On voxel-by-voxel accumulated dose for prostate radiation therapy using deformable image registration.

Since delivered dose is rarely the same with planned, we calculated the delivered total dose to ten prostate radiotherapy patients treated with rectal balloons using deformable dose accumulation (DDA) and compared it with the planned dose. The patients were treated with TomoTherapy using two rectal balloon designs: five patients had the Radiadyne balloon (balloon A), and five patients had the EZ-EM balloon (balloon B). Prostate and rectal wall contours were outlined on each pre-treatment MVCT for all patients. Delivered fractional doses were calculated using the MVCT taken immediately prior to delivery. Dose grids were accumulated to the last MVCT using DDA tools in Pinnacle3 TM (v9.100, Philips Radiation Oncology Systems, Fitchburg, USA). Delivered total doses were compared with planned doses using prostate and rectal wall DVHs. The rectal NTCP was calculated based on total delivered and planned doses for all patients using the Lyman model. For 8/10 patients, the rectal wall NTCP calculated using the delivered total dose was less than planned, with seven patients showing a decrease of more than 5% in NTCP. For 2/10 patients studied, the rectal wall NTCP calculated using total delivered dose was 2% higher than planned. This study indicates that for patients receiving hypofractionated radiotherapy for prostate cancer with a rectal balloon, total delivered doses to prostate is similar with planned while delivered dose to rectal walls may be significantly different from planned doses. 8/10 patients show significant correlation between rectal balloon anterior-posterior positions and some VD values

Electrophysiological hallmarks for event relations and event roles in working memory

The ability to maintain events (i.e., interactions between/among objects) in working memory is crucial for our everyday cognition, yet the format of this representation is poorly understood. The current ERP study was designed to answer two questions: How is maintaining events (e.g., the tiger hit the lion) neurally different from maintaining item coordinations (e.g., the tiger and the lion)? That is, how is the event relation (present in events but not coordinations) represented? And how is the agent, or initiator of the event encoded differently from the patient, or receiver of the event during maintenance? We used a novel picture-sentence match-across-delay approach in which the working memory representation was “pinged” during the delay, replicated across two ERP experiments with Chinese and English materials. We found that maintenance of events elicited a long-lasting late sustained difference in posterior-occipital electrodes relative to non-events. This effect resembled the negative slow wave reported in previous studies of working memory, suggesting that the maintenance of events in working memory may impose a higher cost compared to coordinations. Although we did not observe significant ERP differences associated with pinging the agent vs. the patient during the delay, we did find that the ping appeared to dampen the ongoing sustained difference, suggesting a shift from sustained activity to activity silent mechanisms. These results suggest a new method by which ERPs can be used to elucidate the format of neural representation for events in working memory

Sample-efficient Multi-objective Molecular Optimization with GFlowNets

Many crucial scientific problems involve designing novel molecules with desired properties, which can be formulated as a black-box optimization problem over the discrete chemical space. In practice, multiple conflicting objectives and costly evaluations (e.g., wet-lab experiments) make the diversity of candidates paramount. Computational methods have achieved initial success but still struggle with considering diversity in both objective and search space. To fill this gap, we propose a multi-objective Bayesian optimization (MOBO) algorithm leveraging the hypernetwork-based GFlowNets (HN-GFN) as an acquisition function optimizer, with the purpose of sampling a diverse batch of candidate molecular graphs from an approximate Pareto front. Using a single preference-conditioned hypernetwork, HN-GFN learns to explore various trade-offs between objectives. We further propose a hindsight-like off-policy strategy to share high-performing molecules among different preferences in order to speed up learning for HN-GFN. We empirically illustrate that HN-GFN has adequate capacity to generalize over preferences. Moreover, experiments in various real-world MOBO settings demonstrate that our framework predominantly outperforms existing methods in terms of candidate quality and sample efficiency. The code is available at https://github.com/violet-sto/HN-GFN.Comment: NeurIPS 202

Continuous Finite-Time Terminal Sliding Mode IDA-PBC Design for PMSM with the Port-Controlled Hamiltonian Model

Finite-time control scheme for speed regulation of permanent magnet synchronous motor (PMSM) is investigated under the port-controlled Hamiltonian (PCH), terminal sliding mode (TSM), and fast TSM stabilization theories. The desired equilibrium is assigned to the PCH structure model of PMSM by maximum torque per ampere (MTPA) principle, and the desired Hamiltonian function of state error is constructed in the form of fractional power structure as TSM and fast TSM, respectively. Finite-time TSM and fast TSM controllers are designed via interconnection and damping assignment passivity-based control (IDA-PBC) methodology, respectively, and the finite-time stability of the desired equilibrium point is also achieved under the PCH framework. Simulation results validate the improved performance of the presented scheme

Explanation-aware Soft Ensemble Empowers Large Language Model In-context Learning

Large language models (LLMs) have shown remarkable capabilities in various natural language understanding tasks. With only a few demonstration examples, these LLMs can quickly adapt to target tasks without expensive gradient updates. Common strategies to boost such 'in-context' learning ability are to ensemble multiple model decoded results and require the model to generate an explanation along with the prediction. However, these models often treat different class predictions equally and neglect the potential discrepancy between the explanations and predictions. To fully unleash the power of explanations, we propose EASE, an Explanation-Aware Soft Ensemble framework to empower in-context learning with LLMs. We design two techniques, explanation-guided ensemble, and soft probability aggregation, to mitigate the effect of unreliable explanations and improve the consistency between explanations and final predictions. Experiments on seven natural language understanding tasks and four varying-size LLMs demonstrate the effectiveness of our proposed framework

