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Removal of dissolved organic carbon in the West Pacific hadal zones

Correction: Identifying suitable tester for evaluating Striga resistant lines using DArTseq markers and agronomic traits

Author Correction: LLM-driven multimodal target volume contouring in radiation oncology

Novel deep reinforcement learning based collision avoidance approach for path planning of robots in unknown environment

Reinforcement learning is a remarkable aspect of the artificial intelligence field with many applications. Reinforcement learning facilitates learning new tasks based on action and reward principles. Motion planning addresses the navigation problem for robots. Current motion planning approaches lack support for automated, timely responses to the environment. The problem becomes worse in a complex environment cluttered with obstacles. Reinforcement learning can increase the capacity of robotic systems due to the reward system’s capability and feedback to the environment. This could help deal with a complex environment. Existing algorithms for path planning are slow, computationally expensive, and less responsive to the environment, which causes late convergence to a solution. Furthermore, they are less efficient for task learning due to post-processing requirements. Reinforcement learning can address these issues using its action feedback and reward policies. This research presents a novel Q-learning-based reinforcement algorithm with deep learning integration. The proposed approach is evaluated in a narrow and cluttered passage environment. Further, improvements in the convergence of reinforcement learning-based motion planning and collision avoidance are addressed. The proposed approach’s agent converged in 210th episodes in a cluttered environment and 400th episodes in a narrow passage environment. A state-of-the-art comparison shows that the proposed approach outperformed existing approaches based on the number of turns and convergence of the path by the planner.

Predictive genetic circuit design for phenotype reprogramming in plants

Comparative analysis of salivary antimicrobial resistance genes in dental students: A PCR and questionnaire study

Introduction Antimicrobial resistance (AMR) is a major global healthcare challenge, with limited treatment options due to the decline in new antibiotics. The human oral cavity, home to diverse bacteria, is crucial for maintaining oral and systemic health. Recent studies suggest that saliva may serve as a reservoir for AMR genes. However, there is a lack of research on this topic in the UAE and most Middle Eastern countries. This study investigated the presence of AMR genes in saliva from forty 5th-year dental students and forty 1st-year dental students. Materials & methods Demographic and health information was collected via a 28-question structured questionnaire. Real-Time PCR was used to detect a panel of preselected AMR genes in bacterial DNA from saliva samples. Results Participants’ ages ranged from 20 to 31 years, with 41 females and 39 males. The prevalence of AMR genes varied: blaCTX-M grp 1 (29%), blaCTX-M grp 9 (85%), blaCTX-M grp 8 (39%), blaOXA-48 (69%), blaKPC-1 (6%), blaVIM (49%), DHA (53%), ACC (25%), MOX (59%), armA (83%), and rmtB (63%). There were no significant differences in AMR gene prevalence between 5th-year and 1st-year students or between male and female students. Conclusion The study revealed a high occurrence of AMR genes in the oral microbiome. Comprehensive metagenomic analysis is recommended to further evaluate the prevalence and relative abundance of these genes in the UAE population. Establishing a database for these ARGs could aid in effective future monitoring.

Active learning-assisted directed evolution

Abstract Directed evolution (DE) is a powerful tool to optimize protein fitness for a specific application. However, DE can be inefficient when mutations exhibit non-additive, or epistatic, behavior. Here, we present Active Learning-assisted Directed Evolution (ALDE), an iterative machine learning-assisted DE workflow that leverages uncertainty quantification to explore the search space of proteins more efficiently than current DE methods. We apply ALDE to an engineering landscape that is challenging for DE: optimization of five epistatic residues in the active site of an enzyme. In three rounds of wet-lab experimentation, we improve the yield of a desired product of a non-native cyclopropanation reaction from 12% to 93%. We also perform computational simulations on existing protein sequence-fitness datasets to support our argument that ALDE can be more effective than DE. Overall, ALDE is a practical and broadly applicable strategy to unlock improved protein engineering outcomes.

