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This study aimed to demonstrate the differences in the way cell and gene therapy (CGT) products have been developed and reviewed for approval in Japan, the USA, and the EU by comparing regulations and successfully launched products in each region, and to examine the background to such differences.Information on relevant regulations and approved CGT products were collected from the public source and compared by region.While regulations on CGT products are largely consistent among these regions, some differences could have a substantial impact on the practices defining CGT products, the timing of responses required to comply with the regulations for handling gene-modified organisms, and the acceptable validation processes under good manufacturing practice regulations. Although CGT products are given some preferential status in all regions, the preferential treatment given to CGT products varies across regions. The CGT products launched in each region also differ significantly in type, indications, the nature of the developers, and the clinical evidence submitted. While all the cellular products launched in Japan were approved based on small uncontrolled trials, most cellular products in the USA and EU were approved based on controlled studies. A trend was observed for companies to enter their home markets.Our study showed differences of regulations on CGT products and of features in approved products as well as the trend of their home market entries, which may have been driven by a different context than that of traditional pharmaceuticals.

Background/Aim: This study evaluated the calculation accuracy of the stopping power ratio (SPR) using dual-energy computed tomography with fast kilovoltage switching (FKSCT) for particle therapy. Materials and Methods: A tissue characterization phantom with various reference materials was scanned to obtain single-energy computed tomography (SECT) images and generate virtual monochromatic images at 77 keV (VMI(77keV)) and 140 keV (VMI(140keV)), water density (WD) images, and effective Z (Z(eff)) images. For SECT, VMI(77keV) and VMI(140keV) lookup tables were generated to convert the measured Hounsfield value into the theoretical SPR for a normal phantom size. Subsequently, the reference materials were scanned in small and large phantoms. The SPR was calculated using the lookup tables of SECT (SPR(SECT)) images, VMI(77keV) (SPR(77keV)), and VMI(140keV) (SPR(140keV)), and it was derived from the WD and Z(eff) (SPR(WD)). Results: In the normal-sized phantom, the overall mean difference between SPR(WD) and theoretical SPR was −0.3%, and remained below 2% for most reference materials. For the large phantom, the overall mean absolute difference for SPR(140keV) (3.0%, p=0.006) and SPR(WD) (3.2%, p=0.002) for the reference materials was significantly lower than that for SPR(SECT) (5.9%). For the small phantom, a significant reduction in the mean difference in the SPR calculation was observed in SPR(77keV) (1.0%, p=0.001) and SPR(140keV) (1.1%, p=0.013) compared with SPR(SECT) (2.2%). Conclusion: VMI(140keV) generated using FKSCT significantly improves the estimation accuracy of SPR compared with SECT. Thus, FKSCT may be used to improve the dose calculation accuracy for treatment planning of particle therapy.

Small-angle X-ray scattering (SAXS) coupled with computed tomography (CT), denoted SAXS-CT, enables the spatial distribution of the characteristic parameters of nanoscale structures inside samples to be visualized. In this work, a new scheme with Tikhonov regularization was developed to remove the effects of artifacts caused by streak scattering originating from contour regions of the sample., Small-angle X-ray scattering (SAXS) coupled with computed tomography (CT), denoted SAXS-CT, has enabled the spatial distribution of the characteristic parameters (e.g. size, shape, surface, length) of nanoscale structures inside samples to be visualized. In this work, a new scheme with Tikhonov regularization was developed to remove the effects of artifacts caused by streak scattering originating from the reflection of the incident beam in the contour regions of the sample. The noise due to streak scattering was successfully removed from the sinogram image and hence the CT image could be reconstructed free from artifacts in the contour regions.

We have succeeded in visualizing the spatial heterogeneity of the reaction ratio in epoxy resins by combining medium-angle X-ray scattering (MAXS) and computed tomography (CT). The reaction ratio is proportional to the degree of cross-linking between epoxy and amine in epoxy resins. The reaction ratio and its spatial inhomogeneity affect the toughness of epoxy resins. However, there has been no non-destructive method to measure the spatial inhomogeneity of the reaction ratio, although we can measure only the spatially averaged reaction ratio by Fourier-transform infrared spectroscopy (FT-IR). We found that the scattering peak reflected the cross-linking structures in the

The present study proposes a sensor selection method based on the proximal splitting algorithm and the A-optimal design of experiment using the alternating direction method of multipliers (ADMM) algorithm. The performance of the proposed method was evaluated with a random sensor problem and compared with previously proposed methods, such as the greedy and convex relaxation methods. The performance of the proposed method is better than the existing greedy and convex relaxation methods in terms of the A-optimality criterion. Although, the proposed method requires a longer computational time than the greedy method, it is quite shorter than that of convex relaxation method in large-scale problems. Then the proposed method was applied to the data-driven sparse-sensor-selection problem. The dataset adopted was the National Oceanic and Atmospheric Administration optimum interpolation sea surface temperature dataset. At a number of sensors larger than that of the latent variables, the proposed method showed similar and better performance compared with previously proposed methods in terms of the A-optimality criterion and reconstruction error.

