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Transducing chemical energy through catalysis by an artificial molecular motor

Abstract Cells display a range of mechanical activities generated by motor proteins powered through catalysis 1 . This raises the fundamental question of how the acceleration of a chemical reaction can enable the energy released from that reaction to be transduced (and, consequently, work to be done) by a molecular catalyst 2–7 . Here we demonstrate the molecular-level transduction of chemical energy to mechanical force 8 in the form of the powered contraction and powered re-expansion of a cross-linked polymer gel driven by the directional rotation of artificial catalysis-driven 9 molecular motors. Continuous 360° rotation of the rotor about the stator of the catalysis-driven motor-molecules incorporated in the polymeric framework of the gel twists the polymer chains of the cross-linked network around one another. This progressively increases writhe and tightens entanglements, causing a macroscopic contraction of the gel to approximately 70% of its original volume. The subsequent addition of the opposite enantiomer fuelling system powers the rotation of the motor-molecules in the reverse direction, unwinding the entanglements and causing the gel to re-expand. Continued powered twisting of the strands in the new direction causes the gel to re-contract. In addition to actuation, motor-molecule rotation in the gel produces other chemical and physical outcomes, including changes in the Young modulus and storage modulus—the latter is proportional to the increase in strand crossings resulting from motor rotation. The experimental demonstration of work against a load by a synthetic organocatalyst, and its mechanism of energy transduction 6 , informs both the debate 3,5,7 surrounding the mechanism of force generation by biological motors and the design principles 6,10–14 for artificial molecular nanotechnology.

Continuous photo-oxidation of methane to methanol at an atomically tailored reticular gas-solid interface

RETRACTED: Machine vision model for drip leakage detection of pipeline

The prevailing trend in industrial equipment development is integration, with pipelines as the lifeline connecting system components. Given the often harsh conditions of these industrial equipment pipelines, leakage is a common occurrence that can disrupt normal operations and, in severe cases, lead to safety accidents. Early detection of even minor drips at the onset of leakage can enable timely maintenance measures, preventing more significant leaks and halting the escalation of pipeline failures. In light of this, our study investigates a method for monitoring pipe drips in industrial equipment using machine vision technology. We propose a machine vision model specifically designed for pipe drip detection, aiming to facilitate monitoring of pipe system drips. The system designed to collect the image of the droplet side cross-section with a Charge charge-coupled device (CCD) industrial camera, is aided by the computer image processing system used to analyze and process the collected images. Image enhancement technology is applied to improve the visibility of the image and image filtering technology is applied to remove the noise of the image. With the help of image segmentation technology, target droplet identification and division are achieved. Morphological reconstruction and region-filling techniques are used to remove the noise caused by shooting in the side cross-section image, such as hollow, reflection, and irregular droplet edge, to upgrade the quality of the solution droplet edge. The mathematical model is established for boundary position points extracted from the droplet side cross-section image. Then, the fitting droplet image is drawn. The droplet volume is obtained by calculating the volume of the rotating body. The two-dimensional image of the target droplet is obtained dynamically through the camera capture technology. The droplet boundary extraction algorithm is proposed, and the three-dimensional model of the target droplet is established, so the volume calculation problem of the droplet is solved, which provides a way of thinking for drip leakage detection of the pipeline.

Pregnancy entails a U-shaped trajectory in human brain structure linked to hormones and maternal attachment

Deep learning-based skin lesion analysis using hybrid ResUNet++ and modified AlexNet-Random Forest for enhanced segmentation and classification

Skin cancer is considered globally as the most fatal disease. Most likely all the patients who received wrong diagnosis and low-quality treatment die early. Though if it is detected in the early stages the patient has fairly good chance and the aforementioned diseases can be cured. Consequently, diagnostic identification and management of the patient at this level becomes a rather enormous task. This paper offers a cutting-edge hybrid deep learning approach of better segmentation and classification of skin lesions. The proposed method incorporates three key stages: preprocessing, segmentation of lesions, and classification of lesions. By the stage of preprocessing, a morphology-based technique takes out hair so as to enhance the segmentation precision to use the cleansed images for subsequent analysis. Segmentation cuts off the lesion from the surrounding skin, giving the classification phase a dedicated area of interest and the ability to clear the background noise that may affect classification rates. The isolation enables the model to better analyze anatomical lesion features in order to achieve accurate benign and malignant classifications. Using ResUNet++, the cutting-edge deep learning architecture, we achieved accurate lesion segmentation. Next, we will modify and use an AlexNet-Random Forest (AlexNet-RF) based classifier for robust lesion classification. The proposed hybrid deep learning model is intensively validated on the Ham10000 data set which is one of the most popular datasets for skin lesions analysis. The obtained results show that the utilized approach, compared to the previous ones, is more effective, giving better segmentation and classification results. This method takes advantage of ResUNet++ strong classification skill and modified AlexNet-Random Forest robustness for more accurate segmentation. There is a high probability that ResUNet++, which is highly proficient at medical image segmentation, can produce better segmentation of lesions than the simpler models. The composition of AlexNet’s extraction of features with Random Forest ability to reduce overfitting possibly may be more precise in the classification when compared to using only one model.

Spontaneous curvature in two-dimensional van der Waals heterostructures

Correction: Defending Against Advanced Persistent Threats Using Game-Theory

Developing an alternative medium for in-space biomanufacturing

Protective effects of Euphorbia heterophylla against testicular degeneration in streptozotocin-induced diabetic rats in relation to phytochemical profile

Background Diabetes mellitus (DM) poses a major risk to human health due to an array of implications, one of which is a detrimental effect on the testicular and reproductive functions. Euphorbia heterophylla is widely recognized for its medicinal properties worldwide. Methods and findings The objective of this study was to profile E. heterophylla ethanol extract (EH-EtOH) and elucidate its protective role in oxidative stress, relieving inflammatory action of hyperglycemia-induced testicular degeneration and restoring the normal histological structure with physiological properties of testicular tissue in streptozotocin (STZ)-induced DM. High-resolution ultra-performance liquid chromatography-mass spectrometry (UHPLC-ESI-Orbitrap-MS) analysis was employed to analyze the compounds present in EH-EtOH. The protective effect of EH-EtOH against testicular degeneration in the rat model of DM was evaluated by measuring improvements in blood glucose levels, body weight, testicular inflammation, oxidative damage, testicular microcirculation impairment, and apoptosis of testicular cells induced by STZ. The chemical profiling of EH-EtOH revealed the presence of 52 compounds, including phenolic acids, flavonoids, coumarins, phloroglucinols, and triterpenes. Notably, this study identified isovitexin-C-hexoside, isorhamnetin-O-hexoside, diosmetin, and halfordin for the first time in Euphorbia species. Treatment with EH-EtOH effectively mitigated the damage caused by STZ, as evidenced by restored testosterone (T4) levels and antioxidant capacity, reduced expression of pro-inflammatory cytokines, improved testicular microcirculation, and inhibition of apoptosis in the testes. Conclusions These results emphasize the potential therapeutic effect of E. heterophylla on DM related to male infertility and reproductive dysfunctions via its antioxidant/angiogenetic /anti-apoptotic effect.

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