bluuebunny/crossref_metadata_2025_split huggingface dataset:
Practical Hamiltonian learning with unitary dynamics and Gibbs states
Authors: Gu Andi , Cincio Lukasz , Coles Patrick J. 
Published: January 2024

AbstractWe study the problem of learning the parameters for the Hamiltonian of a quantum many-body system, given limited access to the system. In this work, we build upon recent approaches to Hamiltonian learning via derivative estimation. We propose a protocol that improves the scaling dependence of prior works, particularly with respect to parameters relating to the structure of the Hamiltonian (e.g., its locality k). Furthermore, by deriving exact bounds on the performance of our protocol, we are able to provide a precise numerical prescription for theoretically optimal settings of hyperparameters in our learning protocol, such as the maximum evolution time (when learning with unitary dynamics) or minimum temperature (when learning with Gibbs states). Thanks to these improvements, our protocol has practical scaling for large problems: we demonstrate this with a numerical simulation of our protocol on an 80-qubit system.
Efficient and robust estimation of many-qubit Hamiltonians
Authors: Stilck França Daniel , Markovich Liubov A. , Dobrovitski V. V. , Werner Albert H. , Borregaard Johannes 
Published: January 2024

AbstractCharacterizing the interactions and dynamics of quantum mechanical systems is an essential task in developing quantum technologies. We propose an efficient protocol based on the estimation of the time-derivatives of few qubit observables using polynomial interpolation for characterizing the underlying Hamiltonian dynamics and Markovian noise of a multi-qubit device. For finite range dynamics, our protocol exponentially relaxes the necessary time-resolution of the measurements and quadratically reduces the overall sample complexity compared to previous approaches. Furthermore, we show that our protocol can characterize the dynamics of systems with algebraically decaying interactions. The implementation of the protocol requires only the preparation of product states and single-qubit measurements. Furthermore, we improve a shadow tomography method for quantum channels that is of independent interest and discuss the robustness of the protocol to various errors. This protocol can be used to parallelize the learning of the Hamiltonian, rendering it applicable for the characterization of both current and future quantum devices.
Estimating the heritability of SARS-CoV-2 susceptibility and COVID-19 severity
Authors: Brown Kathleen LaRow , Ramlall Vijendra , Zietz Michael , Gisladottir Undina , Tatonetti Nicholas P. 
Published: January 2024

AbstractSARS-CoV-2 has infected over 340 million people, prompting therapeutic research. While genetic studies can highlight potential drug targets, understanding the heritability of SARS-CoV-2 susceptibility and COVID-19 severity can contextualize their results. To date, loci from meta-analyses explain 1.2% and 5.8% of variation in susceptibility and severity respectively. Here we estimate the importance of shared environment and additive genetic variation to SARS-CoV-2 susceptibility and COVID-19 severity using pedigree data, PCR results, and hospitalization information. The relative importance of genetics and shared environment for susceptibility shifted during the study, with heritability ranging from 33% (95% CI: 20%-46%) to 70% (95% CI: 63%-74%). Heritability was greater for days hospitalized with COVID-19 (41%, 95% CI: 33%-57%) compared to shared environment (33%, 95% CI: 24%-38%). While our estimates suggest these genetic architectures are not fully understood, the shift in susceptibility estimates highlights the challenge of estimation during a pandemic, given environmental fluctuations and vaccine introduction.
Gene drive and genetic sex conversion in the global agricultural pest Ceratitis capitata
Authors: Meccariello Angela , Hou Shibo , Davydova Serafima , Fawcett James Daniel , Siddall Alexandra , Leftwich Philip T. , Krsticevic Flavia , Papathanos Philippos Aris , Windbichler Nikolai 
Published: January 2024

AbstractHoming-based gene drives are recently proposed interventions promising the area-wide, species-specific genetic control of harmful insect populations. Here we characterise a first set of gene drives in a tephritid agricultural pest species, the Mediterranean fruit fly Ceratitis capitata (medfly). Our results show that the medfly is highly amenable to homing-based gene drive strategies. By targeting the medfly transformer gene, we also demonstrate how CRISPR-Cas9 gene drive can be coupled to sex conversion, whereby genetic females are transformed into fertile and harmless XX males. Given this unique malleability of sex determination, we modelled gene drive interventions that couple sex conversion and female sterility and found that such approaches could be effective and tolerant of resistant allele selection in the target population. Our results open the door for developing gene drive strains for the population suppression of the medfly and related tephritid pests by co-targeting female reproduction and shifting the reproductive sex ratio towards males. They demonstrate the untapped potential for gene drives to tackle agricultural pests in an environmentally friendly and economical way.
Uncertainty driven active learning of coarse grained free energy models
Authors: Duschatko Blake R. , Vandermause Jonathan , Molinari Nicola , Kozinsky Boris 
Published: January 2024