Discovering the allure of forests: Exploring adolescent queries in nature-rich environments

This study explores adolescents’ inherent curiosity about nature through the production of self-generated questions during a field visit to a nature-rich environment, followed by descriptive-interpretative analysis using focus groups. Utilizing cultural probes and content-free question tokens, we collected 164 valid questions produced by 36 adolescents during the field session. Biotic elements, like species, turned out to be more intriguing than abiotic elements, originating 89.6% of the questions. The predominant topics were related to species adaptation, extinction, dispersion, and diversity, with younger adolescents showing a notable interest in nature conservation, while older adolescents highlighted biodiversity dynamics. These findings were corroborated by the ranking of the TOP-5 most interesting questions, where biodiversity dynamics, nature conservation and plant physiology occupied the same relative positions. Our results indicate that in a nature-rich environment and through an inquiry-based approach, adolescents were encouraged to express curiosity about nature. This approach could be a valuable educational strategy to enhance their connection to nature, promote conservation responsibility, and benefit the environment.

Lerociclib plus fulvestrant in patients with HR+/HER2− locally advanced or metastatic breast cancer who have progressed on prior endocrine therapy: LEONARDA-1 a phase III randomized trial

Investigating dosage effects of ovulation inhibitors on oocyte maturation in assisted reproductive technology: A retrospective study among patients with normal ovarian reserve

The judicious selection of ovulation inhibitors in ovarian stimulation protocols is crucial for the success of assisted reproductive technology (ART). Herein, we investigate the dose-dependent effects of chlormadinone acetate (CMA) and cetrorelix, two distinct ovulation inhibitors, on oocyte maturation in patients with normal ovarian reserve, using univariable and multivariable Poisson regression analyses. Patients undergoing progestin-primed ovarian stimulation (PPOS) with CMA (n = 299) or gonadotropin-releasing hormone antagonist (GnRH-ant) with cetrorelix (n = 605) during their initial in vitro fertilization cycle were enrolled at our center from March 2018 to October 2020 (N = 904). The primary and secondary outcomes were the oocyte maturation and fertilization rates, respectively. After adjusting for several covariates including age, anti-Müllerian hormone levels, total gonadotropin dose, and type of trigger, we calculated the dose-dependent adjusted relative risk (aRR) and 95% confidence interval (CI) for 1 mg of CMA or 0.25 mg of cetrorelix. In the PPOS group, the median age was 34.0 years, and the median total CMA dosage was 22 mg (interquartile range [IQR]: 18.0–32.0). In the GnRH-ant group, the median age was 35.0 years, and the median total cetrorelix dosage was 0.5 mg (IQR 0.5–0.5). The aRR of the maturation rate was 1.003 (95% CI: 0.999–1.007) with PPOS (p = 0.194) and 1.009 (95% CI: 0.962–1.059) with GnRH-ant (p = 0.717). The aRR of the fertilization rate was 1.002 (95% CI: 0.985–1.020) with PPOS (p = 0.783) and 1.022 (95% CI: 0.839–1.246) with GnRH-ant (p = 0.829). Collectively, these findings indicate that within the applied dosages, ovulation inhibitors do not significantly impact oocyte maturation or fertilization rates in patients with normal ovarian reserve. These valuable insights can be applied when designing ART protocols and may guide clinicians in optimizing infertility treatments.

The human and non-human primate developmental GTEx projects

System- and sample-agnostic isotropic three-dimensional microscopy by weakly physics-informed, domain-shift-resistant axial deblurring

Correction: Functional capacity testing in patients with pulmonary hypertension (PH) using the one-minute sit-to-stand test (1-min STST)

Unusual Phospholipids from <i>Morganella morganii</i> Linked to Depression

Ultra-high electrostriction and ferroelectricity in poly (vinylidene fluoride) by ‘printing of charge’ throughout the film

Abstract Electrostriction is an important electro-mechanical property in poly (vinylidene fluoride) (PVDF) films, which describes the proportional relation between the electro-stimulated deformation and the square of the electric field. Generally, traditional methods to improve the electrostriction of PVDF either sacrifice other crystalline-related key properties or only influence minimal regions around the surface. Here, we design a unique electret structure to fully exploit the benefits of internal crystal in PVDF films. Through the 3D printing of charged ink, we have obtained the best electrostrictive and ferroelectric properties among PVDF-based materials so far. The optimized electrostrictive coefficient M 33 (324 × 10−18 m2 V−2) is 104 times that of normal PVDF films, and the piezoelectric constant d 33 (298 pm V−1) is close to 10 times its traditional limit. The proposed 3D electret structure and the bottom-up approach to ‘print the charge’ open up a new way to design and adapt the electroactive polymers in smart devices and systems.

