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Reporting driving incidents depends on episodic memories formed at a certain time point in the past, and its retrievalwith subjective feelings. This study examined aging effects on episodic memory recollections by analyzing free writing reportsby older and young drivers. Unstructured hand-writing samples from 199 older (Mage = 69.2) long-experienced (Mdriving =43.0 years), and 299 young (Mage = 21.5) novice (Mdriving = 2.2 years) drivers were avalyzed by Latent Dirichlet Allocation.This identified a 6-topic model labeled: (1) operational failures, (2) control aspects, (3) other vehicles, (4) jump-outs, (5) trafficlights, and (6) attention. Posterior distribution analysis revealed that older drivers reported less in topics concerning own drivingoperations. In addition, older drivers less attributed these topics to self than environment relative to young drivers. The agedifferences of episodic memory retrieval for free reports and applicability of natural language processing to psychology arediscussed.

Many cognitive functions are represented as cell assemblies. In the case of spatial navigation, the population activity of place cells in the hippocampus and grid cells in the entorhinal cortex represents self-location in the environment. The brain cannot directly observe self-location information in the environment. Instead, it relies on sensory information and memory to estimate self-location. Therefore, estimating low-dimensional dynamics, such as the movement trajectory of an animal exploring its environment, from only the high-dimensional neural activity is important in deciphering the information represented in the brain. Most previous studies have estimated the low-dimensional dynamics (i.e., latent variables) behind neural activity by unsupervised learning with Bayesian population decoding using artificial neural networks or gaussian processes. Recently, persistent cohomology has been used to estimate latent variables from the phase information (i.e., circular coordinates) of manifolds created by neural activity. However, the advantages of persistent cohomology over Bayesian population decoding are not well understood. We compared persistent cohomology and Bayesian population decoding in estimating the animal location from simulated and actual grid cell population activity. We found that persistent cohomology can estimate the animal location with fewer neurons than Bayesian population decoding and robustly estimate the animal location from actual noisy data. [ABSTRACT FROM AUTHOR]

Background: Inadequate computed tomography (CT) number calibration curves affect dose calculation accuracy. Although CT number calibration curves registered in treatment planning systems (TPSs) should be consistent with human tissues, it is unclear whether adequate CT number calibration is performed because CT number calibration curves have not been assessed for various types of CT number calibration phantoms and TPSs. Purpose: The purpose of this study was to investigate CT number calibration curves for mass density (ρ) and relative electron density (ρe). Methods: A CT number calibration audit phantom was sent to 24 Japanese photon therapy institutes from the evaluating institute and scanned using their individual clinical CT scan protocols. The CT images of the audit phantom and institute‐specific CT number calibration curves were submitted to the evaluating institute for analyzing the calibration curves registered in the TPSs at the participating institutes. The institute‐specific CT number calibration curves were created using commercial phantom (Gammex, Gammex Inc., Middleton, WI, USA) or CIRS phantom (Computerized Imaging Reference Systems, Inc., Norfolk, VA, USA)). At the evaluating institute, theoretical CT number calibration curves were created using a stoichiometric CT number calibration method based on the CT image, and the institute‐specific CT number calibration curves were compared with the theoretical calibration curve. Differences in ρ and ρe over the multiple points on the curve (Δρm and Δρe,m, respectively) were calculated for each CT number, categorized for each phantom vendor and TPS, and evaluated for three tissue types: lung, soft tissues, and bones. In particular, the CT‐ρ calibration curves for Tomotherapy TPSs (ACCURAY, Sunnyvale, CA, USA) were categorized separately from the Gammex CT‐ρ calibration curves because the available tissue‐equivalent materials (TEMs) were limited by the manufacturer recommendations. In addition, the differences in ρ and ρe for the specific TEMs (ΔρTEM and Δρe,TEM, respectively) were calculated by subtracting the ρ or ρe of the TEMs from the theoretical CT‐ρ or CT‐ρe calibration curve. Results: The mean ± standard deviation (SD) of Δρm and Δρe,m for the Gammex phantom were −1.1 ± 1.2 g/cm3 and −0.2 ± 1.1, −0.3 ± 0.9 g/cm3 and 0.8 ± 1.3, and −0.9 ± 1.3 g/cm3 and 1.0 ± 1.5 for lung, soft tissues, and bones, respectively. The mean ± SD of Δρm and Δρe,m for the CIRS phantom were 0.3 ± 0.8 g/cm3 and 0.9 ± 0.9, 0.6 ± 0.6 g/cm3 and 1.4 ± 0.8, and 0.2 ± 0.5 g/cm3 and 1.6 ± 0.5 for lung, soft tissues, and bones, respectively. The mean ± SD of Δρm for Tomotherapy TPSs was 2.1 ± 1.4 g/cm3 for soft tissues, which is larger than those for other TPSs. The mean ± SD of Δρe,TEM for the Gammex brain phantom (BRN‐SR2) was −1.8 ± 0.4, implying that the tissue equivalency of the BRN‐SR2 plug was slightly inferior to that of other plugs. Conclusions: Latent deviations between human tissues and TEMs were found by comparing the CT number calibration curves of the various institutes. [ABSTRACT FROM AUTHOR]

