Human Observers Research Articles

Potential of Accelerometers to Remotely Early Detect Bovine Ephemeral Fever in Cattle Using Pattern Mining1

Abstract Bovine Ephemeral Fever (BEF), caused by an arthropod-borne rhabdovirus, is widespread in tropical and subtropical regions. It affects cattle with symptoms of fever, lameness, inappetence and in some situations can result in mortality. The goal of this study is to determine if accelerometer data can be used to identify the behaviour patterns that occur when cattle become ill from BEF. Eight heifers in a separate experiment were monitored with 3-axis accelerometers sensors. Movement variation (MV) was calculated from accelerometer data (25 Hz) using 1-minute epochs and then averaged hourly. Two different approaches, cosine similarity (CS) and deviation from previous behavioural patterns, were developed to autonomously detect patterns and recognize the onset of sickness in cattle using accelerometer data. Analyses show that one heifer had behavioural changes one day before the manager observed BEF, and another heifer had behavioural changes on the same day the manager observed BEF. The other six heifers did not display any BEF symptoms. To validate the efficacy of our analytical approaches, we employed them on a separate commercial herd of 73 cows where 4 of the 27 monitored cows were observed with BEF symptoms. Predictions were either on the day or even the day prior to the manager's observation and diagnosis. There were likely no false positives in the first or second trials using the deviation algorithm with sum_deviation formula, but there were several false positives with the other algorithms. These case studies demonstrate the potential of accelerometer data to autonomously detect disease onset, in some cases before it was apparent to the human observer. However, more research is needed to minimize false positives that may occur from other similar diseases, abnormal weather events or cyclical changes in behaviour such as oestrus is required.

Cross-species comparisons between pigs and mice reveal conserved sex-specific intraspinal inflammatory responses after spinal cord injury

ObjectiveTherapeutic translation is challenging in spinal cord injury (SCI) and large animal models with high clinical relevance may accelerate therapeutic development. Pigs have important anatomical and physiological similarities to humans. Intraspinal inflammation mediates SCI pathophysiology. The purpose of this study was to evaluate the effect of sex on inflammation and outcomes in a pig thoracic contusion/compression SCI model.MethodsAdult (gonad-intact) male and female Yucatan miniature swine were subjected to either SCI or sham (laminectomy-only) injury.ResultsSCI caused locomotor dysfunction (measured with the Porcine Thoracic Injury Behavior Score) with some recovery over 6 weeks and limited tissue sparing at 6 weeks with no difference between sexes. Immunohistological evaluations of spinal cord tissue at 2 days and 6 weeks post-injury revealed intraspinal microglia/macrophage (IBA-1, CD68) and lymphocyte responses (T-cells (CD3) and B-cells (CD79a)) consistent with observations in rodents and humans. Astrocyte (GFAP) immunoreactivity was observed within the lesion core at 6 weeks in contrast to observations in rodents. No differences were seen for astrocytes, microglia, macrophages, B-cells, and neutrophil infiltration between males and females. Intraspinal CD3 + T-cell counts and T-cell microclusters were significantly higher in females compared to males 6 weeks post-injury. Interestingly, we observed a similar significant increase in intraspinal CD3 + T-cell accumulation in female vs. male mice at 6 weeks post-thoracic contusion SCI.InterpretationOur observations indicate that sex is a potential biological variable for T-cell infiltration and may contribute to sex-based differences in SCI pathophysiology and recovery outcomes.

CamKIIα and VPAC1 Expressions in the Caudal Trigeminal Nucleus of Rats After Systemic Nitroglycerin Treatment: Interaction with Anandamide