This paper proposes an epigraphical relaxation (ERx) technique for non-proximable mixed norm minimization. Mixed norm regularization methods play a central role in signal reconstruction and processing, where their optimization relies on the fact that the proximity operators of the mixed norms can be computed efficiently. To bring out the power of regularization, sophisticated layered modeling of mixed norms that can capture inherent signal structure is a key ingredient, but the proximity operator of such a mixed norm is often unavailable (non-proximable). Our ERx decouples a layered non-proximable mixed norm into a norm and multiple epigraphical constraints. This enables us to handle a wide range of non-proximable mixed norms in optimization, as long as both the proximal operator of the outermost norm and the projection onto each epigraphical constraint are efficiently computable. Moreover, under mild conditions, we prove that ERx does not change the minimizer of the original problem despite relaxing equality constraints into inequality ones. We also develop new regularizers based on ERx: one is decorrelated structure-tensor total variation for color image restoration, and the other is amplitude-spectrum nuclear norm for low-rank amplitude recovery. We examine the power of these regularizers through experiments, which illustrates the utility of ERx., accepted to IEEE Transactions on Signal Processing

BACKGROUND: We investigated the change of dose distributions in volumetric modulated arc therapy (VMAT) under baseline drift (BD) during breath holding. MATERIALS AND METHODS: Ten VMAT plans recalculated to a static field at a gantry angle of 0° were prepared for measurement with a 2D array device and five original VMAT plans were prepared for measurement with gafchromic films. These measurement approaches were driven by a waveform reproducing breath holding with BD. We considered breath holding times of 15 and 10 s, and BD at four speeds; specifically, BD0 (0 mm/s), BD0.2 (0.2 mm/s), BD0.3 (0.3 mm/s), and BD0.4 (0.4 mm/s). The BD was periodically reproduced from the isocenter along the craniocaudal direction and the shift during breath holding (Shift(BH)) ranged 0–6 mm. The dose distribution of BD0.2, BD0.3 and BD0.4 were compared to that of BD0 using gamma analysis with the criterion of 2%/2 mm. RESULTS: The mean pass rates of each Shift(BH) were 99.8% and 98.9% at 0 mm, 96.8% and 99.4% at 2 mm, 94.9% and 98.6% at 3 mm, 91.5% and 98.4% at 4 mm, 70.8% and 94.1% at 4.5 mm, and 55.0% and 83.6% at 6 mm for the array and film measurements, respectively. CONCLUSION: We found significant differences in Shift(BH) above 4 mm (ρ

Grazing-incidence small-angle X-ray scattering coupled with computed tomography (CT) has enabled the visualization of the spatial distribution of nanostructures in thin films. In this study, to optimize the CT image quality, total variation regularization is introduced to minimize sinogram image noise and artifacts., Grazing-incidence small-angle X-ray scattering (GISAXS) coupled with computed tomography (CT) has enabled the visualization of the spatial distribution of nanostructures in thin films. 2D GISAXS images are obtained by scanning along the direction perpendicular to the X-ray beam at each rotation angle. Because the intensities at the q positions contain nanostructural information, the reconstructed CT images individually represent the spatial distributions of this information (e.g. size, shape, surface, characteristic length). These images are reconstructed from the intensities acquired at angular intervals over 180°, but the total measurement time is prolonged. This increase in the radiation dosage can cause damage to the sample. One way to reduce the overall measurement time is to perform a scanning GISAXS measurement along the direction perpendicular to the X-ray beam with a limited interval angle. Using filtered back-projection (FBP), CT images are reconstructed from sinograms with limited interval angles from 3 to 48° (FBP-CT images). However, these images are blurred and have a low image quality. In this study, to optimize the CT image quality, total variation (TV) regularization is introduced to minimize sinogram image noise and artifacts. It is proposed that the TV method can be applied to downsampling of sinograms in order to improve the CT images in comparison with the FBP-CT images.

The spatio-spectral total variation (SSTV) model has been widely used as an effective regularization of hyperspectral images (HSI) for various applications such as mixed noise removal. However, since SSTV computes local spatial differences uniformly, it is difficult to remove noise while preserving complex spatial structures with fine edges and textures, especially in situations of high noise intensity. To solve this problem, we propose a new TV-type regularization called Graph-SSTV (GSSTV), which generates a graph explicitly reflecting the spatial structure of the target HSI from noisy HSIs and incorporates a weighted spatial difference operator designed based on this graph. Furthermore, we formulate the mixed noise removal problem as a convex optimization problem involving GSSTV and develop an efficient algorithm based on the primal-dual splitting method to solve this problem. Finally, we demonstrate the effectiveness of GSSTV compared with existing HSI regularization models through experiments on mixed noise removal. The source code will be available at https://www.mdi.c.titech.ac.jp/publications/gsstv., Comment: Accepted to IEEE Geoscience and Remote Sensing Letters. The code is available at https://www.mdi.c.titech.ac.jp/publications/gsstv