Association of the abdominal aortic calcification with all-cause and cardiovascular disease-specific mortality: Prospective cohort study

Background Abdominal aortic calcification (AAC) is a prevalent form of vascular calcification associated with adverse cardiovascular outcomes. While previous studies on AAC and cardiovascular risk exist, many have limitations such as small sample sizes and limited clinical significance outcomes. This study aims to prospectively investigate the association between AAC and all-cause and cardiovascular disease (CVD)-specific mortality rates in a nationally representative sample of adults in the United States, using data from the National Health and Nutrition Examination Survey (NHANES). Methods The study, conducted on NHANES participants aged 40 years or older during the 2013–2014 cycle, assessed AAC using the Kauppila scoring system. Demographic characteristics, mortality data, and comorbid factors such as age, gender, diabetes, and hypertension were considered. Statistical analyses, including weighted percentages, Kaplan-Meier survival curves, and multivariable Cox proportional hazards regression models, were employed to evaluate the associations between AAC and mortality risks. Results After analyzing a final sample of 2717 participants, the study found a significant association between severe AAC (SAAC) and higher all-cause mortality risk (HR 1.70, 95% CI 1.17–2.48). The dose-response relationship indicated an increased risk with higher AAC scores. However, no independent association was observed between AAC and cardiovascular mortality. Stratified analysis revealed variations in the AAC-all-cause mortality association based on gender and hypertension. Conclusion This population-based study provides valuable insights into the prospective association between AAC and all-cause mortality, emphasizing the potential role of AAC assessment in identifying individuals at higher risk.

Green coal and lubricant via hydrogen-free hydrothermal liquefaction of biomass

Psychometric assessment of scales used to evaluate sexual assault prevention programming in the United States Air Force

Background Preventing sexual assault in the United States (U.S.) military is essential to safeguard the overall well-being of military personnel and support the military to function in alignment with its intended mission and objectives. Valid instruments are needed to accurately and reliably evaluate programming effectiveness. The goal of this research was to psychometrically assess measures used to evaluate the Sexual Communication and Consent (SCC) program within the Air Force Basic Military Training (BMT) context. Methods We evaluated four measures used to assess the SCC program implemented at Air Force BMT in 2019–2020: Date Rape Attitudes, Self-Efficacy to Resist Unwanted Advances, Risky and Protective Dating Behaviors, and Bystander Intentions. The analytic sample included 7,126 BMT trainees (74% male). We assessed structural validity with exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). We used full information maximum likelihood estimation with robust standard errors in Mplus. We refined each scale based on factor analysis results and assessed internal consistency reliability by computing Cronbach’s coefficient alpha for each scale in the overall sample, by sex, and by tailored SCC intervention group. Results We identified a two-factor structure for the Date Rape Attitudes scale; subscale reliability was moderate within the overall sample and among males, though low among females. We found single-factor structures and excellent reliability for both the Self-Efficacy to Resist Unwanted Advances scale and the Bystander Intentions scale. The Dating Behaviors scale CFA did not confirm the two-factor solution suggested by the EFA, but subscale reliability was acceptable. Conclusion This research fills a critical gap in the psychometric literature in military settings. Based on our findings, we recommend approaches for using the finalized scales in future evaluations of sexual assault prevention programming in the U.S. Air Force BMT setting.

Integrated system for electrolyte recovery, product separation, and CO2 capture in CO2 reduction

The effect of perceived organizational fairness on the endogenous motivation of university teachers’ professional development: A study of the mediating role of trust and organizational identification

Objective The purpose of this study is to investigate the effects of perceived organizational fairness, organizational identity, and trust on the intrinsic motivation for the professional development of university teachers. In addition, this study aims to verify the mediating role of organizational identity and trust. Method This study adopts a quantitative research methodology to investigate the relationship between perceived organizational fairness, organizational identity, trust, and intrinsic motivation in the professional development of university teachers by constructing and validating a structural equation model. Result The study shows that perceived organizational fairness has a significant positive effect on the intrinsic motivation for the professional development of university teachers. Moreover, it validates the mediating effects of organizational identity and trust in the relationship between perceived organizational fairness and intrinsic motivation for teachers’ professional development, shedding light on the psychological mechanisms underlying teachers’ professional development motivation. Conclusion Organizational identity and trust not only directly influence teachers’ intrinsic motivation for professional development but also serve as mediators in the relationship between perceived organizational fairness and intrinsic motivation. These findings have important implications for university management in promoting teachers’ professional development and enhancing educational quality.