Pulses (Fabaceae) have regained interest for their high protein level. However, food application of pulses and pulse ingredients is hampered by several issues around their off‐flavor. Off‐flavors in ...pulses are partially inherent and partially produced during harvesting, processing, and storage. Generally, volatile off‐flavor compounds in pulses belong to the categories of aldehydes, alcohols, ketones, acids, pyrazines, sulfur compounds, and others, and off‐taste is strongly correlated to the presence of saponins, phenolic compounds, and sometimes alkaloids. No systematic studies have been performed on the identification of the off‐flavor compounds present in pulses in relation to their contribution to the overall perception of the pulses. This review article aims to provide a concise overview highlighting the most important aspects of the knowledge available on the off‐flavor compounds present in various pulses, their possible origins, and the technologies available to prevent, reduce, or mask these off‐flavor compounds. Rather than attempting to make a full inventory of the literature in the field, this paper addresses the most relevant topics referring to a selected set of relevant papers on each topic to substantiate the observations and conclusions that may guide the reader toward additional literature.
Perception can be described as a process of inference, integrating bottom-up sensory inputs and top-down expectations. However, it is unclear how this process is neurally implemented. It has been ...proposed that expectations lead to prestimulus baseline increases in sensory neurons tuned to the expected stimulus, which in turn, affect the processing of subsequent stimuli. Recent fMRI studies have revealed stimulus-specific patterns of activation in sensory cortex as a result of expectation, but this method lacks the temporal resolution necessary to distinguish pre- from poststimulus processes. Here, we combined human magnetoencephalography (MEG) with multivariate decoding techniques to probe the representational content of neural signals in a time-resolved manner. We observed a representation of expected stimuli in the neural signal shortly before they were presented, showing that expectations indeed induce a preactivation of stimulus templates. The strength of these prestimulus expectation templates correlated with participants’ behavioral improvement when the expected feature was task-relevant. These results suggest a mechanism for how predictive perception can be neurally implemented.
There is an increasing demand for accurate biomarkers for early non-invasive colorectal cancer detection. We employed a genome-scale marker discovery method to identify and verify candidate DNA ...methylation biomarkers for blood-based detection of colorectal cancer.
We used DNA methylation data from 711 colorectal tumors, 53 matched adjacent-normal colonic tissue samples, 286 healthy blood samples and 4,201 tumor samples of 15 different cancer types. DNA methylation data were generated by the Illumina Infinium HumanMethylation27 and the HumanMethylation450 platforms, which determine the methylation status of 27,578 and 482,421 CpG sites respectively. We first performed a multistep marker selection to identify candidate markers with high methylation across all colorectal tumors while harboring low methylation in healthy samples and other cancer types. We then used pre-therapeutic plasma and serum samples from 107 colorectal cancer patients and 98 controls without colorectal cancer, confirmed by colonoscopy, to verify candidate markers. We selected two markers for further evaluation: methylated THBD (THBD-M) and methylated C9orf50 (C9orf50-M). When tested on clinical plasma and serum samples these markers outperformed carcinoembryonic antigen (CEA) serum measurement and resulted in a high sensitive and specific test performance for early colorectal cancer detection.
Our systematic marker discovery and verification study for blood-based DNA methylation markers resulted in two novel colorectal cancer biomarkers, THBD-M and C9orf50-M. THBD-M in particular showed promising performance in clinical samples, justifying its further optimization and clinical testing.
Perception is a process of inference, integrating sensory inputs with prior expectations. However, little is known regarding the temporal dynamics of this integration. It has been proposed that ...expectation plays a role early in the perceptual process, biasing sensory processing. Alternatively, others suggest that expectations are integrated only at later, postperceptual decision-making stages. The current study aimed to dissociate between these hypotheses. We exposed human participants (male and female) to auditory cues predicting the likely direction of upcoming moving dot patterns, while recording neural activity using magnetoencephalography (MEG). Participants' reports of the moving dot directions were biased toward the direction predicted by the cues. To investigate when expectations affected sensory representations, we used inverted encoding models to decode the direction represented in early sensory signals. Strikingly, the cues modulated the direction represented in the MEG signal as early as 150 ms after visual stimulus onset. While this may not reflect a modulation of the initial feedforward sweep, it does reveal a modulation of early sensory representations. Exploratory analyses showed that the neural modulation was related to perceptual expectation effects: participants with a stronger perceptual bias toward the predicted direction also revealed a stronger reflection of the predicted direction in the MEG signal. For participants with this perceptual bias, a correlation between decoded and perceived direction already emerged before visual stimulus onset, suggesting that the prestimulus state of the visual cortex influences sensory processing. Together, these results suggest that expectations play an integral role in the neural computations underlying perception.