This paper proposes focal surface occlusion to provide focal cues of occlusion masks for multiple virtual objects at continuous depths in an occlusion-capable optical see-through head-mounted display. A phase-only spatial light modulator (PSLM) that acts as a dynamic free-form lens is used to conform the focal surface of an occlusion mask to the geometry of the virtual scene. To reproduce multiple and continuous focal blurs while reducing the distortion of the see-through view, an optical design based on afocal optics and edge-based optimization to exploit a property of the occlusion mask is established. The prototype with the PSLM and transmissive liquid crystal display can reproduce the focus blur of occluded objects at multiple and continuous depths with a field of view of 14.6°.

Our study objective was to determine lung cancer chemotherapy attributes that are important to patients in Japan. A discrete choice experiment survey in an anonymous web-based questionnaire format with a reward was completed by 200 lung cancer patients in Japan from November 25, 2019, to November 27, 2019. The relative importance of patient preferences for each attribute was estimated using a conditional logit model. A hierarchical Bayesian logit model was also used to estimate the impact of each demographic characteristic on the relative importance of each attribute. Of the 200 respondents, 191 with consistent responses were included in the analysis. In their preference, overall survival was the most important, followed by diarrhea, nausea, rash, bone marrow suppression (BMS), progression-free survival, fatigue, interstitial lung disease, frequency of administration, and duration of administration. The preferences were influenced by demographic characteristics (e.g., gender and age) and disease background (e.g., cancer type and stage). Interestingly, the experience of cancer drug therapies and adverse events had a substantial impact on the hypothetical drug preferences. For the Japanese lung cancer patients, improved survival was the most important attribute that influenced their preference for chemotherapy, followed by adverse events, including diarrhea, nausea, rash, and BMS. The preferences varied depending on the patient’s demographic and experience. As drug attributes can affect patient preferences, pharmaceutical companies should be aware of the patient preferences and develop drugs that respond to segmented market needs.

Purpose To evaluate the utility of the use of iterative cone-beam computed tomography (CBCT) for machine log file-based dose verification during volumetric modulated arc therapy (VMAT) for prostate cancer patients. Methods All CBCT acquisition data were used to reconstruct images with the Feldkamp-Davis-Kress algorithm (FDK-CBCT) and the novel iterative algorithm (iCBCT). The Hounsfield unit (HU)-electron density curves for CBCT images were created using the Advanced Electron Density Phantom. The I'mRT and anthropomorphic phantoms were irradiated with VMAT after CBCT registration. Subsequently, fourteen prostate cancer patients received VMAT after CBCT registration. Machine log files and both CBCT images were exported to the PerFRACTION software, and a 3D patient dose was reconstructed. Mean dose for planning target volume (PTV), the bladder, and rectum and the 3D gamma analysis were evaluated. Results For the phantom studies, the variation of HU values was observed at the central position surrounding the bones in FDK-CBCT. There were almost no changes in the difference of doses at the isocenter between measurement and reconstructed dose for planning CT (pCT), FDK-CBCT, and iCBCT. Mean dose differences of PTV, rectum, and bladder between iCBCT and pCT were approximately 2% lower than those between FDK-CBCT and pCT. For the clinical study, average gamma analysis for 2%/2 mm was 98.22% ± 1.07 and 98.81% ± 1.25% in FDK-CBCT and iCBCT, respectively. Conclusions A similar machine log file-based dose verification accuracy is obtained for FDK-CBCT and iCBCT during VMAT for prostate cancer patients.

We propose a design method of sampling matrices for graph signals that guarantees perfect recovery for arbitrary graph signal subspaces. When the signal subspace is known, perfect reconstruction is always possible from the samples with an appropriately designed sampling matrix. However, most graph signal sampling methods so far design sampling matrices based on the bandlimited assumption and sometimes violates the perfect reconstruction condition for the other signal models. In this paper, we formulate an optimization problem for the design of the sampling matrix that guarantees perfect recovery, thanks to a generalized sampling framework for standard signals. In experiments with various signal models, our sampling matrix presents better reconstruction accuracy both for noiseless and noisy situations.

