bluuebunny/crossref_metadata_2025_split huggingface dataset:
A diagnostic model based on bioinformatics and machine learning to differentiate bipolar disorder from schizophrenia and major depressive disorder
Authors: Shen Jing , Xiao Chenxu , Qiao Xiwen , Zhu Qichen , Yan Hanfei , Pan Julong , Feng Yu 
Published: February 2024

AbstractBipolar disorder (BD) showed the highest suicide rate of all psychiatric disorders, and its underlying causative genes and effective treatments remain unclear. During diagnosis, BD is often confused with schizophrenia (SC) and major depressive disorder (MDD), due to which patients may receive inadequate or inappropriate treatment, which is detrimental to their prognosis. This study aims to establish a diagnostic model to distinguish BD from SC and MDD in multiple public datasets through bioinformatics and machine learning and to provide new ideas for diagnosing BD in the future. Three brain tissue datasets containing BD, SC, and MDD were chosen from the Gene Expression Omnibus database (GEO), and two peripheral blood datasets were selected for validation. Linear Models for Microarray Data (Limma) analysis was carried out to identify differentially expressed genes (DEGs). Functional enrichment analysis and machine learning were utilized to identify. Least absolute shrinkage and selection operator (LASSO) regression was employed for identifying candidate immune-associated central genes, constructing protein-protein interaction networks (PPI), building artificial neural networks (ANN) for validation, and plotting receiver operating characteristic curve (ROC curve) for differentiating BD from SC and MDD and creating immune cell infiltration to study immune cell dysregulation in the three diseases. RBM10 was obtained as a candidate gene to distinguish BD from SC. Five candidate genes (LYPD1, HMBS, HEBP2, SETD3, and ECM2) were obtained to distinguish BD from MDD. The validation was performed by ANN, and ROC curves were plotted for diagnostic value assessment. The outcomes exhibited the prediction model to have a promising diagnostic value. In the immune infiltration analysis, Naive B, Resting NK, and Activated Mast Cells were found to be substantially different between BD and SC. Naive B and Memory B cells were prominently variant between BD and MDD. In this study, RBM10 was found as a candidate gene to distinguish BD from SC; LYPD1, HMBS, HEBP2, SETD3, and ECM2 serve as five candidate genes to distinguish BD from MDD. The results obtained from the ANN network showed that these candidate genes could perfectly distinguish BD from SC and MDD (76.923% and 81.538%, respectively).
Risk of carditis among adolescents after extending the interdose intervals of BNT162b2
Authors: Fan Min , Peng Kuan , Zhang Yin , Lai Francisco Tsz Tsun , Chui Celine Sze Ling , Wan Eric Yuk Fai , Wong Carlos King Ho , Chan Esther Wai Yin , Li Xue , Wong Ian Chi Kei 
Published: February 2024

AbstractPrevious studies indicate an increased carditis risk among adolescents following the two-dose messenger RNA COVID-19 vaccine. Several jurisdictions have extended the interdose interval between the first and second doses to reduce the risk. However, the effectiveness of such an extension policy remains inconclusive. Using the territory-wide vaccine record-linked electronic health records in Hong Kong, we conducted a nested case–control study from February 23, 2021 to August 15, 2022. Adolescents aged between 12 and 17 who received two-dose BNT162b2 were included for comparing risks between standard interdose interval (21–27 days) versus extended interdose interval ( ≥ 56 days). The carditis cumulative incidence within 28 days following the second dose was calculated. The adjusted odds ratio was estimated from multivariable conditional logistic regression. We identified 49 adolescents with newly diagnosed carditis within 28 days following the second dose. The crude cumulative incidence is 37.41 [95% confidence interval (CI): 27.68–49.46] per million vaccinated adolescents. Compared to the standard interdose interval group, adolescents with an extended interval had a significantly lower risk of carditis [adjusted odds ratio (aOR) 0.34 (95% CI: 0.16–0.73)]. Sensitivity analysis of carditis occurring within 14 days following the second dose yielded a similar estimate [aOR 0.30 (95% CI: 0.13–0.73)]. Extending the interdose interval of the BNT162b2 vaccine from 21 to 27 days to 56 days or longer is associated with 66% lower risk of incident carditis among adolescents. Our findings contribute towards an evidence-based vaccination strategy for a vulnerable population and potentially informs product label updates.
Critical transitions in the Amazon forest system
Authors: Flores Bernardo M. , Montoya Encarni , Sakschewski Boris , Nascimento Nathália , Staal Arie , Betts Richard A. , Levis Carolina , Lapola David M. , Esquível-Muelbert Adriane , Jakovac Catarina , Nobre Carlos A. , Oliveira Rafael S. , Borma Laura S. , Nian Da , Boers Niklas , Hecht Susanna B. , ter Steege Hans , Arieira Julia , Lucas Isabella L. , Berenguer Erika , Marengo José A. , Gatti Luciana V. , Mattos Caio R. C. , Hirota Marina 
Published: February 2024

