Search Results (39)

Understanding how primary structural features and secondary material properties adapt to functional loads is essential to determining their effect on changes in joint biomechanics over time. The objective of this study was to map and correlate spatiotemporal changes in primary structural features, secondary material properties, and dentoalveolar joint (DAJ) stiffness with age in rats subjected to prolonged chewing of soft foods versus hard foods. To probe how loading history shapes the balance between the primary and secondary features, four-week-old rats were fed either a hard-food (HF, N = 25) or soft-food (SF, N = 25) diet for 4, 12, 16, and 20 weeks, and functional imaging of intact mandibular DAJs was performed at 8, 12, 16, 20, and 24 weeks. Across this time course, the primary structural determinants of joint function (periodontal ligament (PDL) space, contact area, and alveolar bone socket morphology) and secondary material and microstructural determinants (tissue-level stiffness encoded by bone and cementum volume fractions, pore architecture, and bone microarchitecture) were quantified. As the joints matured, bone and cementum volume fractions increased in both the HF and SF groups but along significantly different trajectories, and these changes correlated with a pronounced decrease in PDL-space from 12 to 16 weeks in both diets. With further aging, older HF rats maintained significantly wider PDL-spaces than SF rats. These evolving physical features were accompanied by an age-dependent significant increase in the contact ratio in the SF group. The DAJ stiffness was significantly greater in SF than HF animals at younger ages, indicating that food hardness-dependent remodeling alters the relative contribution of structural versus material factors to joint function across the life course. At the tissue level, volumetric strains, representing overall volume changes, and von Mises bone strains, representing shape changes, increased with age in HF and SF joints, with volumetric strain rising rapidly from 16 to 20 weeks and von Mises strain increasing sharply from 12 to 16 weeks. Bone in SF animals exhibited higher and more variable strain values than age-matched HF bone, and changes in joint space, degrees of freedom, contact area, and bone strain correlated with joint biomechanics, demonstrating that multiscale functional biomechanics, including bone strain in intact DAJs, are colocalized with anatomy-specific physical effectors. Together, these spatiotemporal shifts in primary (structure/form), and secondary features (material properties and microarchitecture) define divergent mechanobiological pathways for the DAJ and suggest that altered loading histories can bias joints toward early maladaptation and potential degeneration. Full article

Background/Objectives: Down Syndrome (DS) is frequently associated with oral-motor dysmorphologies, like oral hypotonia, tongue protrusion, short palate, and malocclusion, compromising the oral functions of sucking, chewing, swallowing, and speech production. Therapeutic interventions with stimulating palatal plates (SPP) have been proposed to prevent and improve oral-motor dysmorphologies in DS. This study proposes a new digital workflow for the manufacturing and use of a modified SPP. Methods: We report the application of the new workflow to five clinical cases, all infants with DS showing oral-motor disorders, aged between 5 and 11 months. The workflow is described step-by-step, from the mouth scanning protocol and model printing to SPP manufacturing and delivering, and assessment of oral-morphological features and language abilities via video captures and parental questionnaires. Key novel features include an SPP with an acrylic extension with a pacifier terminal and, importantly, the use of an infant-friendly intraoral scanner. Results: The new workflow had good acceptability by infants and parents, offering a safe, easy-to-implement, and feasible solution for SPP design, as it avoided the high risks associated with impression materials. It also supported the use of the SPP to promote tongue stimulation, retraction, and overall oral-muscle function in oral-motor disorders in children with DS, especially in infants. Conclusions: Within the limitations of the current study, it was shown that the proposed digital workflow constitutes a viable and infant-friendly approach to the production and use of a modified SPP, and thus promises to contribute to improving oral morphology and auditory-motor language abilities. Full article