Migraines are a frequently occurring neurological condition that affects up to 16% of the global population. The precise pathomechanism of the disease remains unknown, but from animal and human observations, it appears that calcium/calmodulin-dependent protein kinase II alpha (CamKIIα), pituitary adenylate cyclase-activating polypeptide (PACAP), and vasoactive intestinal polypeptide (VIP) are involved in its pathogenesis. One of the animal models of migraines uses the systemic administration of nitroglycerin (NTG), which, as a nitric oxide (NO) donor, initiates a self-amplifying process in the trigeminal system, leading to central sensitization. Endocannabinoids, such as anandamide (AEA), are thought to play a modulatory role in trigeminal activation and sensitization phenomena. In the present experiment, we aimed to investigate the effect of NTG and AEA on CamKIIα, PACAP/VIP, and vasoactive intestinal polypeptide type 1 receptor (VPAC1) expression levels in the upper cervical spinal cord (C1-C2) of rats, where trigeminal nociceptive afferents are clustered. Four groups of animals were formed: in the first group, the rats received only the vehicle; in the second group, they were treated with an intraperitoneal injection of NTG (10 mg/kg); animals in the third and fourth groups received AEA (2 × 5 mg/kg) half an hour before and one hour after the placebo or treatment with NTG. Four hours after the placebo/NTG injection, the animals were transcardially perfused, and the cervical spinal cords were removed for Western blot. Our results show that both NTG and AEA alone can increase the expression of CamKIIα and VPAC1 in the C1-C2 segments. Interestingly, the combination of NTG and AEA had no such effect on these markers, possibly due to various negative feedback mechanisms.

Clinical Applications of Artificial Intelligence (AI) in Human Cancer: Is It Time to Update the Diagnostic and Predictive Models in Managing Hepatocellular Carcinoma (HCC)?

In recent years, novel findings have progressively and promisingly supported the potential role of Artificial intelligence (AI) in transforming the management of various neoplasms, including hepatocellular carcinoma (HCC). HCC represents the most common primary liver cancer. Alarmingly, the HCC incidence is dramatically increasing worldwide due to the simultaneous “pandemic” spreading of metabolic dysfunction-associated steatotic liver disease (MASLD). MASLD currently constitutes the leading cause of chronic hepatic damage (steatosis and steatohepatitis), fibrosis, and liver cirrhosis, configuring a scenario where an HCC onset has been reported even in the early disease stage. On the other hand, HCC represents a serious plague, significantly burdening the outcomes of chronic hepatitis B (HBV) and hepatitis C (HCV) virus-infected patients. Despite the recent progress in the management of this cancer, the overall prognosis for advanced-stage HCC patients continues to be poor, suggesting the absolute need to develop personalized healthcare strategies further. In this “cold war”, machine learning techniques and neural networks are emerging as weapons, able to identify the patterns and biomarkers that would have normally escaped human observation. Using advanced algorithms, AI can analyze large volumes of clinical data and medical images (including routinely obtained ultrasound data) with an elevated accuracy, facilitating early diagnosis, improving the performance of predictive models, and supporting the multidisciplinary (oncologist, gastroenterologist, surgeon, radiologist) team in opting for the best “tailored” individual treatment. Additionally, AI can significantly contribute to enhancing the effectiveness of metabolomics–radiomics-based models, promoting the identification of specific HCC-pathogenetic molecules as new targets for realizing novel therapeutic regimens. In the era of precision medicine, integrating AI into routine clinical practice appears as a promising frontier, opening new avenues for liver cancer research and treatment.

Monitoring American woodcock abundance in Tennessee using automated recording units

AbstractAlthough American woodcock (Scolopax minor) have been declining for the past 50 years, their breeding activity is not regularly monitored across much of the southeastern United States. Due to predictable crepuscular display behaviors, woodcock are a good candidate for monitoring with automated recording units (ARUs). Here, we test the efficacy of ARUs for monitoring woodcock against human observers. We also evaluate the use of automated detection software for 2 woodcock display sounds, the peent call and twitter display, and estimate whether abundance estimates can be derived from the acoustic data. We find that human observers detected more woodcock and counted more twitter displays than ARUs with manual human review but counted similar numbers of peent calls. The automated detection program monitoR successfully classified 52% of peent calls captured in recordings but was unable to consistently classify twitter displays. We were unable to successfully predict the number of woodcock in a given area based on the total number of peents or twitters recorded per survey by human observers in the field. Automated recording units can be effectively used to monitor woodcock presence but have limitations compared to human observers for estimating abundance.

