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Functional magnetic resonance imaging (fMRI) studies have often recorded robust univariate group effects in the amygdala of subjects exposed to emotional stimuli. Yet it is unclear to what extent this effect also holds true when multi‐voxel pattern analysis (MVPA) is applied at the level of the individual participant. Here we sought to answer this question. To this end, we combined fMRI data from two prior studies (N = 112). For each participant, a linear support vector machine was trained to decode the valence of emotional pictures (negative, neutral, positive) based on brain activity patterns in either the amygdala (primary region‐of‐interest analysis) or the whole‐brain (secondary exploratory analysis). The accuracy score of the amygdala‐based pattern classifications was statistically significant for only a handful of participants (4.5%) with a mean and standard deviation of 37% ± 5% across all subjects (range: 28–58%; chance‐level: 33%). In contrast, the accuracy score of the whole‐brain pattern classifications was statistically significant in roughly half of the participants (50.9%), and had an across‐subjects mean and standard deviation of 49% ± 6% (range: 33–62%). The current results suggest that the information conveyed by the emotional pictures was encoded by spatially distributed parts of the brain, rather than by the amygdala alone, and may be of particular relevance to studies that seek to target the amygdala in the treatment of emotion regulation problems, for example via real‐time fMRI neurofeedback training. Many emotion provocation functional magnetic resonance imaging (fMRI) tasks are known to induce a response inside the amygdala that is robust across participants, yet recent evidence suggests that the reproducibility of such task effects may actually be quite low at the single‐subject level. To further examine this issue, we trained a linear support vector machine to decode the valence of emotional pictures (negative, neutral, positive) based on brain activity patterns in either the amygdala (primary region‐of‐interest analysis) or the whole‐brain (secondary exploratory analysis). The accuracy score of the amygdala‐based pattern classifications was statistically significant for only a handful of participants, while the accuracy score of the whole‐brain pattern classifications was statistically significant in roughly half of the participants. These findings add to a growing body of research highlighting the low intra‐subject reliability of amygdala activation in emotion provocation fMRI.

Numerous studies have examined the neural substrates of intertemporal decision‐making, but few have systematically investigated separate neural representations of the two attributes of future rewards (i.e., the amount of the reward and the delay time). More importantly, no study has used the novel analytical method of representational connectivity analysis (RCA) to map the two dimensions' functional brain networks at the level of multivariate neural representations. This study independently manipulated the amount and delay time of rewards during an intertemporal decision task. Both univariate and multivariate pattern analyses showed that brain activity in the dorsomedial prefrontal cortex (DMPFC) and lateral frontal pole cortex (LFPC) was modulated by the amount of rewards, whereas brain activity in the DMPFC and dorsolateral prefrontal cortex (DLPFC) was modulated by the length of delay. Moreover, representational similarity analysis (RSA) revealed that even for the regions of the DMPFC that overlapped between the two dimensions, they manifested distinct neural activity patterns. In terms of individual differences, those with large delay discounting rates (k) showed greater DMPFC and LFPC activity as the amount of rewards increased but showed lower DMPFC and DLPFC activity as the delay time increased. Lastly, RCA suggested that the topological metrics (i.e., global and local efficiency) of the functional connectome subserving the delay time dimension inversely predicted individual discounting rate. These findings provide novel insights into neural representations of the two attributes in intertemporal decisions, and offer a new approach to construct task‐based functional brain networks whose topological properties are related to impulsivity. DMPFC represented both amount and delay‐time with distinct activation patterns. RCA‐based amount‐related and time‐related networks topological properties can predict k.

