Significant suppression of two-magnon scattering in ultrathin Co by controlling the surface magnetic anisotropy at the Co/nonmagnet interfaces

To enable suppression of two-magnon scattering (TMS) in nanometer-thick Co (ultrathin Co) layers and realize low-magnon damping in such layers, the magnon damping in ultrathin Co layers grown on various nonmagnetic seed layers with different surface magnetic anisotropy (SMA) energies are investigated. We verify the significantly weak magnon damping realized by varying the seeding layer species used. Although TMS is enhanced in ultrathin Co on Cu and Al seeding layers, the insertion of a Ti seeding layer below the ultrathin Co greatly suppresses the TMS, which is attributed to suppression of the SMA at the interface between Co and Ti. The Gilbert damping constant of the ultrathin Co layer on Ti (3 nm), 0.020, is comparable to the value for bulk Co, although the Co layer thickness here is only 2 nm. Realization of such weak magnon damping can open the door to tunable magnon excitation, thus enabling coupling of magnons with other quanta such as photons, given that the magnetization of ultrathin ferromagnets can be tuned using an external electric field

Passive immunisation of goldfish with the serum of those surviving a Cyprinid herpesvirus 2 infection after high temperature water treatment

Herpesviral haematopoietic necrosis of goldfish caused by cyprinid herpesvirus 2 (CyHV-2) can be controlled by raising water temperature to a virus non-permissive temperature of 34℃. Consequently, the goldfish can survive and acquire resistance to the disease; the underlying mechanism of acquired resistance, however, remains unclear. In this study, we investigated serological changes in the surviving goldfish, with a focus on their humoral immunity, and examined whether sera of the surviving goldfish conferred passive immunity to naive goldfish. Levels of the anti-CyHV-2 antibodies in 8 of the 9 survivors measured via ELISA were higher than those in control fish. Neutralising antibodies were detected in the sera of 2 survivors, but no direct correlation was observed between ELISA optical density value and neutralising antibody titer. Passive immunisation tests showed that recipients injected with the serum containing neutralising antibodies showed higher survival rates than the control group. The sera from 6 other survivors showed no effect on the recipient\u27s mortality regardless of anti-CyHV-2 antibody levels. These results suggest that neutralising antibodies can contribute to acquired immunity in survivors, and other protective factors, including cell-mediated immunity, may work in the survivors that show no detectable neutralising antibodies

Deep Neural Networks for Dental Implant System Classification

In this study, we used panoramic X-ray images to classify and clarify the accuracy of different dental implant brands via deep convolutional neural networks (CNNs) with transfer-learning strategies. For objective labeling, 8859 implant images of 11 implant systems were used from digital panoramic radiographs obtained from patients who underwent dental implant treatment at Kagawa Prefectural Central Hospital, Japan, between 2005 and 2019. Five deep CNN models (specifically, a basic CNN with three convolutional layers, VGG16 and VGG19 transfer-learning models, and finely tuned VGG16 and VGG19) were evaluated for implant classification. Among the five models, the finely tuned VGG16 model exhibited the highest implant classification performance. The finely tuned VGG19 was second best, followed by the normal transfer-learning VGG16. We confirmed that the finely tuned VGG16 and VGG19 CNNs could accurately classify dental implant systems from 11 types of panoramic X-ray images

Multi-Task Deep Learning Model for Classification of Dental Implant Brand and Treatment Stage Using Dental Panoramic Radiograph Images

It is necessary to accurately identify dental implant brands and the stage of treatment to ensure efficient care. Thus, the purpose of this study was to use multi-task deep learning to investigate a classifier that categorizes implant brands and treatment stages from dental panoramic radiographic images. For objective labeling, 9767 dental implant images of 12 implant brands and treatment stages were obtained from the digital panoramic radiographs of patients who underwent procedures at Kagawa Prefectural Central Hospital, Japan, between 2005 and 2020. Five deep convolutional neural network (CNN) models (ResNet18, 34, 50, 101 and 152) were evaluated. The accuracy, precision, recall, specificity, F1 score, and area under the curve score were calculated for each CNN. We also compared the multi-task and single-task accuracies of brand classification and implant treatment stage classification. Our analysis revealed that the larger the number of parameters and the deeper the network, the better the performance for both classifications. Multi-tasking significantly improved brand classification on all performance indicators, except recall, and significantly improved all metrics in treatment phase classification. Using CNNs conferred high validity in the classification of dental implant brands and treatment stages. Furthermore, multi-task learning facilitated analysis accuracy

