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Exploration of contemporary modernization in UWSNs in the context of localization including opportunities for future research in machine learning and deep learning
Fertilizer types and nitrogen rates integrated strategy for achieving sustainable quinoa yield and dynamic soil nutrient-water distribution at high altitude
Abstract Quinoa (Chenopodium quinoa Willd.) is a crop particularly adapted to high-altitude environments characterized by significant variability in climate and soil conditions Fertilization is essential for providing nutrients and influencing soil nutrient cycling and hydrological dynamics. This study aimed to optimize fertilizer type and nitrogen (N) application rates to improve soil nutrient availability, moisture retention, and quinoa yield. We examined three fertilizer types: compound fertilizer (NPK), bio-microbial fertilizer (BM), and slow-release fertilizer (SRF), with nitrogen application rates of 90, 120, and 150 kg ha− 1, compared to a control group (CK) with no fertilization. Our results revealed that applying 120 kg ha− 1 of nitrogen with SRF significantly reduced soil bulk density, improved water retention beyond 60 cm depth, and enhanced water use efficiency by 9.2–16.2%, alleviating water stress. In conjunction with BM, this nitrogen application increased soil organic matter, alkali-hydrolyzed nitrogen, and the availability of phosphorus and potassium, especially during the grain-filling stage, promoting quinoa growth. Elevated nitrogen rates (120 and 150 kg ha− 1) with BM maximized soil urease and sucrase activities, correlating positively with key soil chemical parameters. Additionally, 120 kg ha− 1 of SRF notably boosted quinoa biomass and yield components. Economic analysis indicated that SRF at 120 kg ha− 1 nitrogen provided the highest productivity. These results highlight the importance of fertilizer type and nitrogen rates in enhancing soil nutrient status and optimizing water infiltration in high-altitude soils, offering a drought-resistant strategy for quinoa cultivation.
MEGA PROTAC, MEGA DOCK-based PROTAC mediated ternary complex formation pipeline with sequential filtering and rank aggregation
Abstract Proteolysis-targeting chimaeras (PROTACs), which induce proteolysis by recruiting an E3 ligase to dock into a target protein, are acquiring popularity as a novel pharmacological modality because of the unique features of PROTAC, including high potency, low dosage, and effective on undruggable targets. While PROTACs are promising prospects as chemical probes and therapeutic agents, their discovery usually necessitates the synthesis of numerous analogues to explore variations on the chemical linker structure exhaustively. Without extensive trial and error, it is unknown how to link the two protein-recruiting moieties to facilitate the formation of a productive ternary complex. Although molecular docking-based and optimization pipelines have been designed to predict ternary complexes, guiding rational PROTAC design, they have suffered from limited predictive performance in the quality of the ternary structure and their ranks. Here, MEGA PROTAC has been designed to enhance the performance in quality and ranking of ternary structures. MEGA PROTAC employs MEGADOCK to execute docking for protein-protein complexes (PPCs). The docking establishes an initial exploration area for PPCs. A sequential filtration strategy combined with rank aggregation is employed to choose a subset of PPCs for grid search. Once candidate PPCs are selected, a grid search method is used separately for translation and rotation. The remaining proteins have been grouped into clusters, and MEGA PROTAC further filters these clusters based on the energy score of the proteins within each cluster. MEGA PROTAC utilises rank aggregation to choose the best clusters and then employs MEGADOCK to dock PROTAC into the selected PPCs, forming a ternary structure. Finally, MEGA PROTAC was tested on 22 cases to compare with the state-of-the-art method, Bayesian optimisation for ternary complex prediction (BOTCP). MEGA PROTAC outperformed BOTCP on 16 test cases out of 22 cases, achieving a higher maximum DockQ score with an 18% higher mean and 35% higher median. Also, MEGA PROTAC exhibited 75% superior ranks and a reduced cluster number for maximum DockQ score compared to BOTCP. Also, MEGA PROTAC outperforms BOTCP by achieving a twofold improvement in locating the first acceptable DockQ scores, with a more significant proportion of near-native structures within the detected cluster.
Reactive rate coefficients and machine learning predictions for O(3P) + C2(X1Σg+) collisions on an accurate PIP-NN potential energy surface
A full-dimensional potential energy surface (PES) for the 3A″ state of the [CCO] system has been constructed using neural networks (NNs) with permutationally invariant polynomials. This global analytical PES was accurately fitted from 9293 ab initio energies at the MRCI + Q/aug-cc-pVTZ level of theory. Based on the newly developed surfaces, the microscopic chemical reaction mechanisms of the O(3P) + C2(X1Σg+) → CO(X1Σ+) + C(3P) reactive collision were investigated using the quasi-classical trajectory (QCT) method. The reaction cross sections and rate coefficients obtained from QCT calculations are in good agreement with available theoretical and experimental data reported in the literature. Rate coefficient calculations indicate that for O + C2 collisions, the results for the reactive channel are significantly higher than those for the inelastic channel across a wide temperature range of 1000–20 000 K. Finally, to reduce computational demands, we also established an NN-based model to predict cross section by combining QCT with NNs. The developed model accurately reproduces the original QCT results.