YOLOv8‐ESW: An Improved Oncomelania hupensis Detection Model

ABSTRACTTraditional Oncomelania hupensis detection relies on human eye observation, which results in reduced efficiency due to easy fatigue of the human eye and limited individual cognition, an improved YOLOv8 O. hupensis detection algorithm, YOLOv8‐ESW(expectation–maximization attention [EMA], Small Target Detection Layer, and Wise‐IoU), is proposed. The original dataset is augmented using the OpenCV library. To imitate image blur caused by motion jitter, salt and pepper, and Gaussian noise were added to the dataset; to imitate images from different angles captured by the camera in an instant, affine, translation, flip, and other transformations were performed on the original data, resulting in a total of 6000 images after data enhancement. Considering the insufficient feature fusion problem caused by lightweight convolution, We present the expectation–EMA module (E), which innovatively incorporates a coordinate attention mechanism and convolutional layers to introduce a specialized layer for small target detection (S). This design significantly improves the network's ability to synergize information from both superficial and deeper layers, better focusing on small target O. hupensis and occluded O. hupensis. To tackle the challenge of quality imbalance among O. hupensis samples, we employ the Wise‐IoU (WIoU) loss function (W). This approach uses a gradient gain distribution strategy and improves the model convergence speed and regression accuracy. The YOLOv8‐ESW model, with 16.8 million parameters and requiring 98.4 GFLOPS for computations, achieved a mAP of 92.74% when tested on the O. hupensis dataset, marking a 4.09% improvement over the baseline model. Comprehensive testing confirms the enhanced network's efficacy, significantly elevating O. hupensis detection precision, minimizing both missed and false detections, and fulfilling real‐time processing criteria. Compared with the current mainstream models, it has certain advantages in detection accuracy and has reference value for subsequent research in actual detection.

Estimating Wingbeat Frequency of Hummingbirds using a No-labeling Learning Computer Vision Approach.

Wingbeat frequency estimation is an important aspect for the study of avian flight, energetics, and behavioral patterns, among others. Hummingbirds, in particular, are ideal subjects to test a method for this estimation due to their fast wing motions and unique aerodynamics, which results from their ecological diversification, adaptation to high-altitude environments, and sexually selected displays. Traditionally, wingbeat frequency measurements have been done via "manual" image/sound processing. In this study, we present an automated method to detect, track, classify, and monitor hummingbirds in high-speed video footage, accurately estimating their wingbeat frequency using computer vision techniques and signal analysis. Our approach utilizes a zero-shot learning algorithm that eliminates the need for labeling during training. Results demonstrate that our method can produce automated wingbeat frequency estimations with minimal supervision, closely matching those performed by trained human observers. This comparison indicates that our method can, in some scenarios, achieve low or zero error compared to a human, making it a valuable tool for flight analysis. Automating video analysis can assist wingbeat frequency estimation by reducing processing time and, thus, lowering barriers to analyze biological data on fields such as aerodynamics, foraging behavior, and signaling.

An unsupervised automatic texture classification method for ultrasound images of thyroid nodules

Objective.Ultrasound is the predominant modality in medical practice for evaluating thyroid nodules. Currently, diagnosis is typically based on textural information. This study aims to develop an automated texture classification approach to aid physicians in interpreting ultrasound images of thyroid nodules. However, there is currently a scarcity of pixel-level labeled datasets for the texture classes of thyroid nodules. The labeling of such datasets relies on professional and experienced doctors, requiring a significant amount of manpower. Therefore, the objective of this study is to develop an unsupervised method for classifying nodule textures.Approach.Firstly, we develop a spatial mapping network to transform the one-dimensional pixel value space into a high-dimensional space to extract comprehensive feature information. Subsequently, we outline the principles of feature selection that are suitable for clustering. Then we propose a pixel-level clustering algorithm with a region growth pattern, and a distance evaluation method for texture sets among different nodules is established.Main results.Our algorithm achieves a pixel-level classification accuracy of 0.931 for the cystic and solid region, 0.870 for the hypoechoic region, 0.959 for the isoechoic region, and 0.961 for the hyperechoic region. The efficacy of our algorithm and its concordance with human observation have been demonstrated. Furthermore, we conduct calculations and visualize the distribution of different textures in benign and malignant nodules.Significance.This method can be used for the automatic generation of pixel-level labels of thyroid nodule texture, aiding in the construction of texture datasets, and offering image analysis information for medical professionals.