Deep learning model for analyzing the relationship between mandibular third molar and inferior alveolar nerve in panoramic radiography

In this study, the accuracy of the positional relationship of the contact between the inferior alveolar canal and mandibular third molar was evaluated using deep learning. In contact analysis, we investigated the diagnostic performance of the presence or absence of contact between the mandibular third molar and inferior alveolar canal. We also evaluated the diagnostic performance of bone continuity diagnosed based on computed tomography as a continuity analysis. A dataset of 1279 images of mandibular third molars from digital radiographs taken at the Department of Oral and Maxillofacial Surgery at a general hospital (2014-2021) was used for the validation. The deep learning models were ResNet50 and ResNet50v2, with stochastic gradient descent and sharpness-aware minimization (SAM) as optimizers. The performance metrics were accuracy, precision, recall, specificity, F1 score, and area under the receiver operating characteristic curve (AUC). The results indicated that ResNet50v2 using SAM performed excellently in the contact and continuity analyses. The accuracy and AUC were 0.860 and 0.890 for the contact analyses and 0.766 and 0.843 for the continuity analyses. In the contact analysis, SAM and the deep learning model performed effectively. However, in the continuity analysis, none of the deep learning models demonstrated significant classification performance

Is attention branch network effective in classifying dental implants from panoramic radiograph images by deep learning?

Attention mechanism, which is a means of determining which part of the forced data is emphasized, has attracted attention in various fields of deep learning in recent years. The purpose of this study was to evaluate the performance of the attention branch network (ABN) for implant classification using convolutional neural networks (CNNs). The data consisted of 10191 dental implant images from 13 implant brands that cropped the site, including dental implants as pretreatment, from digital panoramic radiographs of patients who underwent surgery at Kagawa Prefectural Central Hospital between 2005 and 2021. ResNet 18, 50, and 152 were evaluated as CNN models that were compared with and without the ABN. We used accuracy, precision, recall, specificity, F1 score, and area under the receiver operating characteristics curve as performance metrics. We also performed statistical and effect size evaluations of the 30-time performance metrics of the simple CNNs and the ABN model. ResNet18 with ABN significantly improved the dental implant classification performance for all the performance metrics. Effect sizes were equivalent to "Huge" for all performance metrics. In contrast, the classification performance of ResNet50 and 152 deteriorated by adding the attention mechanism. ResNet18 showed considerably high compatibility with the ABN model in dental implant classification (AUC = 0.9993) despite the small number of parameters. The limitation of this study is that only ResNet was verified as a CNN; further studies are required for other CNN models

Effective deep learning for oral exfoliative cytology classification

The use of sharpness aware minimization (SAM) as an optimizer that achieves high performance for convolutional neural networks (CNNs) is attracting attention in various fields of deep learning. We used deep learning to perform classification diagnosis in oral exfoliative cytology and to analyze performance, using SAM as an optimization algorithm to improve classification accuracy. The whole image of the oral exfoliation cytology slide was cut into tiles and labeled by an oral pathologist. CNN was VGG16, and stochastic gradient descent (SGD) and SAM were used as optimizers. Each was analyzed with and without a learning rate scheduler in 300 epochs. The performance metrics used were accuracy, precision, recall, specificity, F1 score, AUC, and statistical and effect size. All optimizers performed better with the rate scheduler. In particular, the SAM effect size had high accuracy (11.2) and AUC (11.0). SAM had the best performance of all models with a learning rate scheduler. (AUC = 0.9328) SAM tended to suppress overfitting compared to SGD. In oral exfoliation cytology classification, CNNs using SAM rate scheduler showed the highest classification performance. These results suggest that SAM can play an important role in primary screening of the oral cytological diagnostic environment

Evaluation of multi-task learning in deep learning-based positioning classification of mandibular third molars

