bluuebunny/crossref_metadata_2025_split huggingface dataset:
Superconducting nonlinear transport in optically driven high-temperature K3C60
Authors: Wang E. , Adelinia J. D. , Chavez-Cervantes M. , Matsuyama T. , Fechner M. , Buzzi M. , Meier G. , Cavalleri A. 
Published: November 2023

AbstractOptically driven quantum materials exhibit a variety of non-equilibrium functional phenomena, which to date have been primarily studied with ultrafast optical, X-Ray and photo-emission spectroscopy. However, little has been done to characterize their transient electrical responses, which are directly associated with the functionality of these materials. Especially interesting are linear and nonlinear current-voltage characteristics at frequencies below 1 THz, which are not easily measured at picosecond temporal resolution. Here, we report on ultrafast transport measurements in photo-excited K3C60. Thin films of this compound were connected to photo-conductive switches with co-planar waveguides. We observe characteristic nonlinear current-voltage responses, which in these films point to photo-induced granular superconductivity. Although these dynamics are not necessarily identical to those reported for the powder samples studied so far, they provide valuable new information on the nature of the light-induced superconducting-like state above equilibrium Tc. Furthermore, integration of non-equilibrium superconductivity into optoelectronic platforms may lead to integration in high-speed devices based on this effect.
Encystation stimuli sensing is mediated by adenylate cyclase AC2-dependent cAMP signaling in Giardia
Authors: Shih Han-Wei , Alas Germain C. M. , Paredez Alexander R. 
Published: November 2023

AbstractProtozoan parasites use cAMP signaling to precisely regulate the place and time of developmental differentiation, yet it is unclear how this signaling is initiated. Encystation of the intestinal parasite Giardia lamblia can be activated by multiple stimuli, which we hypothesize result in a common physiological change. We demonstrate that bile alters plasma membrane fluidity by reducing cholesterol-rich lipid microdomains, while alkaline pH enhances bile function. Through depletion of the cAMP producing enzyme Adenylate Cyclase 2 (AC2) and the use of a newly developed Giardia-specific cAMP sensor, we show that AC2 is necessary for encystation stimuli-induced cAMP upregulation and activation of downstream signaling. Conversely, over expression of AC2 or exogenous cAMP were sufficient to initiate encystation. Our findings indicate that encystation stimuli induce membrane reorganization, trigger AC2-dependent cAMP upregulation, and initiate encystation-specific gene expression, thereby advancing our understanding of a critical stage in the life cycle of a globally important parasite.
Nigrostriatal dopamine modulates the striatal-amygdala pathway in auditory fear conditioning
Authors: Chen Allen P. F. , Chen Lu , Shi Kaiyo W. , Cheng Eileen , Ge Shaoyu , Xiong Qiaojie 
Published: November 2023

AbstractThe auditory striatum, a sensory portion of the dorsal striatum, plays an essential role in learning and memory. In contrast to its roles and underlying mechanisms in operant conditioning, however, little is known about its contribution to classical auditory fear conditioning. Here, we reveal the function of the auditory striatum in auditory-conditioned fear memory. We find that optogenetically inhibiting auditory striatal neurons impairs fear memory formation, which is mediated through the striatal-amygdala pathway. Using calcium imaging in behaving mice, we find that auditory striatal neuronal responses to conditioned tones potentiate across memory acquisition and expression. Furthermore, nigrostriatal dopaminergic projections plays an important role in modulating conditioning-induced striatal potentiation. Together, these findings demonstrate the existence of a nigro-striatal-amygdala circuit for conditioned fear memory formation and expression.
Modulated Kondo screening along magnetic mirror twin boundaries in monolayer MoS2
Authors: van Efferen Camiel , Fischer Jeison , Costi Theo A. , Rosch Achim , Michely Thomas , Jolie Wouter 
Published: November 2023