AbstractCoarse graining techniques play an essential role in accelerating molecular simulations of systems with large length and time scales. Theoretically grounded bottom-up models are appealing due to their thermodynamic consistency with the underlying all-atom models. In this direction, machine learning approaches hold great promise to fitting complex many-body data. However, training models may require collection of large amounts of expensive data. Moreover, quantifying trained model accuracy is challenging, especially in cases of non-trivial free energy configurations, where training data may be sparse. We demonstrate a path towards uncertainty-aware models of coarse grained free energy surfaces. Specifically, we show that principled Bayesian model uncertainty allows for efficient data collection through an on-the-fly active learning framework and opens the possibility of adaptive transfer of models across different chemical systems. Uncertainties also characterize models’ accuracy of free energy predictions, even when training is performed only on forces. This work helps pave the way towards efficient autonomous training of reliable and uncertainty aware many-body machine learned coarse grain models.
Accelerating corrosion inhibitor discovery through computational routes: a case of naphthalene 1-thiocarboxamide
Authors: Kumar Dharmendr , K Venkata Muralidhar , Jain Vinay , Rai Beena 
Published: January 2024

AbstractThe conventional approach to the discovery of corrosion inhibitors is time-consuming and requires a significant amount of resources. In the present study, we highlight the use of a first principles DFT-based approach to expedite the rational design and discovery of corrosion inhibitors for mild steel in acidic media. From among various sulfur containing molecules shortlisted based on quantum chemical descriptors, naphthalene 1-thiocarboxamide (NTC) is found to have the lowest ELUMO and Egap, suggesting best corrosion inhibition. Subsequently, explicit adsorption studies reveal strong chemisorption of NTC onto the Fe (001) surface, characterized by a plethora of Fe-C/N/S covalent bonds. DFT Surface coverage studies additionally indicate the formation of a compact monolayer of NTC on the Fe surface. Gravimetric, potentiodynamic polarization, and Electrochemical Impedance Spectroscopy studies, all confirm NTC as a remarkable inhibitor for mild steel in 1 N HCl at both room and elevated (60 °C) temperatures even at merely 1 mM concentration.
Magnetism and fermiology of kagome magnet YMn6Sn4Ge2
Authors: Bhandari Hari , Dally Rebecca L. , Siegfried Peter E. , Regmi Resham B. , Rule Kirrily C. , Chi Songxue , Lynn Jeffrey W. , Mazin I. I. , Ghimire Nirmal J. 
Published: January 2024

AbstractKagome lattice magnets are an interesting class of materials as they can host topological properties in their magnetic and electronic structures. YMn6Sn6 is one such compound in which various exotic magnetic and electronic topological properties have been realized. Here, by means of a partial substitution of Sn with an isovalent and slightly smaller atom Ge, we demonstrate the sensitivity of such chemical substitution on the magnetic structure and its influence in the electronic properties. Magnetic structure of YMn6Sn4Ge2 determined by neutron diffraction reveals an incommensurate staggered magnetic spiral with a slightly larger spiral pitch than in YMn6Sn6. This change in magnetic structure influences the Fermi surface enhancing the out-of-plane conductivity. Such a sensitivity to the partial chemical substitution provides a great potential for engineering the magnetic phases and associated electronic properties not only in YMn6Sn6, but also in the large family of 166 rare-earth kagome magnet.
A comprehensive dataset of photonic features on spectral converters for energy harvesting
Authors: Ferreira Rute A. S. , Correia Sandra F. H. , Georgieva Petia , Fu Lianshe , Antunes Mário , André Paulo S. 
Published: January 2024

