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The field of the human microbiome is rapidly growing due to the recent advances in high-throughput sequencing technologies. Meanwhile, there have also been many new analytic pipelines, methods and/or tools developed for microbiome data preprocessing and analytics. They are usually focused on microbiome data with continuous (e.g., body mass index) or binary responses (e.g., diseased vs. healthy), yet multi-categorical responses that have more than two categories are also common in reality. In this paper, we introduce a new unified cloud platform, named MiMultiCat, for the analysis of microbiome data with multi-categorical responses. The two main distinguishing features of MiMultiCat are as follows: First, MiMultiCat streamlines a long sequence of microbiome data preprocessing and analytic procedures on user-friendly web interfaces; as such, it is easy to use for many people in various disciplines (e.g., biology, medicine, public health). Second, MiMultiCat performs both association testing and prediction modeling extensively. For association testing, MiMultiCat handles both ecological (e.g., alpha and beta diversity) and taxonomical (e.g., phylum, class, order, family, genus, species) contexts through covariate-adjusted or unadjusted analysis. For prediction modeling, MiMultiCat employs the random forest and gradient boosting algorithms that are well suited to microbiome data while providing nice visual interpretations. We demonstrate its use through the reanalysis of gut microbiome data on obesity with body mass index categories. MiMultiCat is freely available on our web server. [ABSTRACT FROM AUTHOR]

As autonomous vehicles (AVs) are advancing to higher levels of autonomy and performance, the associated technologies are becoming increasingly diverse. Lane-keeping systems (LKS), corresponding to a key functionality of AVs, considerably enhance driver convenience. With drivers increasingly relying on autonomous driving technologies, the importance of safety features, such as fail-safe mechanisms in the event of sensor failures, has gained prominence. Therefore, this paper proposes a reinforcement learning (RL) control method for lane-keeping, which uses surrounding object information derived through LiDAR sensors instead of camera sensors for LKS. This approach uses surrounding vehicle and object information as observations for the RL framework to maintain the vehicle's current lane. The learning environment is established by integrating simulation tools, such as IPG CarMaker, which incorporates vehicle dynamics, and MATLAB Simulink for data analysis and RL model creation. To further validate the applicability of the LiDAR sensor data in real-world settings, Gaussian noise is introduced in the virtual simulation environment to mimic sensor noise in actual operational conditions. [ABSTRACT FROM AUTHOR]

The advent of next-generation sequencing has greatly accelerated the field of human microbiome studies. Currently, investigators are seeking, struggling and competing to find new ways to diagnose, treat and prevent human diseases through the human microbiome. Machine learning is a promising approach to help such an effort, especially due to the high complexity of microbiome data. However, many of the current machine learning algorithms are in a "black box", i.e., they are difficult to understand and interpret. In addition, clinicians, public health practitioners and biologists are not usually skilled at computer programming, and they do not always have high-end computing devices. Thus, in this study, we introduce a unified web cloud analytic platform, named MiTree, for user-friendly and interpretable microbiome data mining. MiTree employs tree-based learning methods, including decision tree, random forest and gradient boosting, that are well understood and suited to human microbiome studies. We also stress that MiTree can address both classification and regression problems through covariate-adjusted or unadjusted analysis. MiTree should serve as an easy-to-use and interpretable data mining tool for microbiome-based disease prediction modeling, and should provide new insights into microbiome-based diagnostics, treatment and prevention. MiTree is an open-source software that is available on our web server. [ABSTRACT FROM AUTHOR]

Recently, smart farming research based on artificial intelligence (AI) has been widely applied in the field of agriculture to improve crop cultivation and management. Predicting the harvest time (time-to-harvest) of crops is important in smart farming to solve problems such as planning the production schedule of crops and optimizing the yield and quality. This helps farmers plan their labor and resources more efficiently. In this paper, our concern is to predict the time-to-harvest (i.e., survival time) of tomatoes on a smart farm. For this, it is first necessary to develop a deep learning modeling approach that takes into account the farm effect on the tomato plants, as each farm has multiple tomato plant subjects and outcomes on the same farm can be correlated. In this paper, we propose deep neural network (DNN) survival models to account for the farm effect as a fixed effect using one-hot encoding. The tomato data used in our study were collected on a weekly basis using the Internet of Things (IoT). We compare the predictive performance of our proposed method with that of existing DNN and statistical survival modeling methods. The results show that our proposed DNN method outperforms the existing methods in terms of the root mean squared error (RMSE), concordance index (C-index), and Brier score. [ABSTRACT FROM AUTHOR]

