Search Results (227)

Anthropic activities have significantly elevated cadmium levels, making it a significant stressor in aquatic ecosystems. Present in high concentrations across water bodies, cadmium is known to bioaccumulate and biomagnify throughout the food chain. While the toxic effects of cadmium on the organs and tissues of aquatic species are well-documented, little is known about its impact on sensory systems crucial for survival. Consequently, this study investigated the impact of short-term exposure (96 h) to 25 µM cadmium chloride on the olfactory system of adult zebrafish. The research aimed to assess structural and functional changes in the zebrafish’s olfactory lamellae, providing a deeper understanding of how cadmium affects the sense of smell in this aquatic species. After exposure, cyto-anatomical alterations in the lamellae were analysed using light microscopy and immunocytochemistry. They revealed severe lamellar edema, epithelial thickening, and an increased number of apoptotic and crypt cells. Rodlet and goblet cells also increased by 3.5- and 2.5-fold, respectively, compared to control lamellae, and collagen density in the lamina propria increased 1.7-fold. Cadmium upregulated metallothioneins and increased the number of PCNA-positive cells. The olfactory function was assessed through a behavioural odour recognition test, followed by a recovery phase in which zebrafish exposed to cadmium were placed in clean water for six days. The exposed fish performed poorly, failing to reach food in five consecutive trials. However, lamellar damage was reduced after the recovery period, and their performance improved, becoming comparable to the control group. These results suggest that cadmium disrupts the sense of smell, and that recovery is possible after short-term exposure. This evidence sheds light on aspects of animal survival that are often overlooked when assessing environmental pollution. Full article

Background/Objectives: Tactile gnosis derives from the interplay between the hand’s tactile input and the memory systems of the brain. It is the prerequisite for complex hand functions. Impaired sensation leads to profound disability. Various invasive and non-invasive sensory substitution strategies for providing feedback from prostheses have been unsuccessful when translated to clinical practice, since they fail to match the feeling to genuine sensation of the somatosensory cortex. Methods: Herein, we describe a novel surgical technique for upper-limb-targeted sensory reinnervation (ulTSR) and report how single digital nerves selectively reinnervate the forearm skin and restore the spatial sensory capacity of single digits of the amputated hand in a case series of seven patients. We explore the interplay of the redirected residual digital nerves and the interpretation of sensory perception after reinnervation of the forearm skin in the somatosensory cortex by evaluating sensory nerve action potentials (SNAPs), somatosensory evoked potentials (SEPs), and amputation-associated pain qualities. Results: Digital nerves were rerouted and reliably reinnervated the forearm skin after hand amputation, leading to somatotopy and limb maps of the thumb and four individual fingers. SNAPs were obtained from the donor digital nerves after stimulating the recipient sensory nerves of the forearm. Matching SEPs were obtained after electrocutaneous stimulation of the reinnervated skin areas of the forearm where the thumb, index, and little fingers are perceived. Pain incidence was significantly reduced or even fully resolved. Conclusions: We propose that ulTSR can lead to higher acceptance of prosthetic hands and substantially reduce the incidence of phantom limb and neuroma pain. In addition, the spatial restoration of lost-hand sensing and the somatotopic reinnervation of the forearm skin may serve as a machine interface, allowing for genuine sensation and embodiment of the prosthetic hand without the need for complex neural coding adjustments. Full article

Recent research has increasingly focused on the benefits of various plants, including hemp, which has gained prominence for its medical and industrial applications. The incorporation of industrial hemp in beer brewing has further popularized this age-old beverage. In several countries, the cultivation of industrial hemp containing a maximum of 0.2% THC (Δ9-tetrahydrocannabinol) has been legalized, leading to a growing recognition of the plant’s potential and its derived products. The objective of this study was to produce beer infused with dried industrial hemp inflorescences. Additionally, a sensory analysis was conducted with panelists to evaluate the drinkability of the produced beer, revealing that both beer variants with added hemp inflorescence received higher scores. The addition of hemp inflorescence during boiling resulted with a higher specific gravity (1.01071 mg/L) in regard to control beer (1.01015 mg/L) and beer subjected to dry hemping (1.01018 mg/L). Generally, a significant difference for most physical–chemical parameters was noted in the sample boiled with hemp inflorescence, while the control sample and dry-hemped sample showed no statistically significant difference. The only physical–chemical property that showed no difference between all samples, including control, was bitterness, which exhibited no change in relation to the mode of hemp inflorescence addition. Full article