The role of dynamic shape cues in the recognition of emotion from naturalistic body motion.

Human observers can often judge emotional or affective states from bodily motion, even in the absence of facial information, but the mechanisms underlying this inference are not completely understood. Important clues come from the literature on "biological motion" using point-light displays (PLDs), which convey human action, and possibly emotion, apparently on the basis of body movements alone. However, most studies have used simplified and often exaggerated displays chosen to convey emotions as clearly as possible. In the current study we aim to study emotion interpretation using more naturalistic stimuli, which we draw from narrative films, security footage, and other sources not created for experimental purposes. We use modern algorithmic methods to extract joint positions, from which we create three display types intended to probe the nature of the cues observers use to interpret emotions: PLDs; stick figures, which convey "skeletal" information more overtly; and a control condition in which joint positions are connected in an anatomically incorrect manner. The videos depicted a range of emotions, including fear, joy, nurturing, anger, sadness, and determination. Subjects were able to estimate the depicted emotion with a high degree of reliability and accuracy, most effectively from stick figures, somewhat less so for PLDs, and least for the control condition. These results confirm that people can interpret emotion from naturalistic body movements alone, and suggest that the mechanisms underlying this interpretation rely heavily on skeletal representations of dynamic shape.

Gestural Iconicity and Alignment as Steps in the Evolution of Language.

Studies of the evolution of language rely heavily on comparisons to nonhuman primates, particularly the gestural communication of nonhuman apes. Differences between human and ape gestures are largely ones of degree rather than kind. For example, while human gestures are more flexible, ape gestures are not inflexible. In this piece, I closely consider two features of the gestural communication of apes and humans that might display differences in kind: iconicity and temporal alignment. Iconicity has long played a privileged role in theories of gestural language origins, the proposal being that it provided a steppingstone into language through a stage of pantomime. However, iconicity is not as easy as it seems. Evidence from co-speech gestures of hearing children and from homesign suggests that iconic reference is both cognitively complex and slow to develop in humans. There is no conclusive evidence that nonhuman apes understand or produce iconic gestures; some gestures may appear iconic to human observers but the apes themselves may not understand the similarity between form and meaning. Far from providing an easy pathway into the emergence of symbolic communication, iconicity relies on sophisticated capacities for analogy and abstraction, ones often lacking in apes and young children. Temporal alignment between gesture and vocalization is another area that may show sharp contrasts between adult humans and apes, though data here is sparser. I discuss the tight synchronization of gesture and speech that emerges over the first year of life in human children and contrast it with gesture and vocalization in apes, which typically are described as overlapping but not simultaneous. Human ancestors probably communicated in ways similar to other apes, but the dual emergence of the ability to use iconic reference and the alignment of hand and mouth may have set our ancestors down a unique evolutionary road toward language.

An AI-Based Digital Scanner for Varroa destructor Detection in Beekeeping.

Beekeeping is a crucial agricultural practice that significantly enhances environmental health and food production through effective pollination by honey bees. However, honey bees face numerous threats, including exotic parasites, large-scale transportation, and common agricultural practices that may increase the risk of parasite and pathogen transmission. A major threat is the Varroa destructor mite, which feeds on honey bee fat bodies and transmits viruses, leading to significant colony losses. Detecting the parasite and defining the intervention thresholds for effective treatment is a difficult and time-consuming task; different detection methods exist, but they are mainly based on human eye observations, resulting in low accuracy. This study introduces a digital portable scanner coupled with an AI algorithm (BeeVS) used to detect Varroa mites. The device works through image analysis of a sticky sheet previously placed under the beehive for some days, intercepting the Varroa mites that naturally fall. In this study, the scanner was tested for 17 weeks, receiving sheets from 5 beehives every week, and checking the accuracy, reliability, and speed of the method compared to conventional human visual inspection. The results highlighted the high repeatability of the measurements (R2 ≥ 0.998) and the high accuracy of the BeeVS device; when at least 10 mites per sheet were present, the device showed a cumulative percentage error below 1%, compared to approximately 20% for human visual observation. Given its repeatability and reliability, the device can be considered a valid tool for beekeepers and scientists, offering the opportunity to monitor many beehives in a short time, unlike visual counting, which is done on a sample basis.