Chewing lice are among the most significant ectoparasites affecting poultry, causing irritation, anemia, and reduced productivity, thereby posing economic and welfare challenges for poultry farmers. Their impact is particularly pronounced in sub-Saharan Africa, where poultry production is predominantly free-range with limited biosecurity, which increases exposure to infestation. This review was conducted to determine the epidemiology of chewing lice species of poultry in sub-Saharan African countries. A search of peer-reviewed literature on the epidemiology of chewing lice species of poultry was conducted on four electronic databases from 1990 to 2024. Nineteen species of chewing lice, namely Menacanthus stramineus, Menacanthus cornutus, Menacanthus pallidulus, Menopon gallinae, Lipeurus caponis, Lipeurus tropicalis, Gallacanthus cornutus, Goniocotes gigas, Goniocotes gallinae, Goniocotes hologaster, Goniodes gigas, Goniodes meleagridis, Goniodes gallinae, Goniodes dissimilis, Cuclotogaster heterographus, Stenocrotaphus gigas, Columbicola columbae, Chelopistes meleagridis, and Amyrsidea powelli, were reported from six poultry species distributed across ten African countries. The identification of the chewing lice species was primarily based on microscopic examination of the morphological features, which resulted in the exclusion of some studies that failed to identify lice to the species level. Poultry species infested included chickens (Gallus gallus domesticus), turkeys (Meleagris gallopavo), ducks (Anas platyrhynchos), guinea fowls (Numida meleagridis), pigeons (Columba livia), and geese (Anser cygnoides). Nigeria recorded the highest number of chewing lice species. The genus Goniodes showed the highest species diversity, and M. stramineus was the most predominant species, reported in nine of the ten reviewed countries. Infestations were mostly reported in chickens compared to other poultry species, and the prevalence ranged from 1.28% in chickens in Ethiopia to 100% in chickens from Zimbabwe. Results from this review provide valuable insights into the species diversity and regional distribution patterns of chewing lice fauna, highlighting their dispersion and host associations. The review will serve as a valuable resource in the design of effective and sustainable prevention and control strategies of chewing lice, especially in free-range chickens reared by resource-poor communities in sub-Saharan Africa. Full article

The feeding behavior of dairy cows constitutes a complex temporal sequence comprising actions such as head lowering, sniffing, arching, eating, head raising, and chewing. Its precise recognition is crucial for refined livestock management. While existing 2D convolution-based models effectively extract features from individual frames, they lack temporal modeling capabilities. Conversely, due to their high computational complexity, 3D convolutional networks suffer from significantly limited recognition accuracy in high-density feeding scenarios. To address this, this paper proposes a Spatio-Temporal Fusion Network (DAS-Net): it designs a collaborative architecture featuring a 2D branch with a triple-attention module to enhance spatial key feature extraction, constructs a 3D branch based on multi-branch dilated convolution and integrates a 3D multi-scale attention mechanism to achieve efficient long-term temporal modeling. On our Spatio-Temporal Dairy Feeding Dataset (STDF Dataset), which contains 403 video clips and 10,478 annotated frames across seven behavior categories, the model achieves an average recognition accuracy of 56.83% for all action types. This result marks a significant improvement of 3.61 percentage points over the original model. Among them, the recognition accuracy of the eating action has been increased to 94.78%. This method provides a new idea for recognizing dairy cow feeding behavior and can provide technical support for developing intelligent feeding systems in real dairy farms. Full article

Background/Objectives: Maxillary defects, whether congenital or acquired, can compromise chewing, speech, and aesthetics. This systematic review aimed to evaluate the application and characteristics of definitive palatal obturators in the rehabilitation of such defects, analyzing techniques of fabrication, materials, outcomes of the fabrication, and limitations reported in the literature. Methods: The review was conducted in accordance with PRISMA 2020 guidelines and was registered in PROSPERO (ID: 1011648). A comprehensive search was performed in PubMed, Scopus, Lilacs, and Google Scholar for studies published from 1 January 2014 to 1 January 2025. Inclusion criteria comprised adult patients treated with definitive palatal obturators and with reported follow-up. Exclusion criteria included studies on children, animals, or lacking patient data. Two reviewers independently screened studies and assessed eligibility. Bias was evaluated qualitatively across five domains. No meta-analysis was conducted; data were synthesized descriptively using charts and tables. The study was funded by the Italian Ministry of Health—Current Research IRCCS. Results: A total of 59 studies involving 83 patients (46 males, 37 females; mean age 54.6 ± 13.8 years) were included. Mucormycosis and squamous cell carcinoma were the primary causes of defects. Conventional impressions using alginate and silicone were most common, while digital techniques were reported in only 6.6% of cases. All definitive obturators were fabricated using acrylic resin, with some featuring hollow bulbs, velopharyngeal extensions, or magnetic retention. Multiple sources of bias were observed. Conclusions: Definitive palatal obturators provide effective functional and aesthetic rehabilitation for maxillary defects. However, evidence is limited by methodological weaknesses, lack of standardization, and underutilization of digital technologies. Future studies should focus on improving reporting quality, adopting innovative fabrication protocols, and generating higher-level clinical evidence to support best practices. Full article