Perception can be thought of as an inferential process in which our brains integrate sensory inputs with prior expectations to make sense of the world. This study investigated whether this integration occurs early or late in the process of perception. We exposed human participants to auditory cues that predicted the likely direction of visual moving dots, while recording neural activity with millisecond resolution using magnetoencephalography. Participants' perceptual reports of the direction of the moving dots were biased toward the predicted direction. Additionally, the predicted direction modulated the neural representation of the moving dots just 150 ms after they appeared. This suggests that prior expectations affected sensory processing at early stages, playing an integral role in the perceptual process.
Perception is guided by the anticipation of future events. It has been hypothesized that this process may be implemented by pattern completion in early visual cortex, in which a stimulus sequence is ...recreated after only a subset of the visual input is provided. Here we test this hypothesis using ultra-fast functional magnetic resonance imaging to measure BOLD activity at precisely defined receptive field locations in visual cortex (V1) of human volunteers. We find that after familiarizing subjects with a spatial sequence, flashing only the starting point of the sequence triggers an activity wave in V1 that resembles the full stimulus sequence. This preplay activity is temporally compressed compared to the actual stimulus sequence and remains present even when attention is diverted from the stimulus sequence. Preplay might therefore constitute an automatic prediction mechanism for temporal sequences in V1.
We constantly exploit the statistical regularities in our environment to help guide our perception. The hippocampus has been suggested to play a pivotal role in both learning environmental ...statistics, as well as exploiting them to generate perceptual predictions. However, it is unclear how the hippocampus balances encoding new predictive associations with the retrieval of existing ones. Here, we present the results of two high resolution human fMRI studies (N = 24 for both experiments) directly investigating this. Participants were exposed to auditory cues that predicted the identity of an upcoming visual shape (with 75% validity). Using multivoxel decoding analysis, we find that the hippocampus initially preferentially represents unexpected shapes (i.e., those that violate the cue regularities), but later switches to representing the cue-predicted shape regardless of which was actually presented. These findings demonstrate that the hippocampus is involved both acquiring and exploiting predictive associations, and is dominated by either errors or predictions depending on whether learning is ongoing or complete.
Cancer gene therapies are usually designed either to express wild-type copies of tumor suppressor genes or to exploit tumor-associated phenotypic changes to endow selective cytotoxicity. However, ...these approaches become less relevant to cancers that contain many independent mutations, and the situation is made more complex by our increased understanding of clonal evolution of tumors, meaning that different metastases and even regions of the same tumor mass have distinct mutational and phenotypic profiles. In contrast, the relatively genetically stable tumor microenvironment (TME) therefore provides an appealing therapeutic target, particularly since it plays an essential role in promoting cancer growth, immune tolerance, and acquired resistance to many therapies. Recently, a variety of different TME-targeted gene therapy and armed oncolytic strategies have been explored, with particular success observed in strategies targeting the cancer stroma, reducing tumor vasculature, and repolarizing the immunosuppressive microenvironment. Herein, we review the progress of these TME-targeting approaches and try to highlight those showing the greatest promise.
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Recent advances in gene therapies and armed oncolytic viruses have been exploring therapeutic strategies that target not only cancer cells but also stromal populations. In this review, Wan et al. highlight different approaches to deplete cancer stroma, degrade tumor vasculature, and repolarize the immunosuppressive tumor microenvironment. The authors also discuss the opportunities this approach brings, together with challenges faced, and suggest directions the field may adopt.
Understanding pedestrian flow patterns in urban areas could support the decision‐making for infrastructure planning. By incorporating computer vision techniques into surveillance video processing, ...human walking trajectories in a wide area could be identified by pedestrian re‐identification (ReID) across multiple cameras. Recent ReID methods mostly use convolutional neural networks equipped with deep learning techniques to extract discriminative human features from images for identity matching. However, they still suffer from realistic challenges such as occlusion and appearance variation. This paper develops a ReID‐based framework for pedestrian trajectory recognition across multiple cameras. Specifically, a generic approach of explainable model design is presented, which intuitively analyzes existing baseline models based on feature visualization. Hence, a new model named OSNet + BDB is developed that extracts discriminative‐and‐distributed features. Additionally, an incremental feature aggregation strategy is designed for more robust identity matching. Our ReID method notably outperforms its baselines by 4% identification F1 accuracy in public benchmarks. Practically, pedestrian flow statistics in a real building are extracted for behavioral modeling. Simulations of several what‐if layouts are then conducted for facility performance evaluation.