Clinical Application Of Deep Learning-assisted Needles Reconstruction In Prostate Ultrasound Brachytherapy.

High dose rate (HDR) prostate brachytherapy (BT) procedure requires image-guided needle insertion. Given that general anesthesia is often employed during the procedure, minimizing overall planning time is crucial. In this study, we explore the clinical feasibility and time-saving potential of artificial intelligence (AI)-driven auto-reconstruction of transperineal needles in the context of US-guided prostate BT planning. This study included a total of 102 US-planned BT images from a single institution and split into three groups: 50 for model training and validation, 11 to evaluate reconstruction accuracy (test set), and 41 to evaluate the AI tool in a clinical implementation (clinical set). Reconstruction accuracy for the test set was evaluated by comparing the performance of AI-derived and manually reconstructed needles from 5 medical physicists on the 3D-US scans after treatment. The needle total reconstruction time for the clinical set was defined as the timestamp difference from scan acquisition to the start of dose calculations and was compared to values recorded before the clinical implementation of the AI-assisted tool. A mean error of (0.44±0.32)mm was found between the AI-reconstructed and the human consensus needle positions in the test set, with 95.0% of AI needle points falling below 1mm from their human-made counterparts. Post-hoc analysis showed only one of the human observers' reconstructions were significantly different from the others including the AI's. In the clinical set, the AI algorithm achieved a true positive reconstruction rate of 93.4% with only 4.5% of these needles requiring manual corrections from the planner before dosimetry. Total time required to perform AI-assisted catheter reconstruction on clinical cases was on average 15.2min lower (p < 0.01) compared to procedure without AI assistance. This study demonstrates the feasibility of an AI-assisted needle reconstructing tool for 3D-US based HDR prostate BT. This is a step toward treatment planning automation and increased efficiency in HDR prostate BT.

FAST: Fast, free, consistent, and unsupervised oligodendrocyte segmentation and tracking system.

To develop reparative therapies for neurological disorders like multiple sclerosis (MS), we need to better understand the physiology of loss and replacement of oligodendrocytes, the cells that make myelin and are the target of damage in MS. In vivo two-photon fluorescence microscopy allows direct visualization of oligodendrocytes in the intact brain of transgenic mouse models, promising a deeper understanding of the longitudinal dynamics of replacing oligodendrocytes after damage. However, the task of tracking the fate of individual oligodendrocytes requires extensive effort for manual annotation and is especially challenging in three-dimensional images. While several models exist for annotating cells in two-dimensional images, few models exist to annotate cells in three-dimensional images and even fewer are designed for tracking cells in longitudinal imaging. Notably, existing options often come with a substantial financial investment, being predominantly commercial or confined to proprietary software. Furthermore, the complexity of processes and myelin formed by individual oligodendrocytes can result in the failure of algorithms that are specifically designed for tracking cell bodies alone. Here, we propose a fast, free, consistent, and unsupervised beta-mixture oligodendrocyte segmentation system (FAST) that is written in open-source software, and can segment and track oligodendrocytes in three-dimensional images over time with minimal human input. We showed that the FAST model can segment and track oligodendrocytes similarly to a blinded human observer. Although FAST was developed to apply to our studies on oligodendrocytes, we anticipate that it can be modified to study four-dimensional in vivo data of any brain cell with associated complex processes.Significance Statement We have developed "FAST: Fast, free, consistent, and unsupervised oligodendrocyte segmentation and tracking system" to solve our challenge of quantification of four-dimensional data acquired from longitudinal in vivo imaging. Although it was developed for oligodendrocytes, we will make the code entirely open source and user-friendly, and expect that it will be useful for segmentation for any cell body from a complex cell amenable to longitudinal in vivo imaging.

A practical approach to the spatial-domain calculation of nonprewhitening model observers in computed tomography.