The olfactory sensory system plays vital roles in daily activities, such as locating mate partners, foraging, and risk avoidance. Natural enemies can locate their prey through characteristic volatiles. However, little is known about whether prey can recognize the volatiles of their predators and if this recognition can increase the efficiency of prey escaping from predators. Xylocoris flavipes is a predator of Tribolium castaneum (Herbst) and Oryzaephilus surinamensis (Linne) that has been widely used in stored pest control. Herein, we analyze the volatile components of Xylocoris flavipes and their impacts on the olfactory behavior of T. castaneum and O. surinamensis. We found that T. castaneum and O. surinamensis preferred blank air rather than odors of X. flavipes and X. flavipes emissions, which significantly decreased the orientation preference of T. castaneum and O. surinamensis to wheat. X. flavipes emits three major volatiles, including linalool, α-terpineol, and geraniol. Y-tube bioassays showed that T. castaneum and O. surinamensis can recognize linalool and geraniol at certain concentrations, especially at 200 μg/mL. EAG recordings verified that linalool and geraniol elicit higher olfactory responses in the two pests, but very small EAG responses were observed in the insects to α-terpineol. A further repellency evaluation also proved that linalool and geraniol are repellent to the two pests, and this repellency can be slightly enhanced by mixing them together. T. castaneum and O. surinamensis can recognize the predator X. flavipes by perceiving its volatiles and using them as signals for escaping. The two most potent volatiles, linalool and geraniol, may have potential values as repellents in controlling pests in these two stored products. Full article

Federated learning (FL) has emerged as a promising solution for the Internet of Things (IoT), facilitating distributed artificial intelligence while ensuring communication efficiency and data privacy. Traditional methods involve transmitting raw sensory data from IoT devices to servers or base-stations for processing, resulting in significant communication overhead. This overhead not only increases energy consumption but also diminishes device longevity within IoT networks. By focusing on model updates rather than raw data transmission, FL reduces the volume of data communicated to the base-station; however, FL still faces challenges due to the multiple communication rounds required for convergence. This research introduces an innovative approach that leverages the in-network processing capabilities of IoT devices by integrating a hierarchical clustering routing protocol with FL. This approach enhances energy efficiency through single-round pattern recognition, minimizing the need for multiple communication rounds to achieve convergence. It is envisaged that the proposed approach will prolong the lifespan of IoT devices and maintain high accuracy in event detection, all while ensuring robust data privacy. Full article

This pilot study explored how muscle activation influences the pattern recognition of tactile cues delivered using electrical stimulation (ES) during each 10% window interval of the normal walking gait cycle (GC). Three healthy adults participated in the experiment. After identifying the appropriate threshold, ES as the haptic cue was applied to the gastrocnemius lateralis (GL) and biceps brachii (BB) of participants walking on a treadmill. Findings revealed variable recognition patterns across participants, with the BB showing more variability during walking due to its minimal activity compared to the actively engaged GL. Dynamic time warping (DTW) was used to assess the similarity between muscle activation and electro-stimulated haptic perception. The DTW distance between electromyography (EMG) signals and muscle recognition patterns was significantly smaller for the GL (4.87 ± 0.21, mean ± SD) than the BB (8.65 ± 1.36, mean ± SD), showing a 78.6% relative difference, indicating that higher muscle activation was generally associated with more consistent haptic perception. However, individual differences and variations in recognition patterns were observed, suggesting personal variability influenced the perception outcomes. The study underscores the complexity of human neuromuscular responses to artificial sensory stimuli and suggests a potential link between muscle activity and haptic perception. Full article

