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Lung adenocarcinoma (LUAD) is the most common subtype of non-small cell lung cancer associated with an unstable prognosis. Thus, there is an urgent demand for the identification of novel diagnostic and prognostic biomarkers as well as targeted drugs for LUAD. The present study aimed to identify potential new biomarkers associated with the pathogenesis and prognosis of LUAD. Three microarray datasets (GSE10072, GSE31210, and GSE40791) from the Gene Expression Omnibus database were integrated to identify the differentially expressed genes (DEGs) in normal and LUAD samples using the limma package. Bioinformatics tools were used to perform functional and signaling pathway enrichment analyses for the DEGs. The expression and prognostic values of the hub genes were further evaluated by Gene Expression Profiling Interactive Analysis and real-time quantitative polymerase chain reaction. Furthermore, we mined the “Connectivity Map” (CMap) to explore candidate small molecules that can reverse the tumoral of LUAD based on the DEGs. A total of 505 DEGs were identified, which included 337 downregulated and 168 upregulated genes. The PPI network was established with 1,860 interactions and 373 nodes. The most significant pathway and functional enrichment associated with the genes were cell adhesion and extracellular matrix-receptor interaction, respectively. Seven DEGs with high connectivity degrees (ZWINT, RRM2, NDC80, KIF4A, CEP55, CENPU, and CENPF) that were significantly associated with worse survival were chosen as hub genes. Lastly, top 20 most important small molecules which reverses the LUAD gene expressions were identified. The findings contribute to revealing the molecular mechanisms of the initiation and progression of LUAD and provide new insights for integrating multiple biomarkers in clinical practice.

This paper studies an LSTM-based adaptive transmission method for a 360-degree video and proposes a non-uniform encoding transmission strategy based on LSTM. Our goal is to maximize the user's video experience by dynamically dividing the 360-degree video into tiles of different numbers and sizes, and selecting different bitrates for each tile. This aims to reduce buffering events and video jitter. To determine the optimal number and size of tiles at the current moment, we constructed a dual-layer stacked LSTM network model. This model predicts, in real-time, the number, size, and bitrate of the tiles needed for the next moment of the 360-degree video based on the distance between the user's eyes and the screen. In our experiments, we used an exhaustive algorithm to calculate the optimal tile division and bitrate selection scheme for a 360-degree video under different network conditions, and used this dataset to train our prediction model. Finally, by comparing with other advanced algorithms, we demonstrated the superiority of our proposed method. [ABSTRACT FROM AUTHOR]

The nanoconfinement approach for constructing ultra‐small nanoparticles has been effectively utilized in the development of non‐precious metal catalysts. Herein, the catalyst precursor was prepared by host‐guest assembly method, using PCN‐222 as the host and 1,1′‐bis(diphenylphosphino) ferrocene (DPPF) as the guest. After calcination, the sub‐2 nm iron oxide nanoparticles within a phosphorus‐nitrogen co‐doped carbon matrix (Fe3O4@P/N‐CDCM) was obtained. Benefiting from the nanoconfinement effect, the sub‐2 nm Fe3O4 nanoparticles were uniformly dispersed on the P/N‐CDCM matrix. As a representative application, Fe3O4@P/N‐CDCM‐600 showed remarkable catalytic activity in the reduction of various nitroaromatic compounds. Under ambient conditions, the efficient reduction of nitro compounds in aqueous solution led to excellent conversion rates (96–99 %) and selectivity (99 %) within 45 minutes. Additionally, the catalyst maintained high efficiency over 5 cycles without experiencing a noticeable decrease in activity. This work provides an innovative and cost‐effective strategy for fabricating highly dispersed non‐precious metal nanoparticles. [ABSTRACT FROM AUTHOR]

