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Abstract Pharmaceutical companies have adopted biomarker-based enrichment (personalized) strategies to improve research and development productivity. We explored the background in which personalized strategies are adopted and examined whether their adoption is linked to improved efficacy of new drugs approved for non-small cell lung cancer (NSCLC) by US Food and Drug Administration (FDA). We extracted data from the first labels of drugs approved for NSCLC between May 2003 and February 2021, and performed a qualitative comparative analysis and meta-analysis. Personalized strategies were adopted in more than half of the trials (16/27) and were often used in trials aimed at obtaining first-line indications and in drugs that were not first-in-class. The meta-analysis showed that personalized trials had significantly improved progression-free survival (PFS) hazard ratio (HR) than trials without personalization but not for relative response rate ratio (RRR) or overall survival (OS) HR. Trials in which PFS HR was the primary endpoint tended to have improved PFS HR, and trials in which OS HR was the primary endpoint had worse PFS HR. The efficacy endpoints that are substantially affected by personalized strategies appear to differ, especially for new drugs with novel mechanism of action (MOA), because trial designs are employed to validate drug-specific advantages.

Sparse index tracking is a prominent passive portfolio management strategy that constructs a sparse portfolio to track a financial index. A sparse portfolio is preferable to a full portfolio in terms of reducing transaction costs and avoiding illiquid assets. To achieve portfolio sparsity, conventional studies have utilized $\ell _{p}$-norm regularizations as a continuous surrogate of the $\ell _{0}$-norm regularization. Although these formulations can construct sparse portfolios, their practical application is challenging due to the intricate and time-consuming process of tuning parameters to define the precise upper limit of assets in the portfolio. In this paper, we propose a new problem formulation of sparse index tracking using an $\ell _{0}$-norm constraint that enables easy control of the upper bound on the number of assets in the portfolio. Moreover, our approach offers a choice between constraints on portfolio and turnover sparsity, further reducing transaction costs by limiting asset updates at each rebalancing interval. Furthermore, we develop an efficient algorithm for solving this problem based on a primal-dual splitting method. Finally, we illustrate the effectiveness of the proposed method through experiments on the S&P500 and Russell3000 index datasets.

Hyperspectral (HS) unmixing is the process of decomposing an HS image into material-specific spectra (endmembers) and their spatial distributions (abundance maps). Existing unmixing methods have two limitations with respect to noise robustness. First, if the input HS image is highly noisy, even if the balance between sparse and piecewise-smooth regularizations for abundance maps is carefully adjusted, noise may remain in the estimated abundance maps or undesirable artifacts may appear. Second, existing methods do not explicitly account for the effects of stripe noise, which is common in HS measurements, in their formulations, resulting in significant degradation of unmixing performance when such noise is present in the input HS image. To overcome these limitations, we propose a new robust HS unmixing method based on constrained convex optimization. Our method employs, in addition to the two regularizations for the abundance maps, regularizations for the HS image reconstructed by mixing the estimated abundance maps and endmembers. This strategy makes the unmixing process much more robust in highly noisy scenarios, under the assumption that the abundance maps used to reconstruct the HS image with desirable spatio-spectral structure are also expected to have desirable properties. Furthermore, our method is designed to accommodate a wider variety of noise including stripe noise. To solve the formulated optimization problem, we develop an efficient algorithm based on a preconditioned primal-dual splitting method, which can automatically determine appropriate stepsizes based on the problem structure. Experiments on synthetic and real HS images demonstrate the advantages of our method over existing methods.

This paper proposes an automatic image correction method for portrait photographs, which promotes consistency of facial skin color by suppressing skin color changes due to background colors. In portrait photographs, skin color is often distorted due to the lighting environment (e.g., light reflected from a colored background wall and over-exposure by a camera strobe). This color distortion is emphasized when artificially synthesized with another background color, and the appearance becomes unnatural. In our framework, we, first, roughly extract the face region and rectify the skin color distribution in a color space. Then, we perform color and brightness correction around the face in the original image to achieve a proper color balance of the facial image, which is not affected by luminance and background colors. Our color correction process attains natural results by using a guide image, unlike conventional algorithms. In particular, our guided image filtering for the color correction does not require a perfectly-aligned guide image required in the original guide image filtering method proposed by He et al. Experimental results show that our method generates more natural results than conventional methods on not only headshot photographs but also natural scene photographs. We also show automatic yearbook style photo generation as another application.

