probabilities of occurrence in the stream network coincided with those segments in the stream network most densely and frequently sampled, indicating both a spatial and temporal sampling bias. Species richness curves confirmed the temporal sampling bias. Next to spatial bias, sampling frequency also plays an important role in data collection, affecting further analysis and modeling procedures. Results indicate an underrepresentation of low order streams, an important aspect that should be addressed by both monitoring schemes and modeling approaches.     

An interactive method to select a set of sustainable urban development indicators

The paper proposes a method for selecting a set of sustainable development indicators which can be used for various sustainability-related tasks, such as assessment of current condition, measure of progress toward specific goals of sustainable development, in general, and sustainable urban development, in particular. The method is based on variable clustering, selecting cluster representative, and multivariate linear regression in combination with experts and stakeholders’ input in an interactive process. The small set of indicators derived from the proposed method was able to account for a significant amount of information from the initial indicator set while effectively assisting stakeholders in making informed decision based on objective quantitative information and meeting their preference simultaneously.     

Incorporating spatial autocorrelation in rarefaction methods: Implications for ecologists and conservation biologists

Recently, methods for constructing Spatially Explicit Rarefaction (SER) curves have been introduced in the scientific literature to describe the relation between the recorded species richness and sampling effort and taking into account for the spatial autocorrelation in the data. Despite these methodological advances, the use of SERs has not become routine and ecologists continue to use rarefaction methods that are not spatially explicit. Using two study cases from Italian vegetation surveys, we demonstrate that classic rarefaction methods that do not account for spatial structure can produce inaccurate results. Furthermore, our goal in this paper is to demonstrate how SERs can overcome the problem of spatial autocorrelation in the analysis of plant or animal communities. Our analyses demonstrate that using a spatially-explicit method for constructing rarefaction curves can substantially alter estimates of relative species richness. For both analyzed data sets, we found that the rank ordering of standardized species richness estimates was reversed between the two methods. We strongly advise the use of Spatially Explicit Rarefaction methods when analyzing biodiversity: the inclusion of spatial autocorrelation into rarefaction analyses can substantially alter conclusions and change the way we might prioritize or manage nature reserves.     

Machine learning approaches in MALDI-MSI: clinical applications

Introduction: Despite the unquestionable advantages of Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging in visualizing the spatial distribution and the relative abundance of biomolecules directly on-tissue, the yielded data is complex and high dimensional. Therefore, analysis and interpretation of this huge amount of information is mathematically, statistically and computationally challenging.

Areas covered: This article reviews some of the challenges in data elaboration with particular emphasis on machine learning techniques employed in clinical applications, and can be useful in general as an entry point for those who want to study the computational aspects. Several characteristics of data processing are described, enlightening advantages and disadvantages. Different approaches for data elaboration focused on clinical applications are also provided. Practical tutorial based upon Orange Canvas and Weka software is included, helping familiarization with the data processing.

Expert commentary: Recently, MALDI-MSI has gained considerable attention and has been employed for research and diagnostic purposes, with successful results. Data dimensionality constitutes an important issue and statistical methods for information-preserving data reduction represent one of the most challenging aspects. The most common data reduction methods are characterized by collecting independent observations into a single table. However, the incorporation of relational information can improve the discriminatory capability of the data.     

Effective aerial monitoring of cyanobacterial harmful algal blooms is dependent on understanding cellular migration

Cyanobacterial harmful algal blooms (CHABs) degrade water quality and may produce toxins. The distribution of CHABs can change rapidly due to variations in population dynamics and environmental conditions. Biological and ecological aspects of CHABs were studied in order to better understand CHABs dynamics. Field experiments were conducted near Hartington, Ontario, Canada in ponds dominated by Microcystis aeruginosa and CHABs floating experiments were conducted at Lake Taihu during the summers of 2015 and 2016. Single colonies composed of hundreds to thousands of cells with an average median of 0.2–0.5 mm in diameter were the basic form assumed by the Microcystis, and this remained the same throughout the growing season. Thorough mixing of the water column followed by calm conditions resulted in over 90% of the cyanobacteria floating after 1 h. Multiple colonies floated on the water surface in four types of assemblages: aggregates, ribbons, patches, and mats. It is the mats that are conventionally considered the blooming stage of cyanobacteria.Presence of CHABs on open water surfaces also depends on environmental influences such as direct and indirect wind effects. For example, field tests revealed that the surface coverage of CHABs can be reduced to half within an hour at wind speeds of 0.5 m/s.Because our findings indicated that blooming involves surface display of cyanobacteria essentially presenting as a two-dimensional plane under defined conditions, the use of surface imagery to quantify CHABs was justified. This is particularly important in light of the fact that traditional detection methods do not provide accurate distribution information. Nor do they portray CHABs events in a real-time manner due to limitations in on-demand surveillance and delays between sample time and analyzed results. Therefore, a new CHAB detection method using small unmanned aerial systems with consumer-grade cameras was developed at a maximum detection altitude of 80 m. When cyanobacteria were floating on the surface, CHABs detection through RGB band cameras and spectral enhancement techniques was efficient and accurate. Small unmanned aerial systems were capable of providing coverage up to 1 km² per mission and the short intervals between sampling and results (approx. 2 h) allowed for the rapid analysis of data and for implementing follow-up monitoring or treatments. This method is very cost-effective at an estimate of as low as $100 CAD per mission with an average cyanobacterial detection accuracy of 86%. Thus, it is a good candidate method to fill the urgent need for CHABs detection, providing cost effective, rapid, and accurate information to improve risk management at a local level as well as to help quickly allocate resources for purposes of mitigation.     