AbstractWhen a single electron is confined to an impurity state in a metal, a many-body resonance emerges at the Fermi energy if the electron bath screens the impurity’s magnetic moment. This is the Kondo effect, originally introduced to explain the abnormal resistivity behaviour in bulk magnetic alloys, and it has been realized in many quantum systems over the past decades, ranging from heavy-fermion lattices down to adsorbed single atoms. Here we describe a Kondo system that allows us to experimentally resolve the spectral function consisting of impurity levels and a Kondo resonance in a large Kondo temperature range, as well as their spatial modulation. Our approach is based on a discrete half-filled quantum confined state within a MoS2 grain boundary, which—in conjunction with numerical renormalization group calculations—enables us to test the predictive power of the Anderson model that is the basis of the microscopic understanding of Kondo physics.
Circular extrachromosomal DNA promotes tumor heterogeneity in high-risk medulloblastoma
Authors: Chapman Owen S. , Luebeck Jens , Sridhar Sunita , Wong Ivy Tsz-Lo , Dixit Deobrat , Wang Shanqing , Prasad Gino , Rajkumar Utkrisht , Pagadala Meghana S. , Larson Jon D. , He Britney Jiayu , Hung King L. , Lange Joshua T. , Dehkordi Siavash R. , Chandran Sahaana , Adam Miriam , Morgan Ling , Wani Sameena , Tiwari Ashutosh , Guccione Caitlin , Lin Yingxi , Dutta Aditi , Lo Yan Yuen , Juarez Edwin , Robinson James T. , Korshunov Andrey , Michaels John-Edward A. , Cho Yoon-Jae , Malicki Denise M. , Coufal Nicole G. , Levy Michael L. , Hobbs Charlotte , Scheuermann Richard H. , Crawford John R. , Pomeroy Scott L. , Rich Jeremy N. , Zhang Xinlian , Chang Howard Y. , Dixon Jesse R. , Bagchi Anindya , Deshpande Aniruddha J. , Carter Hannah , Fraenkel Ernest , Mischel Paul S. , Wechsler-Reya Robert J. , Bafna Vineet , Mesirov Jill P. , Chavez Lukas 
Published: November 2023

AbstractCircular extrachromosomal DNA (ecDNA) in patient tumors is an important driver of oncogenic gene expression, evolution of drug resistance and poor patient outcomes. Applying computational methods for the detection and reconstruction of ecDNA across a retrospective cohort of 481 medulloblastoma tumors from 465 patients, we identify circular ecDNA in 82 patients (18%). Patients with ecDNA-positive medulloblastoma were more than twice as likely to relapse and three times as likely to die within 5 years of diagnosis. A subset of tumors harbored multiple ecDNA lineages, each containing distinct amplified oncogenes. Multimodal sequencing, imaging and CRISPR inhibition experiments in medulloblastoma models reveal intratumoral heterogeneity of ecDNA copy number per cell and frequent putative ‘enhancer rewiring’ events on ecDNA. This study reveals the frequency and diversity of ecDNA in medulloblastoma, stratified into molecular subgroups, and suggests copy number heterogeneity and enhancer rewiring as oncogenic features of ecDNA.
A helical fulcrum in eIF2B coordinates allosteric regulation of stress signaling
Authors: Lawrence Rosalie E. , Shoemaker Sophie R. , Deal Aniliese , Sangwan Smriti , Anand Aditya A. , Wang Lan , Marqusee Susan , Walter Peter 
Published: November 2023

AbstractThe integrated stress response (ISR) enables cells to survive a variety of acute stresses, but chronic activation of the ISR underlies age-related diseases. ISR signaling downregulates translation and activates expression of stress-responsive factors that promote return to homeostasis and is initiated by inhibition of the decameric guanine nucleotide exchange factor eIF2B. Conformational and assembly transitions regulate eIF2B activity, but the allosteric mechanisms controlling these dynamic transitions and mediating the therapeutic effects of the small-molecule ISR inhibitor ISRIB are unknown. Using hydrogen–deuterium exchange–mass spectrometry and cryo-electron microscopy, we identified a central α-helix whose orientation allosterically coordinates eIF2B conformation and assembly. Biochemical and cellular signaling assays show that this ‘switch-helix’ controls eIF2B activity and signaling. In sum, the switch-helix acts as a fulcrum of eIF2B conformational regulation and is a highly conserved actuator of ISR signal transduction. This work uncovers a conserved allosteric mechanism and unlocks new therapeutic possibilities for ISR-linked diseases.
Harmonized geospatial data to support infrastructure siting feasibility planning for energy system transitions
Authors: Vernon Chris R. , Mongird Kendall , Nelson Kristian D. , Rice Jennie S. 
Published: November 2023

AbstractClimate change, energy system transitions, and socioeconomic change are compounding influences affecting the growth of electricity demand. While energy efficiency initiatives and distributed resources can address a significant amount of this demand, the United States will likely still need new utility-scale generation resources. The energy sector uses capacity expansion planning models to determine the aggregate need for new generation, but these models are typically at the state or regional scale and are not equipped to address the wide range of location- and technology-specific issues that are increasingly a factor in power plant siting. To help address these challenges, we have developed the Geospatial Raster Input Data for Capacity Expansion Regional Feasibility (GRIDCERF) data package, a high-resolution product to evaluate siting suitability for renewable and non-renewable power plants in the conterminous United States. GRIDCERF offers 264 suitability layers for use with 56 power plant technologies in a harmonized format that can be easily ingested by geospatially-enabled modeling software allowing for customization to robustly address science objectives when evaluating varying future conditions.
Leveraging three-tier deep learning model for environmental cleaner plants production
Authors: Tarek Zahraa , Elhoseny Mohamed , Alghamdi Mohamemd I. , EL-Hasnony Ibrahim M. 
Published: November 2023