The internal and external cost of motor vehicle crashes
Abstract Crash cost estimates are essential for evaluating road safety management policies and assessing the economic benefits of safety improvements. Existing studies often rely on aggregated crash data, assuming an even distribution of incidents, which overlooks significant spatial variations influenced by road characteristics and traffic conditions. This research presents a methodological framework for link-based crash cost analysis that considers both internal and external costs, enabling detailed quantification at a localized level. By employing safety performance functions and ordered probit models, we estimate on-road crash rates by crash type and injury severity, distinguishing between internal costs borne by individuals involved in crashes and external costs that impact victims, insurers, and government agencies. This framework is applied to the Minneapolis-St. Paul metropolitan area for a proof-of-concept. Our findings reveal that the costs incurred by drivers are higher than those imposed on others, and that highways are generally safer than surface streets. However, these crash costs are too low compared to the value of travel time to significantly influence route choices, even when drivers are aware of these costs. To enhance effective decision-making, related policies should consider offering incentives for safe driving practices. Future research on the practical applications of this framework is encouraged to maintain a dynamic dataset that reflects ongoing changes in road safety conditions.
Water intrusion in hydrophobic MOFs with complex topology: A glimpse of the intrusion mechanism of Cu2(tebpz)
Despite water intrusion in microporous materials being extensively investigated, obtaining a detailed overview of the intrusion mechanism in materials with more complex morphology, topology, and physical–chemical characteristics, such as metal–organic frameworks (MOFs), is far from trivial. In this work, we present a qualitative study on the mechanism of water intrusion in a crystallite of hydrophobic Cu2(tebpz) (tebpz = 3,3′,5,5′-tetraethyl-4,4′-bipyrazolate) MOF. This MOF is characterized by a complex morphology; it consists of primary (main channels) and secondary (lateral apertures) porosities. This is similar to some zeolites, such as the so-called ITT-type zeolite framework, but it presents the additional characteristics of high flexibility of the material and non-uniform hydrophobicity. Interestingly, in Cu2(tebpz), water intrusion occurs first for some of the channels lying tangent to the surface of the MOF’s crystallite. This is due to hydrogen bonding bridging with bulk water across the (thin) lateral apertures of these channels. In macroscopic terms, this can be understood as a local reduction of hydrophobicity favoring intrusion. Temperature and pressure influence the average number of hydrogen bonds and the number of intruded water molecules, explaining the effect of these thermodynamic parameters on the intrusion/extrusion characteristics of this porous material. Molecular dynamics simulations allowed us to glimpse liquid intrusion in this complex hydrophobic material, highlighting how the classical models valid for mesoporous systems, namely, Young–Laplace’s law, are not quite appropriate to describe intrusion in such materials.
Few-shot learning for non-vitrified ice segmentation
Abstract This study introduces Ice Finder, a novel tool for quantifying crystalline ice in cryo-electron tomography, addressing a critical gap in existing methodologies. We present the first application of the meta-learning paradigm to this field, demonstrating that diverse tomographic tasks across datasets can be unified under a single meta-learning framework. By leveraging few-shot learning, our approach enhances domain generalization and adaptability to domain shifts, enabling rapid adaptation to new datasets with minimal examples. Ice Finder’s performance is evaluated on a comprehensive set of in situ datasets from EMPIAR, showcasing its ease of use, fast processing capabilities, and millisecond inference times.
Autobiography of Y. Ron Shen
Elimination of apoptotic cells by non-professional embryonic phagocytes can be stimulated or inhibited by external stimuli
Inertia effects in the spatial distribution and dynamics of active particles with space-dependent activity
The activity of particles can be modulated by external conditions such as light irradiation. Research on active particles with spatially varying activity has demonstrated that active particles tend to accumulate in low-activity regions and form a polarity layer at the interface, directed from the high-activity to the low-activity region. Here, we investigate the distribution and dynamics of individual or an ideal gas of inertial particles in a space with alternating active and passive regions. Our findings reveal that high inertia leads to a pronounced depletion layer in the passive region. At the interface between the active and passive regions, in addition to the usual polarity layer, an adjacent anti-polarity layer forms on the active-region side. In extreme situations (narrow region width and long persistence times), the interfacial polarity layer can even reverse orientation. Dynamically, we observe long-time peaks in the velocity autocorrelation function of particles within the active region. For particles with high inertia, the peak can even exceed 1. Correspondingly, the mean squared displacement of high-inertia particles in the active region exhibits an unusual superdiffusive behavior (∼t3). In addition, kinetic temperature and pressure differences arise between the active and passive regions. The effective temperature of particles with high inertia exhibits a gradual gradient across the active region. Our study provides new insights into the behavior of inertial active particles under spatially modulated activity and lays the groundwork for further exploration of their collective behaviors when interactions are included.
