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The leaching behavior of arsenic (As), lead (Pb), nickel (Ni), iron (Fe), and manganese (Mn) was investigated from subsurface core sediment of marine and nonmarine depositional environments in central Kanto Plain, Japan. A four-step sequential extraction technique was adopted to determine the chemical speciation, potential mobility, and bioavailability of metals under natural conditions in variable depositional environments. In addition, a correlation of these properties with pore water and total metal content was carried out. The concentration of As in pore water was found to be 2–3 times higher than the permissible limit (10 µg/L) for drinking water and leachate in fluvial, transitional, and marine environments. The trend of potential mobile fractions of As, Pb, and Ni showed Fe–Mn oxide bound > carbonate bound > ion exchangeable bound > water soluble in the fluvial environment. However, in the marine environment, it showed Fe–Mn oxide bound > water soluble > carbonate bound > ion exchangeable bound for As. The leaching of As in this fluvial environment is due to the organic matter-mediated, reductive dissolution of Fe–Mn oxide bound, where Mn is the scavenger. The amount of total content of As and sulfur (S) in transitional sediment reflects an elevated level of leachate in pore water, which is controlled by S reduction. However, the leaching of As in marine sediment is controlled by pH and organic matter content.

Polycrystalline Ge thin films have recently attracted renewed attention as a material for various electronic and optical devices. However, the difficulty in the Fermi level control of polycrystalline Ge films owing to their high density of defect-induced acceptors has limited their application in the aforementioned devices. Here, we experimentally estimated the origin of acceptor defects by significantly modulating the crystallinity and electrical properties of polycrystalline Ge layers and investigating their correlation. Our proposed linear regression analysis method, which is based on deriving the acceptor levels and their densities from the temperature dependence of the hole concentration, revealed the presence of two different acceptor levels. A systematic analysis of the effects of grain size and post annealing on the hole concentration suggests that deep acceptor levels (53–103 meV) could be attributed to dangling bonds located at grain boundaries, whereas shallow acceptor levels (< 15 meV) could be attributed to vacancies in grains. Thus, this study proposed a machine learning-based simulation method that can be widely applied in the analysis of physical properties, and can provide insights into the understanding and control of acceptor defects in polycrystalline Ge thin films.

Microscopic evaluation is one of the most effective methods in materials research. High-quality images are essential to analyze microscopic images using artificial intelligence. To overcome this challenge, we propose the machine learning of “fake micrographs” in this study. To verify the effectiveness of this method, we chose to analyze the optical microscopic images of the crystal growth process of a Ge thin film, which is a material in which it is difficult to obtain a contrast between the crystal and amorphous states. By learning the automatically generated fake micrographs that mimic the crystal growth process, the machine learning model can now identify the low-resolution real micrographs as crystalline or amorphous. Comparing the three types of machine learning models, it was found that ResUNet ++ exhibited high accuracy, exceeding 90%. The technology developed in this study for the automatic and rapid analysis of low-resolution images is widely helpful in material research.

Kolmogorov–Sinai (KS) entropy is an indicator of the chaotic behavior of entire systems from an information-theoretic viewpoint. Here, we used KS entropy values derived from the heart sounds of four fetus–mother pairs to identify the changes in fetal and maternal informational patterns during the four phases of pregnancy (early, mid, late, and postnatal). Time-series data of the heart sounds were reconstructed in a five-dimensional phase space to obtain the Lyapunov spectrum, and KS entropy was calculated. Statistical analyses were then conducted separately for the fetus and mother for the four phases of pregnancy. The fetal KS entropy significantly increased from early pregnancy to the postnatal period (0.054 ± 0.007 vs. 0.097 ± 0.007; p < 0.001), whereas the maternal KS entropy decreased in late pregnancy and then significantly increased after birth (0.098 ± 0.002 vs. 0.133 ± 0.003; p < 0.001). The increase in KS entropy with the course of fetal gestation reflects an increase in information generation and adaptive capacity during the developmental process. Thus, changes in maternal KS entropy play a dual role, temporarily enhancing physiological stability to support fetal development and helping to rebuild the mother’s own adaptive capacity in the postpartum period.