Scaling Data Generation in Vision-and-Language Navigation

Recent research in language-guided visual navigation has demonstrated a significant demand for the diversity of traversable environments and the quantity of supervision for training generalizable agents. To tackle the common data scarcity issue in existing vision-and-language navigation datasets, we propose an effective paradigm for generating large-scale data for learning, which applies 1200+ photo-realistic environments from HM3D and Gibson datasets and synthesizes 4.9 million instruction trajectory pairs using fully-accessible resources on the web. Importantly, we investigate the influence of each component in this paradigm on the agent's performance and study how to adequately apply the augmented data to pre-train and fine-tune an agent. Thanks to our large-scale dataset, the performance of an existing agent can be pushed up (+11% absolute with regard to previous SoTA) to a significantly new best of 80% single-run success rate on the R2R test split by simple imitation learning. The long-lasting generalization gap between navigating in seen and unseen environments is also reduced to less than 1% (versus 8% in the previous best method). Moreover, our paradigm also facilitates different models to achieve new state-of-the-art navigation results on CVDN, REVERIE, and R2R in continuous environments.Comment: ICCV 202

Generative AI for Controllable Protein Sequence Design: A Survey

The design of novel protein sequences with targeted functionalities underpins a central theme in protein engineering, impacting diverse fields such as drug discovery and enzymatic engineering. However, navigating this vast combinatorial search space remains a severe challenge due to time and financial constraints. This scenario is rapidly evolving as the transformative advancements in AI, particularly in the realm of generative models and optimization algorithms, have been propelling the protein design field towards an unprecedented revolution. In this survey, we systematically review recent advances in generative AI for controllable protein sequence design. To set the stage, we first outline the foundational tasks in protein sequence design in terms of the constraints involved and present key generative models and optimization algorithms. We then offer in-depth reviews of each design task and discuss the pertinent applications. Finally, we identify the unresolved challenges and highlight research opportunities that merit deeper exploration.Comment: 9 page

Bone marrow adipocytes and lung cancer bone metastasis: unraveling the role of adipokines in the tumor microenvironment

Bone is a common site of metastasis for lung cancer. The “seed and soil” hypothesis suggests that the bone marrow microenvironment (“soil”) may provide a conducive survival environment for metastasizing tumor cells (“seeds”). The bone marrow microenvironment, comprising a complex array of cells, includes bone marrow adipocytes (BMAs), which constitute about 70% of the adult bone marrow volume and may play a significant role in tumor bone metastasis. BMAs can directly provide energy for tumor cells, promoting their proliferation and migration. Furthermore, BMAs participate in the tumor microenvironment’s osteogenesis regulation, osteoclast(OC) regulation, and immune response through the secretion of adipokines, cytokines, and inflammatory factors. However, the precise mechanisms of BMAs in lung cancer bone metastasis remain largely unclear. This review primarily explores the role of BMAs and their secreted adipokines (leptin, adiponectin, Nesfatin-1, Resistin, chemerin, visfatin) in lung cancer bone metastasis, aiming to provide new insights into the mechanisms and clinical treatment of lung cancer bone metastasis

Chemistry of hydrolysis of FeCl3 in the presence of phosphate to form hematite nanotubes and nanorings

JC thanks joint scholarship from Chinese Ministry of Education (CSC grant) and University of St Andrews. The authors thank the EPSRC for financial support on FEG-SEM equipment (EP/F019580/1) and Titan Themis S/TEM microscope (EP/L017008/01).Through investigation of the intermediate specimens during the hydrolysis of FeCl3 in the presence of phosphate using Mass spectroscopy, XRD, EDX, SEM, HRTEM and ICP-OES Spectrometry, the formation mechanisms of α-Fe2O3 nanotubes and nanorings were revealed. At early stages, the precursor molecules polymerized and aggregated into large disordered particles, from which β-FeOOH nanorods grew up. When the NaH2PO4 concentration was low (e.g. 1 mM in a solution of 23 mM FeCl3), the β-FeOOH nanorods were relatively stable and underwent side-by-side aggregation into spindle-like particles. Phase transformation into self-orientated α-Fe2O3 nanocrystallites then took place on the surface of these spindle particles, followed by Ostwald ripening, to form a single-crystalline shell. The ends and the core of the spindle particles were dissolved, forming α-Fe2O3 nanotubes. When the NaH2PO4 concentration was high (e.g. 4 mM), the individual β-FeOOH nanorods decomposed into α-Fe2O3 nanocrystallites, which underwent self-orientated aggregation into polycrystalline disks. Surface Ostwald ripening and dissolution of the central area turned these disks into nanorings. The exposed surface in the nanotubes is mainly (hk0), while it is (001) in the nanorings. Photoelectrochemical measurement indicated that photocurrent response of the nanotubes was three times higher than the nanorings. This newly established non-classical formation mechanisms of these crystals may help us to understand the development of many other novel morphologies of metal oxides via a hydrolysis process.PostprintPeer reviewe