Aims: Patterns of spatial community dissimilarity have inspired a large body of theory in ecology and biogeography. Yet key gaps remain in our understanding of the local-scale ecological processes underlying species replacement and species nestedness, the two fundamental components of spatial community dissimilarity. Here, we examined the relative influence of dispersal limitation, habitat filtering and interspecific species interactions on local-scale patterns of the replacement and nestedness components in eight stem-mapped temperate forest mega-plots at different ontogenetic stages (large versus small trees). Location: Eight large (20–35 ha), fully mapped temperate forest plots in northern China and northern U.S.A. Time period: 2004–2016. Major taxa studied: Woody plants. Methods: We combined decomposition of community dissimilarity (based on the Ružička index) and spatial point-pattern analysis to compare the spatial (i.e., distance-dependent) replacement and nestedness components of each plot with that expected under five spatially explicit null models representing different hypotheses on community-assembly mechanisms. Results: Our analyses revealed complex results. In all eight forests, spatial community dissimilarity was best explained by species replacement among local tree assemblages and by a null model based on dispersal limitation. In contrast, spatial nestedness for large and small trees was best explained by random placement and habitat filtering, respectively, in addition to dispersal limitation. However, interspecific interactions did not contribute to local replacement and nestedness. Main conclusions: Species replacement is the predominant process accounting for spatial community dissimilarity in these temperate forests and caused largely by local-scale species clustering associated with dispersal limitation. Nestedness, in contrast, is less prevalent and primarily associated with larger variation in local species richness as caused by spatial richness gradients or 'hotspots' of local species richness. The novel use of replacement and nestedness measures in point pattern analysis is a promising approach to assess local-scale biodiversity patterns and to explore their causes.

Many bloodstain pattern classification methods exist in the literature that analysts could use in casework. Currently, no research demonstrates which classification methods bloodstain pattern analysts use or why they use those specific methods; therefore, this study aims to address this gap and support the development of a standardised classification approach. This research surveyed 79 participants working in Bloodstain Pattern Analysis (BPA) to determine which classification methods are used and why. The most used classification methods were the ‘Passive, Spatter, and Altered,’ ‘other methods’ (such as OSAC BPA terminology and Passive, Spatter, Transfer), and ‘Taxonomic methodology,’ and that job role and court system influenced the method chosen. Participants also used their classification methods to classify ten bloodstain patterns. The average percentage of correct classifications was 85 %, consistent with previous research. The percentage of correct classifications was then compared to the classification methods used. No single classification method was shown to be more accurate than any other method for this specific sample. However, as assessing the accuracy and effectiveness of the classification methods was not the main aim of this study, further work is needed to conduct a thorough assessment that will aid in developing a standardised procedure. •The bloodstain classification methods being used by analysts are currently unknown.•Why analysts use the classification method they do is also unknown.•The most used classification methods have been identified.•Training was the most common reason for using a classification method.•The accuracy of classifications is in line with previously published work.

Several studies have examined the neural substrates of probabilistic decision‐making, but few have systematically investigated the neural representations of the two objective attributes of probabilistic rewards, that is, the reward amount and the probability. Specifically, whether there are common or distinct neural activity patterns to represent the objective attributes and their association with the neural representation of the subjective valuation remains largely underexplored. We conducted two studies ( n Study1  = 34, n Study2  = 41) to uncover distributed neural representations of the objective attributes and subjective value as well as their association with individual probability discounting rates. The amount and probability were independently manipulated to better capture brain signals sensitive to these two attributes and were presented simultaneously in Study 1 and successively in Study 2. Both univariate and multivariate pattern analyses showed that the brain activities in the superior parietal lobule (SPL), including the postcentral gyrus, were modulated by the amount of rewards and probability in both studies. Further, representational similarity analysis revealed a similar neural representation between these two objective attributes and between the attribute and valuation. Moreover, the SPL tracked the subjective value integrated by the hyperbolic function. Probability‐related brain activations in the inferior parietal lobule were associated with the variability in individual discounting rates. These findings provide novel insights into a similar neural representation of the two attributes during probabilistic decision‐making and perhaps support the common neural coding of stimulus objective properties and subjective value in the field of probabilistic discounting.

This study aimed to identify the types of foods that constitute a vegan diet and establish patterns within the diet. Dietary pattern analysis, a key instrument for exploring the correlation between health and disease, was used to identify patterns within the vegan diet. A modified version of the EPIC-Norfolk FFQ was created and validated to include vegan foods and launched on social media. UK participants, recruited online. A convenience sample of 129 vegans voluntarily completed the FFQ. Collected data were converted to reflect weekly consumption to enable factor and cluster analyses. Factor analysis identified four distinct dietary patterns including: (1) convenience (22 %); (2) health conscious (12 %); (3) unhealthy (9 %) and (4) traditional vegan (7 %). Whilst two healthy patterns were defined, the convenience pattern was the most identifiable pattern with a prominence of vegan convenience meals and snacks, vegan sweets and desserts, sauces, condiments and fats. Cluster analysis identified three clusters, cluster 1 'convenience' (26·8 %), cluster 2 'traditional' (22 %) and cluster 3 'health conscious' (51·2 %). Clusters 1 and 2 consisted of an array of ultraprocessed vegan food items. Together, both clusters represent almost half of the participants and yielding similar results to the predominant dietary pattern, strengthens the factor analysis. These novel results highlight the need for further dietary pattern studies with full nutrition and blood metabolite analysis in larger samples of vegans to enhance and ratify these results.