Studying the properties of thermoelectric materials needs substantial effort owing to the interplay of the trade-off relationships among the influential parameters. In view of this issue, artificial intelligence has recently been used to investigate and optimize thermoelectric materials. Here, we used Bayesian optimization to improve the thermoelectric properties of multicomponent III–V materials; this domain warrants comprehensive investigation due to the need to simultaneously control multiple parameters. We designated the figure of merit ZT as the objective function to improve and search for a five-dimensional space comprising the composition of InGaAsSb thin films, dopant concentration, and film-deposition temperatures. After six Bayesian optimization cycles, ZT exhibited an approximately threefold improvement compared to its values obtained in the random initial experimental trials. Additional analysis employing Gaussian process regression elucidated that a high In composition and low substrate temperature were particularly effective at increasing ZT. The optimal substrate temperature (205 °C) demonstrated the potential for depositing InGaAsSb thermoelectric thin films onto plastic substrates. These findings not only promote the development of thermoelectric devices based on III–V semiconductors but also highlight the effectiveness of using Bayesian optimization for multicomponent materials.Bayesian optimization improved the thermoelectric properties of InGaAsSb thin films; this domain warrants comprehensive investigation due to the need to simultaneously control multiple parameters, such as, the composition, dopant concentration, and film-deposition temperatures. After six optimization cycles, the dimensionless figure of merit exhibited an approximately threefold improvement compared to its values obtained in the random initial experimental trials. These findings not only promote the development of thermoelectric devices based on III–V semiconductors but also highlight the effectiveness of using Bayesian optimization for multicomponent materials.

Polycrystalline Ge thin films have attracted considerable attention as potential materials for use in various electronic and optical devices. We recently developed a low-temperature solid-phase crystallization technology for a doped Ge layer and achieved the highest electron mobility in a polycrystalline Ge thin film. In this study, we investigated the effects of strain on the crystalline and electrical properties of n-type polycrystalline Ge layers. By inserting a GeO x interlayer directly under Ge and selecting substrates with different coefficients of thermal expansion, we modulated the strain in the polycrystalline Ge layer, ranging from approximately 0.6% (tensile) to − 0.8% (compressive). Compressive strain enlarged the grain size to 12 µm, but decreased the electron mobility. The temperature dependence of the electron mobility clarified that changes in the potential barrier height of the grain boundary caused this behavior. Furthermore, we revealed that the behavior of the grain boundary barrier height with respect to strain is opposite for the n- and p-types. This result strongly suggests that this phenomenon is due to the piezoelectric effect. These discoveries will provide guidelines for improving the performance of Ge devices and useful physical knowledge of various polycrystalline semiconductor thin films.

Objectives MotionFree® (AMF) is a data-driven respiratory gating (DDG) algorithm for image processing that has recently been introduced into clinical practice. The present study aimed to verify the accuracy of respiratory waveform and the effects of normal and irregular respiratory motions using AMF with the DDG algorithm. Methods We used a NEMA IEC body phantom comprising six spheres (37-, 28-, 22-, 17-, 13-, and 10 mm diameter) containing 18 F. The sphere-to-background ratio was 4:1 (21.2 and 5.3 kBq/mL). We acquired PET/CT images from a stationary or moving phantom placed on a custom-designed motion platform. Respiratory motions were reproduced based on normal (sinusoidal or expiratory-paused waveforms) and irregular (changed amplitude or shifted baseline waveforms) movements. The “width” parameters in AMF were set at 10–60% and extracted data during the expiratory phases of each waveform. We verified the accuracy of the derived waveforms by comparing those input from the motion platform and output determined using AMF. Quantitative accuracy was evaluated as recovery coefficients (RCs), improvement rate, and %change that were calculated based on sphere diameter or width. We evaluated statistical differences in activity concentrations of each sphere between normal and irregular waveforms. Results Respiratory waveforms derived from AMF were almost identical to the input waveforms on the motion platform. Although the RCs in each sphere for expiratory-paused and ideal stationary waveforms were almost identical, RCs except the expiratory-paused waveform were lower than those for the stationary waveform. The improvement rate decreased more for the irregular, than the normal waveforms with AMF in smaller spheres. The %change was improved by decreasing the width of waveforms with a shifted baseline. Activity concentrations significantly differed between normal waveforms and those with a shifted baseline in spheres < 28 mm. Conclusions The PET images using AMF with the DDG algorithm provided the precise waveform of respiratory motions and the improvement of quantitative accuracy in the four types of respiratory waveforms. The improvement rate was the most obvious in expiratory-paused waveforms, and the most subtle in those with a shifted baseline. Optimizing the width parameter in irregular waveform will benefit patients who breathe like the waveform with the shifted baseline.