AbstractThe world's population is expected to exceed 9 billion people by 2050, necessitating a 70% increase in agricultural output and food production to meet the demand. Due to resource shortages, climate change, the COVID-19 pandemic, and highly harsh socioeconomic predictions, such a demand is challenging to complete without using computation and forecasting methods. Machine learning has grown with big data and high-performance computers technologies to open up new data-intensive scientific opportunities in the multidisciplinary agri-technology area. Throughout the plant's developmental period, diseases and pests are natural disasters, from seed production to seedling growth. This paper introduces an early diagnosis framework for plant diseases based on fog computing and edge environment by IoT sensors measurements and communication technologies. The effectiveness of employing pre-trained CNN architectures as feature extractors in identifying plant illnesses has been studied. As feature extractors, standard pre-trained CNN models, AlexNet are employed. The obtained in-depth features are eliminated by proposing a revised version of the grey wolf optimization (GWO) algorithm that approved its efficiency through experiments. The features subset selected were used to train the SVM classifier. Ten datasets for different plants are utilized to assess the proposed model. According to the findings, the proposed model achieved better outcomes for all used datasets. As an average for all datasets, the accuracy of the proposed model is 93.84 compared to 85.49, 87.89, 87.04 for AlexNet, GoogleNet, and the SVM, respectively.
Micro-nano oxygenated irrigation improves the yield and quality of greenhouse cucumbers under-film drip irrigation
Authors: Ouyang Zan , Tian Juncang , Yan Xinfang , Yang Zhenfeng 
Published: November 2023

AbstractTo study the influence mechanism of micro-nano oxygenated irrigation (MNOI) on greenhouse fruit cucumber in arid and semi-arid cold regions, the yield and quality of greenhouse fruit cucumber were evaluated and verified based on 2 years of observation data. Taking fruit cucumber in Ningxia solar greenhouse as the research object, three dissolved oxygen (DO) levels of MNOI (DO; 6, 7.5, and 9 mg L−1, O1, O2, and O3, respectively) and non-oxygenated irrigation (CK, 4 mg L−1) were set up as the control treatment. Through comparative design, the influence mechanism of different levels of aerobic irrigation on the yield and quality of greenhouse fruit cucumber was studied. The main indicators of fruit cucumber yield and quality increased with dissolved oxygen in irrigation water from 4 to 9 mg L−1. In spring–summer (autumn–winter), compared with CK, the leaf area index (LAI) and net photosynthetic rate (A) increased by 28.83% (28.77%) and 44.90% (35.00%), respectively, and Vitamin C, soluble protein, soluble sugar, soluble solids and total acid content increased by 100.00% (51.88%), 37.78% (61.11%), 34.17% (54.17%), 37.07% (78.72%) and 26.92% (30.67%) respectively, while nitrate content decreased by 44.88% (51.15%), and dry matter accumulation (DMA), soil respiration rate (SRR), microbial carbon (MC), and microbial nitrogen (MN) increased by 49.81% (127.25%), 55.22% (110.34%), 117.50% (90.91%) and 70.37% (74.42%) respectively, and yield, irrigation water use efficiency (IWUE) and soil oxygen content (SO) increased by 22.47% (28.04%), 22.39% (28.05%) and 33.21% (35.33%) respectively. A model of DO in irrigation water and SO was established and the applicability of the model was verified with an average relative error of 2% (less than 5%). MNOI increased SO and soil enzyme activity, enriched soil microorganisms, improved soil microenvironment, promoted water nutrient uptake and growth of root system, increased chlorophyll, photosynthesis and DMA, which improved fruit cucumber yield and quality, and the better DO concentration in irrigation water is 9 mg L−1. The research results provide theoretical support for regulating soil water, fertilizer and air environment, and at the same time, provide feasible ways to improve the quality and efficiency of crops in arid and semi-arid cold regions.
Identification of endoplasmic reticulum stress-related signature characterizes the tumor microenvironment and predicts prognosis in lung adenocarcinoma
Authors: Wan Li , Chen Zhike , Yang Jian , Wu Gaotian , Xu Yao , Cui Jian , Zhao Xueping 
Published: November 2023