When an organism is exposed to environmental stimuli of varying intensity, the adaptive changes in the CNS can be explained by several conceptual provisions: the law of motivation–activation by Yerkes and Dodson, the laws of force and pessimal protective inhibition, and the theory of emotion activation. Later, reinforcement sensitivity theory was developed in the fields of psychology and psychophysics. At the same time, cortical prepulse inhibition (PPI), the prepulse inhibition of perceived stimulus intensity (PPIPSI), and the augmentation/reduction phenomenon were proposed in sensory neurophysiology, which expanded our understanding of consciousness. The aim of this study was to identify markers of levels of activity of cognitive processes under normal and in psychopathological conditions while amplifying the information stimulus. For this purpose, we changed the contrast level of reversible checkerboard patterns and mapped the visual evoked potentials (VEPs) in 19 monopolar channels among 52 healthy subjects and 39 patients with a mental illness without an active productive pathology. Their cognitive functions were assessed by presenting visual tests to assess invariant pattern recognition, short-term visual memory, and Gestalt perception. The personalities of the healthy subjects were assessed according to Cattell’s 16-factor questionnaire, linking the data from neurophysiological and cognitive studies to factors Q4 (relaxation/tension) and C (emotional stability). According to the N₇₀ and N₁₅₀ VEP waves, the healthy subjects and the patients were divided into two groups. In some, there was a direct relationship between VEP amplitude and contrast intensity (21 patients and 29 healthy persons), while in the others, there was an inverse relationship, with a reduction in VEP amplitude (18 patients and 23 healthy persons). The relationship and mechanisms of subjects’ cognitive abilities and personality traits are discussed based on the data acquired from the responses to information stimuli of varied intensity. Full article

Cerebrotendinous xanthomatosis (CTX) is a rare metabolic disorder caused by mutations in the CYP27A1 gene, leading to cholestanol accumulation in various tissues, including peripheral nerves. Polyneuropathy is an underrecognized feature with considerable variability in clinical presentation and neurophysiological findings in CTX. This review assesses the prevalence, clinical manifestations, and diagnostic methodologies of polyneuropathy in CTX, exploring its underlying mechanisms and potential treatment outcomes. A literature review was conducted using PubMed, Embase, and the Virtual Health Library databases with search terms related to CTX and polyneuropathy. A total of 892 articles were initially identified, with 59 selected for in-depth analysis. The review focused on studies examining peripheral nerve involvement in CTX, including nerve conduction studies, electromyography, and nerve ultrasound. Polyneuropathy in CTX was observed in 50% to 77.7% of patients across multiple case series. Neurophysiological findings varied, with reports of axonal, demyelinating, and mixed polyneuropathies. Clinical presentation included lower limb atrophy, pes cavus, and distal weakness, with sensory symptoms less frequently reported. Treatment with chenodeoxycholic acid (CDCA) showed potential in improving nerve conduction parameters, although the response was variable and dependent on the timing of intervention. Polyneuropathy in CTX presents significant diagnostic challenges due to its heterogeneous presentation and varying neurophysiological findings. Early recognition and intervention are crucial for improving patient outcomes. Peripheral nerve ultrasound is a promising diagnostic tool, complementing traditional neurophysiological assessments. Further research is needed to standardize protocols and explore the full therapeutic potential of CDCA in managing CTX-related polyneuropathy. Full article

Background: Headache is one of the leading causes of disability in the world. Neck proprioception, pain, and postural control are interconnected in both healthy individuals and those with chronic neck pain. This study examines the effects of proprioceptive training using a gaze direction recognition task on postural stability and pain in cervicogenic headache patients. Methods: Patients with cervicogenic headache (n = 34, age: 35–49 y) were randomized into a control group (CON), receiving only selected physical therapy rehabilitation or to an experimental group (EXP), performing proprioceptive training using a gaze direction recognition task plus selected physical therapy rehabilitation. Both programs consisted of 24, 60 min long sessions over 8 weeks. Postural stability was assessed by the modified clinical test of sensory integration of balance (mCTSIB) and a center of pressure test (COP) using the HUMAC balance system. Neck pain was assessed by a visual analog scale. Results: In all six tests, there was a time main effect (p < 0.001). In three of the six tests, there were group by time interactions so that EXP vs. CON improved more in postural stability measured while standing on foam with eyes closed normalized to population norms, COP velocity, and headache (all p ≤ 0.006). There was an association between the percent changes in standing on foam with eyes closed normalized to population norms and percent changes in COP velocity (r = 0.48, p = 0.004, n = 34) and between percent changes in COP velocity and percent changes in headache (r = 0.44, p = 0.008, n = 34). Conclusions: While we did not examine the underlying mechanisms, proprioceptive training in the form of a gaze direction recognition task can improve selected measures of postural stability, standing balance, and pain in cervicogenic headache patients. Full article

