A new fossil species of Meliolinites Selkirk associated with Rhodoleia leaves from the Upper Pliocene of southwestern China.

Fossil epifoliar fungi are valuable indicators of paleoenvironment and paleoecology. The Meliolaceae, members of which typically inhabit the surface of living plants as biotrophs or pathogens, is one of the largest groups of epifoliar fungi. In this study, we report a novel fossil species of Meliolinites Selkirk (fossil Meliolaceae), Meliolinites tengchongensis, on the lower epidermis of compressed fossil Rhodoleia (Hamamelidaceae) leaves from the Upper Pliocene Mangbang Formation of Tengchong, Yunnan, southwestern China. Meliolinites tengchongensis is characterized by web-like, superficial, brown to dark brown, septate, and branching mycelia bearing 2-celled appressoria and unicellular phialides. The fungal colonies also include ellipsoidal, 5-celled, 4-septate ascospores and dark brown perithecia with suborbicular outline and verrucose surface. The well-preserved vegetative and reproductive organs help us to explore the potential disease process of the new fossil species. Besides, the presence of fungal remains indicates that the fungal taxon might have maintained its host preference since at least the Late Pliocene. Furthermore, the occurrence of both fossil fungi and their host plants in Tengchong indicate a subtropical-tropical, warm, and humid climate during the Late Pliocene, whereas the distribution pattern of the fungi on the host leaves suggests that Rhodoleia may have been a part of the middle-upper canopies in the Tengchong Late Pliocene multilayered forest.

Modeling the Heating Dynamics of a Semiconductor Bridge Initiator with Deep Neural Network.

A semiconductor bridge (SCB) is an ignition device that provides a safe and efficient method widely used in civilian and military fields. The heating process of an SCB under electrical stimulation has a wide range of applications owing to its unique energy release process. However, the temperature variation of an SCB is challenging to obtain, both experimentally because of the rapid reaction on a microscale and with simulation due to its high demand in nonlinear calculations. In this study, we propose deep learning (DL) approach to study the electrothermal-coupled multi-physical heating process of the SCB initiator. We generated training data with multi-physics simulation (MPS), producing surface temperature distributions of SCBs under different voltages. The model was then trained with partial data in this database and evaluated on a separate test set. A generative adversarial network (GAN) with a customized loss function was used for modeling point-wise temperature dynamics. In the test set, our proposed method can predict the temperature distribution of an SCB under different voltages with high accuracy of over 0.9 during the heating process. We reduced the computation time by several orders of magnitude by replacing MPS with a deep neural network.

Predicting patient experience of Invisalign treatment: An analysis using artificial neural network.

Objective: Poor experience with Invisalign treatment affects patient compliance and, thus, treatment outcome. Knowing the potential discomfort level in advance can help orthodontists better prepare the patient to overcome the difficult stage. This study aimed to construct artificial neural networks (ANNs) to predict patient experience in the early stages of Invisalign treatment. Methods: In total, 196 patients were enrolled. Data collection included questionnaires on pain, anxiety, and quality of life (QoL). A four-layer fully connected multilayer perception with three backpropagations was constructed to predict patient experience of the treatment. The input data comprised 17 clinical features. The partial derivative method was used to calculate the relative contributions of each input in the ANNs. Results: The predictive success rates for pain, anxiety, and QoL were 87.7%, 93.4%, and 92.4%, respectively. ANNs for predicting pain, anxiety, and QoL yielded areas under the curve of 0.963, 0.992, and 0.982, respectively. The number of teeth with lingual attachments was the most important factor affecting the outcome of negative experience, followed by the number of lingual buttons and upper incisors with attachments. Conclusions: The constructed ANNs in this preliminary study show good accuracy in predicting patient experience (i.e., pain, anxiety, and QoL) of Invisalign treatment. Artificial intelligence system developed for predicting patient comfort has potential for clinical application to enhance patient compliance.

Growth differentiation factor 11 impairs titanium implant healing in the femur and leads to mandibular bone loss.

BACKGROUND: Growth differentiation factor 11 (GDF11), a secreted member of the transforming growth factor-ß superfamily, has recently been suggested as an anti-aging factor that declines with age in the bloodstream, and restoration of the youthful level by administration of its recombinant protein could reverse age-related dysfunctions. However, its effects on titanium implant osseointegration and mandibular bone during aging remain unknown. METHODS: Two-month-old and 18-month-old C57BL male mice were given daily intraperitoneal injections of recombinant GDF11 (rGDF11) or vehicle for 6 weeks. Experimental titanium implants were inserted into femurs on the fourth week. Inhibition of GDF11 function was achieved by GDF11 antibody. Implant-bearing femurs were subjected to histomorphometric analysis and biomechanical evaluation. Mandibles were scanned with micro-CT and decalcified for histological measurements. RESULTS: In both young adult and aged mice, supraphysiologic GDF11 leads to a significantly decreased bone-to-implant contact ratio (BIC) and peri-implant bone volume/total volume (BV/TV) at the histologic level and reduced resistance at the biomechanical level, indicating weakened implant fixation. Moreover, rGDF11 administration resulted in less trabecular bone volume and thinner cortical thickness in the mandible, which was further compromised in the old animals. In contrast, inhibition of GDF11 improved peri-implant bone healing in old mice. CONCLUSIONS: Rather than functioning as a rejuvenating factor, exogenous GDF11 negatively affects not only titanium implant healing but also mandibular bone in both young and old mice. In contrast, neutralization of endogenous GDF11 has positive effects on implant fixation in aged mice.