In this paper, a biologically congruent parallel mechanism (PM) inspired by the masticatory system of human beings has been proposed to recreate complete chewing behaviours in three-dimensional space. The mechanism is featured by direct constraints from the base (DCFB) to its end effector at two higher kinematic pairs (HKPs), which greatly raise its topological complexity. Meanwhile, friction effects occur at HKPs and actuators, causing wear and then reducing motion accuracy. Regarding these, an inverse dynamic model that can raise the computational efficiency and the modelling fidelity is proposed, being prepared to be applied to realise accurate real-time motion and/or force control. In it, Euler parameters are employed to express the motions of the constrained end effector, and Newton–Euler’s law is applied, which can conveniently incorporate friction effects at both HKPs and actuators into the dynamic model. Numerical results show that the time consumption of the model using Euler parameters is only approximately 23% of that of the model using Euler angles, and friction effects significantly increase the model’s nonlinearity. Further, from the comparison between the models of the target PM and its counterpart free of DCFB, these constraints sharply raise the modelling complexity in terms of the transformation between Euler parameters and Euler angles in the end effector and the computational cost of inverse dynamics. Full article

Gorlin-Goltz syndrome (GGS) is also known as Nevoid basal cell carcinoma syndrome (NBCCS). In the most common manifestation, GGS is diagnosed based on multiple cysts in the jaw bones, namely OKCs (odontogenic keratocysts). Other features might include major and minor clinical and radiological criteria to confirm this syndrome. Quite commonly, BCCs (basal cell carcinomas), bifid ribs, palmar and plantar pits, and ectopic calcification of the falx cerebri can be found in the majority of patients. Currently, the mutation of the PTCH1 gene seems to be responsible for GGS occurrence, while the male-to-female ratio is 1:1. The following radiological study based on OPGs and CBCT confirmed multiple cystic lesions in jaw bones, confirmed to be OKCs in the histopathological evaluation with an occurrence of numerous skin BCC lesions. In cases of most oral OKC cystic lesions, either surgical removal, curettage, or enucleation with or without any bone grafting can be used with a good amount of success. Rarely, some stable bone osteosynthesis procedures have to be carried out to avoid pathological bone fractures after cyst removal. A special consideration should include the temporomandibular joint. TMJ surgery and the replacement of the joint with an alloplastic material can be performed to improve biting, chewing, proper mouth opening, and maintain good patient occlusion. The authors want to present how effective and simple a standard dental panoramic radiograph combined with CBCT is and how it is suitable for GGS detection. They also want to underline how a standard TMJ prosthesis can be used as an alternative to a custom-made prosthesis. Full article

This study uses bionics as an enabling methodology to bridge the gap between biology and engineering for generating innovative designs for implementation into novel technology development. A product lifecycle management (PLM) methodology framework is proposed that uses bionics as a technical discipline. The manuscript presents a novel, reverse biomimetics as a shape abstraction methodology to investigate, analyse, and de-feature biological structures through functional morphology as the enabling methodology for studying the relationships between form and function. The novel reverse engineering (RE) format with eleven stages supports technical biology, addressing the abstraction issues which have been identified as the most difficult steps in Fayemi’s eight-step framework. Inverse biomimetics and RE changes functional modelling (FM) from highly abstracted principles to low- or even reality-level abstraction, achieving nature design intents. The goal of the reverse biomimetic approach is to implement functional feature extraction, surface reconstruction, and solid modelling into five stages of a design process. The benefit of virtually mapping this in a pictorial fashion with high-end software fosters a simpler understanding and representation of knowledge transfer from biology to engineering, and can lead to innovative bio-inspired developments. The study aims to present the bionics PLM framework and its comprehensive processes of bionic design and biomimetic modelling, simulation, optimisation, and clinical validation techniques for two large-scale, human skeletal biological systems: a drug-releasing chewing robot and an anthropometric prosthetic hand suitable for introduction to engineering courses. Integration into undergraduate courses would be one route to bolster interest and encourage growth within the subject area in future. Full article

Brain–computer interface (BCI)-based assistive technologies enable intuitive and efficient user interaction, significantly enhancing the independence and quality of life of elderly and disabled individuals. Although existing wet EEG-based systems report high accuracy, they suffer from limited practicality. This study presents a hybrid BCI system combining dry-type EEG-based flash visual-evoked potentials (FVEP) and pupillary light reflex (PLR) designed to control an LED-based meal-assist robot. The hybrid system integrates dry-type EEG and eyewear-type infrared cameras, addressing the preparation challenges of wet electrodes, while maintaining practical usability and high classification performance. Offline experiments demonstrated an average accuracy of 88.59% and an information transfer rate (ITR) of 18.23 bit/min across the four target classifications. Real-time implementation uses PLR triggers to initiate the meal cycle and EMG triggers to detect chewing, indicating the completion of the cycle. These features allow intuitive and efficient operation of the meal-assist robot. This study advances the BCI-based assistive technologies by introducing a hybrid system optimized for real-world applications. The successful integration of the FVEP and PLR in a meal-assisted robot demonstrates the potential for robust and user-friendly solutions that empower the users with autonomy and dignity in their daily activities. Full article