Reinforcement learning agents that have not been seen during training must be robust in test environments. However, the generalization problem is challenging to solve in reinforcement learning using high-dimensional images as the input. The addition of a self-supervised learning framework with data augmentation in the reinforcement learning architecture can promote generalization to a certain extent. However, excessively large changes in the input images may disturb reinforcement learning. Therefore, we propose a contrastive learning method that can help manage the trade-off relationship between the performance of reinforcement learning and auxiliary tasks against the data augmentation strength. In this framework, strong augmentation does not disturb reinforcement learning and instead maximizes the auxiliary effect for generalization. Results of experiments on the DeepMind Control suite demonstrate that the proposed method effectively uses strong data augmentation and achieves a higher generalization than the existing methods. [ABSTRACT FROM AUTHOR]

Simple Summary: In patients with gastric mucosa-associated lymphoid tissue lymphoma (GML), irradiation of the pancreas is inevitable owing to its anatomical proximity to the stomach. This is the first study to demonstrate the association between RT exposure and the risk of development of diabetes mellitus (DM) in patients with GML by measuring individual-based dose parameters of the pancreas. The findings of this study demonstrate the association between incidental pancreatic irradiation and DM. Obesity and sex were key risk factors for DM. Considering the risk of DM, the pancreas should no longer be neglected during RT planning, particularly in male patients and patients with obesity. The long-term effect of radiation on the pancreas in pediatric patients has been studied without individual radiation dosimetric data. This study investigated the effect of radiotherapy on the risk of developing diabetes mellitus (DM) in patients with gastric mucosa-associated lymphoid tissue lymphoma (GML), using individual radiation dosimetric analysis. Retrospective analysis reviewed the data of 225 patients without a history of DM receiving curative treatment for stage IE GML. Involved-site radiotherapy was delivered to the whole stomach in 83 patients. The pancreas was delineated in each patient's computed tomography scan for dosimetric analysis. At a median follow-up of 49.0 months, the 5-year cumulative incidence of DM was 4.5%, 9.6%, and 1.6% in all patients, patients who received radiotherapy, and patients who did not receive radiotherapy, respectively (p = 0.009). Mean pancreatic dose (Dmean; p = 0.009), sex (p = 0.043), and body mass index (BMI; p = 0.008) were independently associated with DM. Using recursive partitioning analysis, patients were classified into low, intermediate, and high-risk groups, with 5-year DM incidence rates of 0.0%, 3.1%, and 15.6%, respectively (p < 0.001). Incidental irradiation of the pancreas can increase the risk of DM, which may be stratified according to patient sex and BMI. [ABSTRACT FROM AUTHOR]

In this work we report on the improvement in cold temperature characteristics of PMOSFETs and inverter circuits by removing the plasma-damaged layer of the source/drain contacts. We removed the plasma-induced damage on the Si using a simple in situ Si soft treatment technique. We found by transmission electron microscope (TEM) analysis that the damaged amorphous layer reduced from 52 Å to 42 Å and 35 Å with a treatment time of 10 and 20 s, respectively. As a result, the resistances of both the n+ and p+ contacts decreased for all contact sizes and the standard deviations at the cold temperature were suppressed by 45%. At −25 °C, the saturation current of the PMOSFET increased by 3% and the propagation delay time (tPD) decreased by 2%. The tPD increases by 19.3% when the temperature decreases from 85 °C to −25 °C, and the operating voltage decreases from 1.2 V to 0.95 V at the same time. However, this increase can be reduced to 17% by applying the soft treatment for 10 s. This simple and short time process will be considered essential for both mobile applications and automotive applications of dynamic random access memory (DRAM) devices requiring a low-voltage and low-temperature operation. [ABSTRACT FROM AUTHOR]