Rhizoma Panacis Japonici (RPJ) is an ancient herbal medicine from China that has long been employed for its medicinal benefits in relieving arthritis physical debility and diverse afflictions. The primary bioactive constituents found in RPJ are triterpene saponins, which exhibit numerous pharmacological actions, including anti-inflammatory, antioxidant, and immunomodulating effects. The present study established a straightforward and effective approach for characterizing triterpene saponins in RPJ. An offline HILIC × RP LC/QTOF-MS method was developed, along with a self-constructed in-house database containing 612 saponins reported in the Panax genus and 228 predicted metabolites. The approach achieved good chromatographic performance in isolating triterpene saponins of RPJ, with the HILIC column as the first dimension (1D) and the BEH C18 column as the second dimension (2D). The developed two-dimensional liquid chromatography system exhibited an orthogonality of 0.61 and a peak capacity of 1249. Detection was performed using a QTOF mass spectrometer in a data-independent manner (MSE) in a negative ion mode. Using the in-house database, the collected MS data were processed by an automatic workflow on UNIFI 1.8.2 software, which included data correction, matching of precursor and product ions, and peak annotation. In this study, 307 saponins were characterized from RPJ and 76 saponins were identified for the first time in Panax japonicus. This research not only enhances our understanding of the chemical characteristics of RPJ but also offers a simple and efficient method for analyzing the complex composition of herbal medicine. [ABSTRACT FROM AUTHOR]

The issue of hypersonic boundary layer transition prediction is a critical aerodynamic concern that must be addressed during the aerodynamic design process of high-speed vehicles. In this context, we propose an advanced mesoscopic method that couples the gas kinetic scheme (GKS) with the Langtry–Menter transition model, including its three high-speed modification methods, tailored for accurate predictions of high-speed transition flows. The new method incorporates the turbulent kinetic energy term into the Maxwellian velocity distribution function, and it couples the effects of high-speed modifications on turbulent kinetic energy within the computational framework of the GKS solver. This integration elevates both the transition model and its high-speed enhancements to the mesoscopic level, enhancing the method's predictive capability. The GKS-coupled mesoscopic method is validated through a series of test cases, including supersonic flat plate simulation, multiple hypersonic cone cases, the Hypersonic International Flight Research Experimentation (HIFiRE)-1 flight test, and the HIFiRE-5 case. The computational results obtained from these cases exhibit favorable agreement with experimental data. In comparison with the conventional Godunov method, the new approach encompasses a broader range of physical mechanisms, yielding computational results that closely align with the true physical phenomena and marking a notable elevation in computational fidelity and accuracy. This innovative method potentially satisfies the compelling demand for developing a precise and rapid method for predicting hypersonic boundary layer transition, which can be readily used in engineering applications. [ABSTRACT FROM AUTHOR]

Conducting large-scale numerical computations to obtain flow field during the hypersonic vehicle engineering design phase can be excessively costly. Although deep learning algorithms enable rapid flow field prediction with high-precision, they require a significant investment in training samples, contradicting the motivation of reducing the cost of acquiring flow field. The combination of feature extraction algorithms and regression algorithms can also achieve high-precision prediction of flow fields, which is more suitable to tackle three-dimensional flow prediction with a small dataset. In this study, we propose a reduced-order model (ROM) for the three-dimensional hypersonic vehicle flow prediction utilizing proper orthogonal decomposition to extract representative features and Gaussian process regression with improved automatic kernel construction (AKC-GPR) to perform a nonlinear mapping of physical features for prediction. The selection of variables is based on sensitivity analysis and modal assurance criterion. The underlying relationship is unveiled between flow field variables and inflow conditions. The ROM exhibits high predictive accuracy, with mean absolute percentage error (MAPE) of total field less than 3.5%, when varying altitudes and Mach numbers. During angle of attack variations, the ROM only effectively reconstructs flow distribution by interpolation with a MAPE of 7.02%. The excellent small-sample fitting capability of our improved AKC-GPR algorithm is demonstrated by comparing with original AKC-GPRs with a maximum reduction in a MAPE of 35.28%. These promising findings suggest that the proposed ROM can serve as an effective approach for rapid and accurate vehicle flow predicting, enabling its application in engineering design analysis. [ABSTRACT FROM AUTHOR]

It is a difficult problem to find a low cost oxygen evolution catalyst which can run stably under large current. To face this challenge, in this paper, we followed the displacement reaction principle to load Mn and Fe on nickel foam (NF) by a simple two-step soaking method to prepare an oxygen evolution reaction electrocatalyst, FeMn-NF, which has excellent performance under large current. The catalyst only needs 252 mV overpotential to reach the current density of 500 mA/cm², and almost no performance attenuate even after stable operation of more than 1500 h, showing ultra-good catalytic stability under large current. Through the analysis of the scanning electron microscope test results of each catalyst after the long--term catalytic oxygen evolution reason, we found that during the oxygen evolution reaction under large current, the morphology of the catalyst will obviously evolve towards the direction of more stable. Meanwhile, the active element can stably exist WITHout loss. This evolution Is conducive to the combination of the active substance on the catalyst surface with the substrate and the smooth release of the reaction product O2 bubbles, which as more conducive to the catalyst to maintain the efficient catalytic performance. The simple synthesized catalyst with low cost and excellent performance provides a new concept for the design of industrial oxygen evolution reaction catalysts. [ABSTRACT FROM AUTHOR]