木麻黄海防林种子雨的时空动态

为了解木麻黄(Casuarina equisetifolia)种子雨的时空间分布格局和萌发性能对其天然更新的影响,对海口木麻黄海防林种子雨的时空分布特征和种子雨的萌发动态进行了研究。结果表明,木麻黄种子落雨时间始于7月中旬,结束于次年4月中旬,持续长达9个月,期间种子雨年际平均密度为1 764.63 grain m~(-2)a~(-1);种子雨高峰期在12月上中旬,落种量占总散落量的43.38%;种子雨呈聚集分布,且种子雨密度与附近球果数量呈极显著正相关;种子雨萌发率较低,仅为11.31%,但由于种子雨密度较大,可萌发的种子雨密度仍然较大,为199.58grainm~(-2)a~(-1)。因此,种子数量和质量均不是制约木麻黄天然更新困难的主要障碍因子,但聚集分布特征使木麻黄种子在林内扩散能力十分有限。     

Walsh Hadamard Transform for Simple Linear Iterative Clustering (SLIC) Superpixel Based Spectral Clustering of Multimodal MRI Brain Tumor Segmentation

The automated brain tumor segmentation methods are challenging due to the diverse nature of tumors. Recently, the graph based spectral clustering method is utilized for brain tumor segmentation to make high-quality segmentation output. In this paper, a new Walsh Hadamard Transform (WHT) texture for superpixel based spectral clustering is proposed for segmentation of a brain tumor from multimodal MRI images. First, the selected kernels of WHT are utilized for creating texture saliency maps and it becomes the input for the Simple Linear Iterative Clustering (SLIC) algorithm, to generate more precise texture based superpixels. Then the texture superpixels become nodes in the graph of spectral clustering for segmenting brain tumors of MRI images. Finally, the original members of superpixels are recovered to represent Complete Tumor (CT), Tumor Core (TC) and Enhancing Tumor (ET) tissues. The observational results are taken out on BRATS 2015 datasets and evaluated using the Dice Score (DS), Hausdorff Distance (HD) and Volumetric Difference (VD) metrics. The proposed method produces competitive results than other existing clustering methods.     

Influence of Stimuli Spatial Proximity on a SSVEP-Based BCI Performance

Objective: Steady-State Visual Evoked Potentials based Brain-Computer Interfaces (SSVEP-based BCIs) systems have been shown as promising technology due to their short response time and ease of use. SSVEP-based BCIs use brain responses to a flickering visual stimulus as an input command to an external application or device, and it can be influenced by stimulus properties, signal recording, and signal processing. We aim to investigate the system performance varying the stimuli spatial proximity (a stimulus property).Material and methods: We performed a comparative analysis of two visual interface designs (named cross and square) for an SSVEP-based BCI. The power spectrum density (PSD) was used as feature extraction and the Support Machine Vector (SVM) as classification method. We also analyzed the effects of five flickering frequencies (6.67, 8.57, 10, 12 e 15 Hz) between and within interfaces.Results: We found higher accuracy rates for the flickering frequencies of 10, 12, and 15 Hz. The stimulus of 10 Hz presented the highest SSVEP amplitude response for both interfaces. The system presented the best performance (highest classification accuracy and information transfer rate) using the cross interface (lower visual angle).Conclusion: Our findings suggest that the system has the highest performance in the spatial proximity range from 4° to 13° (visual angle). In addition, we conclude that as the stimulus spatial proximity increases, the interference from other stimuli reduces, and the SSVEP amplitude response decreases, which reduces system accuracy. The inter-stimulus distance is a visual interface parameter that must be chosen carefully to increase the efficiency of an SSVEP-based BCI.