Although previous research has improved the energy efficiency of humanoid robots to increase mobility, no study has considered the offset between hip joints to this end. Here, we optimized the offsets of hip joints in humanoid robots via the Taguchi method to maximize energy efficiency. During optimization, the offsets between hip joints were selected as control factors, and the sum of the root-mean-square power consumption from three actuated hip joints was set as the objective function. We analyzed the power consumption of a humanoid robot model implemented in physics simulation software. As the Taguchi method was originally devised for robust optimization, we selected turning, forward, backward, and sideways walking motions as noise factors. Through two optimization stages, we obtained near-optimal results for the humanoid hip joint offsets. We validated the results by comparing the root-mean-square (RMS) power consumption of the original and optimized humanoid models, finding that the RMS power consumption was reduced by more than 25% in the target motions. We explored the reason for the reduction of power consumption through bio-inspired analysis from human gait mechanics. As the distance between the left and right hip joints in the frontal plane became narrower, the amplitude of the sway motion of the upper body was reduced. We found that the reduced sway motion of the upper body of the optimized joint configuration was effective in improving energy efficiency, similar to the influence of the pathway of the body's center of gravity (COG) on human walking efficiency. [ABSTRACT FROM AUTHOR]

Sensor fusion frameworks for indoor localization are developed with the specific goal of reducing positioning errors. Although many conventional localization frameworks without fusion have been improved to reduce positioning error, sensor fusion frameworks generally provide a further improvement in positioning accuracy. In this paper, we propose a sensor fusion framework for indoor localization using the smartphone inertial measurement unit (IMU) sensor data and Wi-Fi received signal strength indication (RSSI) measurements. The proposed sensor fusion framework uses location fingerprinting and trilateration for Wi-Fi positioning. Additionally, a pedestrian dead reckoning (PDR) algorithm is used for position estimation in indoor scenarios. The proposed framework achieves a maximum of 1.17 m localization error for the rectangular motion of a pedestrian and a maximum of 0.44 m localization error for linear motion. [ABSTRACT FROM AUTHOR]

1-repetition maximum (1RM), a representative index for an individual's weightlifting capacity, provides an organized workout guide, but to measure 1RM needs several repetitive exercises up to one's limit and has a risk of injury, thus, not adequate for beginners, elders, or disabled people. This study suggests a simpler and safer 1RM measurement method using a hydraulic fitness machine. We asked twenty-five female subjects with less than a month of experience in weight training to repeat chest exercises using a conventional plate-loaded bench press machine and a hydraulic bench press machine and measured 1RMs. Repeated-measures ANOVA and paired t-test reported the difference between the plate and hydraulic 1RMs insignificant (p-value = 0.082) and confirmed the generality of 1RM across the different types of fitness machines. We then derived several 1RM equations in terms of load weight W and lifting speed v during non-1RM exercise and reduced it to a first-order polynomial expression 1 RM = − 0.3908 + 0.8251 W + 0.1054 v with adjusted R-square of 0.8849. Goodness-of-fit test and comparison with 1RM equations from reference studies ( v = − 1.46 × W 1 RM + 1.7035 ,   W 1 RM × 100 = 7.5786 v 2 − 75.865 v + 113.02 ) verified our formula valid. We finally simplified the 1RM measurement process up to a maximum of three repetitions. [ABSTRACT FROM AUTHOR]

Quantum dot (QD)-based luminescent down-shifting (LDS) layers were deposited on Cu2ZnSn(S,Se)4 (CZTSSe) solar cells via the drop-casting method. The LDS layers can easily widen the narrow absorption wavelength regions of single-junction solar cells and enable improvement of the short-circuit current. The optical properties of LDS layers deposited on glass and containing different QD contents were analyzed based on their transmittance, reflectance, and absorbance. The absorber films to be used in the CZTSSe solar cells were determined by X-ray diffraction measurements and Raman spectroscopy to determine their crystal structures and secondary phases, respectively. The completed CZTSSe solar cells with LDS layers showed increased ultraviolet responses of up to 25% because of wavelength conversion by the QDs. In addition, the impact of the capping layer, which was formed to protect the QDs from oxygen and moisture, on the solar cell performance was analyzed. Thus, a maximal conversion efficiency of 7.3% was achieved with the 1.0 mL QD condition; furthermore, to the best of our knowledge, this is the first time that LDS layers have been experimentally demonstrated for CZTSSe solar cells. [ABSTRACT FROM AUTHOR]