AbstractThe possibility that the Amazon forest system could soon reach a tipping point, inducing large-scale collapse, has raised global concern1–3. For 65 million years, Amazonian forests remained relatively resilient to climatic variability. Now, the region is increasingly exposed to unprecedented stress from warming temperatures, extreme droughts, deforestation and fires, even in central and remote parts of the system1. Long existing feedbacks between the forest and environmental conditions are being replaced by novel feedbacks that modify ecosystem resilience, increasing the risk of critical transition. Here we analyse existing evidence for five major drivers of water stress on Amazonian forests, as well as potential critical thresholds of those drivers that, if crossed, could trigger local, regional or even biome-wide forest collapse. By combining spatial information on various disturbances, we estimate that by 2050, 10% to 47% of Amazonian forests will be exposed to compounding disturbances that may trigger unexpected ecosystem transitions and potentially exacerbate regional climate change. Using examples of disturbed forests across the Amazon, we identify the three most plausible ecosystem trajectories, involving different feedbacks and environmental conditions. We discuss how the inherent complexity of the Amazon adds uncertainty about future dynamics, but also reveals opportunities for action. Keeping the Amazon forest resilient in the Anthropocene will depend on a combination of local efforts to end deforestation and degradation and to expand restoration, with global efforts to stop greenhouse gas emissions.
A 2D ferroelectric vortex pattern in twisted BaTiO3 freestanding layers
Authors: Sánchez-Santolino G. , Rouco V. , Puebla S. , Aramberri H. , Zamora V. , Cabero M. , Cuellar F. A. , Munuera C. , Mompean F. , Garcia-Hernandez M. , Castellanos-Gomez A. , Íñiguez J. , Leon C. , Santamaria J. 
Published: February 2024

AbstractThe wealth of complex polar topologies1–10 recently found in nanoscale ferroelectrics results from a delicate balance between the intrinsic tendency of the materials to develop a homogeneous polarization and the electric and mechanical boundary conditions imposed on them. Ferroelectric–dielectric interfaces are model systems in which polarization curling originates from open circuit-like electric boundary conditions, to avoid the build-up of polarization charges through the formation of flux-closure11–14 domains that evolve into vortex-like structures at the nanoscale15–17 level. Although ferroelectricity is known to couple strongly with strain (both homogeneous18 and inhomogeneous19,20), the effect of mechanical constraints21 on thin-film nanoscale ferroelectrics has been comparatively less explored because of the relative paucity of strain patterns that can be implemented experimentally. Here we show that the stacking of freestanding ferroelectric perovskite layers with controlled twist angles provides an opportunity to tailor these topological nanostructures in a way determined by the lateral strain modulation associated with the twisting. Furthermore, we find that a peculiar pattern of polarization vortices and antivortices emerges from the flexoelectric coupling of polarization to strain gradients. This finding provides opportunities to create two-dimensional high-density vortex crystals that would enable us to explore previously unknown physical effects and functionalities.
Online images amplify gender bias
Authors: Guilbeault Douglas , Delecourt Solène , Hull Tasker , Desikan Bhargav Srinivasa , Chu Mark , Nadler Ethan 
Published: February 2024