What is known and objective The objectives of this study were to explore completeness of direct adverse event (AE) reports from consumers and healthcare professionals (HCPs), and to discuss the reasons completeness varied among reporters with different occupations. Methods We used a total of 5475 direct AE reports to the United States (US) Food and Drug Administration (FDA) from the first and second quarters of 2016 and assessed completeness of basic information (eg, patient sex, age, weight) and information relevant to AEs (eg, suspect and concomitant drugs). Logistic regression analysis was conducted to evaluate the associations between report completeness and reporting backgrounds. Results and discussion The completeness of AE reports from consumers was generally greater than that of reports from HCPs. Completeness of specific items varied among different occupations, which may reflect accessibility to, and/or availability of, relevant information for each type of reporter. There was a clear association between the proportion of ‘known’ ADRs in a report and completeness, suggesting that consumers and HCPs are likely to consult labelling information when reporting AEs. What is new and conclusion The quality of AE reports seemed to depend on information costs accrued to potential reporters. Researchers should consider the impact of database heterogeneity and possible sample selection bias when using spontaneous AE reports as a sample of events in the United States., The quality of AE reports in the US seems to depend on information costs accrued to potential reporters. Researchers may need to consider heterogeneity and sample selection bias when using spontaneous AE reports.

The basic components of regulatory and supporting policies for orphan drug development appear similar between the United States and Japan, but drugs designated as orphan drugs have been different between the 2 countries. The probabilities of development success (ie, marketing approval) in designated orphan drugs have also been significantly different. In this study, we analyzed recent outcomes of development for orphan drugs designated from 1993 to 2017 in Japan, considering their development and approval status in the United States. Our analysis showed that success for orphan drug development in Japan was apparently associated with prior approval status in the United States. Company size, orphan development experience, and patient enrichment were also positively associated with successful marketing approval. Although similar designations and priority review systems for orphan drugs have been enacted, economic incentives and regulatory conditions provided by the systems seem to be different between the 2 countries, which may lead to varied performance in orphan designation and approval. We need to pay close attention to the impact of industrial global development strategies when comparing the outcomes and performance of different orphan drug promotion systems.

Recent International Conference on Harmonization (ICH) guidelines provide pharmaceutical companies with regulatory justifications to pursue various global drug‐development pathways, in some of which “local” dose‐ranging and/or pivotal phase III studies are skipped. We examined the association between the clinical development pathway and postmarketing safety in Japan for 177 new molecular entities approved between 2004 and 2013 focusing on dose setting histories for each drug. The risk of drug‐related deaths was higher when companies did not conduct local (i.e., Japanese) dose‐ranging studies and/or pivotal studies. Even when local dose‐ranging studies were conducted, the risk remained higher in some drugs for which the approved dose in Japan was set equal to that in the United States. Drugs developed under a bridging strategy tended to show lower risks. These results suggested that local clinical studies may play a substantial role in achieving optimization of postmarketing drug use in each local target population.

Pixel-domain weighting methods for multiple-exposure blending can efficiently remove noise and under-/over-exposed pixels simultaneously in high dynamic range (HDR) image generation. Various types of noise such as non-Gaussian noise, e.g., Poisson, impulse noise, and pixel saturation, are often superimposed to multiple-exposure images taken with a high ISO setting in a low-light condition. Because almost all existing methods assume Gaussian noise, these methods cannot sufficiently reduce these types of noise. To achieve high-quality HDR image generation in such difficult conditions, we propose a novel multiple-exposure blending method in which image blending is performed in a wavelet domain so as to enhance the denoising performance. In addition, the Huber loss function is utilized as a fidelity measure in blending to make the method robust against outliers. We also introduce an efficient algorithm based on a primal-dual splitting method for solving our optimization problem. The experimental results demonstrate the advantages of the proposed method over several conventional methods.

This paper proposes robust acoustic scene classification (ASC) to multiple devices using maximum classifier discrepancy (MCD) and knowledge distillation (KD). The proposed method employs domain adaptation to train multiple ASC models dedicated to each device and combines these multiple device-specific models using a KD technique into a multi-domain ASC model. For domain adaptation, the proposed method utilizes MCD to align class distributions that conventional DA for ASC methods have ignored. The multi-device robust ASC model is obtained by KD, combining the multiple device-specific ASC models by MCD that may have a lower performance for non-target devices. Our experiments show that the proposed MCD-based device-specific model improved ASC accuracy by at most 12.22% for target samples, and the proposed KD-based device-general model improved ASC accuracy by 2.13% on average for all devices.

In this study, we examine the relationship between financial literacy, financial education, and smoking behavior among the Japanese population. We hypothesize that financially literate and financially educated people, who have the ability to make more rational decisions, are less likely to smoke. Using the Preference Parameters Study of Osaka University, conducted in 2010 (N = 3,706), the probit regression results show that both financial literacy (with an emphasis on knowledge of investments) and financial education (with an emphasis on savings behavior) have a significant negative impact on smoking behavior. In addition, gender, age, education, marital status, household income and assets, risky behaviors, a myopic view of the future, risk preference, and level of happiness also significantly predict the likelihood of a person being a current smoker. This study provides empirical evidence that enhancing the rational decision-making ability of individuals through financial literacy and financial education may curtail smoking behavior.