Small‐molecule induction can lead to the oriented migration of metal elements, which affords functional materials with synergistic components. In this study, phosphating nickel foam (NF)‐supported octahedral WO3 with phosphine affords P‐WO3/NF electrocatalyst. Ni is found to form Ni−P bonds that migrate from NF to WO3 under the induction of P, resulting in the complex oxides W1.3Ni0.24O4 and Ni2P2O7 in the particle interior and nickel phosphide on the octahedral grain surface. The catalytic activity of P‐WO3/NF in the urea oxidation reaction (UOR) is improved by synergistic action of the components in the synthesized hybrid particles. A current density of 10 mA cm−2 can be reached at a potential of 1.305 V, the double layer capacitance of the catalyst is significantly increased, and the electron transfer impedance in catalytic UOR is reduced. This work demonstrates that small‐molecule induction is suitable for constructing co‐catalysts with complex components in a simple protocol, which provides a new route for the design of highly efficient urea oxidation electrocatalysts. [ABSTRACT FROM AUTHOR]

For intercity railway transportation enterprises, a reasonable intercity train operation plan is not only the foundation of the intercity railway operation organization, but also the key to the sustainable development of the intercity railway (ICR). In this paper, taking into account the economic benefits of railway transportation enterprises and the social benefits of passenger travel, an optimization model is established with the intercity railway train operation plan as the research object. The model aims to minimize the operating cost of railway transportation enterprises and minimize the travel time of passengers, and considers constraints such as passenger seat utilization, passenger flow, train frequency, and stops. It is a multi-objective optimization model that accumulates two objectives by introducing the passenger time value coefficient. According to the characteristics of the model, a genetic algorithm is designed to solve the model. Taking the Beijing-Xiong'an Intercity Railway (BXICR) as an example, the "smart business card" of China's high-speed railway, two scenarios of passenger time value are designed, and the optimized train operation plan is obtained according to the existing OD passenger flow data, which verifies the effectiveness of the model and algorithm. The results show that compared with the original train operation plan, the number of stops per train of the optimized train operation plan under the two passenger time value scenarios decreased by 8.8% and 14.9%, the operating cost of the enterprise decreased by 7.7% and 1.6%, the travel time of passengers decreased by 0.7% and 1.5%, respectively. Under the condition of meeting the demand of passenger flow, the optimized train operation plan can effectively reduce the operating cost of enterprises and save the travel time of passengers, which is conducive to the sustainable development of intercity railways. [ABSTRACT FROM AUTHOR]

The extensive application of vector phase shift modulation method in permanent magnet synchronous motor drive systems with single dc-link current sensor results in obvious current ripple in the low-speed region. The patterns of these ripples vary greatly in different sectors, leading to nonnegligible phase current reconstruction errors. In this article, an estimation of the local average current change rate (ELAC2R) based reconstruction error suppression method within the switching cycle is proposed to resolve this problem. By comparing the actual and the ideal sampling points, the increase of current ripple and the reconstruction error caused by the combination of the vector phase shift and the single current sensor sampling in the low-speed region are modelled and analyzed. Then according to equivalent modelling of the current ripple between the ideal and the actual sampling points, the more accurate phase current information can be quickly obtained based on the sampling value. The compensation process is significantly simplified and the computation burden of the processor is reduced. Experimental results show that the performance of current reconstruction can be improved effectively with less harmonic content with the proposed method. [ABSTRACT FROM AUTHOR]

For aerodynamic shape design using gradient-free searching algorithms, which can be applied more flexibly than gradient-based ones, computing costs increase dramatically with dimensionality. Rational design variables are considered vital to elevate design performance in gradient-free optimization. In this paper, the Bèzier surface free-form deformation (FFD) parameterization based on adjoint surface sensitivity analysis is proposed for aerodynamic shape global optimization. Specifically, FFD point lattice is located where a wide variation is identified in adjoint surface sensitivity. In addition, input space has been adjusted accordingly to enhance space coverage due to the smoothness feature of Bernstein polynomial basis in Bèzier surface FFD. The proposed parameterization was applied to a transonic inviscid drag reduction problem for NACA 0012 with thickness constraints in dimensionality from 5 to 11, and 360.5 counts reduction in drag was achieved. In general, compared with the regularly spaced control lattice, the proposed parameterization effectively weakens the strong shock wave, and drag is decreased considerably using a small-scale sample database. [ABSTRACT FROM AUTHOR]