AbstractEach year, people spend less time reading and more time viewing images1, which are proliferating online2–4. Images from platforms such as Google and Wikipedia are downloaded by millions every day2,5,6, and millions more are interacting through social media, such as Instagram and TikTok, that primarily consist of exchanging visual content. In parallel, news agencies and digital advertisers are increasingly capturing attention online through the use of images7,8, which people process more quickly, implicitly and memorably than text9–12. Here we show that the rise of images online significantly exacerbates gender bias, both in its statistical prevalence and its psychological impact. We examine the gender associations of 3,495 social categories (such as ‘nurse’ or ‘banker’) in more than one million images from Google, Wikipedia and Internet Movie Database (IMDb), and in billions of words from these platforms. We find that gender bias is consistently more prevalent in images than text for both female- and male-typed categories. We also show that the documented underrepresentation of women online13–18 is substantially worse in images than in text, public opinion and US census data. Finally, we conducted a nationally representative, preregistered experiment that shows that googling for images rather than textual descriptions of occupations amplifies gender bias in participants’ beliefs. Addressing the societal effect of this large-scale shift towards visual communication will be essential for developing a fair and inclusive future for the internet.
A single-cell time-lapse of mouse prenatal development from gastrula to birth
Authors: Qiu Chengxiang , Martin Beth K. , Welsh Ian C. , Daza Riza M. , Le Truc-Mai , Huang Xingfan , Nichols Eva K. , Taylor Megan L. , Fulton Olivia , O’Day Diana R. , Gomes Anne Roshella , Ilcisin Saskia , Srivatsan Sanjay , Deng Xinxian , Disteche Christine M. , Noble William Stafford , Hamazaki Nobuhiko , Moens Cecilia B. , Kimelman David , Cao Junyue , Schier Alexander F. , Spielmann Malte , Murray Stephen A. , Trapnell Cole , Shendure Jay 
Published: February 2024

AbstractThe house mouse (Mus musculus) is an exceptional model system, combining genetic tractability with close evolutionary affinity to humans1,2. Mouse gestation lasts only 3 weeks, during which the genome orchestrates the astonishing transformation of a single-cell zygote into a free-living pup composed of more than 500 million cells. Here, to establish a global framework for exploring mammalian development, we applied optimized single-cell combinatorial indexing3 to profile the transcriptional states of 12.4 million nuclei from 83 embryos, precisely staged at 2- to 6-hour intervals spanning late gastrulation (embryonic day 8) to birth (postnatal day 0). From these data, we annotate hundreds of cell types and explore the ontogenesis of the posterior embryo during somitogenesis and of kidney, mesenchyme, retina and early neurons. We leverage the temporal resolution and sampling depth of these whole-embryo snapshots, together with published data4–8 from earlier timepoints, to construct a rooted tree of cell-type relationships that spans the entirety of prenatal development, from zygote to birth. Throughout this tree, we systematically nominate genes encoding transcription factors and other proteins as candidate drivers of the in vivo differentiation of hundreds of cell types. Remarkably, the most marked temporal shifts in cell states are observed within one hour of birth and presumably underlie the massive physiological adaptations that must accompany the successful transition of a mammalian fetus to life outside the womb.
IL-23 stabilizes an effector Treg cell program in the tumor microenvironment
Authors: Wertheimer Tobias , Zwicky Pascale , Rindlisbacher Lukas , Sparano Colin , Vermeer Marijne , de Melo Bruno Marcel Silva , Haftmann Claudia , Rückert Tamina , Sethi Aakriti , Schärli Stefanie , Huber Anna , Ingelfinger Florian , Xu Caroline , Kim Daehong , Häne Philipp , Fonseca da Silva André , Muschaweckh Andreas , Nunez Nicolas , Krishnarajah Sinduya , Köhler Natalie , Zeiser Robert , Oukka Mohamed , Korn Thomas , Tugues Sonia , Becher Burkhard 
Published: February 2024