The FADD-like interleukin-1β converting enzyme (FLICE)-inhibitory protein (c-FLIP) plays a crucial role in various biological processes, including apoptosis and inflammation. However, the complete transcriptional profile altered by the c-FLIP is not fully understood. Furthermore, the impact of the c-FLIP deficiency on the transcriptome during a Zika virus (ZIKV) infection, which induces apoptosis and inflammation in the central nervous system (CNS), has not yet been elucidated. In this study, we compared transcriptome profiles between wild-type (WT) and the c-Flip heterozygous knockout mice (c-Flip^+/−) fetal heads at embryonic day 13.5 from control and PBS-infected WT dams mated with c-Flip^+/− sires. In the non-infected group, we observed differentially expressed genes (DEGs) mainly involved in embryonic development and neuron development. However, the ZIKV infection significantly altered the transcriptional profile between WT and the c-Flip^+/− fetal heads. DEGs in pattern recognition receptor (PRR)-related signaling pathways, such as the RIG-I-like receptor signaling pathway and Toll-like receptor signaling pathway, were enriched. Moreover, the DEGs were also enriched in T cells, indicating that the c-FLIP participates in both innate and adaptive immune responses upon viral infection. Furthermore, our observations indicate that DEGs are associated with sensory organ development and eye development, suggesting a potential role for the c-FLIP in ZIKV-induced organ development defects. Overall, we have provided a comprehensive transcriptional profile for the c-FLIP and its modulation during a ZIKV infection. Full article

In recent years, assistive technology usage among the visually impaired has risen significantly worldwide. While traditional aids like guide dogs and white canes have limitations, recent innovations like RFID-based indoor navigation systems and alternative sensory solutions show promise. Nevertheless, there is a need for a user-friendly, comprehensive system to address spatial orientation challenges for the visually impaired. This research addresses the significance of developing a deep learning-based walking assistance device for visually impaired individuals to enhance their safety during mobility. The proposed system utilizes real-time ultrasonic sensors attached to a cane to detect obstacles, thus reducing collision risks. It further offers real-time recognition and analysis of diverse obstacles, providing immediate feedback to the user. A camera distinguishes obstacle types and conveys relevant information through voice assistance. The system’s efficacy was confirmed with a 90–98% object recognition rate in tests involving various obstacles. This research holds importance in providing safe mobility, promoting independence, leveraging modern technology, and fostering social inclusion for visually impaired individuals. Full article

Safety is the eternal theme of power systems. In view of problems such as time-consuming and poor real-time performance in the correct use of seat belt hooks by manual supervision operators in the process of power operation, this paper proposes an improved YOLOv7 seat belt hook suspension state recognition algorithm. Firstly, the feature extraction part of the YOLOv7 backbone network is improved, and the M-Spatial Pyramid Pooling Concurrent Spatial Pyramid Convolution (M-SPPCSPC) feature extraction module is constructed to replace the Spatial Pyramid Pooling Concurrent Spatial Pyramid Convolution (SPPCSPC) module of the backbone network, which reduces the amount of computation and improves the detection speed of the backbone network while keeping the sensory field of the backbone network unchanged. Second, a decoupled head, which realizes the confidence and regression frames separately, is introduced to alleviate the negative impact of the conflict between the classification and regression tasks, consequently improving the network detection accuracy and accelerating the network convergence. Ultimately, a dynamic non-monotonic focusing mechanism is introduced in the output layer, and the Wise Intersection over Union (WioU) loss function is used to reduce the competitiveness of high-quality anchor frames while reducing the harmful gradient generated by low-quality anchor frames, which ultimately improves the overall performance of the detection network. The experimental results show that the mean Average Precision ([email protected]) value of the improved network reaches 81.2%, which is 7.4% higher than that of the original YOLOv7, therefore achieving better detection results for multiple-state recognition of hooks. Full article

