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Parallel evolution of plant alkaloid biosynthesis from bacterial-like decarboxylases
No full textAlkaloids are nitrogen-containing natural products derived from amino acids. The basic amino acids lysine and ornithine are precursors to a wide range of alkaloids including the bioactive compounds nicotine, hyoscyamine and securinine. Isotope feeding experiments have shown that the amino acids can be incorporated into alkaloids in a symmetric or nonsymmetric manner. The symmetric pathway is catalysed by two enzymes, a decarboxylase and oxidase, forming a cyclic iminium which acts as the electrophile in the scaffold forming step. Here, we describe the ornithine/lysine/arginine decarboxylase-oxidases (OLADOs), PLP-dependent enzymes responsible for the nonsymmetric pathway, catalysing the single step decarboxylative oxidative deamination of lysine, ornithine or arginine. These enzymes are part of the group III ornithine/lysine/arginine decarboxylase-like family (OLADLs), previously exclusively associated with prokaryotes. We reveal OLADLs to be widespread in plants and show that OLADOs have repeatedly emerged through parallel evolution from OLADLs, via similar active site substitutions. This investigation introduces a new class of eukaryotic decarboxylases, and describes enzymes involved in multiple alkaloid biosynthesis pathways. It furthermore demonstrates how the principle of parallel evolution at a genomic and enzymatic level can be leveraged for gene discovery across multiple lineages
The Leverage of Terrorists on Democratic Regimes:Evidence from Natural Experiments in Sub-Saharan Africa
No full textThis paper quantifies the impact of terrorism on electoral support for incumbent parties in sub-Saharan Africa, using survey data collected during fieldwork that coincided with terrorist attacks. By comparing respondents surveyed immediately before a local attack with those in the same area surveyed shortly after, we observe a 5-6 percentage point decrease in incumbent support following attacks. This decline is more pronounced when attacks are proximate, target civilians, or incur higher casualties. The impact is strongest in states with weaker institutions, areas with extensive media coverage, and among politically engaged and educated citizens, though effective government response can mitigate this adverse effect. Further investigation reveals that the reduction in support stems primarily from a significant erosion of trust in the incumbent party, rather than shifts in counter-terrorism policy preferences. These findings underscore terrorism's considerable influence on democratic stability in affected regions
Weekly train timetabling integrating stop planning for high-speed rail lines
No full textIn high-speed rail (HSR) train planning and scheduling, traditional approaches often focus on passenger demand over short periods, such as one or two hours or a single day, while overlooking demand fluctuations over an entire week. This study proposes an integrated model for weekly train timetabling and stop planning, aiming to optimize both train stops and schedules across different times of day and days of the week. To improve computational efficiency for large-scale, real-world applications, a Lagrangian relaxation algorithm is developed. Case studies based on Chinese HSR lines demonstrate that the proposed model and algorithm outperform both the commercial solver CPLEX and the conventional sequential approach of line planning followed by timetabling. The weekly timetable generated by the proposed algorithm significantly reduces train and passenger traveling costs by improving traveling speeds and the proportion of passengers traveling within their preferred periods compared to current practical timetables, making it widely applicable to a wide range of HSR lines
Unseen data detection using routing entropy in mixture-of-experts for autonomous vehicles
No full textUnseen data that differ significantly from the training data can cause machine learning models to behave unpredictably, which is particularly problematic in safety-critical systems like autonomous vehicles. Detecting such data, commonly called out-of-distribution (OOD) data, is essential for ensuring the robustness of these models. Existing methods often rely on the model’s final output, which are limited since the model can be overconfident on unseen data. In this paper, we propose Routing Entropy, a novel OOD detection method that leverages the internal routing behavior of Mixture-of-Experts (MoE) models, a design increasingly adopted in modern neural networks. We hypothesize that MoE models exhibit high confidence routing for in-distribution (ID) inputs, but greater uncertainty for OOD inputs. We quantify this uncertainty by calculating the entropy of the routing scores for a given input. Experimental results on a MoE-based semantic segmentation model used for perception in autonomous driving demonstrate that Routing Entropy is effective on its own and, more importantly, provides a complementary signal to existing output-based methods. Combining Routing Entropy with an existing method significantly improves OOD detection performance. These results suggest that leveraging internal routing behavior of MoE models is a promising direction for robust OOD detection
Advancing Equity for People with Intellectual Disabilities:Closing the Neglected Cancer Policy Gap
No full textThe prevalence and nature of animal maltreatment in young people referred to a community Forensic CAMHS:a Service Evaluation
No full textSpatiotemporal patterns and propagation characteristics of convective activity on the northeast slope of Tibetan Plateau: A high-resolution radar perspective