Problem gambling is becoming a growing concern in the United States because of the proliferation of, and state support for, gambling opportunities. The economic cost along with the physical and mental health problems associated with problem gambling make it necessary to study how problem gambling can be reduced. Our study examines whether financial literacy could be a means to reducing gambling frequency in the United States. We use data from the Preference Parameter Study of Osaka University, Japan, and apply instrumental variable probit regression models. The results show that, generally, financial literacy does not have a relationship with gambling frequency, but the relationship is significant in the states where electronic gambling machines (EGMs) are available. The results imply that gamblers are irrational and fail to assess the risks of gambling as well as the probabilities that maximize expected payoffs. It appears that gamblers' psychological gain from gambling outweighs the negative expected utility when there is easy access to gambling. Thus, rationality with regard to gambling decisions does not work unless the easy access to EGMs is controlled. Our results further show that males, older people, people with higher household income, and people who have easy access to gambling are likely to be frequent gamblers.

Food is not just evaluated based on taste alone but is influenced by various complicated factors. For this reason, there are many kinds of research on foods with various perspectives and multiple angles. Our research team proposed the margin support system based on the concept of “Freedom from the need to prepare many plates to fit each various foods.” In this study, we aimed to elucidate the margin support system’s effect on people’s emotions and sensibilities. We conducted Factor analysis to explore latent variables in participants’ impressions of our system’s projected plate ratio size. We extracted the following two factors from Factor analysis: “Assertiveness of art in the food expression” and “Assertiveness as a food.” Furthermore, we clarify a relationship between each factor scores and the percentage of margins to be projected.

Regulatory agencies as well as private organizations pursue programs that advocate patient centricity and emphasize the importance of dialog with patients. Various methods are applied to elicit the preferences of patients regarding the aspects of treatment they lend more importance to. Decisions on treatment choices are critical to patients with lung cancer because of their poor prognosis and the serious trade-off between safety and efficacy in traditional cytotoxic chemotherapy. We conducted a systematic literature review of quantitative patient preference studies of patients with lung cancer. Our exhaustive search of MEDLINE, CINAHL, EMBASE, PLOS, and SpringerLink identified 15 relevant studies published from January 2000 to April 2020 that enabled us to assess the relative importance of treatment attributes according to lung cancer patients’ perspective. The literature review revealed that patients with lung cancer tend to place a higher weight on efficacy and quality of life (QoL) attributes than on other attributes. Overall survival was found to be the most important among the efficacy attributes. The consequences of adverse events seemed less important than the possible efficacy from therapies. The clinical utility of treatment, such as the route of administration, was generally not considered important. It remains inconclusive whether sociodemographic factors and/or medical history affect the relative importance of a patient’s preference. Our systematic review clarified that patients generally prefer a better efficacy profile to a better safety profile, which underscores the importance of improved benefits in anti-lung cancer drug development.

This paper proposes an epigraphical reformulation (ER) technique for non-proximable mixed norm regularization. Various regularization methods using mixed norms have been proposed, where their optimization relies on efficient computation of the proximity operator of the mixed norms. Although the sophisticated design of mixed norms significantly improves the performance of regularization, the proximity operator of such a mixed norm is often unavailable. Our ER decouples a non-proximable mixed norm function into a proximable norm and epigraphical constraints. Thus, it can handle a wide range of non-proximable mixed norms as long as the proximal operator of the outermost norm, and the projection onto the epigraphical constraints can be efficiently computed. Moreover, we prove that our ER does not change the minimizer of the original problem despite using a certain inequality approximation. We also provide a new structure-tensor-based regularization as an application of our framework, which illustrates the utility of ER.

Background: The aim of the study was to evaluate analysis criteria for the identification of the presence of rectal gas during volumetric modulated arc therapy (VMAT) for prostate cancer patients by using electronic portal imaging device (EPID)-based in vivo dosimetry (IVD). Materials and methods: All measurements were performed by determining the cumulative EPID images in an integrated acquisition mode and analyzed using PerFRACTION commercial software. Systematic setup errors were simulated by moving the anthropomorphic phantom in each translational and rotational direction. The inhomogeneity regions were also simulated by the I’mRT phantom attached to the Quasar phantom. The presence of small and large air cavities (12 and 48 cm 3 ) was controlled by moving the Quasar phantom in several timings during VMAT. Sixteen prostate cancer patients received EPID-based IVD during VMAT. Results: In the phantom study, no systematic setup error was detected in the range that can happen in clinical ( < 5-mm and < 3 degree). The pass rate of 2% dose difference (DD2%) in small and large air cavities was 98.74% and 79.05%, respectively, in the appearance of the air cavity after irradiation three quarter times. In the clinical study, some fractions caused a sharp decline in the DD2% pass rate. The proportion for DD2% < 90% was 13.4% of all fractions. Rectal gas was confirmed in 11.0% of fractions by acquiring kilo-voltage X-ray images after the treatment. Conclusions: Our results suggest that analysis criteria of 2% dose difference in EPID-based IVD was a suitable method for identification of rectal gas during VMAT for prostate cancer patients.