Accuracy of computer-aided prediction in soft tissue changes after orthodontic treatment.

INTRODUCTION: An accurate prediction in the soft tissue changes is of great importance for orthodontic treatment planning. Previous studies on the accuracy of the Dolphin visual treatment objective (VTO) in predicting treatment results were mainly focused on orthognathic treatment. The accuracy of Dolphin VTO prediction for orthodontic treatment is, however, poorly understood. The aim of this study was to evaluate the accuracy of Dolphin VTO prediction in soft tissue changes after orthodontic treatment by comparing the changes between predicted and actual values. METHODS: A total of 157 patients were screened for eligibility, and 34 young adult patients (8 males, 26 females; mean age 24.8 ± 3.9 years) were finally included in the study based on the inclusion and exclusion criteria. The landmarks and parameters of the Holdaway soft tissue analysis were used for the cephalometric analyses. The cephalometric tracings of the actual treatment result and the Dolphin predicted treatment outcome were superimposed to calculate the prediction errors. Paired t test was used to compare the statistical differences between the predicted and actual treatment outcomes of the parameters used in the Holdaway soft tissue analysis. RESULTS: There were significant differences between the predicted and actual values in parameters of the Holdaway soft tissue analysis (P < 0.05). The prediction of the landmarks in the lips region (ie, subnasale, soft tissue A-point, upper lip, lower lip, and soft tissue B-point) was inclined to be overestimated horizontally and underestimated vertically, whereas the prediction of the landmarks belonging to the chin region (ie, soft tissue pogonion, soft tissue gnathion, and soft tissue menton) was inclined to be underestimated horizontally and overestimated vertically. The most accurate prediction was found in the soft tissue A-point, whereas the least accurate one was found in the soft tissue in the chin region. The prediction was relatively more accurate in the vertical direction than in the horizontal direction. CONCLUSIONS: The Dolphin VTO prediction in soft tissue changes after the orthodontic treatment in patients with bimaxillary protrusion is the most accurate for the soft tissue A-point and the least accurate for the soft tissue chin region.

Sclerostin injection enhances orthodontic tooth movement in rats.

OBJECTIVE: It was aimed to investigate the in vivo effects of local injection of sclerostin protein on orthodontic tooth movement. DESIGN: A total of 48 rats underwent orthodontic mesialization of the maxillary first molars on both sides. Local injection was given at the compression side in the alveolar bone on both maxillary sides, with sclerostin protein carried by hydrogel on one side, and the same volume of normal saline carried by hydrogel on the other side serving as the control. After two weeks, the tooth movement amount and effects on the periodontium were assessed through micro-computed tomography (µCT) analysis, tartrate-resistant acid phosphatase (TRAP) staining and immunohistochemistry (IHC) analysis. RESULTS: After two weeks of intervention, tooth movement was significantly greater in the 4 µg/kg and 20 µg/kg sclerostin injection groups, compared to the control. Analysis of the furcation area of the maxillary first molar showed that the 20 µg/kg group had significantly decreased BV/TV. At the compression side, the number of TRAP-positive osteoclasts was significantly increased in 20 µg/kg group compared to the control. The expression of RANKL was statistically higher in all the sclerostin groups, while the expression of OPG was statistically lower in the 4 µg/kg and 20 µg/kg groups, compared to the control. At the tension side, the expression of RUNX2 and COL-1 was statistically higher in the 20 µg/kg group compared to the control. CONCLUSIONS: Local injection of sclerostin protein in the alveolar bone at the compression side accelerates OTM in rats by promoting osteoclastogenesis.

Spatial Dynamics of the Communities and the Role of Major Countries in the International Rare Earths Trade: A Complex Network Analysis.

Rare earths (RE) are critical materials in many high-technology products. Due to the uneven distribution and important functions for industrial development, most countries import RE from a handful of suppliers that are rich in RE, such as China. However, because of the rapid growth of RE exploitation and pollution of the mining and production process, some of the main suppliers have gradually tended to reduce the RE production and exports. Especially in the last decade, international RE trade has been changing in the trade community and trade volume. Based on complex network theory, we built an unweighted and weighted network to explore the evolution of the communities and identify the role of the major countries in the RE trade. The results show that an international RE trade network was dispersed and unstable because of the existence of five to nine trade communities in the unweighted network and four to eight trade communities in the weighted network in the past 13 years. Moreover, trade groups formed due to the great influence of geopolitical relations. China was often associated with the South America and African countries in the same trade group. In addition, Japan, China, the United States, and Germany had the largest impacts on international RE trade from 2002 to 2014. Last, some policy suggestions were highlighted according to the results.