Ontology-guided machine learning outperforms zero-shot foundation models for cardiac ultrasound text reports
Abstract Big data can revolutionize research and quality improvement for cardiac ultrasound. Text reports are a critical part of such analyses. Cardiac ultrasound reports include structured and free text and vary across institutions, hampering attempts to mine text for useful insights. Natural language processing (NLP) can help and includes both statistical- and large language model based techniques. We tested whether we could use NLP to map cardiac ultrasound text to a three-level hierarchical ontology. We used statistical machine learning (EchoMap) and zero-shot inference using GPT. We tested eight datasets from 24 different institutions and compared both methods against clinician-scored ground truth. Despite all adhering to clinical guidelines, institutions differed in their structured reporting. EchoMap performed best with validation accuracy of 98% for the first ontology level, 93% for first and second levels, and 79% for all three. EchoMap retained performance across external test datasets and could extrapolate to examples not included in training. EchoMap’s accuracy was comparable to zero-shot GPT at the first level of the ontology and outperformed GPT at second and third levels. We show that statistical machine learning can map text to structured ontology and may be especially useful for small, specialized text datasets.
Thermo-orientation and anomalous rotational diffusion of cone-shaped particles under a temperature gradient
Thermophoresis, the translational motion of particles in response to temperature gradients, has been well-studied, but the rotational response remains less understood. This work investigates the thermo-orientation and rotational diffusion of non-spherical particles, with special focus on shape asymmetry, through non-equilibrium molecular dynamics simulations. Our results indicate that the degree of thermo-orientation of asymmetric particles (cone-shaped) is positively correlated with both the aspect ratio (R/H) and the temperature gradient; however, the Soret coefficient exhibits a negative correlation with thermo-orientation. To explore the underlying mechanisms further, we analyzed the variation in the torque experienced by the particles. We propose that the thermo-orientation of particles originates from the combined effects of thermophoretic torque and random torque, which in turn lead to anomalous rotational diffusion behavior. Consequently, we investigated the rotational diffusion characteristics of the particles, observing that the probability density functions of angular displacement transition from Gaussian to thin-tailed distributions, with the degree of non-Gaussianity increasing as the R/H values rise. These results could provide a new perspective based on rotational diffusion dynamics for studying the thermo-orientation of asymmetric particles.
Enhancing drought tolerance in blackgram (Vigna mungo L. Hepper) through physiological and biochemical modulation by peanut shell carbon dots
Bats on film: scientific storytelling from a recovering academic
Transcorrelated methods applied to second row elements
We explore the applicability of the transcorrelated method to the elements in the second row of the periodic table. We use transcorrelated Hamiltonians in conjunction with full configuration interaction quantum Monte Carlo and coupled cluster techniques to obtain total energies and ionization potentials, investigating their dependence on the nature and size of the basis sets used. Transcorrelation accelerates convergence to the complete basis set limit relative to conventional approaches, and chemically accurate results can generally be obtained with the cc-pVTZ basis, even with a frozen Ne core in the post-Hartree–Fock treatment.
Small traffic sign recognition method based on improved YOLOv7
‘Researching climate change feels like standing in the path of an approaching train’
Transport coefficients from equilibrium molecular dynamics
The determination of transport coefficients through the time-honored Green–Kubo theory of linear response and equilibrium molecular dynamics requires significantly longer simulation times than those of equilibrium properties while being further hindered by the lack of well-established data-analysis techniques to evaluate the statistical accuracy of the results. Leveraging recent advances in the spectral analysis of the current time series associated with molecular trajectories, we introduce a new method to estimate the full (diagonal as well as off-diagonal) Onsager matrix of transport coefficients from a single statistical model. This approach, based on the knowledge of the statistical distribution of the Onsager-matrix samples in the frequency domain, unifies the evaluation of diagonal (conductivities and viscosities) and off-diagonal (e.g., thermoelectric) transport coefficients within a comprehensive framework, significantly improving the reliability of transport coefficient estimation for materials ranging from molten salts to solid-state electrolytes. We validate the accuracy of this method against existing approaches using benchmark data on molten cesium fluoride and liquid water and conclude our presentation with the computation of various transport coefficients of the Li3PS4 solid-state electrolyte.