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Charting a path for microbial conservation in the IUCN: report on “Conservation in a Microbial World” meeting in San Diego, CA, May 2025
No full textIn May 2025, the “Conservation in a Microbial World” conference was hosted at UC San Diego’s Scripps Institution of Oceanography to address the lack of microbial representation in international conservation frameworks (i.e. International Union for Conservation of Nature—IUCN). Organized by Drs. Redford, Gilbert, Friedman, and Rodríguez, the meeting explored the importance of the microbial world in conservation and what can be done to include microbes in global conservation. The meeting gathered together experts in fungi, bacteria and viruses with backgrounds in climate science, genomics, terrestrial and aquatic ecosystems, and human health, spanning research, industry, conservation, and policy. Key themes included: how to communicate the importance of microbes, how to conserve microbes themselves, and how microbes could be integrated into existing conservation. The primary outcome was the launch of the IUCN Species Survival Commission—Microbial Conservation Specialist Group (MCSG). This initiative marks a pivotal step toward incorporating microbial life into global biodiversity conservation policy and practice.This meeting would not have been made possible without generous funding from the Gordon and Betty Moore Foundation. Continuing financial support from the Moore Foundation and Applied Microbiology International will allow the goals of the Microbial Conservation Specialist Group to gain real traction moving forward. Much work remains, but as one participant observed, “It’s difficult to preserve the things that we don’t know exist, but that shouldn’t prevent us from trying.”
The meeting was sponsored by the Gordon and Betty Moore Foundation
SAM4EM: Efficient memory-based two stage prompt-free segment anything model adapter for complex 3D neuroscience electron microscopy stacks
No full textWe present SAM4EM, a novel approach for 3D segmentation of complex neural structures in electron microscopy (EM) data by leveraging the Segment Anything Model (SAM) alongside advanced fine-tuning strategies. Our contributions include the development of a prompt-free adapter for SAM using two stage mask decoding to automatically generate prompt embeddings, a dual-stage fine-tuning method based on Low-Rank Adaptation (LoRA) for enhancing segmentation with limited annotated data, and a 3D memory attention mechanism to ensure segmentation consistency across 3D stacks. We further release a unique benchmark dataset for the segmentation of astrocytic processes and synapses. We evaluated our method on challenging neuroscience segmentation benchmarks, specifically targeting mitochondria, glia, and synapses, with significant accuracy improvements over state-of-the-art (SOTA) methods, including recent SAM-based adapters developed for the medical domain and other vision transformer-based approaches. Experimental results indicate that our approach outperforms existing solutions in the segmentation of complex processes like glia and post-synaptic densities. Our code and models are available at https://github.com/Uzshah/SAM4EM.This publication was funded by the PPM7th Cycle grant (PPM 07-0409-240041, AMAL-For-Qatar) from
the Qatar National Research Fund, a member of the Qatar Foundation. The findings herein reflect the work and are solely the
responsibility, of the author
Leveraging solvent affinity for phase-selective doping to enhance doping efficiency in a DPP-based n-type conjugated polymer.
No full textPhase-selective doping holds promise for optimizing the electronic properties of conjugated polymers. This approach has been observed on a selection of polymer hosts and dopants, but it is unclear how to translate such an effect to other materials. In our study, we show that in coprocessing techniques, the affinity of the solvent to the polymer host, determined by Hansen solubility parameters, influences whether a dopant is primarily located within the amorphous phase or distributed across both the crystalline and amorphous phases. We used tetrabutylammonium fluoride (TBAF) as the dopant, an n-type DPP-based polymer host (2PyDPP-2CNTVT), and five common solvents. Optical, electrical, and structural characterizations reveal that solvents with high polymer affinity lead to low solid-state order and high Seebeck coefficients S but exhibit low electrical conductivity σ. Alternatively, solvents with partial affinity for both polymer and dopant produce films with mixed phases, where the dopant concentrates in amorphous regions. These films retain a higher structural order at elevated doping levels, achieving electrical conductivity σ, an order of magnitude higher than high-affinity solvents at similar doping concentrations. Our findings propose a solvent-centric strategy for phase-selective doping, potentially fine-tuning the electronic properties for various applications.This publication is based upon work supported by the King Abdullah University of Science and Technology (KAUST) and Office of Research Administration (ORA) under award no. ORA-CRG2022-4668. A. S. acknowledges KAUST support under award no: OSR-CARF/CCF-3079