Salinity is one of major environmental constraints that adversely affect plant growth and pose a serious threat to crop productivity. One basic concern is related to tissue sensitivity differences to salt stress. It is intriguing that much more research efforts have been directed to shoots/leaves than roots, although roots are the first tissues directly encountering salt stress and acting as its sensor. We focused on the tissue sensitivity differences to salt stress in wheat seedlings by adopting a novel tip bioassay. The tip bioassay employed a set of 5-mL pipette tips filled with vermiculite placed over racks containing NaCl solution. Using this simple device, after determining the time course of growth inhibition under varying concentrations of NaCl, we compared the salt sensitivity in 19 diverse germplasms from five Triticum species after 7 and 14 days of exposure to 200 mmol/L NaCl. Markedly greater reduction in root length than shoot length was observed in all lines. Both the magnitude and relative rate of inhibition were much greater in roots than shoots and significant differences were observed in tissue, line, stress duration and their interactions. Our tip bioassay demonstrated that roots were more sensitive than shoots to the growth-suppressing effect of salt during the early seedling stage in wheat. These results point out the need of further research targeted on the role of and communication between roots and shoots in salt stress sensing and response.

This study examined the outcomes of recent confirmatory randomized controlled trials (RCTs) in phase III that were initiated between 2005 and 2017 for oncologic drugs in the United States and identified several factors that were associated with the success of RCTs. Our regression analysis showed that studies with progression‐free survival or response rate as primary end point were more likely to succeed than studies with overall survival (odds ratio (OR) = 2.94 and 6.23, respectively). The status of development was also linked with success rates. Studies for non‐lead indication tended to have lower success rates than studies for lead indication (OR = 0.68). Studies for first‐line therapy were observed to have low success rates compared with studies for post second‐line therapies (OR = 0.37). Studies for which strong prior evidence was not listed in their publication tended to be more successful than studies that followed rigorous RCTs or single arm studies for the indication. These results suggest that historical success rates may reflect not only the important features of trials, which can be observed directly from study design and results, but also the background status of trials in clinical development pathways.

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.

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.

Non-local self-similarity (NLSS) is widely used as prior information in an image restoration method. In particular, a low-rankness-based prior has a significant effect on performance. On the other hand, a number of color extensions of NLSS-based grayscale image restoration methods have been developed. These extensions focus on the pixel-wise correlation among color channels. However, a natural color image also has a complex dependency, known as an inter-channel dependency, among local regions from different color channels. As a result, color artifacts appear in a denoised image obtained by using the existing methods. In this paper, we propose a novel non-local and inter-channel dependency-aware prior called the weighted tensor nuclear norm (WTNN). The proposed prior is derived by incorporating inter-channel dependency to low-rank-based NLSS prior. The WTNN is a low-rankness-of-the-third-order patch tensor, and we apply it to the tensors constructed with non-local similar patches. It enables us to naturally represent the higher-order dependencies among similar color patches. We propose an image denoising algorithm using the WTNN and image restoration algorithm by using a non-trivial generalization of this algorithm. The experimental results clearly show that the proposed WTNN-based color image denoising and restoration algorithms outperform state-of-the-art methods.

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.

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.

Background/objectives: The results of phase 2 and 3 clinical trials, which justify decisions regarding marketing approval for new drugs, are used for comparison of drugs in the post-marketing phase. A number of meta-analyses of approved antidiabetics have been performed, but the heterogeneity of trials has not been fully examined. The aim of this study was to explore factors that may influence baseline HbA1c in trial samples and treatment outcomes (i.e. HbA1c reductions and effect sizes), with the goal of providing unbiased and fair retrospective comparisons between different antidiabetics. Method: We conducted three meta-regression analyses using 78 randomized or non-randomized comparative phase 2 or 3 trials of 24 approved antidiabetics in Japan, conducted from 1987 to 2012. Results: Baseline HbA1c of each arm was higher in phase 2 trials, trials with a greater number of subjects, trials with a lower proportion of male subjects, trials of combination therapy, or trials with longer subject disease duration. Entry criteria were different among drug classes and caused variations in baseline HbA1c. HbA1c reductions were larger in non-randomized trials, trials with a shorter treatment period, or trials with a lower proportion of male subjects. Effect sizes were larger in phase 2 trials, or trials of combination therapy. Larger effect sizes were observed in drugs with later market entry for alpha-glucosidase inhibitors and glinides. Conclusion: Baseline HbA1c, an important characteristic of subjects enrolled in trials of antidiabetics, differed significantly across trials. Differences in features of study subjects were caused by explicit stipulations in eligibility criteria of HbA1c and also by other conditions (e.g. trial design, regulatory guidance, treatment guideline) and/or interventions of investigators and pharmaceutical companies that were specific to drugs and trials. Healthcare professionals should carefully consider these heterogeneities in trials used for marketing approval review when making a retrospective comparison to select the best treatment option for patients.