Construction sites are among the most hazardous places with various safety issues. The high rate of hazards on construction sites can be attributed to the dynamic and complex characteristics of ...construction-related entities, such as the movement of construction equipment and workers as well as the interactions among them. Tracking construction equipment and workers can help avoid potential collisions and other accidents to achieve safer on-site conditions. As construction equipment (e.g. excavators, trucks, cranes, and bulldozers) plays a significant role in construction projects, it is important to track the location, pose and movement of construction equipment. Currently, with the wide installation of surveillance cameras on construction sites, computer vision techniques are explored to process the captured surveillance videos and images, such as to monitor the site conditions and prevent potential hazards. Previous studies have attempted to identify and locate different types of construction equipment on construction sites based on surveillance videos using computer vision techniques. However, there are limited studies that automatically estimate the full body pose and movement of on-site construction equipment, which can greatly influence the safety condition of construction sites and the utilization of the equipment itself.
In this study, a methodology framework is developed for automatically estimating the poses of different construction equipment in videos captured on construction sites using computer vision and deep learning techniques. Firstly, keypoints of equipment are defined, based on which the images collected from the surveillance cameras are annotated to generate the ground truth labels. 70%, 10%, and 20% of the annotated image dataset are used for training, validation and testing, respectively. Then, the architectures of three types of deep learning networks i.e. Stacked Hourglass Network (HG), Cascaded Pyramid Network (CPN), and an ensemble model (HG-CPN) integrating Stacked Hourglass and Cascaded Pyramid Network are constructed and trained in the same training environment. After training, the three models are evaluated on the testing dataset in terms of normalized errors (NE), percentage of correct keypoints (PCK), area under the curve (AUC), detection speed, and training time. The experiment results demonstrate the promising performance of our proposed methodology framework for automatically estimating different full body poses of construction equipment with high accuracy and fast speed. It is indicated by experiments that both HG and CPN can achieve relative high accuracy, with a PCK value of 91.19% and 91.78% respectively for estimating the equipment full body poses. In addition, the ensemble model with online data augmentation can further improve the accuracy, achieving a NE of 14.57 × 10−3, a PCK of 93.43%, and an AUC of 39.72 × 10−3 at the detection speed of 125 millisecond (ms) per image. This study lays the foundation for applying computer vision and deep learning techniques in the full body pose estimation of construction equipment, which can contribute to the real-time safety monitoring on construction sites.
•A methodology framework is proposed to estimate on-site equipment full body poses.•Equipment keypoints are defined and an equipment pose dataset is built and labeled.•Two models (HG and CPN) achieved accuracies over 90% for estimating equipment full body pose.•The ensemble model (HG-CPN) further improved the full body pose estimation accuracy.•HG, CPN, and HG-CPN are compared, and insights are provided for potential improvement.
Early visual areas contain specific information about visual items maintained in working memory, suggesting a role for early visual cortex in more complex cognitive functions 1–4. It is an open ...question, however, whether these areas also underlie the ability to internally generate images de novo (i.e., mental imagery). Research on mental imagery has to this point focused mostly on whether mental images activate early sensory areas, with mixed results 5–7. Recent studies suggest that multivariate pattern analysis of neural activity patterns in visual regions can reveal content-specific representations during cognitive processes, even though overall activation levels are low 1–4. Here, we used this approach 8, 9 to study item-specific activity patterns in early visual areas (V1–V3) when these items are internally generated. We could reliably decode stimulus identity from neural activity patterns in early visual cortex during both working memory and mental imagery. Crucially, these activity patterns resembled those evoked by bottom-up visual stimulation, suggesting that mental images are indeed “perception-like” in nature. These findings suggest that the visual cortex serves as a dynamic “blackboard” 10, 11 that is used during both bottom-up stimulus processing and top-down internal generation of mental content.
•Mental images can be decoded from activity patterns in human early visual cortex•Activity patterns evoked by mental images resemble stimulus-driven activity patterns•Mental imagery and working memory have shared representations in early visual cortex
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