High-Nuclearity Copper Molecular Catalysts for Electrocatalytic CO-to-Acetate Conversion
No full textLigand-modified metal nanoclusters (NCs) have emerged as candidate materials for catalysis owing to their well-defined yet tunable structure and their metal centers’ high nuclearity. We posited that NC-based catalytic behavior will depend on ligand properties, the accessibility of active sites, and their atomic configuration. We synthesized a series of Cu NC-based catalysts, tuned local hydrophobicity through ligand adjustment, balanced the ligand coverage and active site exposure, and found that we were, in this way, able to engender efficient electrosynthesis of acetate via CO electroreduction. Computation and operando spectroscopy show that asymmetric Cu–Cu sites, which determine the CO binding strength, impact the bifurcation step after C–C coupling. The best of these catalysts, Cu13Nap, achieved an acetate Faradaic efficiency (FE) of 86% and an energy efficiency of 29% in a 5 bar system, exceeding the single C2+ FE of <50% previously achieved by NC-based catalysts.We acknowledge support of the King Abdullah University of Science and Technology (KAUST), Saudi Arabia, Office of Sponsored Research (URF/1/5033-01-01), and the Government of Canada’s New Frontiers in Research Fund (NFRF), NFRFT-2022-0019. L.F. acknowledges the financial support of the Swedish Research Council for an International Postdoc grant (2021-00282). We thank I. Munroe for assistance in the manuscript editing
High-Performance Statistical Computing ( <scp>HPSC</scp> ): Challenges, Opportunities, and Future Directions
No full textWe recognize the emergence of a statistical computing community focused on working with large computing platforms and producing software and applications that exemplify high-performance statistical computing (HPSC). The statistical computing (SC) community develops software that is widely used across disciplines. However, it remains largely absent from the high-performance computing (HPC) landscape, particularly on platforms such as those featured on the www.top500.org or Green500 lists. Many disciplines already participate in HPC, mostly centered around simulation science, although data-focused efforts under the artificial intelligence (AI) label are gaining popularity. Bridging this gap requires both community adaptation and technical innovation to align statistical methods with modern HPC technologies. We can accelerate progress in fast and scalable statistical applications by building strong connections between the SC and HPC communities. We present a brief history of SC, a vision for how its strengths can contribute to statistical science in the HPC environment (such as HPSC), the challenges that remain, and the opportunities currently available, culminating in a possible roadmap toward a thriving HPSC community
Hydrogen jet characteristics with an outwardly opening piezo injector
No full textHydrogen is a promising renewable energy vector for decarbonizing transportation, offering high energy density and clean combustion. In this study, experimental investigation of hydrogen jets discharged from an outwardly opening piezo injector (Bosch HDEV4) was conducted. The HDEV4 injector was suggested by the Engine Combustion Network (ECN) to investigate hydrogen injection and jet mixing dynamics for hydrogen spark-ignition direct injection engines. This choice was based on the injector's compatibility with hydrogen, its comprehensive characterization in literature, and its suitability for open-access research. High-speed z-type Schlieren imaging was employed to visualize jet structure and time-resolved evolution within a constant pressure flow vessel. The injector needle lift was measured using high-magnification, high-speed photography. The needle lift shows a positive correlation with increased driving voltage and some fluctuations after reaching a fully opened position. Jet-projected area and tip penetration decrease monotonically with ambient pressure. The comparison of the theoretically predicted and experimentally measured injected mass across varying injection pressures enabled estimating a discharge coefficient between 0.6 and 0.8. Increasing the injection pressure speeds up penetration due to the higher mass flow injected and increased jet momentum. However, the jet penetration pattern remained almost unaltered across various injector heating temperatures. The jet self-similarity parameter enables scaling correlations for non-dimensional penetration across voltages, revealing a two-phase linear relationship between needle damping and stable stages. This study provides valuable experimental data for the ECN community, supporting comparative studies and serving as a reference for validating computational fluid dynamics simulations.The work was supported by the Saudi Aramco Research and Development Center FUELCOM program under Master Research Agreement Number 6600024505/01. The authors thank Professor Gilles Lubineau and Ph.D. candidate Hassan Al-kady from the Mechanics of Composites for Energy and Mobility Lab. at KAUST for providing x-ray tomography of the injectors used in the current study
Rising occurrence of compound droughts and heatwaves in the Arabian Peninsula linked to large-scale atmospheric circulations.