AbstractInterleukin-23 (IL-23) is a proinflammatory cytokine mainly produced by myeloid cells that promotes tumor growth in various preclinical cancer models and correlates with adverse outcomes. However, as to how IL-23 fuels tumor growth is unclear. Here, we found tumor-associated macrophages to be the main source of IL-23 in mouse and human tumor microenvironments. Among IL-23-sensing cells, we identified a subset of tumor-infiltrating regulatory T (Treg) cells that display a highly suppressive phenotype across mouse and human tumors. The use of three preclinical models of solid cancer in combination with genetic ablation of Il23r in Treg cells revealed that they are responsible for the tumor-promoting effect of IL-23. Mechanistically, we found that IL-23 sensing represents a crucial signal driving the maintenance and stabilization of effector Treg cells involving the transcription factor Foxp3. Our data support that targeting the IL-23/IL-23R axis in cancer may represent a means of eliciting antitumor immunity.
The social anatomy of climate change denial in the United States
Authors: Gounaridis Dimitrios , Newell Joshua P. 
Published: February 2024

AbstractUsing data from Twitter (now X), this study deploys artificial intelligence (AI) and network analysis to map and profile climate change denialism across the United States. We estimate that 14.8% of Americans do not believe in climate change. This denialism is highest in the central and southern U.S. However, it also persists in clusters within states (e.g., California) where belief in climate change is high. Political affiliation has the strongest correlation, followed by level of education, COVID-19 vaccination rates, carbon intensity of the regional economy, and income. The analysis reveals how a coordinated social media network uses periodic events, such as cold weather and climate conferences, to sow disbelief about climate change and science, in general. Donald Trump was the strongest influencer in this network, followed by conservative media outlets and right-wing activists. As a form of knowledge vulnerability, climate denialism renders communities unprepared to take steps to increase resilience. As with other forms of misinformation, social media companies (e.g., X, Facebook, YouTube, TikTok) should flag accounts that spread falsehoods about climate change and collaborate on targeted educational campaigns.
Global coastal wave storminess
Authors: Lobeto Hector , Semedo Alvaro , Lemos Gil , Dastgheib Ali , Menendez Melisa , Ranasinghe Roshanka , Bidlot Jean-Raymond 
Published: February 2024

AbstractCoastal wave storms pose a massive threat to over 10% of the world’s population now inhabiting the low elevation coastal zone and to the trillions of $ worth of coastal zone infrastructure and developments therein. Using a ~ 40-year wave hindcast, we here present a world-first assessment of wind-wave storminess along the global coastline. Coastal regions are ranked in terms of the main storm characteristics, showing Northwestern Europe and Southwestern South America to suffer, on average, the most intense storms and the Yellow Sea coast and the South-African and Namibian coasts to be impacted by the most frequent storms. These characteristics are then combined to derive a holistic classification of the global coastlines in terms of their wave environment, showing, for example, that the open coasts of northwestern Europe are impacted by more than 10 storms per year with mean significant wave heights over 6 m. Finally, a novel metric to classify the degree of coastal wave storminess is presented, showing a general latitudinal storminess gradient. Iceland, Ireland, Scotland, Chile and Australia show the highest degree of storminess, whereas Indonesia, Papua-New Guinea, Malaysia, Cambodia and Myanmar show the lowest.
A population of faint, old, and massive quiescent galaxies at $$3<z<4$$ revealed by JWST NIRSpec Spectroscopy
Authors: Nanayakkara Themiya , Glazebrook Karl , Jacobs Colin , Kawinwanichakij Lalitwadee , Schreiber Corentin , Brammer Gabriel , Esdaile James , Kacprzak Glenn G. , Labbe Ivo , Lagos Claudia , Marchesini Danilo , Marsan Z. Cemile , Oesch Pascal A. , Papovich Casey , Remus Rhea-Silvia , Tran Kim-Vy H. 
Published: February 2024

AbstractHere we present a sample of 12 massive quiescent galaxy candidates at $$z\sim 3-4$$

z
∼
3
-
4

observed with the James Webb Space Telescope (JWST) Near Infrared Spectrograph (NIRSpec). These galaxies were pre-selected from the Hubble Space Telescope imaging and 10 of our sources were unable to be spectroscopically confirmed by ground based spectroscopy. By combining spectroscopic data from NIRSpec with multi-wavelength imaging data from the JWST Near Infrared Camera (NIRCam), we analyse their stellar populations and their formation histories. We find that all of our galaxies classify as quiescent based on the reconstruction of their star formation histories but show a variety of quenching timescales and ages. All our galaxies are massive ($$\sim 0.1-1.2\times 10^{11}$$