Hydrogen energy is one of the key technologies that can help to prevent global warming. A water electrolysis process can be used to produce hydrogen, in which hydrogen is produced at one electrode of the electrochemical cell, and oxygen is produced at the other electrode. On the other hand, the oxygen evolution reaction (OER) requires multiple reaction steps and precious-metal-based catalysts (e.g., Ru/C, Ir/C, RuO2, and IrO2) as electrocatalysts to improve the reaction rate. Their high cost and limited supply, however, limit their applications to the mass production of hydrogen. In this study, boron, nitrogen-doped carbon incorporated with molybdenum carbide (MoC-BN/C) was synthesized to replace the precious-metal-based catalysts in the OER. B, N-doped carbon with nanosized molybdenum nanoparticles was fabricated by plasma engineering. The synthesized catalysts were heat-treated at 600, 700, and 800 °C in nitrogen for one hour to enhance the conductivity. The best MoC-BN/C electrocatalysts (heated at 800 °C) exhibited superior OER catalytic activity: 1.498 V (vs. RHE) and 1.550 V at a current density of 10 and 100 mA/cm2, respectively. The hybrid electrocatalysts even outperformed the noble electrocatalyst (5 wt.% Ru/C) with higher stability. Therefore, the hybrid electrocatalyst can replace expensive precious-metal-based catalysts for the upcoming hydrogen economy. [ABSTRACT FROM AUTHOR]

Streptomyces venezuelae is well known to produce various secondary metabolites, including chloramphenicol, jadomycin, and pikromycin. Although many strains have been classified as S. venezuelae species, only a limited number of strains have been explored extensively for their genomic contents. Moreover, genomic differences and diversity in secondary metabolite production between the strains have never been compared. Here, we report complete genome sequences of three S. venezuelae strains (ATCC 10712, ATCC 10595, and ATCC 21113) harboring chloramphenicol and jadomycin biosynthetic gene clusters (BGC). With these high-quality genome sequences, we revealed that the three strains share more than 85% of total genes and most of the secondary metabolite biosynthetic gene clusters (smBGC). Despite such conservation, the strains produced different amounts of chloramphenicol and jadomycin, indicating differential regulation of secondary metabolite production at the strain level. Interestingly, antagonistic production of chloramphenicol and jadomycin was observed in these strains. Through comparison of the chloramphenicol and jadomycin BGCs among the three strains, we found sequence variations in many genes, the non-coding RNA coding regions, and binding sites of regulators, which affect the production of the secondary metabolites. We anticipate that these genome sequences of closely related strains would serve as useful resources for understanding the complex secondary metabolism and for designing an optimal production process using Streptomyces strains. [ABSTRACT FROM AUTHOR]

All-optical fiber magnetic field sensor based on the Gd₂O₃ nano-particles (NPs)-doped alumino-silicate glass optical fiber was developed, and its temperature and vibration dependence on the Faraday Effect were investigated. Uniformly embedded Gd₂O₃ NPs were identified to form in the core of the fiber, and the measured absorption peaks of the fiber appearing at 377 nm, 443 nm, and 551 nm were attributed to the Gd₂O₃ NPs incorporated in the fiber core. The Faraday rotation angle (FRA) of the linearly polarized light was measured at 650 nm with the induced magnetic field by the solenoid. The Faraday rotation angle was found to increase linearly with the magnetic field, and it was about 18.16° ± 0.048° at 0.142 Tesla (T) at temperatures of 25 °C-120 °C, by which the estimated Verdet constant was 3.19 rad/(T∙m) ± 0.01 rad/(T∙m). The variation of the FRA with time at 0.142 T and 120 °C was negligibly small (-9.78 × 10 -4 °/min). The variation of the FRA under the mechanical vibration with the acceleration below 10 g and the frequency above 50 Hz was within 0.5°., Competing Interests: The authors declare no conflict of interest.