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Comprehensive Evaluation of the Capacities of Microbial Cell Factories for Producing 235 Different Bio-Based Chemicals
No full textGAUSS'S FORM CLASS GROUPS AND SHIMURA'S CANONICAL MODELS
No full textLet N be a positive integer and Gamma be a subgroup of SL2(Z) containing Gamma 1(N). Let K be an imaginary quadratic field and O be an order of discriminant DO in K. Under some assumptions, we show that Gamma induces a form class group of discriminant DO (or of order O) and level N if and only if there is a certain canonical model of the modular curve for Gamma defined over a suitably small number field. In this way we can find an interesting link between two different subjects.
Structure of the Huntingtin F-actin complex reveals its role in cytoskeleton organization
No full textThe Huntingtin protein (HTT), named for its role in Huntington's disease, has been best understood as a scaffolding protein that promotes vesicle transport by molecular motors along microtubules. Here, we show that HTT also interacts with the actin cytoskeleton, and its loss of function disturbs the morphology and function of the axonal growth cone. We demonstrate that HTT organizes F-actin into bundles. Cryo-electron tomography (cryo-ET) and subtomogram averaging (STA) structural analyses reveal that HTT's N-terminal HEAT and Bridge domains wrap around F-actin, while the C-terminal HEAT domain is displaced; furthermore, HTT dimerizes via the N-HEAT domain to bridge parallel actin filaments separated by similar to 20 nanometers. Our study provides the structural basis for understanding how HTT interacts with and organizes the actin cytoskeleton.
Leveraging Whole-Exome Sequencing and Mutational Signatures to Detect Homologous Recombination Deficiency in Cancer
No full textHomologous recombination is a high-fidelity DNA repair mechanism essential for maintaining genome stability. Impairment of this pathway, often due to BRCA1 or BRCA2 inactivation, leads to homologous recombination deficiency (HRD), forcing cells to rely on error-prone mechanisms for repairing DNA double-strand breaks, such as nonhomologous or microhomology-mediated end joining. HRD is a clinically important biomarker, particularly in breast and ovarian cancers, as it predicts responsiveness to platinum-based chemotherapies and PARP inhibitors. However, current tests in the clinical setting, mostly based on targeted panel sequencing, lack specificity and lead to a substantial number of false positives. In contrast, whole-genome sequencing, despite its high accuracy, remains largely confined to research because of high costs and logistical constraints. In this issue of Cancer Research, Abbasi and colleagues present HRProfiler, a machine learning-based tool that accurately detects HRD using whole-exome sequencing (WES) data, which is increasingly accessible in clinical oncology. Notably, it demonstrates improved sensitivity in the WES setting compared with existing tools, such as HRDetect and SigMA. As WES continues to gain traction, HRProfiler offers a promising step toward democratizing HRD detection and enabling more precise, genomics-guided treatment strategies.
Video-Foley: Two-Stage Video-to-Sound Generation via Temporal Event Condition for Foley Sound
No full textFoley sound synthesis is crucial for multimedia production, enhancing user experience by synchronizing audio and video both temporally and semantically. Recent studies on automating this labor-intensive process through video-to-sound generation face significant challenges. Systems lacking explicit temporal features suffer from poor alignment and controllability, while timestamp-based models require costly and subjective human annotation. We propose Video-Foley, a video-to-sound system using Root Mean Square (RMS) as an intuitive condition with semantic timbre prompts (audio or text). RMS, a frame-level intensity envelope closely related to audio semantics, acts as a temporal event feature to guide audio generation from video. The annotation-free self-supervised learning framework consists of two stages, Video2RMS and RMS2Sound, incorporating mu-law scaled RMS discretization and RMS-ControlNet with a pretrained text-to-audio model. Our extensive evaluation shows that Video-Foley achieves state-of-the-art performance in audio-visual alignment and controllability for sound timing, intensity, timbre, and nuance.
Triboelectric charge transfer theory driven by thermoelectric effect
No full textDespite extensive study and the practical significance of friction-driven static electricity, a quantitative triboelectric charge transfer theory has yet to be established. Here, we elucidate the detailed dynamics of triboelectric charge transfer driven by interfacial thermoelectric bias, maintaining a steady state at the interface. We demonstrate that transferred triboelectric charge exists in a deltalike distribution at a steady state and is dictated by half of the difference between thermoelectrically induced surface charges. Moreover, we quantitatively discuss electrostatic adhesion and static discharge between the transferred charges, which we may experience every day, including the role of surface charge inhomogeneity. Our findings may have significant implications for applications ranging from static electricity phenomena to advanced energy harvesting devices.
Deep-Learning-Based Automated REM Sleep Detection in Patients With REM Sleep Behavior Disorder: Is It Reliable?