To address behavioral interferences such as head turning and lowering during rumination in group-housed dairy cows, an enhanced network algorithm combining the YOLOv5s and DeepSort algorithms was developed. Initially, improvements were made to the YOLOv5s algorithm by incorporating the C3_CA module into the backbone to enhance the feature interaction and representation at different levels. The Slim_Neck paradigm was employed to strengthen the feature extraction and fusion, and the CIoU loss function was replaced with the WIoU loss function to improve the model’s robustness and generalization, establishing it as a detector of the upper and lower jaws of dairy cows. Subsequently, the DeepSort tracking algorithm was utilized to track the upper and lower jaws and plot their movement trajectories. By calculating the difference between the centroid coordinates of the tracking boxes for the upper and lower jaws during rumination, the rumination curve was obtained. Finally, the number of rumination chews and the false detection rate were calculated. The system successfully monitored the frequency of the cows’ chewing actions during rumination. The experimental results indicate that the enhanced network model achieved a mean average precision (mAP_@0.5) of 97.5% and 97.9% for the upper and lower jaws, respectively, with precision (P) of 95.4% and 97.4% and recall (R) of 97.6% and 98.4%, respectively. Two methods for determining chewing were proposed, which showed false detection rates of 8.34% and 3.08% after the experimental validation. The research findings validate the feasibility of the jaw movement tracking method, providing a reference for the real-time monitoring of the rumination behavior of dairy cows in group housing environments. Full article

In overcoming the worldwide problem of overweight and obesity, automatic dietary monitoring (ADM) is introduced as support in dieting practises. ADM aims to automatically, continuously, and objectively measure dimensions of food intake in a free-living environment. This could simplify the food registration process, thereby overcoming frequent memory, underestimation, and overestimation problems. In this study, an eating event detection sensor system was developed comprising a smartwatch worn on the wrist containing an accelerometer and gyroscope for eating gesture detection, a piezoelectric sensor worn on the jaw for chewing detection, and a respiratory inductance plethysmographic sensor consisting of two belts worn around the chest and abdomen for food swallowing detection. These sensors were combined to determine to what extent a combination of sensors focusing on different steps of the dietary cycle can improve eating event classification results. Six subjects participated in an experiment in a controlled setting consisting of both eating and non-eating events. Features were computed for each sensing measure to train a support vector machine model. This resulted in F${}_1$-scores of 0.82 for eating gestures, 0.94 for chewing food, and 0.58 for swallowing food. Full article

Rumination behavior is closely associated with factors such as cow productivity, reproductive performance, and disease incidence. For multi-object scenarios of dairy cattle, ruminant mouth area images accounted for little characteristic information, which was first put forward using an improved Faster R-CNN target detection algorithm to improve the detection performance model for the ruminant area of dairy cattle. The primary objective is to enhance the model’s performance in accurately detecting cow rumination regions. To achieve this, the dataset used in this study is annotated with both the cow head region and the mouth region. The ResNet-50-FPN network is employed to extract the cow mouth features, and the CBAM attention mechanism is incorporated to further improve the algorithm’s detection accuracy. Subsequently, the object detection results are combined with optical flow information to eliminate false detections. Finally, an interpolation approach is adopted to design a frame complementary algorithm that corrects the detection frame of the cow mouth region. This interpolation algorithm is employed to rectify the detection frame of the cow’s mouth region, addressing the issue of missed detections and enhancing the accuracy of ruminant mouth region detection. To overcome the challenges associated with the inaccurate extraction of small-scale optical flow information and interference between different optical flow information in multi-objective scenes, an enhanced GMFlowNet-based method for multi-objective cow ruminant optical flow analysis is proposed. To mitigate interference from other head movements, the MeanShift clustering method is utilized to compute the velocity magnitude values of each pixel in the vertical direction within the intercepted ruminant mouth region. Furthermore, the mean square difference is calculated, incorporating the concept of range interquartile, to eliminate outliers in the optical flow curve. Finally, a final filter is applied to fit the optical flow curve of the multi-object cow mouth movement, and it is able to identify rumination behavior and calculate chewing times. The efficacy, robustness, and accuracy of the proposed method are evaluated through experiments, with nine videos capturing multi-object cow chewing behavior in different settings. The experimental findings demonstrate that the enhanced Faster R-CNN algorithm achieved an 84.70% accuracy in detecting the ruminant mouth region, representing an improvement of 11.80 percentage points over the results obtained using the Faster R-CNN detection approach. Additionally, the enhanced GMFlowNet algorithm accurately identifies the ruminant behavior of all multi-objective cows, with a 97.30% accuracy in calculating the number of ruminant chewing instances, surpassing the accuracy of the FlowNet2.0 algorithm by 3.97 percentage points. This study provides technical support for intelligent monitoring and analysis of rumination behavior of dairy cows in group breeding. Full article