AbstractLung adenocarcinoma (LUAD) remains one of the most lethal malignancies worldwide, with a high mortality rate and unfavorable prognosis. Endoplasmic reticulum (ER) stress is a key regulator of tumour growth, metastasis, and the response to chemotherapy, targeted therapies and immune response. It acts via responding to misfolded proteins and triggering abnormal activation of ER stress sensors and downstream signalling pathways. Notably, the expression patterns of ER-stress-related-genes (ERSRGs) are indicative of survival outcomes, especially in the context of immune infiltration. Through consensus clustering of prognosis-associated ERSRGs, we delineated two distinct LUAD subtypes: Cluster 1 and Cluster 2. Comprehensive analyses revealed significant disparities between these subtypes in terms of prognosis, immune cell infiltration, and tumor progression. Leveraging the robustness of LASSO regression and Multivariate stepwise regression, we constructed and validated an ER Stress-associated risk signature for LUAD. This signature underwent assessments for its prognostic value, correlation with clinical attributes, and interaction within the tumour immune microenvironment. By integrating this signature with multivariate cox analysis of distinct pathological stages, we devised an enhanced nomogram, validated through various statistical metrics, with an area under the curve for overall survival at 1, 3, and 5 years post-diagnosis being 0.79, 0.80, and 0.81, respectively. In conclusion, our findings introduce a composite signature of 11 pivotal ERSRGs, holding promise as a potent prognostic tool for LUAD, and offering insights for immunotherapeutic and targeted intervention strategies.
Singly-excited resonant open quantum system Tavis-Cummings model with quantum circuit mapping
Authors: Marinkovic Marina Krstic , Radulaski Marina 
Published: November 2023

AbstractTavis-Cummings (TC) cavity quantum electrodynamical effects, describing the interaction of N atoms with an optical resonator, are at the core of atomic, optical and solid state physics. The full numerical simulation of TC dynamics scales exponentially with the number of atoms. By restricting the open quantum system to a single excitation, typical of experimental realizations in quantum optics, we analytically solve the TC model with an arbitrary number of atoms with linear complexity. This solution allows us to devise the Quantum Mapping Algorithm of Resonator Interaction with N Atoms (Q-MARINA), an intuitive TC mapping to a quantum circuit with linear space and time scaling, whose N+1 qubits represent atoms and a lossy cavity, while the dynamics is encoded through 2N entangling gates. Finally, we benchmark the robustness of the algorithm on a quantum simulator and superconducting quantum processors against the quantum master equation solution on a classical computer.
Genome interpretation in a federated learning context allows the multi-center exome-based risk prediction of Crohn’s disease patients
Authors: Raimondi Daniele , Chizari Haleh , Verplaetse Nora , Löscher Britt-Sabina , Franke Andre , Moreau Yves 
Published: November 2023

AbstractHigh-throughput sequencing allowed the discovery of many disease variants, but nowadays it is becoming clear that the abundance of genomics data mostly just moved the bottleneck in Genetics and Precision Medicine from a data availability issue to a data interpretation issue. To solve this empasse it would be beneficial to apply the latest Deep Learning (DL) methods to the Genome Interpretation (GI) problem, similarly to what AlphaFold did for Structural Biology. Unfortunately DL requires large datasets to be viable, and aggregating genomics datasets poses several legal, ethical and infrastructural complications. Federated Learning (FL) is a Machine Learning (ML) paradigm designed to tackle these issues. It allows ML methods to be collaboratively trained and tested on collections of physically separate datasets, without requiring the actual centralization of sensitive data. FL could thus be key to enable DL applications to GI on sufficiently large genomics data. We propose FedCrohn, a FL GI Neural Network model for the exome-based Crohn’s Disease risk prediction, providing a proof-of-concept that FL is a viable paradigm to build novel ML GI approaches. We benchmark it in several realistic scenarios, showing that FL can indeed provide performances similar to conventional ML on centralized data, and that collaborating in FL initiatives is likely beneficial for most of the medical centers participating in them.
Pachychoroid neovasculopathy versus macular neovascularization in age-related macular degeneration with and without shallow irregular pigment epithelial detachment
Authors: Riazi-Esfahani Hamid , Asadi Khameneh Esmaeil , Ghassemi Fariba , Mehrabi Bahar Mohammadreza , torkashvand Ali , Mahmoudi Alireza , Husein Ahmed Ahmed , Faghihi Shahin , Rahimi Masoud , Akbarzadeh Ali , Faghihi Hooshang , Khalili Pour Elias 
Published: November 2023