•Conclusions by bloodstain pattern analysts were often erroneous and often contradicted other analysts.•On samples with known causes, 11.2% of responses were erroneous.•Both semantic differences and contradictory interpretations contributed to errors and disagreements. Although the analysis of bloodstain pattern evidence left at crime scenes relies on the expert opinions of bloodstain pattern analysts, the accuracy and reproducibility of these conclusions have never been rigorously evaluated at a large scale. We investigated conclusions made by 75 practicing bloodstain pattern analysts on 192 bloodstain patterns selected to be broadly representative of operational casework, resulting in 33,005 responses to prompts and 1760 short text responses. Our results show that conclusions were often erroneous and often contradicted other analysts. On samples with known causes, 11.2% of responses were erroneous. The results show limited reproducibility of conclusions: 7.8% of responses contradicted other analysts. The disagreements with respect to the meaning and usage of BPA terminology and classifications suggest a need for improved standards. Both semantic differences and contradictory interpretations contributed to errors and disagreements, which could have serious implications if they occurred in casework.

Bloodstain Pattern Analysis (BPA) helps us understand how bloodstains form, with a focus on their size, shape, and distributions. This aids in crime scene reconstruction and provides insight into victim positions and crime investigation. One challenge in BPA is distinguishing between different types of bloodstains, such as those from firearms, impacts, or other mechanisms. Our study focuses on differentiating impact spatter bloodstain patterns from gunshot backward spatter bloodstain patterns. We distinguish patterns by extracting well-designed individual stain features, applying effective data consolidation methods, and selecting boosting classifiers. As a result, our model exhibits competitive accuracy and efficiency on the tested dataset, suggesting its potential in similar scenarios. •A novel method distinguishes gunshot and impact bloodstain patterns using images.•Ellipse- and shade-based features improve interpretability and classification accuracy.•XGBoost achieves 92.89% accuracy, outperforming previous BPA models.•A new Stability Importance Score offers consistent feature ranking across model runs.•The method shows strong potential for practical forensic bloodstain analysis.

Spatial processes underlie major species coexistence mechanisms. A range of spatial analysis techniques are increasingly applied to data of fully mapped communities to quantify spatial structures in species and phylogenetic and functional diversity at some given spatial scale with the goal of gaining insights into processes of community assembly and dynamics. We review these techniques, including spatial point pattern analysis, quadrat-based analyses, and individual-based neighborhood models, and provide a practical roadmap for ecologists in the analysis of local spatial structures in species and phylogenetic and functional diversity. We show how scale-dependent metrics of spatial diversity can be used in concert with ecological null models, statistical models, and dynamic community simulation models to detect spatial patterns, reveal the influence of the biotic neighborhood on plant performance, and quantify the relative contribution of species interactions, habitat heterogeneity, and stochastic processes to community assembly across scale. Future works should integrate these approaches into a dynamic spatiotemporal framework.

An improved automated bloodstain pattern analysis method has been developed and validated, which utilises computer vision techniques to identify bloodstains on a plain background within a digital image. The method generates metrics relating to the individual stains as well as the overall pattern, including bloodstain pattern specific metrics such as the gamma angle, circularity, solidity, area of convergence, stain density and pattern linearity. This method provides an objective approach to the analysis of bloodstains and bloodstain patterns and can generate a wealth of quantitative data that is currently not obtainable using manual techniques or other image-based programs currently utilised in the discipline. This method will be useful to analysts and researchers investigating the application of quantitative methods to bloodstain pattern analysis. •A method that automatically analyses digital images of bloodstain spatter patterns is described.•Improved stain segmentation detects even very small stains under varied lighting conditions.•A novel approach to fitting ellipses allows for accurate BPA specific metrics to be calculated.•Quantitative data is quickly and reliably extracted from patterns increasing objectivity.