This research paper introduces a novel methodology for classifying jaw movements in dairy cattle into four distinct categories: bites, exclusive chews, chew-bite combinations, and exclusive sorting, under conditions of tall and short particle sizes in wheat straw and Alfalfa hay feeding. Sound signals were recorded and transformed into images using a short-time Fourier transform. A total of 31 texture features were extracted using the gray level co-occurrence matrix, spatial gray level dependence method, gray level run length method, and gray level difference method. Genetic Algorithm (GA) was applied to the data to select the most important features. Six distinct classifiers were employed to classify the jaw movements. The total precision found was 91.62%, 94.48%, 95.9%, 92.8%, 94.18%, and 89.62% for Naive Bayes, k-nearest neighbor, support vector machine, decision tree, multi-layer perceptron, and k-means clustering, respectively. The results of this study provide valuable insights into the nutritional behavior and dietary patterns of dairy cattle. The understanding of how cows consume different types of feed and the identification of any potential health issues or deficiencies in their diets are enhanced by the accurate classification of jaw movements. This information can be used to improve feeding practices, reduce waste, and ensure the well-being and productivity of the cows. The methodology introduced in this study can serve as a valuable tool for livestock managers to evaluate the nutrition of their dairy cattle and make informed decisions about their feeding practices. Full article

Chewing is essential in regulating metabolism and initiating digestion. Various methods have been used to examine chewing, including analyzing chewing sounds and using piezoelectric sensors to detect muscle contractions. However, these methods struggle to distinguish chewing from other movements. Electromyography (EMG) has proven to be an accurate solution, although it requires sensors attached to the skin. Existing EMG devices focus on detecting the act of chewing or classifying foods and do not provide self-awareness of chewing habits. We developed a non-invasive device that evaluates a personalized chewing style by analyzing various aspects, like chewing time, cycle time, work rate, number of chews and work. It was tested in a case study comparing the chewing pattern of smokers and non-smokers, as smoking can alter chewing habits. Previous studies have shown that smokers exhibit reduced chewing speed, but other aspects of chewing were overlooked. The goal of this study is to present the device and provide additional insights into the effects of smoking on chewing patterns by considering multiple chewing features. Statistical analysis revealed significant differences, as non-smokers had more chews and higher work values, indicating more efficient chewing. The device provides valuable insights into personalized chewing profiles and could modify unhealthy chewing habits. Full article

Objective: The coronavirus belongs to the family of Coronaviridae, which are not branched single-stranded RNA viruses. COVID-19 creates respiratory problems and infections ranging from mild to severe. The virus features mechanisms that serve to delay the cellular immune response. The host’s response is responsible for the pathological process that leads to tissue destruction. Temporomandibular disorders are manifested by painful jaw musculature and jaw joint areas, clicks, or creaks when opening or closing the mouth. All these symptoms can be disabling and occur during chewing and when the patient yawns or even speaks. The pandemic situation has exacerbated anxieties and amplified the vulnerability of individuals. Therefore, from this mechanism, how the COVID-19 pandemic may have increased the incidence of temporomandibular disorders is perceived. The purpose of this review is to evaluate whether COVID-19-related anxiety has caused an increase in temporomandibular dysfunction symptoms in adults to children. Methods: PubMed, Web of Science, Lilacs, and Scopus were systematically searched, until 30 July 2022, to identify studies presenting: the connection between COVID-19 with temporomandibular disorders. Results: From 198 papers, 4 studies were included. Literature studies have shown that the state of uncertainty and anxiety has led to an increase in the incidence of this type of disorder, although not all studies agree. Seventy-three studies were identified after viewing all four search engines; at the end of the screening phase, only four were considered that met the PECO, the planned inclusion, and the exclusion criteria. All studies showed a statistically significant correlation between temporomandibular disorders and COVID-19 with a p < 0.05. Conclusions: All studies agreed that there is an association between COVID-19 and increased incidence of temporomandibular disorders. Full article