Purpose Perioperative shivering is common and can occur as a result of hypothermia or changes in the threshold of thermoregulation. Droperidol usage for anesthesia is currently limited to its sedative and antiemetic effects. We investigated the effects of high and low doses of droperidol on the shivering threshold in rabbits. Methods Forty-two male Japanese white rabbits were anesthetized with isoflurane and randomly assigned to the control, high-dose, or low-dose group. Rabbits in the high-dose group received a 5 mg/kg droperidol bolus followed by continuous infusion at 5 mg/kg/h, those in the low-dose group received a 0.5 mg/kg droperidol bolus, and those in the control group received the same volume of saline as the high-dose group. Body temperature was reduced at a rate of 2–3 °C/h, and the shivering threshold was defined as the subject’s core temperature (°C) at the onset of shivering. Results The shivering thresholds in the control, high-dose, and low-dose groups were 38.1 °C ± 1.1 °C, 36.7 °C ± 1.2 °C, and 36.9 °C ± 1.0 °C, respectively. The shivering thresholds were significantly lower in the high-dose and low-dose groups than in the control group ( P  < 0.01). The thresholds were comparable between the high-dose and low-dose groups. Conclusions Droperidol in high and low doses effectively reduced the shivering threshold in rabbits. Droperidol has been used in low doses as an antiemetic. Low doses of droperidol can reduce the incidence of shivering perioperatively and during the induction of therapeutic hypothermia.

Background The number of patients who die from causes other than gastric cancer after R0 resection is increasing in Japan, due in part to the aging population. However, few studies have comprehensively investigated the clinicopathological risks associated with deaths from other causes after gastrectomy. This study aimed to build a risk score for predicting such deaths. Methods We retrospectively reviewed clinical data for 3575 patients who underwent gastrectomy for gastric cancer at nine institutions in Japan between January 2010 and December 2014. Results The final study population of 1758 patients were assigned to Group A ( n  = 187): patients who died from other causes within 5 years of surgery, and Group B ( n  = 1571): patients who survived ≥ 5 years after surgery. Multivariate analysis identified nine characteristics as risk factors for poor survival: age ≥ 75 years, male sex, body mass index < 22 kg/m 2 , Eastern Cooperative Oncology Group Performance Status (≥ 1), diabetes mellitus, cardiovascular/cerebrovascular disease, other malignant diseases, preoperative albumin level < 3.5 g/dL, and total gastrectomy. Patients with risk scores of 0–2, 3–4, or 5–9 (based on 1 point per characteristics) were classified into Low-risk, Intermediate-risk, and High-risk groups, respectively. The 5-year survival rates were 96.5%, 85.3%, and 56.5%, for the Low-, Intermediate-, and High-risk groups, respectively, and the hazard ratio (95% confidence intervals) was 16.33 (10.85–24.58, p  < 0.001) for the High-risk group. Conclusions The risk score defined here may be useful for predicting deaths from other causes after curative gastrectomy.

BackgroundRepeat hepatectomy is an acceptable treatment for recurrent hepatocellular carcinoma (HCC). However, repeat laparoscopic liver resection (LLR) has not been widely adopted due to its technical difficulty. This study aimed to assess the feasibility and efficacy of repeat LLR compared with repeat open liver resection (OLR) for recurrent HCC.MethodsWe performed 42 repeat OLR and 30 repeat LLR for cases of recurrent HCC between January 2007 and March 2018. This study retrospectively compared the patients’ clinicopathological characteristics and operative and short-term outcomes including surgical time, intraoperative blood loss, duration of hospital stay, and postoperative complications between the two groups.ResultsThere were no significant differences in patient characteristics between the two groups except in terms of Child–Pugh grade. The repeat LLR group had lower median intraoperative blood loss (100 mL vs. 435 mL; P = 0.001) and shorter median postoperative hospital stay (10 days vs. 14.5 days; P = 0.002). The other results including postoperative complications were comparable between the two groups. Further, comparison of two subpopulations of the repeat LLR group stratified by previous hepatectomy type (open or laparoscopic) or tumor location (segments 7 and 8 or other) revealed no significant differences in the postoperative clinical characteristics between them, although the morbidity rate tended to be higher in patients who underwent open hepatectomy for primary HCC than in patients who underwent laparoscopic hepatectomy.ConclusionsRepeat LLR for recurrent HCC is feasible and useful with good short-term outcomes although an appropriate patient selection seems to be necessary.