Torque smoothness is a strict requirement for synchronous reluctance motor (SynRM) industrial applications. The speed fluctuations induced by the high-frequency and low-frequency torque ripples deteriorate the drive performance. To suppress high-frequency torque ripples chronically omitted by conventional high-frequency signals injection methods at low-speed operation, this article proposes a bi-axes high-frequency signals injection (Bi-HFSI) scheme. A novel signal injection method is adopted, and the torque power spectrum can be broadened with discrete peaks effectively suppressed. In addition, aiming at decreasing low-frequency torque ripples, the Fourier iterative learning control algorithm that is effective in suppressing periodic disturbance is applied. It is combined with the Bi-HFSI scheme which mainly limits torque ripples at low frequencies including 1st, 2nd, and 6th harmonics. Theoretical analysis is presented, and the proposed method is verified by experiments on a 3-kW SynRM drive platform. [ABSTRACT FROM AUTHOR]

The remarkable inductance nonlinearities of synchronous reluctance motors (SynRMs) could result in operating-point-dependent high-frequency (HF) current responses, and more HF losses with audible noises with conventional constant amplitude high-frequency signal injection (HFSI) methods applied. This article proposes an adaptive pseudorandom high-frequency square-wave voltage injection (AP-HFSVI) based sensorless control method for SynRM drives at low-speed operation. An adaptive HF current control strategy is applied using pseudorandom injection patterns to adapt to the nonlinear characteristic of SynRM inductances. The magnitude of the injected HF voltage signals can be adjusted adaptively with the load variation online via HF current adaptive control scheme, which processes and regulates the HF current components to be constant. Hence, the HF losses and audible noises can be effectively suppressed due to the decreasing magnitude of injected signals. The current power spectra density of the AP-HFSVI method is analyzed and compared with the conventional HFSI method. The effectiveness and superiority of the proposed sensorless control method are verified with experiments on a 3-kW SynRM drive platform. [ABSTRACT FROM AUTHOR]

Offline parameter identification of permanent magnet synchronous machines (PMSMs) is of great importance for general-purpose drives with sensorless control. This paper proposes an amplitude-auto-adjusting signal injection (ASI) method for the parameter self-learning of PMSMs at standstill considering inverter nonlinearities and the digital time-delay effect. The ASI method achieves the inductance identification process under various dq-axis currents and at the same time prevents the unexpected rotor rotation during the self-commissioning process. For the test PMSM, the spatial inductance maps of dq-axes and abc-phases concerning the magnetic saturation and cross-coupling effects are identified along with the stator resistance. To enhance the estimation accuracy, an error model of the inverter nonlinearities in dq-axes is established, and a compensation method independent of inverter parameters is proposed based on the Hermite interpolation. In addition, the influence of the digital time-delay effect is analyzed and compensated based on the transient model of the circuits. The effectiveness of the proposed parameter self-learning scheme is confirmed on a 2.2-kW PMSM drive. The accuracy of the experimental results is validated by finite element analysis on the test machine. [ABSTRACT FROM AUTHOR]

Pulsating high-frequency (HF) signal injection is a common saliency-based sensorless control method applied for synchronous reluctance motor (SynRM) drives. However, the accuracy of the rotor position estimation deteriorates due to severe cross-coupling effect and voltage fluctuations. To solve this problem, a measured reference frame HF voltage injection (MHFVI) method is proposed in this paper. A total of two discontinuous pulsating square-wave voltage signals are injected along with two measured axes, and position information is extracted with a cross-coupling factor (CCF) introduced, which can inherently account for the estimation error caused by the cross-coupling effect. The signal processing scheme of the proposed MHFVI method is further developed to be robust to voltage errors, meanwhile has less fluctuation in the position estimation error. The position estimation error is investigated and analyzed based on the finite element analysis results and the introduced CCF is calculated based on the SynRM current–flux characteristics models. Finally, the proposed method is verified by experiments on a 3-kW SynRM drive platform. [ABSTRACT FROM AUTHOR]