No full textCompound droughts and heatwaves (CDHWs) have emerged as a critical threat to global populations, with serious implications for water resources, agriculture, and ecosystems. Despite their significance, the occurrence and drivers of CDHW events remain inadequately understood, particularly in arid regions such as the Arabian Peninsula (AP). Using fifth-generation ECMWF reanalysis (ERA5) data, we analyze the spatiotemporal dynamics and variability of CDHW events over the AP from 1980 to 2023. Our results reveal a substantial increase in the frequency and intensity of CDHW events over this period, with a fourfold increase in the affected area since 1998. The northern parts of the AP are particularly vulnerable to CDHW events, due to the strong synchronization of heatwaves and droughts, derived by dominant role of temperature. We identify a robust linkage between CDHWs and large-scale circulation indices, notably the positive phase of Atlantic Multidecadal Oscillation (AMO) and the negative phase of Pacific Decadal Oscillation (PDO) have favored pronounced CDHWs over the past two decades. The AMO variability primary influences the mid-tropospheric pressure system leading to substantial temperature variations in the AP. However, the PDO largely impacts upper-level zonal winds, which modulate the strength of the subtropical westerly jet and subsequently changes the AP precipitation. Our findings highlight the urgent need for adaptive strategies and resilient measures to mitigate the adverse effects of CDHWs in a rapidly changing climate.This research work was supported by the Climate Change Center, an initiative of the National Center for Meteorology (NCM), Kingdom of Saudi Arabia (Ref No: RGC/03/4829-01-01). We are thankful to all of the agencies for making datasets free available
An EDCC-EMD analysis-based network for DAS VSP data denoising in frequency domain
No full textDistributed acoustic sensing (DAS) has rapidly emerged as a transformative technology in seismic exploration, particularly in vertical seismic profiles (VSP). However, the acquired VSP data suffer from strong coherent DAS coupling noise and random noise. Current deep learning denoising methods, dependent on noise labels derived from conventional denoising techniques, fall short in addressing the unique noise properties inherent in DAS data. To address this challenge, we propose an exponential decay curve-constrained empirical mode decomposition (EDCC-EMD) analysis-based supervised denoising network. Our method begins with extracting the initial noise from the field DAS VSP data through the traditional EMD method. Despite containing some signal leakage, this noise is further processed through EMD to derive intrinsic mode functions (IMFs). We, then, analyze the correlation coefficients between these IMFs and the initial noise, applying an exponential decay curve (EDC) law to isolate pure noise. This refined noise data serves as accurate labels, enhancing the denoising network's precision. Meanwhile, most of the methods usually consider the t-x domain features and ignore the important frequency-domain features. Consequently, we train our network with frequency-domain data instead of time domain data, capitalizing on the more distinct separation of noise and signal characteristics, thereby facilitating more effective noise-signal discrimination. The experimental results demonstrate that our method significantly enhances the denoising performance and successfully recovers weak signals.This work is supported by the National Natural Science Foundation of China (Nos. 42404140, 42130808) and the National Key Research and Development Program of China under grant 2021YFA0716802. We thank Professor Xiangfang Zeng from Innovation Academy for Precision measurement Science and Technology, Chinese Academy of Sciences for his valuable discussions
NHC-Cracker: A Platform for the In Silico Engineering of <i>N</i>-Heterocyclic Carbenes for Diverse Chemical Applications
No full textWe present an in silico workflow to streamline the identification of promising N-heterocyclic carbenes (NHCs) as ligands in metal catalysis or as catalysts in organocatalysis. Central to this workflow is the NHC-cracker database, which contains over 200 descriptors for 1781 nonredundant NHCs, each documented as an NHC-metal complex in the Cambridge Structural Database. To demonstrate its utility, we applied it to two catalytic problems using literature data. First, we analyzed 21 Ru-NHC complexes active in the ethenolysis of cyclic olefins. An MLR (multivariate linear regression) model trained on 11 Ru complexes based on NHCs in NHC-cracker successfully rationalized the behavior of the remaining 10 complexes. Second, we examined an Ir-Ni dual-catalyzed Csp2-Csp3 cross-coupling reaction involving five experimentally tested NHC skeletons. Using a multiscale workflow, we created DFT-based data sets to train two MLR models: one for productive substrate activation and another for detrimental NHC dimerization. Consistent with experiments, the models identified oxazoles as reactive, while benzimidazoles, triazoles, thiazoles, and untested cyclic (alkyl)(amino)carbenes were predicted as nonreactive. Experimental validation confirmed the latter’s lack of productive substrate activation, supporting the proposed mechanistic scenario
Deciphering the stability of two-dimensional III-V semiconductors: Building blocks and their versatile assembly
No full textTwo-dimensionalization unlocks the unique and superior physical properties of materials, but extending it to nonlayered crystals is challenging. Using density functional theory and machine learning, we unveil a universal rule for creating stable two-dimensional counterparts of traditional high-performance III-V semiconductors, i.e., the versatile assembly of building blocks originating from orbital hybridization and electron transfers adhering to the electron counting rule. Akin to LEGO construction, the various building blocks are arranged in different configurations, introducing diverse two-dimensional structures with higher energetic stability than previous structures. Regression analysis reveals the energies of these structures as a linear superposition of the energies of their building blocks, further confirming the LEGO concept. Notably, the predicted two-dimensional GaSb exhibits a hole mobility (~10
8
square centimeters per volt per second) that far surpasses that of graphene (2 × 10
5
square centimeters per volt per second). This study highlights the expansion of nonlayered materials into two dimensions and the potential of two-dimensional confinement in traditional materials.
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