No full textThe northeastern slope of the Tibetan Plateau, situated in a complex terrain and the monsoon-westerly transition zone, experiences frequent convective storms and high disaster risk. Based on CINRAD radar and ERA5 reanalysis data from 2015 to 2019, a high-resolution convective climatology was established, and its environmental fields were diagnosed. Results indicate a significant topographic anchoring effect on convection, with persistent hotspots located in the Yellow River valley-Xinglong Mountain, the eastern Qilian Mountains, and the sharp-bend reach of the Yellow River. Moderate convection dominates (64.7 %), while deep convection has a low frequency but high local intensity. The most active month seasonally is July, with June and August exhibiting similar levels of activity. The convective activity in July is most active during the season, with levels in June and August being similar. The configuration of synoptic patterns indicates a synergistic mode of “upper-level trough, lower-level convergence, and strong moisture transport” for convective days. Diurnal variation is characterized by a peak in the afternoon (13:00–18:00 BJT) and is weakest in the early morning to morning, consistent with the synergistic trigger between solar radiation and terrain convergence. Atmospheric environment diagnostics reveal that, compared to non-convective events, convective events have higher CAPE, stronger updrafts, greater vertical wind shear, and more abundant water vapor in the two hours prior to triggering. The statistical distribution of storm scales exhibits an exponential decay with a “long-tail” pattern, with approximately 80 %–85 % of convective events having a propagation distance of less than 20 km and a max area of less than 200 km². The propagation direction of convective activity exhibits inter-monthly shifts, trending eastward/southward in June, shifting northward to east-southeastward in July, and westward/northward in August. These findings reveal the mechanisms by which large-scale circulation and local topography jointly influence convection, providing critical scientific support for monitoring and early warning systems for severe convection in the Tibetan Plateau and its surrounding areas, as well as for disaster risk prevention and control
Evaluation of heat extraction using compact shallow ground heat panels with the interaction of stormwater- a residential case study
No full textA bespoke parallel shallow horizontal Ground Source Heat Pump system (GSHP) with a small footprint (50 m2) was installed to provide space heating and domestic hot water for a residential house in the North of England. The shallow GSHP was combined with a storm water infiltration trench and both were installed with an adjacent control house which was fitted with a standard gas boiler for space heating and hot water. Up to 350 metres of High Density Polyethylene (HDPE) pipe with an external diameter of 40 mm connected in 2 parallel compact panels was used at the front and back of the house with the GSHP. The paper aims to (i) present data for the response of the ground to heat extraction using shallow ground heat panels and (ii) analytically model the heat gain in the ground heat exchanger panel, accounting for the thermal resistivity between the heat exchanger pipes and the surrounding soil, as well as the varriations in ground temperature and thermal conductivity. Internal and external ambient air temperatures, rainfall, coolant flow rate, coolant temperature, ground temperature and ground water level were monitored for a full year. The comprehensive field data were analysed to demonstrate the ground response and evaluate the performance of the shallow parallel ground heat extraction panels. Field data indicated that rainwater enhanced heat extraction and caused temporary increase in the ground temperature. Results from the analytical model are compared with measured temperature at five points on the ground heat extraction panel. The model showed good levels of predictive performance of the coolant temperature along the ground panel. However, it was noted that the model overestimates the coolant temperature at the centre point of the ground panel
No-Code ML pipeline development: Leveraging knowledge graphs and language models
No full textConstructing machine learning (ML) pipelines is challenging for non-ML experts due to various tasks and methods. Despite several no-code tools, their ML catalogs remain difficult to navigate. To address these challenges, we present an interactive system that simplifies ML pipeline creation through a graphical user interface (GUI) powered by ExeKGLib , a knowledge graph (KG)-based ML framework. The GUI features a drag-and-drop interface, allowing users to design ML workflows visually without coding. In addition, a large language model (LLM)-powered assistant provides context-aware recommendations for selecting pipeline steps from the ExeKGLib graph. We also utilize ontologies and semantic validation to ensure logical dependencies within the pipeline, guaranteeing usability and correctness. The resulting pipelines are automatically translated into executable KGs and executed by ExeKGLib. We demonstrate the system’s capabilities through a detailed walkthrough, highlighting its role in streamlining ML workflow creation and execution. This demo showcases the synergy between ontologies, KGs, and LLM-powered recommendations, democratizing ML pipeline development for both experts and non-experts
Simulated Comparison of On-Chip Terahertz Filters for Sub-Wavelength Dielectric Sensing
No full textThis paper discusses the application of on-chip terahertz (THz) filters attached to waveguides that can act as sensor elements, including for scanned imaging applications. Our
work presents a comparative numerical study of several different geometries (comprising
five split-ring resonator geometries and a quarter-wavelength stub resonator, the latter
being well established as a sensor at THz frequencies and therefore able to act as a benchmark). We designed each structure to have a resonant frequency of 500 GHz, allowing
the impact of resonator geometry on sensing performance to be isolated; the performance
was quantified by assessing each design using four figures of merit: resonance quality
factor, sensitivity (relative frequency shift under dielectric loading), responsivity (sensitivity
weighted by resonance sharpness), and the electric field confinement area. Simulations
were conducted using Ansys HFSS using the properties of a commercially available photoresist (Shipley 1813) as a dielectric load to assess performance under conditions comparable
to previous experimental studies. The analysis showed that while sensitivity remained
broadly similar across geometries, responsivity and quality factor differed substantially
between resonators. Furthermore, the spatial distribution of the electric field and current
density, particularly in rotated configurations, was found to significantly impact coupling
efficiency between the resonator and transmission line. Our findings provide guidance for
the general design of systems employing THz sensors while establishing a framework with
which to benchmark future sensor geometries