This paper proposes a new sensorless control scheme of interior permanent magnet synchronous motors (IPMSMs) using high-frequency (HF) orthogonal square-wave voltage injection into a stationary reference frame (SRF). This method is an improvement on the conventional HF rotating sinusoidal voltage injection into the SRF. First, discrete sequences of the HF currents are applied to establish the whole signal process. Then, a position estimation method without low-pass filters is proposed, and the phase shifts caused by high-pass filters are also reduced. Therefore, the proposed sensorless control method would not be adversely affected by digital filters, which can solve the common drawbacks of the current SRF-based injection methods. Furthermore, the digital delay in drive systems is considered, and its negative influence can be eliminated as well. The sensorless control method is verified by experiments on a 2.2-kW IPMSM drive platform. [ABSTRACT FROM AUTHOR]

We extend several ideas developed in E. Dinaburg, Ya. G. Sinai and D. Li's papers (see Lemma , Remark  , Lemma , Remark  , Remark) and construct a unique mild solution (u,θ) of the system satisfying with spatially almost periodic initial data (u0,θ0)∈FM0,σ(R3,C3)×FM0(R3). Moreover, (u,θ) is also a spatially almost periodic complex‐valued mild solution. Assume further that (u0,θ0)∈FM0,σ∞(R3,C3)×FM0∞(R3) (see Definition). Then, we overcome the difficulty in the work of Y. Giga et al. (see Introduction below) and show that the above mild solution also satisfies for all S∈N. [ABSTRACT FROM AUTHOR]

We are concerned with a family of dissipative active scalar equation on R 2. By using similar methods from the previous paper of Y. Giga et al. (see Introduction below), we construct a unique real, spatially almost periodic mild solution θ of satisfying . In this paper, we consider some countable sum‐closed frequency sets (see Remark ). We show that the property of the solution is rather different from Chae et al and obtain that ( − Δ ) α 2 θ ( · , t ) ∉ L p ( R 2 ) with some initial data θ0 for all t≥0, 0 < p < ∞ and 0≤α≤ω, where ω is a fixed constant. Furthermore, arranging the elements of a countable sum‐closed frequency set Fδ as in Remark , we have for any 0≤α≤ω that ( − Δ ) α 2 θ ( x , t ) belongs to B U C ( R 2 × ( 0 , ∞ ) ), where Fδ is defined in or . [ABSTRACT FROM AUTHOR]

Non-line-start permanent magnet synchronous machines (NLSPMSMs) are normally considered not able to operate stably under grid drive. However, this paper proposes an effective control strategy to achieve soft start and synchronous switching of NLSPMSMs from inverter to grid drives so that to extend the application field. The system electromechanical model during switching is deduced and analyzed numerically to verify the stability under grid drive. For the control strategy, model-based sliding-mode observer is adopted during soft start for low-cost position sensorless drive, and two digital phase lock loops are used to obtain the phases of grid and inverter voltages for phase tracking. Experiments are carried out on five NLSPMSMs with different rated powers and velocities, and test results show that all these prototypes can be safely switched to grid drive and robust to load variation. Tested waveforms are coincident with numerical analysis, and the system efficiencies of NLSPMSMs under inverter and grid drives are compared. [ABSTRACT FROM AUTHOR]

Alternating-current motor drive systems consume the majority of global electricity, but most of them are still not efficient enough. This paper proposes a complete solution to the efficiency enhancement of general induction motor (IM) drive systems with the least cost and a high reliability. On the motor side, the squirrel-cage rotors of IMs are replaced with optimally designed interior permanent-magnet (PM) rotors without damping windings; meanwhile, other assemblies remained unchanged. Stators with both wye- and delta-connected windings are investigated, and super premium efficiency (IE4) is achieved on all the remanufactured motors in this paper. On the drive side, the maximum efficiency per ampere algorithm is realized based on the position-sensorless control to better exert the motor efficiency and reduce the system cost. For applications where an original IM is used under a grid drive, this paper safely switches the remanufactured interior PM synchronous motors from an inverter to the grid drive. A total of seven prototypes with different rated powers and velocities are remanufactured, and various experiments are provided as the verifications of the effectiveness and practicability of the proposed solution. [ABSTRACT FROM PUBLISHER]

Studying the complex interactions between different brain regions is crucial in neuroscience. Various statistical methods have explored the latent communication across multiple brain regions. Two main categories are the Gaussian Process (GP) and Linear Dynamical System (LDS), each with unique strengths. The GP-based approach effectively discovers latent variables with frequency bands and communication directions. Conversely, the LDS-based approach is computationally efficient but lacks powerful expressiveness in latent representation. In this study, we merge both methodologies by creating an LDS mirroring a multi-output GP, termed Multi-Region Markovian Gaussian Process (MRM-GP). Our work establishes a connection between an LDS and a multi-output GP that explicitly models frequencies and phase delays within the latent space of neural recordings. Consequently, the model achieves a linear inference cost over time points and provides an interpretable low-dimensional representation, revealing communication directions across brain regions and separating oscillatory communications into different frequency bands.