AbstractBuilding integrated photovoltaics is a promising strategy for solar technology, in which luminescent solar concentrators (LSCs) stand out. Challenges include the development of materials for sunlight harvesting and conversion, which is an iterative optimization process with several steps: synthesis, processing, and structural and optical characterizations before considering the energy generation figures of merit that requires a prototype fabrication. Thus, simulation models provide a valuable, cost-effective, and time-efficient alternative to experimental implementations, enabling researchers to gain valuable insights for informed decisions. We conducted a literature review on LSCs over the past 47 years from the Web of ScienceTM Core Collection, including published research conducted by our research group, to gather the optical features and identify the material classes that contribute to the performance. The dataset can be further expanded systematically offering a valuable resource for decision-making tools for device design without extensive experimental measurements.
Socio-economic pandemic modelling: case of Spain
Authors: Snellman Jan E. , Barreiro Nadia L. , Barrio Rafael A. , Ventura Cecilia I. , Govezensky Tzipe , Kaski Kimmo K. , Korpi-Lagg Maarit J. 
Published: January 2024

AbstractA global disaster, such as the recent Covid-19 pandemic, affects every aspect of our lives and there is a need to investigate these highly complex phenomena if one aims to diminish their impact in the health of the population, as well as their socio-economic stability. In this paper we present an attempt to understand the role of the governmental authorities and the response of the rest of the population facing such emergencies. We present a mathematical model that takes into account the epidemiological features of the pandemic and also the actions of people responding to it, focusing only on three aspects of the system, namely, the fear of catching this serious disease, the impact on the economic activities and the compliance of the people to the mitigating measures adopted by the authorities. We apply the model to the specific case of Spain, since there are accurate data available about these three features. We focused on tourism as an example of the economic activity, since this sector of economy is one of the most likely to be affected by the restrictions imposed by the authorities, and because it represents an important part of Spanish economy. The results of numerical calculations agree with the empirical data in such a way that we can acquire a better insight of the different processes at play in such a complex situation, and also in other different circumstances.
Effects of abdominal aortic aneurysm on long-term survival in lung cancer patients
Authors: Kim Hyangkyoung , Kwon Tae-Won , Cho Yong-Pil , Gwon Jun Gyo , Han Youngjin , Lee Sang Ah , Kim Ye-Jee , Kim Seonok 
Published: January 2024

AbstractThe major causes of death in patients with abdominal aortic aneurysm (AAA) are cardiovascular disease and cancer. The purpose of this study was to evaluate the effect of AAA on long-term survival in lung cancer patients. All patient data with degenerative type AAA and lung cancer over 50 years of age during the period 2009 to 2018 was collected retrospectively from a National Health Insurance Service (NHIS) administrative database and matched to lung cancer patients without AAA by age, sex, metastasis, and other comorbidities. Mortality rate was compared between the groups. A total of 956 AAA patients who could be matched with patients without AAA were included, and 3824 patients in the matched group were used for comparison. Patients with AAA showed higher risk of death compared with the matched cohort (adjusted hazard ratio (HR) 1.14, 95% confidence interval (CI) 1.06–1.23, p < 0.001). When compared to a matched group of untreated AAA patients, patients with of history of AAA exhibited a significantly increased risk of overall mortality [HR (95%CI) 1.219 (1.113–1.335), p < .001, adjusted HR (95% CI) 1.177 (1.073–1.291), p = .001]. By contrast, mortality risk of AAA patients treated either by endovascular abdominal aortic repair or open surgical repair was not significantly different from that of the matched group (p = 0.079 and p = 0.625, respectively). The mortality risk was significantly higher when AAA was present in lung cancer patients, especially in patients with unrepaired AAA, suggesting the need for continuous cardiovascular risk management.
Physico- and phytochemical properties of Brassica juncea as affected by agroclimatic conditions
Authors: Batool Uzma , Nawaz Rab , Ahmad Sajjad , Irshad Muhammad Atif , Irfan Ali , Gaafar Abdel-Rhman Z. , Arshad Muhammad , Wondmie Gezahign Fentahun , Qayyum Mir Muhammad Nasir , Bourhia Mohammed 
Published: January 2024