Objective This study evaluated the prognostic impact of the quality of dose distribution using dosiomics in patients with prostate cancer, stratified by pretreatment prostate-specific antigen (PSA) levels and Gleason grade (GG) group. Methods A total of 721 patients (Japanese Foundation for Cancer Research [JFCR] cohort: N  = 489 and Tokyo Radiation Oncology Clinic [TROC] cohort: N  = 232) with localized prostate cancer treated by intensity-modulated radiation therapy were enrolled. Two predictive dosiomic features for biochemical recurrence (BCR) were selected and patients were divided into certain groups stratified by pretreatment PSA levels and GG. Freedom from biochemical failure (FFBF) was estimated using the Kaplan–Meier method based on each dosiomic feature and univariate discrimination was evaluated using the log-rank test. As an exploratory analysis, a dosiomics hazard (DH) score was developed and its prognostic power for BCR was examined. Results The dosiomic feature extracted from planning target volume (PTV) significantly distinguished the high- and low-risk groups in patients with PSA levels >10 ng/mL (7-year FFBF: 86.7% vs 76.1%, P  <  .01), GG 4 (92.2% vs 76.9%, P  <  .01), and GG 5 (83.1% vs 77.8%, P  =  .04). The DH score showed significant association with BCR (hazard score: 2.04; 95% confidence interval: 1.38-3.01; P  <  .001). Conclusion The quality of planned dose distribution on PTV may affect the prognosis of patients with poor prognostic factors, such as PSA levels >10 ng/mL and higher GGs. Advances in knowledge The effects of planned dose distribution on prognosis differ depending on the patient's clinical background. [ABSTRACT FROM AUTHOR]

Background: Predicting models of the gamma passing rate (GPR) have been studied to substitute the measurement‐based gamma analysis. Since these studies used data from different radiotherapy systems comprising TPS, linear accelerator, and detector array, it has been difficult to compare the performances of the predicting models among institutions with different radiotherapy systems. Purpose: We aimed to develop unbiased scoring methods to evaluate the performance of the models predicting the GPR, by introducing both best and worst limits for the performance of the GPR prediction. Methods: Two hundred head‐and‐neck VMAT plans were used to develop a framework. The GPRs were measured using the ArcCHECK device. The predicted GPR [p] was generated using a deep learning‐based model [pDL]. The predicting model was evaluated using four metrics: standard deviation (SD) [σ], Pearson's correlation coefficient (CC) [r], mean squared error (MSE) [s], and mean absolute error (MAE) [a]. The best limit [σm${\sigma _m}$, rm${r_m}$, sm${s_m}$, and am${a_m}$] was estimated by measuring the SD of measured GPR [m] by shifting the device along the longitudinal direction to measure different sampling points. Mimicked best and worst p's [pbest and pworst] were generated from pDL. The worst limit was defined such that m and p have no correlation [CC ∼ 0]. The worst limit [σMix, rMix, sMix, and aMix] was generated using the event‐mixing (EM) technique originally introduced in high‐energy physics experiments. The range of σ, r, s, and a was defined to be [σm,σMix]$[ {{\sigma _m},{\sigma _{{\mathrm{Mix}}}}} ]$, [0,rm]$[ {0,{r_m}} ]$, [sm,sMix]$[ {{s_m},{s_{{\mathrm{Mix}}}}} ]$, and [am,aMix]$[ {{a_m},{a_{{\mathrm{Mix}}}}} ]$. The achievement score (AS) independently based on σ, r, s, and a were calculated for pDL, pbest and pworst. The probability that p fails the gamma analysis (alert frequency; AF) was estimated as a function of σd${\sigma _d}$ values within the [σm${\sigma _m}$, σMix] range for the 3%/2 mm data with a 95% criterion. Results: SDs of the best limit were well reproduced by σm=0.531100−m${\sigma _m} = \;0.531\sqrt {100 - m} $. The EM technique successfully generated the (m,p)$({m,p})$ pairs with no correlation. The AS using four metrics showed good agreement. This agreement indicates successful definitions of both best and worst limits, consistent definitions of the AS, and successful generations of mixed events. The AF for the DL‐based model with the 3%/2 mm tolerance was 31.5% and 63.0% with CL's 99% and 99.9%, respectively. Conclusion: We developed the AS to evaluate the predicting model of the GPR in an unbiased manner by excluding the effects of the precision of the radiotherapy system and the spreading of the GPR. The best and worst limits of the GPR prediction were successfully generated using the measured precision of the GPR and the EM technique, respectively. The AS and σp${\sigma _p}$ are expected to enable objective evaluation of the predicting model and setting exact achievement goal of precision for the predicted GPR. [ABSTRACT FROM AUTHOR]