Small-molecule induction can lead to the oriented migration of metal elements, which affords functional materials with synergistic components. In this study, phosphating nickel foam (NF)-supported octahedral WO 3 with phosphine affords P-WO 3 /NF electrocatalyst. Ni is found to form Ni-P bonds that migrate from NF to WO 3 under the induction of P, resulting in the complex oxides W 1.3 Ni 0.24 O 4 and Ni 2 P 2 O 7 in the particle interior and nickel phosphide on the octahedral grain surface. The catalytic activity of P-WO 3 /NF in the urea oxidation reaction (UOR) is improved by synergistic action of the components in the synthesized hybrid particles. A current density of 10 mA cm -2 can be reached at a potential of 1.305 V, the double layer capacitance of the catalyst is significantly increased, and the electron transfer impedance in catalytic UOR is reduced. This work demonstrates that small-molecule induction is suitable for constructing co-catalysts with complex components in a simple protocol, which provides a new route for the design of highly efficient urea oxidation electrocatalysts., (© 2022 Wiley-VCH GmbH.)

Oxygen glucose deprivation (OGD)/re-oxygenation (OGDR) induces profound oxidative injury and neuronal cell death. It mimics ischemia-reperfusion neuronal injury. CPI-1189 is a novel tumor necrosis factor alpha-inhibiting compound with potential neuroprotective function. Here in SH-SY5Y neuronal cells and primary murine cortical neurons, CPI-1189 pretreatment potently inhibited OGDR-induced viability reduction and cell death. In OGDR-stimulated neuronal cells, p38 phosphorylation was blocked by CPI-1189. In addition, CPI-1189 alleviated OGDR-induced reactive oxygen species production, lipid peroxidation, and glutathione consumption. OGDR-induced neuronal cell apoptosis was also inhibited by CPI-1189 pretreatment. Furthermore, in SH-SY5Y cells and cortical neurons, CPI-1189 alleviated OGDR-induced programmed necrosis by inhibiting mitochondrial p53-cyclophilin D-adenine nucleotide translocase 1 association, mitochondrial depolarization, and lactate dehydrogenase release to the medium. In summary, CPI-1189 potently inhibited OGDR-induced oxidative injury and neuronal cell death.

Objective: To determine the prevalence of cochlear-facial dehiscence (CFD) and to examine the influence of otic capsule area, age, sex, and race on CFD., Study Design: Descriptive study of archived temporal bone specimens., Materials and Methods: Targeted sections from 1,020 temporal bone specimens were scanned and examined for CFD. Cochlear-facial partition width (CFPW) and otic capsule area (OCA), a marker of bone thickness, were measured using image analysis software. Demographic data were analyzed using multiple linear regression analysis., Results: The mean CFPW was 0.23 mm (range, 0-0.92 mm; SD, 0.15 mm). Six patients were completely dehiscent (0.59%). Fallopian canal width, age, sex, race, and OCA were found to be significant predictors of CFPW. Age was found to be negatively correlated with CFPW (β = -0.001) (p < 0.005). Thicker CFPW was associated with males (β = 0.024) and non-Caucasian individuals (β = 0.031). The mean OCA for dehiscent specimens (mean, 9.48 mm; range, 6.65-11.58 mm; SD 3.21 mm) was significantly smaller than the mean OCA for nondehiscent specimens, (mean, 12.88 mm; range, 6.63-21.92 mm; SD, 2.47 mm) (p < 0.01)., Conclusion: CFD occurred in nearly 0.6% of specimens in this temporal bone collection. Close to 35% of patients were sufficiently thin (<0.1 mm) to appear dehiscent on computed tomography scanning. Smaller OCA correlated with thinner CFPW, suggesting a developmental factor. Older, female, and Caucasian patients may have a greater risk for CFD and its associated symptoms.