AbstractPhysicochemical and phytochemical assessment of leaf mustard (Brassica juncea L.) grown in different agroclimatic conditions is essential to highlight their compositional variability and evaluate the most suitable bunch of agroclimatic and agronomic practices. B. juncea is one of the important leafy vegetables that serve as source of vitamin A and C and iron, and plenty of antioxidants. This in situ research was executed to assess the quality variability of B. juncea grown in different agroecosystems. Leaves’ samples of B. juncea were procured from 15 farmers’ fields exhibiting different agroclimatic conditions i.e., elevation, nutrient management, temperature, irrigation, and tillage practices. Leaves’ samples were subjected to physicochemical and phytochemical analysis, i.e., moisture, pH, TSS, ascorbic acid, carotenoids, phenolics, flavonoids, and antioxidant potential. In the leaves’ samples of B. juncea, the target properties were found to vary significantly (P ≤ 0.05) in different agroclimatic conditions. The moisture content, ascorbic acid, phenolic content, carotenoids, and antioxidants were found in the range of 62.7–79.3%, 74–91 mg/100 g, 49.2–49.2 mg GAE/100 g, 436.3–480 mg β carotene/100 g, 32.7–46.67%, respectively. This study elaborates the significant variation of physicochemical and phytochemical attributes of B. juncea due to the prevailing agroclimatic conditions. This necessitates the appropriate choice of B. juncea concerning its composition and ecological conditions of its cultivation in the prospective health benefits.
Risk of adverse pregnancy outcomes in pregnant women with gestational diabetes mellitus by age: a multicentric cohort study in Hebei, China
Authors: Zhang Ting , Tian Meiling , Zhang Ping , Du Liyan , Ma Xuyuan , Zhang Yingkui , Tang Zengjun 
Published: January 2024

AbstractGestational diabetes mellitus (GDM) is an unique metabolic disorder that occurs during pregnancy. Both GDM and advanced age increase the risk of adverse pregnancy outcomes. This study used a GDM cohort study to investigate the role of age in the adverse pregnancy outcomes for pregnant women with GDM. From 2015 to 2021, 308,175 pregnant women were selected, and the data received from 22 hospitals by the Hebei Province Maternal Near Miss Surveillance System. There were 24,551 pregnant women with GDM that were divided into five groups by age (20–24, 25–29, 30–34, 35–39, 40–44 years old). Because the prevalence of adverse pregnancy outcomes was lower in pregnant women with GDM aged 25–29, they were used as a reference group (P < 0.05). Compared with GDM women aged 25–29 years, GDM women aged 35–44 years had a significant higher risk of cesarean delivery (aOR: 2.86, 95% CI 2.52–3.25) (P < 0.001), abnormal fetal position (aOR: 1.78, 95% CI 1.31–2.37) (P < 0.001), pre-eclampsia (aOR: 1.28, 95% CI 1.01–1.61) (P < 0.05), macrosomia (aOR: 1.25, 95% CI 1.08–1.45) (P < 0.05), and large for gestational age (LGA) (aOR: 1.16, 95% CI 1.02–1.31) (P < 0.05), GDM women aged 40–44 years had a higher risk of placenta previa (aOR: 2.53, 95% CI 1.01–6.35) (P < 0.05), anemia (aOR: 3.45, 95% CI 1.23–9.68) (P < 0.05) and small for gestational age (aOR: 1.32, 95% CI 1.01–1.60) (P < 0.05). Advanced maternal age was an independent risk factor for abnormal fetal position, pre-eclampsia, anemia, macrosomia, and LGA in pregnant women with GDM.
Solving real-world optimization tasks using physics-informed neural computing
Authors: Seo Jaemin 
Published: January 2024

AbstractOptimization tasks are essential in modern engineering fields such as chip design, spacecraft trajectory determination, and reactor scenario development. Recently, machine learning applications, including deep reinforcement learning (RL) and genetic algorithms (GA), have emerged in these real-world optimization tasks. We introduce a new machine learning-based optimization scheme that incorporates physics with the operational objectives. This physics-informed neural network (PINN) could find the optimal path in well-defined systems with less exploration and also was capable of obtaining narrow and unstable solutions that have been challenging with bottom-up approaches like RL or GA. Through an objective function that integrates governing laws, constraints, and goals, PINN enables top-down searches for optimal solutions. In this study, we showcase the PINN applications to various optimization tasks, ranging from inverting a pendulum, determining the shortest-time path, to finding the swingby trajectory. Through this, we discuss how PINN can be applied in the tasks with different characteristics.
Exploring public interest in gut microbiome dysbiosis, NAFLD, and probiotics using Google Trends
Authors: Pezzino Salvatore , Sofia Maria , Mazzone Chiara , Litrico Giorgia , Agosta Marcello , La Greca Gaetano , Latteri Saverio 
Published: January 2024