AbstractTo compare the choroidal neovascular features of individuals with pachychoroid neovasculopathy (PNV) and neovascular age-related macular degeneration (nAMD) with and without shallow irregular pigment epithelial detachment (SIPED). Using optical coherence tomography angiography, the choroidal neovascular complexes of 27 patients with PNV, 34 patients with nAMD and SIPED, and 15 patients with nAMD without SIPED were analyzed with FIJI and AngioTool software. PNV compared to nAMD with SIPED had a greater vessel percentage area (P = 0.034), junction density (P = 0.045), average vessel length (P < 0.001), and fractal dimension (P < 0.001). PNV, compared to nAMD without SIPED, had a greater total vessel length (P = 0.002), total number of junctions (P < 0.001), junction density (P = 0.034), and fractal dimension (P = 0.005). nAMD with SIPED, compared to nAMD without SIPED, had greater vessel area, total number of junctions, total vessel length, and average vessel length (all P values < 0.001). Patients with nAMD plus SIPED and individuals with nAMD without SIPED have similar fractal dimension values (P = 0.703). Biomarkers of choroidal neovascular complexity, such as fractal dimension, can be used to differentiate PNV from nAMD with or without SIPED.
Computational design of highly efficient thermostable MHET hydrolases and dual enzyme system for PET recycling
Authors: Zhang Jun , Wang Hongzhao , Luo Zhaorong , Yang Zhenwu , Zhang Zixuan , Wang Pengyu , Li Mengyu , Zhang Yi , Feng Yue , Lu Diannan , Zhu Yushan 
Published: November 2023

AbstractRecently developed enzymes for the depolymerization of polyethylene terephthalate (PET) such as FAST-PETase and LCC-ICCG are inhibited by the intermediate PET product mono(2-hydroxyethyl) terephthalate (MHET). Consequently, the conversion of PET enzymatically into its constituent monomers terephthalic acid (TPA) and ethylene glycol (EG) is inefficient. In this study, a protein scaffold (1TQH) corresponding to a thermophilic carboxylesterase (Est30) was selected from the structural database and redesigned in silico. Among designs, a double variant KL-MHETase (I171K/G130L) with a similar protein melting temperature (67.58 °C) to that of the PET hydrolase FAST-PETase (67.80 °C) exhibited a 67-fold higher activity for MHET hydrolysis than FAST-PETase. A fused dual enzyme system comprising KL-MHETase and FAST-PETase exhibited a 2.6-fold faster PET depolymerization rate than FAST-PETase alone. Synergy increased the yield of TPA by 1.64 fold, and its purity in the released aromatic products reached 99.5%. In large reaction systems with 100 g/L substrate concentrations, the dual enzyme system KL36F achieved over 90% PET depolymerization into monomers, demonstrating its potential applicability in the industrial recycling of PET plastics. Therefore, a dual enzyme system can greatly reduce the reaction and separation cost for sustainable enzymatic PET recycling.
Enhancing antibody affinity through experimental sampling of non-deleterious CDR mutations predicted by machine learning
Authors: Clark Thomas , Subramanian Vidya , Jayaraman Akila , Fitzpatrick Emmett , Gopal Ranjani , Pentakota Niharika , Rurak Troy , Anand Shweta , Viglione Alexander , Raman Rahul , Tharakaraman Kannan , Sasisekharan Ram 
Published: November 2023

AbstractThe application of machine learning (ML) models to optimize antibody affinity to an antigen is gaining prominence. Unfortunately, the small and biased nature of the publicly available antibody-antigen interaction datasets makes it challenging to build an ML model that can accurately predict binding affinity changes due to mutations (ΔΔG). Recognizing these inherent limitations, we reformulated the problem to ask whether an ML model capable of classifying deleterious vs non-deleterious mutations can guide antibody affinity maturation in a practical setting. To test this hypothesis, we developed a Random Forest classifier (Antibody Random Forest Classifier or AbRFC) with expert-guided features and integrated it into a computational-experimental workflow. AbRFC effectively predicted non-deleterious mutations on an in-house validation dataset that is free of biases seen in the publicly available training datasets. Furthermore, experimental screening of a limited number of predictions from the model (<10^2 designs) identified affinity-enhancing mutations in two unrelated SARS-CoV-2 antibodies, resulting in constructs with up to 1000-fold increased binding to the SARS-COV-2 RBD. Our findings indicate that accurate prediction and screening of non-deleterious mutations using machine learning offers a powerful approach to improving antibody affinity.