The randomized subspace Newton convex methods for the sensor selection problem are proposed. The randomized subspace Newton algorithm is straightforwardly applied to the convex formulation, and the customized method in which the part of the update variables are selected to be the present best sensor candidates is also considered. In the converged solution, almost the same results are obtained by original and randomized-subspace-Newton convex methods. As expected, the randomized-subspace-Newton methods require more computational steps while they reduce the total amount of the computational time because the computational time for one step is significantly reduced by the cubic of the ratio of numbers of randomly updating variables to all the variables. The customized method shows superior performance to the straightforward implementation in terms of the quality of sensors and the computational time.

When we evaluate a dish, various factors affect the evaluation in a complicated way, it is not just the taste. In this study, we focus on the ‘margin’ of a plate with food. The ‘margin’ is one of the factors of the arrangement, and we aim to elucidate the effect of the margin of plate on people’s emotions and sensibilities. The results were analyzed using factor analysis, one of the multivariate analyses. Furthermore, based on the results of the experiment, we proposed a Kansei system using the technology of projection mapping and a table with a translucent tabletop.

Background Voluntary reports on adverse events (AEs) submitted by consumers have been facilitated through the MedWatch program in the United States (US), but few studies have described the characteristics of voluntary reports. Objective The aim of this study was to reveal the characteristics of current voluntary reports on AEs reported by consumers and healthcare professionals. Methods We performed analysis on voluntary reports of AEs in the US Food and Drug Administration AE Reporting System (FAERS) database submitted in 2016. We compared reports by consumers with those by healthcare professionals. Results The number of voluntary reports by consumers has increased since 2013 in the US. Reports by consumers were different from ones by health professionals in several important aspects such as demographics and outcomes of patients, AEs, and suspect drugs. The proportion of reports on female patients and on disability as a patient outcome were higher in reports by consumers than in those by healthcare professionals. Consumers more frequently reported concomitant drugs compared with healthcare professionals. Time to report varied among the occupations and depending on seriousness of outcomes. Conclusions Our analysis of voluntary AE reports in the US FAERS database has shown that voluntary reports tended to include AEs related to subjective symptoms, as in some previous studies on patient reporting in the EU. Voluntary reports by consumers seemed to be different from ones by healthcare professionals in important aspects including demographics and reporting behaviors. These findings suggest that the heterogeneities should be addressed appropriately in using spontaneous reports. Electronic supplementary material The online version of this article (10.1007/s40801-018-0134-0) contains supplementary material, which is available to authorized users.

We propose an approximation method for thresholding of singular values using Chebyshev polynomial approximation (CPA). Many signal processing problems require iterative application of singular value decomposition (SVD) for minimizing the rank of a given data matrix with other cost functions and/or constraints, which is called matrix rank minimization. In matrix rank minimization, singular values of a matrix are shrunk by hard-thresholding, soft-thresholding, or weighted soft-thresholding. However, the computational cost of SVD is generally too expensive to handle high dimensional signals such as images; hence, in this case, matrix rank minimization requires enormous computation time. In this paper, we leverage CPA to (approximately) manipulate singular values without computing singular values and vectors. The thresholding of singular values is expressed by a multiplication of certain matrices, which is derived from a characteristic of CPA. The multiplication is also efficiently computed using the sparsity of signals. As a result, the computational cost is significantly reduced. Experimental results suggest the effectiveness of our method through several image processing applications based on matrix rank minimization with nuclear norm relaxation in terms of computation time and approximation precision., Comment: This is a journal paper

Background Mechanisms underlying safety events may be heterogeneous and depend on conditions of development and marketing, including the populations studied in clinical trials and the amount of data required for approval, especially under pathways for accelerated access. Objective This study was conducted to investigate possible factors affecting the first post-marketing safety-related regulatory actions (SRRAs) after launch of new drugs in Japan. Methods We studied 338 new molecular entities (NMEs) approved in Japan between 2004 and 2014. We focused on three different types of SRRAs: (1) all-SRRAs (i.e. SRRAs from domestic cases and other countries), (2) domestic-SRRAs (i.e. SRRAs from domestic cases) and (3) domestic unknown-SRRAs (i.e. SRRAs of unknown risks from domestic cases). Occurrences of the three types of SRRAs were analyzed using Kaplan–Meier analysis and Cox-regression. Results SRRAs tended to occur sooner for NMEs launched in recent years versus those launched towards the beginning of the study period. Risk of SRRA was high for antineoplastics. Drugs for cardiovascular diseases, central nervous system, and diabetes had positive associations with all-SRRAs, but the associations were weaker with domestic-SRRAs. Domestic-SRRAs were more likely for drugs with relatively novel modes of action (MOAs). Longer lag to Japanese launch after first global launch significantly lowered SRRA risks. While most of the variables showed similar associations across the three types of SRRAs, adoption of bridging strategies showed higher risks only for domestic-SRRAs, not for all-SRRAs. FDA safety labeling changes and non-orphan priority review drugs presented higher domestic-SRRA risks. The number of adverse drug reactions (ADRs) from spontaneous reports had positive correlations with the three types of SRRAs, whereas the number from company-led surveillance showed no association. Conclusions Our results indicated that global clinical development pathways and marketing status should be considered more seriously in implementing locally optimized pharmacovigilance activities. Caution may be needed not only for drugs with novel MOAs, but also for drugs for which local dose-finding studies have been skipped, expedited review status has been given, timing of launch is close to those in the USA and the EU, and spontaneous reports rather than company-lead surveillance suggest possible safety risks.