Word segmentation, part-of-speech (POS) tagging, and syntactic parsing are three fundamental Chinese analysis tasks for Chinese language processing, which are also crucial for various downstream tasks such as machine translation and information extraction. To achieve high accuracy for these tasks, treebanks that contain sentences manually annotated with word segmentation, part-of-speech tags, and phrase structures are essential. Although there are large-scale Chinese treebanks in the news domain, such treebanks are unavailable in the scientific domain. This significantly limits the performance of Chinese language processing for scientific text. To address this problem, we annotate the 2nd version of the Chinese treebank in the scientific domain (SCTB-V2). SCTB-V2 contains 12,175 sentences annotated with word segmentation, part-of-speech tags, and phrase structures. We conducted Chinese analyses and machine translation experiments on SCTB-V2. The results show the effectiveness of SCTB-V2. We release this treebank to promote scientific Chinese language processing research http://nlp.ist.i.kyoto-u.ac.jp/EN/index.php?A%20Chinese%20Treebank%20 in%20Scientific%20Domain%20%28SCTB%29. [ABSTRACT FROM AUTHOR]

This study aimed to expand the biological conversion factor (BCF) model, which converts the physical dosimetric margin (PDM) to the biological dosimetric margin (BDM) for point prescription with 3-dimensional conformal radiation therapy (3DCRT) and the marginal prescription method with volumetric-modulated arc radiotherapy (VMAT). The VMAT of the marginal prescription and the 3DCRT of the point prescription with lung stereotactic body radiation therapy (SBRT) by using RayStation were planned. The biological equivalent dose (BED) for a dose per fraction (DPF) of 3–20 Gy was calculated from these plans. The dose was perturbed with the calculation using a 1-mm step isocenter shift. The dose covering 95% of the target was greater than or equal to 90% of the prescribed physical dose, and the BED were defined as the PDM and BDM, respectively. The BCF was created as a function of the DPF. The PDM and BDM for all DPFs were larger with the point prescription method than with the marginal prescription method. The marginal prescription method with a 60% isodose line had a larger PDM and BDM. The BCF with the point prescription was smaller than that with the marginal prescription in the left–right (LR), anterior–posterior (AP) and cranio–caudal (CC) directions. In the marginal prescription method, the 60% isodose line had a higher BCF. In conclusion, the improved BCF method could be converted to BDM for point prescription with 3DCRT and marginal prescription method with VMAT, which is required for stereotactic radiation therapy in radiobiology-based treatment planning. [ABSTRACT FROM AUTHOR]

This study aims to evaluate the effect of different air computed tomography (CT) numbers of the image value density table (IVDT) on the retrospective dose calculation of head‐and‐neck (HN) radiotherapy using TomoTherapy megavoltage CT (MVCT) images. The CT numbers of the inside and outside air and each tissue‐equivalent plug of the "Cheese" phantom were obtained from TomoTherapy MVCT. Two IVDTs with different air CT numbers were created and applied to MVCT images of the HN anthropomorphic phantom and recalculated by Planned Adaptive to verify dose distribution. We defined the recalculation dose with MVCT images using both inside and outside air of the IVDT as IVDTMVCTinair${\mathrm{IVDT}}_{\mathrm{MVCT}}^{\mathrm{inair}}$ and IVDTMVCToutair${\mathrm{IVDT}}_{\mathrm{MVCT}}^{\mathrm{outair}}$, respectively. Treatment planning doses calculated on kVCT images were compared with those calculated on MVCT images using two different IVDT tables, namely, IVDTMVCTinair${\mathrm{IVDT}}_{\mathrm{MVCT}}^{\mathrm{inair}}$ and IVDTMVCToutair${\mathrm{IVDT}}_{\mathrm{MVCT}}^{\mathrm{outair}}$. The difference between average MVCT numbers ±1 standard deviation on inside and outside air of the calibration phantom was 65 ± 36 HU. This difference in MVCT number of air exceeded the recommendation lung tolerance for dose calculation error of 2%. The dose differences between the planning target volume (PTV): D98%, D50%, D2% and the organ at risk (OAR): Dmax, Dmean recalculated by IVDTMVCTinair${\mathrm{IVDT}}_{\mathrm{MVCT}}^{\mathrm{inair}}$ and IVDTMVCToutair${\mathrm{IVDT}}_{\mathrm{MVCT}}^{\mathrm{outair}}$ using MVCT images were a maximum of 0.7% and 1.2%, respectively. Recalculated doses to the PTV and OAR with MVCT showed that IVDTMVCToutair${\mathrm{IVDT}}_{\mathrm{MVCT}}^{\mathrm{outair}}$ was 0.5%–0.7% closer to the kVCT treatment planning dose than IVDTMVCTinair${\mathrm{IVDT}}_{\mathrm{MVCT}}^{\mathrm{inair}}$. This study showed that IVDTMVCToutair${\mathrm{IVDT}}_{\mathrm{MVCT}}^{\mathrm{outair}}$ was more accurate than IVDTMVCTinair${\mathrm{IVDT}}_{\mathrm{MVCT}}^{\mathrm{inair}}$ in recalculating the dose HN cases of MVCT using TomoTherapy. [ABSTRACT FROM AUTHOR]