We propose a new constrained optimization approach to hyperspectral (HS) image restoration. Most existing methods restore a desirable HS image by solving some optimization problems, consisting of a regularization term(s) and a data-fidelity term(s). The methods have to handle a regularization term(s) and a data-fidelity term(s) simultaneously in one objective function; therefore, we need to carefully control the hyperparameter(s) that balances these terms. However, the setting of such hyperparameters is often a troublesome task because their suitable values depend strongly on the regularization terms adopted and the noise intensities on a given observation. Our proposed method is formulated as a convex optimization problem, utilizing a novel hybrid regularization technique named Hybrid Spatio-Spectral Total Variation (HSSTV) and incorporating data-fidelity as hard constraints. HSSTV has a strong noise and artifact removal ability while avoiding oversmoothing and spectral distortion, without combining other regularizations such as low-rank modeling-based ones. In addition, the constraint-type data-fidelity enables us to translate the hyperparameters that balance between regularization and data-fidelity to the upper bounds of the degree of data-fidelity that can be set in a much easier manner. We also develop an efficient algorithm based on the alternating direction method of multipliers (ADMM) to efficiently solve the optimization problem. We illustrate the advantages of the proposed method over various HS image restoration methods through comprehensive experiments, including state-of-the-art ones.

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A versatile numerical model for hydrogen absorption into metals was developed. Our model addresses the kinetics of surface adsorption, subsurface transport (which plays an important role for metals with active surfaces), and bulk diffusion processes. This model can allow researchers to perform simulations for various conditions, such as different material species, dimensions, structures, and operating conditions. Furthermore, our calculation scheme reflects the relationship between the temperature changes in metals caused by the heat of adsorption and absorption and the temperature-dependent kinetic parameters for simulation precision purposes. We demonstrated the numerical fitting of the experimental data for various Pd temperatures and sizes, with a single set of kinetic parameters, to determine the unknown kinetic constants. Using the developed model and determined kinetic constants, the transitions of the rate-determining steps on the conditions of metal-hydrogen systems are systematically analyzed. Conventionally, the temperature change of metals during hydrogen adsorption and absorption has not been a favorable phenomenon because it can cause errors when numerically estimating the hydrogen absorption rates. However, by our calculation scheme, the experimental data obtained under temperature changing conditions can be positively used for parameter fitting to efficiently and accurately determine the kinetic constants of the absorption process, even from a small number of experimental runs. In addition, we defined an effectiveness factor as the ratio between the actual absorption rate and the virtually calculated non-bulk-diffusion-controlled rate, to evaluate the quantitative influence of each individual transport process on the overall absorption process. Our model and calculation scheme may be a useful tool for designing high-performance hydrogen storage systems.

IntroductionInternational norms and ethical standards have suggested that compensation for research-related injury should be provided to injured research volunteers. However, statistical data of incidence of compensation claims and the rate of awarding them have been rarely reported.MethodQuestionnaire surveys were sent to pharmaceutical companies and medical institutions, focusing on industry-initiated clinical trials aiming at new drug applications (NDAs) on patient volunteers in Japan.ResultsWith the answers from pharmaceutical companies, the incidence of compensation was 0.8%, including 0.06% of monetary compensation. Of the cases of compensation claims, 99% were awarded. In turn, with the answers from medical institutions, the incidence of compensation was 0.6%, including 0.4% of serious but not death cases, and 0.04% of death cases. Furthermore, most claims for compensation were initiated by medical institutions, rather than by the patients. On the other hand, with the answers from clinical trial volunteers, 3% of respondents received compensations. These compensated cases were 25% of the injuries which cannot be ruled out from the scope of compensation.ConclusionOur study results demonstrated that Japanese pharmaceutical companies have provided a high rate of compensation for clinical trial-related injuries despite the possibility of overestimation. In the era of global clinical development, our study indicates the importance of further surveys to find each country's compensation policy by determining how it is being implemented based on a survey of the actual status of compensation coming from statistical data.