In this paper, we propose a new design method of discrete-valued control for continuous-time linear time-invariant systems based on sum-of-absolute-values (SOAV) optimization. We first formulate the discrete-valued control design as a finite-horizon SOAV optimal control, which is an extended version of ${\mathbb{L}^{1}}$ optimal control. We then give simple conditions that guarantee the existence, discreteness, and uniqueness of the SOAV optimal control. Also, we show that the value function is continuous, by which we prove the stability of infinite-horizon model predictive SOAV control systems. We provide a fast algorithm for the SOAV optimization based on the alternating direction method of multipliers (ADMM), which has an important advantage in real-time control computation. A simulation result shows the effectiveness of the proposed method.

Pyrolysis is an important method for breaking down organic waste to obtain useful low molecular weight compounds. To investigate the pyrolysis behavior of bisphenol A (BPA) and BPA pyrolysates at low pyrolytic temperatures, BPA and p -isopropenyl phenol (IPP) were pyrolyzed at 240–320 °C using a batch reactor. BPA decomposition was strongly enhanced at temperatures higher than 240 °C, and was almost complete after 30 min at 320 °C. Theoretically, pyrolysis of BPA gives phenol (PhOH) and IPP in a 1:1 molar ratio, corresponding to yields of 41 wt% PhOH and 59 wt% IPP. However, an unexpectedly low IPP yield (∼10 wt%) was obtained experientially, with a simultaneous increase in the yields of high molecular weight compounds. Compounds resulting from IPP polymerization were detected using electrospray ionization time-of-flight mass spectrometry (ESI-TOF-MS), suggesting that IPP was rapidly polymerized into high molecular weight compounds via radical propagation. On the other hand, a maximum PhOH yield of 62 wt% was obtained after 30 min at 320 °C, which is higher than the theoretical yield. As direct dealkylation of IPP is difficult, the excess PhOH is likely derived from thermal decomposition of the IPP polymerization products. Thus, the present work revealed the high reactivity of IPP for polymerization at pyrolytic temperatures, which facilitates increased recovery of PhOH from BPA.

While efficient and less onerous for the industry, the globalization of clinical drug development may lead to limited efforts to optimize drugs for regional conditions. We examined the association between clinical development pathways, approved doses, and postmarketing safety risks in Japan for 135 new molecular entities approved between 2004 and 2011. The risk of drug-related deaths seemed higher when pharmaceutical companies chose exactly the same dose as in the United States, even after conducting Japanese dose-ranging studies. We also found a positive association with drug-related deaths when the review process was expedited and when Japanese dose-ranging studies were not conducted for nonexpedited drugs. Our findings suggest that the decisions on regional dose settings and the choice of global clinical development pathways are associated in ways that may influence the postmarketing outcomes in the target populations.

Background In drug development, animal toxicology data are very important for the evaluation of clinical safety. We quantitatively assessed the safety profiles of blood cancer drugs approved in Japan from category I (high) to V (low). We examined the ratios of drug exposure in animals at the no observed adverse effect level to those in humans at the expected therapeutic dose. In addition, qualitative analysis of the relationship between toxicological findings and adverse drug reactions (ADRs) is one of the primary approaches for determining the risk–benefit profile of a pharmaceutical. This study thus aimed to evaluate the potential of nonclinical safety assessments for predicting ADRs in humans. Methods We examined toxicological findings at the lowest observed adverse effect level and ADRs in pivotal clinical studies. We calculated concordance rates as the ratio of the number of concordant ADRs to all ADRs. Results Twenty-seven drugs were eligible for analysis. Concordance rates ranged from 0 to 84.8%. No significant differences were observed in concordance rates between antibodies (median 14.3%) and small molecules (median 18.5%). There was a significant correlation between concordance rates and quantitative safety profiles (p = 0.047), suggesting that some drugs with low safety profiles (categories III, IV, or V) have high concordance rates. Conclusion The results suggested that ADRs in clinical trials could be predicted based on toxicity data obtained in animal tests, especially for some drugs with a low quantitative safety profile.