AbstractScientific interest related to the role of gut microbiome dysbiosis in the pathogenesis of non-alcoholic fatty liver disease (NAFLD) has now been established and is constantly growing. Therefore, balancing dysbiosis, through probiotics, would be a potential therapy. In addition to scientific interest, on the other hand, it is important to evaluate the interest in these topics among the population. This study aimed to analyze, temporally and geographically, the public's interest in gut microbiome dysbiosis, NAFLD, and the use of gut probiotics. The most widely used free tool for analyzing online behavior is Google Trends. Using Google Trends data, we have analyzed worldwide volume searches for the terms “gut microbiome”, “dysbiosis”, “NAFLD” and “gut probiotic” for the period from 1, January 2007 to 31 December 2022. Google's relative search volume (RSV) was collected for all terms and analyzed temporally and geographically. The RSV for the term “gut microbiome” has a growth rate of more than 1400% followed, by “gut probiotics” (829%), NAFLD (795%), and “dysbiosis” (267%) from 2007 to 2012. In Australia and New Zealand, we found the highest RSV score for the term “dysbiosis” and “gut probiotics”. Moreover, we found the highest RSV score for the term “NAFLD” in the three countries: South Korea, Singapore, and the Philippines. Google Trends analysis showed that people all over the world are interested in and aware of gut microbiome dysbiosis, NAFLD, and the use of gut probiotics. These data change over time and have a geographical distribution that could reflect the epidemiological worldwide condition of NAFLD and the state of the probiotic market.
Exosome microRNA-22 inhibiting proliferation, migration and invasion through regulating Twist1/CADM1 axis in osteosarcoma
Authors: Ruan Qing , Wang Cuijie , Wu Yuntao , Zhu Qingsan 
Published: January 2024

AbstractThis study aims to the function of miR-22 original mesenchymal stem cells (MSC) on osteosarcoma (OS) proliferation, migration and invasion. Bio-informatics analysis including GEO2R analysis, Gene Ontology analysis, integration analysis were used to confirmed the target genes (miR-22, Twist1, CADM1) in OS. RT-qPCR and western blotting confirmed the different expression of miR-22, Twist1, CADM1 in OS tissues, MG63 and Saos cell lines. MTS assay, CCK8 assay, colony forming assay, EdU assay were performed to detect the proliferation effect of miR-22 on MG63. Transwell migration assay, transwell invasion assay, wound healing assay were used to verify the migration and invasion effect of miR-22 on MG63. Luciferase reporter assay confirm the binding sites between miR-22 and Twist1. RT-qPCR confirmed miR-22 and CADM1 downregulated and Twist1 upregulated in OS tissues, MG63 and Saos. Exosome original MSC labeled with PKH-26 could be uptake by MG63, which upregulated the expression of miR-22 in MG63. High expression of miR-22 in MG63 inhibited proliferation, migration and invasion, which could be rescued by Twist1. Dual luciferase reporter analysis confirmed Twist1 was a target of miR-22. Exosome modified with miR-22 mimic inhibit proliferation, migration and invasion more efficient than exosome original MSC. miR-22 cargo in exo-MSC could uptake by MG63 and supply MG63 with miR-22, which inhibit MG63 proliferation, migration and invasion through targeting Twist1.
Time of day bias for biological sampling in studies of mammary cancer
Authors: Walton James C. , Walker William H. , Nelson Randy J. , DeVries A. Courtney 
Published: January 2024

AbstractDespite its demonstrated biological significance, time of day is a broadly overlooked biological variable in preclinical and clinical studies. How time of day affects the influence of peripheral tumors on central (brain) function remains unspecified. Thus, we tested the hypothesis that peripheral mammary cancer tumors alter the transcriptome of immune responses in the brain and that these responses vary based on time of day; we predicted that time of day sampling bias would alter the interpretation of the results. Brain tissues collected at mid dark and mid light from mammary tumor-bearing and vehicle injected mice were analyzed using the Nanostring nCounter immune panel. Peripheral mammary tumors significantly affected expression within the brain of over 100 unique genes of the 770 represented in the panel, and fewer than 25% of these genes were affected similarly across the day. Indeed, between 65 and 75% of GO biological processes represented by the differentially expressed genes were dependent upon time of day of sampling. The implications of time-of-day sampling bias in interpretation of research studies cannot be understated. We encourage considering time of day as a significant biological variable in studies and to appropriately control for it and clearly report time of day in findings.