Modern reconstruction algorithms for computed tomography (CT) can exhibit nonlinear properties, including non-stationarity of noise and contrast dependence of both noise and spatial resolution. Model observers have been recommended as a tool for the task-based assessment of image quality (Samei E etal., Med Phys. 2019; 46(11): e735-e756), but the common Fourier domain approach to their calculation assumes quasi-stationarity. A practical spatial-domain approach is proposed for the calculation of the nonprewhitening (NPW) family of model observers in CT, avoiding the disadvantages of the Fourier domain. The methodology avoids explicit estimation of a noise covariance matrix. A formula is also provided for the uncertainty on estimates of detectability index, for a given number of slices and repeat scans. The purpose of this work is to demonstrate the method and provide comparisons to the conventional Fourier approach for both iterative reconstruction (IR) and a deep Learning-based reconstruction (DLR) algorithm. Acquisitions were made on a Revolution CT scanner (GE Healthcare, Waukesha, Wisconsin, USA) and reconstructed using the vendor's IR and DLR algorithms (ASiR-V and TrueFidelity). Several reconstruction kernels were investigated (Standard, Lung, and Bone for IR and Standard for DLR). An in-house developed phantom with two flat contrast levels (2 and 8 mgI/mL) and varying feature size (1-10mm diameter) was used. Two single-energy protocols (80 and 120kV) were investigated with two dose levels (CTDIvol=5 and 13 mGy). The spatial domain calculations relied on repeated scanning, region-of-interest placement and simple operations with image matrices. No more repeat scans were utilized than required for Fourier domain estimations. Fourier domain calculations were made using techniques described in a previous publication (Thor D etal., Med Phys. 2023;50(5):2775-2786). Differences between the calculations in the two domains were assessed using the normalized root-mean-square discrepancy (NMRSD). Fourier domain calculations agreed closely with those in the spatial domain for all zero-strength IR reconstructions, which most closely resemble traditional filtered backprojection. The Fourier-based calculations, however, displayed higher detectability compared to those in the spatial domain for IR with strong iterative strength and for the DLR algorithm. The NRMSD remained within 10% for the NPW model observer without eye filter, but reached larger values when an eye filter was included. The formula for the uncertainty on the detectability index was validated by bootstrap estimates. A practical methodology was demonstrated for calculating NPW observers in the spatial domain. In addition to being a valuable tool for verifying the applicability of typical Fourier-based methodologies, it lends itself to routine calculations for features embedded in a phantom. Higher estimates of detectability were observed when adopting the Fourier domain methodology for IR and for a DLR algorithm, demonstrating that use of the Fourier domain can indicate greater benefit to noise suppression than suggested by spatial domain calculations. This is consistent with the results of previous authors for the Fourier domain, who have compared to human and other model observers, but not, as in this study, to the NPW model observer calculated in the spatial domain.

Attractive and repulsive visual aftereffects depend on stimulus contrast.

Visual perception has been described as a dynamic process where incoming visual information is combined with what has been seen before to form the current percept. Such a process can result in multiple visual aftereffects that can be attractive toward or repulsive away from past visual stimulation. A lot of research has been conducted on what functional role the mechanisms that produce these aftereffects may play. However, there is a lack of understanding of the role of stimulus uncertainty on these aftereffects. In this study, we investigate how the contrast of a stimulus affects the serial aftereffects it induces and how the stimulus itself is affected by these effects depending on its contrast. We presented human observers with a series of Gabor patches and monitored how the perceived orientation of stimuli changed over time with the systematic manipulation of orientation and contrast of presented stimuli. We hypothesized that repulsive serial effects would be stronger for the judgment of high-contrast than low-contrast stimuli, but the other way around for attractive serial effects. Our experimental findings confirm such a strong interaction between contrast and sign of aftereffects. We present a Bayesian model observer that can explain this interaction based on two principles, the dynamic changes of orientation-tuned channels in short timescales and the slow integration of prior information over long timescales. Our findings have strong implications for our understanding of orientation perception and can inspire further work on the identification of its neural mechanisms.

Opposite asymmetry in visual perception of humans and macaques.