Pell and Gregory, and Winter's classifications are frequently implemented to classify the mandibular third molars and are crucial for safe tooth extraction. This study aimed to evaluate the classification accuracy of convolutional neural network (CNN) deep learning models using cropped panoramic radiographs based on these classifications. We compared the diagnostic accuracy of single-task and multi-task learning after labeling 1330 images of mandibular third molars from digital radiographs taken at the Department of Oral and Maxillofacial Surgery at a general hospital (2014-2021). The mandibular third molar classifications were analyzed using a VGG 16 model of a CNN. We statistically evaluated performance metrics [accuracy, precision, recall, F1 score, and area under the curve (AUC)] for each prediction. We found that single-task learning was superior to multi-task learning (all p < 0.05) for all metrics, with large effect sizes and low p-values. Recall and F1 scores for position classification showed medium effect sizes in single and multi-task learning. To our knowledge, this is the first deep learning study to examine single-task and multi-task learning for the classification of mandibular third molars. Our results demonstrated the efficacy of implementing Pell and Gregory, and Winter's classifications for specific respective tasks

Associations between non-motor symptoms and patient characteristics in Parkinson’s disease: a multicenter cross-sectional study

ObjectiveParkinson’s disease (PD) is characterized by various non-motor symptoms (NMS), such as constipation, olfactory disturbance, sleep disturbance, mental disorders, and motor symptoms. This study aimed to investigate factors associated with NMS in patients with PD.MethodsSymptoms of PD were evaluated using the Movement Disorder Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS), Parts I–IV. NMS was assessed using the MDS-UPDRS Part I (self-assessment of NMS) and rapid eye movement sleep behavior disorder (RBD) questionnaires. Patients were categorized by age into &lt;70 years and ≥ 70 years (older adults) groups, according to disease duration into early-stage and advanced-stage groups with a cut-off value of 5 years for motor symptoms, and by sex into male and female groups.ResultsA total of 431 patients with PD (202 males and 229 females) with a mean age of 67.7 years, a mean disease duration of 6.4 years, and a mean Part I total score of 9.9 participated in this study. The Part I total score was significantly positively correlated (p &lt; 0.01) with disease duration and Part II, III, and IV scores. For Part I sub-item scores, the older group had significantly higher scores for cognitive impairment, hallucinations, sleep problems, urinary problems, and constipation than the &lt;70 years group, whereas the advanced-stage group had significantly higher scores for hallucinations, sleep problems, daytime sleepiness, pain, urinary problems, and constipation (p &lt; 0.05) than the early-stage group. Anxiety was higher in female patients than in male patients, whereas daytime sleepiness, urinary problems, and RBD were higher in male patients than in female patients (p &lt; 0.05). Factors affecting Part I included disease duration, Part II total scores, Part IV total scores, and RBD.ConclusionAccording to the self-questionnaire assessment, NMS was highly severe in older adult patients, those with longer illness duration, subjective and objective motor function impairments, and RBD. Sex-based differences were also observed

Genetic and Molecular Basis of Individual Differences in Human Umami Taste Perception

Umami taste (corresponds to savory in English) is elicited by L-glutamate, typically as its Na salt (monosodium glutamate: MSG), and is one of five basic taste qualities that plays a key role in intake of amino acids. A particular property of umami is the synergistic potentiation of glutamate by purine nucleotide monophosphates (IMP, GMP). A heterodimer of a G protein coupled receptor, TAS1R1 and TAS1R3, is proposed to function as its receptor. However, little is known about genetic variation of TAS1R1 and TAS1R3 and its potential links with individual differences in umami sensitivity. Here we investigated the association between recognition thresholds for umami substances and genetic variations in human TAS1R1 and TAS1R3, and the functions of TAS1R1/TAS1R3 variants using a heterologous expression system. Our study demonstrated that the TAS1R1-372T creates a more sensitive umami receptor than -372A, while TAS1R3-757C creates a less sensitive one than -757R for MSG and MSG plus IMP, and showed a strong correlation between the recognition thresholds and in vitro dose - response relationships. These results in human studies support the propositions that a TAS1R1/TAS1R3 heterodimer acts as an umami receptor, and that genetic variation in this heterodimer directly affects umami taste sensitivity