No full textBackground and Purpose Rapid eye movement (REM) sleep without atonia makes it difficult to detect REM sleep stages using electromyography in patients with REM sleep behavior disorder (RBD). The objectives of this study were to propose an automated REM sleep detector that requires only electroencephalography (EEG) and electrooculography (EOG) data, and to evaluate its performance using real-world polysomnography (PSG) data in RBD patients. Methods This multicenter study used 310 PSG datasets obtained from 5 tertiary hospitals. The data were divided into RBD (n=200) and non-RBD (n=110), as well as, into Parkinson's disease (PD) with RBD (n=76), PD without RBD (n=46), idiopathic RBD (iRBD) (n=124), and healthy controls (n=64). An automated computerized REM detection algorithm was implemented using U-Sleep's publicly available pretrained network. Results The U-Sleep-based REM sleep-detection algorithm correctly identified REM sleep with an area under the receiver operating characteristic curve (AUC) of 0.90 +/- 0.14. The classification performance of the REM sleep detector differed significantly between RBD and non-RBD patients (AUC=0.88 +/- 0.13 vs. 0.93 +/- 0.14, p=0.007). The REM sleep detector accurately classified REM sleep in the order of healthy controls, PD without RBD, iRBD, and PD with RBD, with AUC values of 0.94 +/- 0.02, 0.92 +/- 0.03, 0.90 +/- 0.02, and 0.86 +/- 0.02, respectively. Conclusions Our U-Sleep-based REM sleep detector based on only EEG and EOG data showed good performance in detecting REM sleep. However, it performed considerably worse in RBD, especially in PD with RBD. Using transfer learning with fine-tuning by expert review, a high-performance REM sleep-detecting system will be realized.
Smart exercise device using triboelectric self-powered sensor for high intensity interval training (HIIT)
No full textHigh intensity interval training (HIIT) where periods of high intensity exercise are interspersed with periods of low intensity exercise or rest, packs a high-power workout into a short period resulting in higher oxygen uptake compared to other training methods. Commercial equipment used for such exercise protocols either requires manual adjustment or provides pre-programmed options without adapting to the user's performance. It would be much more convenient if the exercise equipment could detect how well the user is exercising and automatically adjust the training intensity level. Thus, in this work, we developed a smart exercise device using a triboelectric nanogenerator (Exercise-TENG). The device utilized the input torque increasing characteristic of the planetary gear system. As the subject operated the Exercise-TENG, energy harvested by the TENG was stored into a capacitor. The level of energy harvested into the capacitor was both an indicator of the user's exercise performance, and a trigger to increase or decrease the input torque required to operate the device. Furthermore, the user could be motivated to exercise by providing real-time feedback on the amount of energy harvested via a LabVIEW interface. Finally, we conducted HIIT bicep curl and triceps extension exercises using the ExerciseTENG and measured muscle activation levels to verify its effectiveness. Compared to other exercise devices, Exercise-TENG is a personalized solution that automatically adapts to the users' abilities with a unique motivation element especially needed for patient rehabilitation. We expect that our device will be a viable product for home exercise or patient rehabilitation applications.
When automation hits jobs: Entrepreneurship as an alternative career path
No full textThis study investigates the relationship between occupational automation risks and workers' transitions to entrepreneurship using data from the Current Population Survey. We find that employees facing automation-related job displacement are inclined to shift toward unincorporated entrepreneurship, emphasizing entrepreneurship as a viable alternative career path. Noteworthy variations emerge when examining specific automation technologies, revealing a positive association between industrial robots and entrepreneurial transitions, whereas artificial intelligence displays a negative relationship. Gender disparities are observed, with female workers exhibiting a lower likelihood than males of transitioning into entrepreneurship. This study also shows a heightened prominence of entrepreneurial transitions during the early stages of the COVID-19 pandemic. By illuminating entrepreneurship as a response to job displacement, our results offer crucial policy insights into the labor market implications of automation.
Emergency Lane Change Trajectory Planning for High Center of Gravity Vehicles using Time-Domain Dual Connecting Points Optimization
No full textThis study proposes a computationally efficient trajectory planning algorithm for emergency collision avoidance in high center of gravity (CoG) vehicles, building upon the previously developed Dual Connecting Points Optimization (DCPO) framework. While existing methods often suffer from computational inefficiency or limited avoidance performance, the DCPO approach effectively utilizes lateral acceleration up to the rollover threshold, enabling safe and responsive maneuvers for high CoG vehicles. By employing exponential lateral acceleration profiles and only two optimization variables, the algorithm achieves both rapid lane changes and real-time feasibility. This work enhances the prior DCPO formulation by redesigning the cost function to adaptively adjust trajectories based on obstacle proximity-producing smoother paths for moderate threats and sharper maneuvers for imminent ones. Simulation results confirm the proposed method's effectiveness across diverse emergency scenarios