In human adults, visual perception varies throughout the visual field. Performance decreases with eccentricity1,2 and varies around polar angle. At isoeccentric locations, performance is typically higher along the horizontal than vertical meridian (horizontal-vertical asymmetry [HVA]) and along the lower than the upper vertical meridian (vertical meridian asymmetry [VMA]).3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23 It is unknown whether the macaque visual system, the leading animal model for understanding human vision,24,25 also exhibits these performance asymmetries. Here, we investigated whether and how visual field asymmetries differ between these two groups. Human adults and adult macaque monkeys (Macaca nemestrina) performed a two-alternative forced choice (2AFC) motion direction discrimination task for a target presented among distractors at isoeccentric locations. Both groups showed heterogeneous visual sensitivity around the visual field, but there were striking differences between them. Human observers showed a large VMA-their sensitivity was poorest at the upper vertical meridian-a weak horizontal-vertical asymmetry, and lower sensitivity at intercardinal locations. Macaque performance revealed an inverted VMA-their sensitivity was poorest in the lower vertical meridian. The opposite pattern of VMA in macaques and humans revealed in this study may reflect adaptive behavior by increasing discriminability at locations with greater relevance for visuomotor integration. This study reveals that performance also varies as a function of polar angle for monkeys, but in a different manner than in humans, and highlights the need to investigate species-specific similarities and differences in brain and behavior to constrain models of vision and brain function.

A Handheld Colorimeter for Remote and Onsite Recognition of Baking Levels at High Temperature — Pork Floss as a Case Study

AbstractPork floss is a common dried meat product in Asia. The endpoint of the baking process is traditionally determined by subjective human experts and indirect temperature measurements, which can often result in unstandardized production. Current colorimeters are unavailable for onsite measurement due to limitations associated with contact measurement and environmental temperature. Instead of the abovementioned human experts and tabletop colorimeters, a handheld colorimeter was built based on the expertise of human specialists and utilizing a tabletop colorimeter and other optical steps. First, the selected samples were used to determine the upper and lower limits distinguishing light, medium, and heavy baking levels by using a tabletop colorimeter. Second, independent light sources and spectrometers were utilized to choose the characteristic and reference wavelengths at 450 and 830 nm, separately. Third, the handheld colorimeter, instead of human expert observation, was designed with functions such as distance sensing and Internet of Things capabilities. The baked index was derived from the calibration reflection and established statistical models. Here, the calibration reflection was defined by the normalized intensity at 450 nm relative to 830 nm, and statistical models were founded from the determined samples of upper and lower limits at 95–700 mm. The developed handheld colorimeter demonstrated high agreement rates of 96.84% and 93.86% in separate comparisons with tabletop colorimeters and human experts, respectively. This work indicated the accurate and stable recognition of samples within two limits and overall. Field validation confirmed the performance of remote, economic, and onsite recognition against environmental temperature and noise.

A spatiotemporal style transfer algorithm for dynamic visual stimulus generation.

Understanding how visual information is encoded in biological and artificial systems often requires the generation of appropriate stimuli to test specific hypotheses, but available methods for video generation are scarce. Here we introduce the spatiotemporal style transfer (STST) algorithm, a dynamic visual stimulus generation framework that allows the manipulation and synthesis of video stimuli for vision research. We show how stimuli can be generated that match the low-level spatiotemporal features of their natural counterparts, but lack their high-level semantic features, providing a useful tool to study object recognition. We used these stimuli to probe PredNet, a predictive coding deep network, and found that its next-frame predictions were not disrupted by the omission of high-level information, with human observers also confirming the preservation of low-level features and lack of high-level information in the generated stimuli. We also introduce a procedure for the independent spatiotemporal factorization of dynamic stimuli. Testing such factorized stimuli on humans and deep vision models suggests a spatial bias in how humans and deep vision models encode dynamic visual information. These results showcase potential applications of the STST algorithm as a versatile tool for dynamic stimulus generation in vision science.

