Catalogue Search | MBRL
Are you sure you want to remove the book from the shelf?
{{itemTitle}}
3,016
result(s) for
"Motoki"
Sort by:
Fully endoscopic occipital transtentorial approach for pineal region tumors
Purpose
Aggressive surgical resection for pineal region tumors, which is a mainstay of management in almost all cases, remains technically challenging because these tumors are located at the geometric center of the brain and surrounded by critical neurovascular structures. Therefore, any refinement in the surgical procedure is desirable. We have recently introduced an endoscope to the occipital transtentorial approach (OTA), which has been one of the most common procedures for reaching the pineal region, to overcome some of the disadvantages of the procedure, including the necessity of a relatively large skin incision and bone opening, the difficulty in orientation due to the obliqueness of the approach, the possibility of retraction injury on the occipital lobe and the corpus callosum, and blind spots at the ipsilateral wall and the roof of the third ventricle.
Methods
We performed endoscopic OTA with corresponding advantages, including less invasiveness by virtue of a small entrance limiting the retraction of the occipital lobe, the elimination of blind spots, and the facilitation of fine manipulation based on the bright endoscopic view even at substantial depth, which is panoramic and can be magnified as needed. This procedure was substantially different from a conventional microscopic OTA, though both use a similar corridor. To maximize maneuverability within the limited space in endoscopic OTA, it was necessary to thoroughly understand each step of the procedure, as well as optimal sequence in the procedure.
Results and conclusion
We found endoscopic OTA to be a promising technique for resectioning pineal region tumors, with the potential for extensive and routine application for surgeons familiar with endoscopic surgery.
Journal Article
Spiciness Anomalies of Subantarctic Mode Water in the South Indian Ocean
This study investigates spreading and generation of spiciness anomalies of the Subantarctic Mode Water (SAMW) located on 26.6 to 26.8 σθ
in the south Indian Ocean, using in situ hydrographic observations, satellite measurements, reanalysis datasets, and numerical model output. The amplitude of spiciness anomalies is about 0.03 psu or 0.13°C and tends to be large along the streamline of the subtropical gyre, whose upstream end is the outcrop region south of Australia. The speed of spreading is comparable to that of themean current, and it takes about a decade for a spiciness anomaly in the outcrop region to spread into the interior up to Madagascar. In the outcrop region, interannual variability inmixed layer temperature and salinity tends to be density compensating, which indicates that Eulerian temperature or salinity changes account for the generation of isopycnal spiciness anomalies. It is known that wintertime temperature and salinity in the surfacemixed layer determine the temperature and salinity relationship of a subducted water mass. Considering this, the mixed layer heat budget in the outcrop region is estimated based on the concept of effective mixed layer depth, the result of which shows the primary contribution from horizontal advection. The contributions from Ekman and geostrophic currents are comparable. Ekman flow advection is caused by zonal wind stress anomalies and the resulting meridional Ekman current anomalies, as is pointed out by a previous study. Geostrophic velocity is decomposed into large-scale and mesoscale variability, both of which significantly contribute to horizontal advection.
Journal Article
Evaluation of an artificial intelligence system for detecting vertical root fracture on panoramic radiography
ObjectivesThe aim of this study was to evaluate the use of a convolutional neural network (CNN) system for detecting vertical root fracture (VRF) on panoramic radiography.MethodsThree hundred panoramic images containing a total of 330 VRF teeth with clearly visible fracture lines were selected from our hospital imaging database. Confirmation of VRF lines was performed by two radiologists and one endodontist. Eighty percent (240 images) of the 300 images were assigned to a training set and 20% (60 images) to a test set. A CNN-based deep learning model for the detection of VRFs was built using DetectNet with DIGITS version 5.0. To defend test data selection bias and increase reliability, fivefold cross-validation was performed. Diagnostic performance was evaluated using recall, precision, and F measure.ResultsOf the 330 VRFs, 267 were detected. Twenty teeth without fractures were falsely detected. Recall was 0.75, precision 0.93, and F measure 0.83.ConclusionsThe CNN learning model has shown promise as a tool to detect VRFs on panoramic images and to function as a CAD tool.
Journal Article
More human than human: measuring ChatGPT political bias
We investigate the political bias of a large language model (LLM), ChatGPT, which has become popular for retrieving factual information and generating content. Although ChatGPT assures that it is impartial, the literature suggests that LLMs exhibit bias involving race, gender, religion, and political orientation. Political bias in LLMs can have adverse political and electoral consequences similar to bias from traditional and social media. Moreover, political bias can be harder to detect and eradicate than gender or racial bias. We propose a novel empirical design to infer whether ChatGPT has political biases by requesting it to impersonate someone from a given side of the political spectrum and comparing these answers with its default. We also propose dose-response, placebo, and profession-politics alignment robustness tests. To reduce concerns about the randomness of the generated text, we collect answers to the same questions 100 times, with question order randomized on each round. We find robust evidence that ChatGPT presents a significant and systematic political bias toward the Democrats in the US, Lula in Brazil, and the Labour Party in the UK. These results translate into real concerns that ChatGPT, and LLMs in general, can extend or even amplify the existing challenges involving political processes posed by the Internet and social media. Our findings have important implications for policymakers, media, politics, and academia stakeholders.