Recently, the research community has taken great interest in human activity recognition (HAR) due to its wide range of applications in different fields of life, including medicine, security, and gaming. The use of sensory data for HAR systems is most common because the sensory data are collected from a person’s wearable device sensors, thus overcoming the privacy issues being faced in data collection through video cameras. Numerous systems have been proposed to recognize some common activities of daily living (ADLs) using different machine learning, image processing, and deep learning techniques. However, the existing techniques are computationally expensive, limited to recognizing short-term activities, or require large datasets for training purposes. Since an ADL is made up of a sequence of smaller actions, recognizing them directly from raw sensory data is challenging. In this paper, we present a computationally efficient two-level hierarchical framework for recognizing long-term (composite) activities, which does not require a very large dataset for training purposes. First, the short-term (atomic) activities are recognized from raw sensory data, and the probabilistic atomic score of each atomic activity is calculated relative to the composite activities. In the second step, the optimal features are selected based on atomic scores for each composite activity and passed to the two classification algorithms: random forest (RF) and support vector machine (SVM) due to their well-documented effectiveness for human activity recognition. The proposed method was evaluated on the publicly available CogAge dataset that contains 890 instances of 7 composite and 9700 instances of 61 atomic activities. The data were collected from eight sensors of three wearable devices: a smartphone, a smartwatch, and smart glasses. The proposed method achieved the accuracy of 96.61% and 94.1% by random forest and SVM classifiers, respectively, which shows a remarkable increase in the classification accuracy of existing HAR systems for this dataset. Full article

Farm aerial survey layers can assist in unmanned farm operations, such as planning paths and early warnings. To address the inefficiencies and high costs associated with traditional layer construction, this study proposes a high-precision instance segmentation algorithm based on SparseInst. Considering the structural characteristics of farm elements, this study introduces a multi-scale attention module (MSA) that leverages the properties of atrous convolution to expand the sensory field. It enhances spatial and channel feature weights, effectively improving segmentation accuracy for large-scale and complex targets in the farm through three parallel dense connections. A bottom-up aggregation path is added to the feature pyramid fusion network, enhancing the model’s ability to perceive complex targets such as mechanized trails in farms. Coordinate attention blocks (CAs) are incorporated into the neck to capture richer contextual semantic information, enhancing farm aerial imagery scene recognition accuracy. To assess the proposed method, we compare it against existing mainstream object segmentation models, including the Mask R-CNN, Cascade–Mask, SOLOv2, and Condinst algorithms. The experimental results show that the improved model proposed in this study can be adapted to segment various complex targets in farms. The accuracy of the improved SparseInst model greatly exceeds that of Mask R-CNN and Cascade–Mask and is 10.8 and 12.8 percentage points better than the average accuracy of SOLOv2 and Condinst, respectively, with the smallest number of model parameters. The results show that the model can be used for real-time segmentation of targets under complex farm conditions. Full article

The variety and content of high-quality proteins in sunflower seeds are higher than those in other cereals. However, sunflower seeds can suffer from abnormalities, such as breakage and deformity, during planting and harvesting, which hinder the development of the sunflower seed industry. Traditional methods such as manual sensory and machine sorting are highly subjective and cannot detect the internal characteristics of sunflower seeds. The development of spectral imaging technology has facilitated the application of terahertz waves in the quality inspection of sunflower seeds, owing to its advantages of non-destructive penetration and fast imaging. This paper proposes a novel terahertz image classification model, MobileViT-E, which is trained and validated on a self-constructed dataset of sunflower seeds. The results show that the overall recognition accuracy of the proposed model can reach 96.30%, which is 4.85%, 3%, 7.84% and 1.86% higher than those of the ResNet-50, EfficientNeT, MobileOne and MobileViT models, respectively. At the same time, the performance indices such as the recognition accuracy, the recall and the F1-score values are also effectively improved. Therefore, the MobileViT-E model proposed in this study can improve the classification and identification of normal, damaged and deformed sunflower seeds, and provide technical support for the non-destructive detection of sunflower seed quality. Full article