Weight illusions explained by efficient coding based on correlated natural statistics

In our everyday experience, the sizes and weights of objects we encounter are strongly correlated. When objects are lifted, visual information about size can be combined with haptic feedback about weight, and a naive application of Bayes’ rule predicts that the perceived weight of larger objects should be exaggerated and smaller objects underestimated. Instead, it is the smaller of two objects of equal weight that is perceived as heavier, a phenomenon termed the Size-Weight Illusion (SWI). Here we provide a normative explanation of the SWI based on principles of efficient coding, which dictate that stimulus properties should be encoded with a fidelity that depends on how frequently those properties are encountered in the environment. We show that the precision with which human observers estimate object weight varies as a function of both mass and volume in a manner consistent with the estimated joint distribution of those properties among everyday objects. We further show that participants’ seemingly “anti-Bayesian” biases (the SWI) are quantitatively predicted by Bayesian estimation when taking into account the gradient of discriminability induced by efficient encoding. The related Material-Weight Illusion (MWI) can also be accounted for on these principles, with surface material providing a visual cue that changes expectations about object density. The efficient coding model is further compatible with a wide range of previous observations, including the adaptability of weight illusions and properties of “non-illusory” objects. The framework is general and predicts perceptual biases and variability in any sensory properties that are correlated in the natural environment.

Impact of nighttime human behavior on exposure to malaria vectors and effectiveness of using long-lasting insecticidal nets in the Ethiopian lowlands and highlands

BackgroundEthiopia continues to grapple with a persistent malaria burden, characterized by ongoing transmission and recurrent outbreaks. Human behavior influences both malaria exposure and the effectiveness of vector interventions, complicating malaria control efforts. Implementing tailored strategies that account for the complex interplay between human activities and vector behavior remains a challenge in both high- and low-transmission areas in Ethiopia, particularly for vulnerable highland populations and temporary labor migrants, due to lack of data. The aim of this study was to examine the spatiotemporal patterns of human—mosquito interactions and evaluate the effectiveness and suitability of long-lasting insecticidal nets (LLINs) in settings involving lowland resident populations, seasonal migrant workers and highland communities.MethodsConcurrent human and vector behavior data were collected from high-transmission lowlands (residents and temporary migrant workers) and vulnerable highlands populations. Hourly human behavior observations (HBOs), which examined LLIN use, indoor versus outdoor human presence and sleeping patterns, were paired in a crossover design with mosquito sampling using US Centers for Disease Control light traps (CDC LT) as a proxy for mosquito biting behavior. The study was conducted during the peak (October–December 2022) and minor (March–May 2023) malaria transmission seasons (‘peak’ and ‘minor’) for a total of 368 nights. In the highlands, four villages consisting of eight households per village were selected for surveillance; in the lowlands, four villages consisting of two resident villages and two farm sites with migrant workers, with eight households/structures per village or farm, were used for data collection. CDC LT and HBO data were integrated to evaluate HBO-adjusted human biting rates (HBO-adjusted HBR) of Anopheles mosquitoes.ResultsIn the highland villages, residents predominantly engaged in indoor activities, with their peak activity overlapping with the peak biting hours (1800-2200 hours). A substantial proportion of inhabitants slept indoors without LLINs in the peak and minor seasons (42.8% and 39.2%, respectively). Highland residents were significantly more exposed to malaria vectors indoors (88.4% peak, 88.6% minor) than outdoors during both transmission seasons. In lowland villages, both resident and seasonal migrant worker populations exhibited predominantly outdoor activity, particularly during peak biting hours (1800-2300 hours). Both residents and temporary migrants were significantly more exposed to Anopheles mosquitoes outdoors (resident: 65.0% peak, 67.1% minor; migrant: 70.5% peak, 80.0% minor) than indoors during both transmission seasons. LLIN usage was minimal and offered limited protection, with < 16.63% of person-time spent under nets by resident populations and 10.7% by migrant workers.ConclusionsMalaria control in Ethiopia requires context-specific strategies tailored to diverse ecological settings that consider the impact of human behavior on exposure to Anopheles mosquitoes. Limited LLIN effectiveness, human activities coinciding with peak biting times and minimal LLIN usage create significant protection gaps. Comprehensive control necessitates supplemental tools addressing exposure in all locations and times. In the Ethiopian highlands, where indoor activities predominate, increased LLIN usage combined with targeted indoor residual spraying could reduce transmission. In lowland areas, both residents and seasonal migrant workers face relatively higher outdoor exposure risks, requiring additional measures, such as topical and spatial repellents. We recommend implementing data-driven, hyperlocal approaches based on specific human—vector interactions to enhance malaria control effectiveness across the Ethiopian highlands and lowlands.Graphical