SummaryWhat is known and objective Multicriteria decision analysis (MCDA) has been generally considered a promising decision-making methodology for the assessment of drug benefit–risk profiles. There have been many discussions in both public and private sectors on its feasibility and applicability, but it has not been employed in official decision-makings. For the purpose of examining to what extent MCDA would reflect the first-hand, intuitive preference of evaluators in practical pharmaceutical assessments, we conducted a questionnaire survey involving the participation of employees of pharmaceutical companies. Methods Showing profiles of the efficacy and safety of four hypothetical drugs, each respondent was asked to rank them following the standard MCDA process and then to rank them intuitively (i.e. without applying any analytical framework). Results and discussion These two approaches resulted in substantially different ranking patterns from the same individuals, and the concordance rate was surprisingly low (17%). Although many respondents intuitively showed a preference for mild, balanced risk-benefit profiles over profiles with a conspicuous advantage in either risk or benefit, the ranking orders based on MCDA scores did not reflect the intuitive preference. What is new and conclusion Observed discrepancies between the rankings seemed to be primarily attributed to the structural characteristics of MCDA, which assumes that evaluation on each benefit and risk component should have monotonic impact on final scores. It would be difficult for MCDA to reflect commonly observed non-monotonic preferences for risk and benefit profiles. Possible drawbacks of MCDA should be further investigated prior to the real-world application of its benefit–risk assessment.

This work proposes a novel restoration model for optical coherence tomography (OCT) data. The authors have been developing a multi-frequency swept (MS) en-face OCT device that can help understand the mechanism of the sensory epithelium in the cochlear. Although the device has merit in acquiring moving tissues, the broadened light gives a weak response; thus, some signal restorations are demanded. This work proposes the introduction of a formulation for OCT data restoration as a convex optimization problem by assuming a latent refractive index distribution. An algorithm to solve the problem with the primal-dual splitting (PDS) framework is then derived. The PDS has an advantage of requiring no inverse matrix operation and being able to handle high-dimensional data. The significance of the proposed model is verified by simulations on artificial data, followed by an experiment with the actual observation of 256 256 2000 voxels.

This paper proposes a new mixed noise removal method using a hybrid spatio-spectral total variation (HSSTV) for hy-perspectral (HS) images. HSSTV effectively evaluates spatial and spectral piecewise smoothness and would be a powerful regularization for HS image restoration. Existing mixed noise removal methods evaluate a-priori knowledge of an HS image via multiple regularizations. However, they do not appropriately evaluate spatial piecewise smoothness, resulting in oversmoothing or artifacts. Moreover, parameter settings in existing methods are troublesome tasks because multiple balancing parameters are interdependent. In contrast, thanks to HSSTV, the proposed method can restore a clean HS image while keeping sharp edges and details. In addition, parameter settings in our method are much easier than existing ones because data fidelity is imposed as hard constraints. In the experiments, we demonstrate the advantages of our proposed method over existing regularizations.

This paper proposes multiscale structure-tensor total variation (MSTV) for image recovery. Gradient vectors in local patches usually have similar directions, and thus each local gradient matrix (the set of gradient vectors) tends to be low rank. STV introduces this property by calculating the sum of nuclear norms from all the 10-cal gradient matrices over the input image. By STV regularization, fine textures are recovered efficiently. However, since STV only considers differences of vertically and horizontally adjacent pixels, if neighboring samples are not reliable due to severe degradation, a latent image cannot be recovered efficiently. In this work, we assume that, for any two target pixels in a local patch, two vectors consisting of multiple differences not only between each target and adjacent pixels but also each target and further distant pixels exhibit a similar direction. According to this assumption, our MSTV firstly applies wavelet-based multiscale decomposition to vertical/horizontal gradient vectors and then evaluates the sum of nuclear norms of all the local wavelet coefficients. Experimental results show that the MSTV improves both numerical reconstruction error and subjective visual quality, compared with the conventional STV.

A hyperspectral (HS) image has high spectral resolution information but low spatial resolution information. To get an HS image of high spatial and spectral resolution (high-spatial HS image), fusion techniques are actively studied, which synthesize an HS image of low spatial and high spectral resolution and a multispectral (MS) image. The techniques can generate a high-spatial HS image by exploiting the high spectral and spatial resolution information of HS and MS images, respectively. However, the methods do not evaluate the edge similarity between generated HS and observed MS images, and do not denoise the MS image. As a result, when an observed MS image is noisy, these methods produce artifacts and spectral distortion. To tackle this problem, we propose a new HS and MS data fusion method using a hybrid spatio-spectral total variation (HSSTV), which is a regularization for HS image restoration. The method not only generates a high-spatial HS image but also denoises a given MS image, so that we obtain a high-spatial HS image even if the observed images are contaminated by noise. In the experiments, we demonstrate the advantages of our method over existing fusion methods and the effectiveness of HSSTV for MS image restoration.