Journal Article
Narrowband deep-blue organic light-emitting diode featuring an organoboron-based emitter
Luminescent materials that exhibit narrowband emission are vital for full-colour displays. Here, we report a thermally activated delayed-fluorescence material that exhibits ultrapure blue emission with full-width at half-maximum of just 14 nm. The emitter consists of five benzene rings connected by two boron and four nitrogen atoms and two diphenylamino substituents. The multiple resonance effect of the boron and nitrogen atoms induces significant localization of the highest occupied and lowest unoccupied molecular orbitals on different atoms to minimize not only the vibronic coupling between the ground state (S0) and the singlet excited state (S1) but also the energy gap between the S1 state and triplet excited state (T1). Organic light-emitting diode devices employing the emitter emit light at 469 nm with full-width at half-maximum of 18 nm with an external quantum efficiency of 34.4% at the maximum and 26.0% at 1,000 cd m−2.
Journal Article
Relationship between Daily and In-laboratory Gait Speed among Healthy Community-dwelling Older Adults
Gait speed in laboratory settings (in-laboratory gait speed) is one of the important indicators associated with the decline in functional abilities in older adulthood. Recently, it has become possible to measure gait speed during daily living (daily gait speed) using accelerometers. However, the relationship between these two gait speed parameters is unclear. This study aimed to compare in-laboratory gait speed, measured by a sheet-type pressure sensor, and daily gait speed, measured by an accelerometer, in healthy community-dwelling older adults. Participants were aged ≥60 years, residing in Takahama city, Aichi, Japan. To calculate daily gait speed, participants were instructed to wear a tri-axial accelerometer on their waist. A total of 1965 participants were included in the final analysis. The results showed a weak association (
r
= 0.333,
p
< 0.001) between the two gait speed parameters. Furthermore, average daily gait speed was significantly lower than average in-laboratory gait speed. However, both gait speed parameters declined significantly with age. These results suggest that, in addition to in-laboratory gait speed, daily gait speed may be a helpful parameter for predicting decline in functional abilities.
Journal Article
Autologous Induced Stem-Cell–Derived Retinal Cells for Macular Degeneration
A goal of regenerative medicine is to use induced pluripotent stem cells to generate an autologous graft for transplantation. This study tests the feasibility of the approach to treat age-related macular degeneration.
Age-related macular degeneration (AMD) is one of the most prevalent retinal diseases that threaten vision in older populations in developed countries.
1
–
4
Neovascular (also called “wet”) AMD is more prevalent than atrophic (or “dry”) AMD in Japan
5
and is associated with the ectopic development of a choroidal neovascular membrane in the subretinal space of the center of the retina (the macula). Physical disruption and functional impairment of the retinal pigment epithelium (RPE), a monolayer sheet of cells that supports the overlying photoreceptors and underlying choroidal vasculature, occur in the course of wet AMD.
6
Current treatments of AMD that involve the . . .
Journal Article
The existence of Walrasian equilibrium: infinitely many commodities, measure space of agents, and discontinuous preferences
This study establishes two equilibrium existence results for large economies with infinitely many commodities. The novel results allow for nontransitive, incomplete, discontinuous, and price-dependent preferences and do not require an interiority condition on initial endowments. The first result is an existence result when the positive cone of the commodity space has a nonempty interior. The second result is an existence result under a nonsatiation condition, including the case of the empty interior of the positive cone. The second result covers infinite-dimensional commodity spaces which could not be covered before due to the interiority condition, such as the space of square integrable functions. Specifically, we employ a saturated measure space of agents to appeal to the convexifying effect of aggregation. The notion of the continuous inclusion property introduced for finite-agent economies is applied to large economies, enabling us to dispense with the continuity assumption regarding preferences. In addition, we provide examples of Walrasian equilibrium and infinite-dimensional commodity spaces newly covered by our results.
Journal Article
Optimization technique combined with deep learning method for teeth recognition in dental panoramic radiographs
Computer-assisted analysis of dental radiograph in dentistry is getting increasing attention from the researchers in recent years. This is mainly because it can successfully reduce human-made error due to stress, fatigue or lack of experience. Furthermore, it reduces diagnosis time and thus, improves overall efficiency and accuracy of dental care system. An automatic teeth recognition model is proposed here using residual network-based faster R-CNN technique. The detection result obtained from faster R-CNN is further refined by using a candidate optimization technique that evaluates both positional relationship and confidence score of the candidates. It achieves 0.974 and 0.981 mAPs for ResNet-50 and ResNet-101, respectively with faster R-CNN technique. The optimization technique further improves the results i.e.
F
1
score improves from 0.978 to 0.982 for ResNet-101. These results verify the proposed method’s ability to recognize teeth with high degree of accuracy. To test the feasibility and robustness of the model, a tenfold cross validation (CV) is presented in this paper. The result of tenfold CV effectively verifies the robustness of the model as the average
F
1
score obtained is more than 0.970. Thus, the proposed model can be used as a useful and reliable tool to assist dental care professionals in dentistry.
Journal Article