∼
0.1
-
1.2
×

10
11

M$$_\odot$$

⊙

), with masses comparable to massive galaxies in the local Universe. We find that the oldest galaxy in our sample formed $$\sim 1.0\times 10^{11}$$

∼
1.0
×

10
11

M$$_\odot$$

⊙

of mass within the first few hundred million years of the Universe and has been quenched for more than a billion years by the time of observation at $$z\sim 3.2$$

z
∼
3.2

($$\sim 2$$

∼
2

billion years after the Big Bang). Our results point to very early formation of massive galaxies requiring a high conversion rate of baryons to stars in the early Universe.
Distinguishing recurrence from radiation-induced lung injury at the time of RECIST progressive disease on post-SABR CT scans using radiomics
Authors: Dammak Salma , Gulstene Stephanie , Palma David A. , Mattonen Sarah A. , Senan Suresh , Ward Aaron D. 
Published: February 2024

AbstractStereotactic ablative radiotherapy (SABR) is a highly effective treatment for patients with early-stage lung cancer who are inoperable. However, SABR causes benign radiation-induced lung injury (RILI) which appears as lesion growth on follow-up CT scans. This triggers the standard definition of progressive disease, yet cancer recurrence is not usually present, and distinguishing RILI from recurrence when a lesion appears to grow in size is critical but challenging. In this study, we developed a tool to do this using scans with apparent lesion growth after SABR from 68 patients. We performed bootstrapped experiments using radiomics and explored the use of multiple regions of interest (ROIs). The best model had an area under the receiver operating characteristic curve of 0.66 and used a sphere with a diameter equal to the lesion’s longest axial measurement as the ROI. We also investigated the effect of using inter-feature and volume correlation filters and found that the former was detrimental to performance and that the latter had no effect. We also found that the radiomics features ranked as highly important by the model were significantly correlated with outcomes. These findings represent a key step in developing a tool that can help determine who would benefit from follow-up invasive interventions when a SABR-treated lesion increases in size, which could help provide better treatment for patients.
Two-dimensional high resolution electron properties of femtosecond laser-induced plasma filament in atmospheric pressure argon
Authors: Bak Junhwi , Urdaneta Gerardo , Pokharel Sagar , Miles Richard B. , Tropina Albina 
Published: February 2024

AbstractThis work reports the measurement of two-dimensional electron properties over a nanosecond scale integration time across a femtosecond laser-induced plasma filament in atmospheric pressure argon. Radial electron properties across the $$\sim 100$$

∼
100

$$\upmu$$
μ
m diameter filament are obtained at discrete axial locations at 2.5 mm steps by one-dimensional high-resolution laser Thomson scattering with a spatial resolution of 10 $$\upmu$$
μ
m. These measurements reveal plasma structural information in the filament. The Thomson spectral lineshapes exhibit clear spectral sidebands with an $$\alpha$$
α
parameter $$\sim 1$$

∼
1

, enabling the measurement of both electron temperature and density profiles. These measurements yield electron densities on the order of $$10^{22}$$

10
22

/m$$^{3}$$

3

and electron temperatures of $$\sim 2$$

∼
2

eV. Heating from the probe laser due to inverse bremsstrahlung is taken into account to correct the Thomson scattering electron temperature measurements. Under these conditions, electron-neutral collision induced bremsstrahlung becomes the dominant laser-induced plasma heating process associated with the probe laser. The measurements reveal structural features of the filament, including an asymmetrically skewed density structure in the axial direction and reversed radial distributions of electron density and temperature.
Research on time series prediction of multi-process based on deep learning
Authors: Zheng Huali , Cao Yu , Sun Dong , Wang Mingjun , Yan Binglong , Ye Chunming 
Published: February 2024

AbstractAiming at the problem of data fluctuation in multi-process production, a Soft Update Dueling Double Deep Q-learning (SU-D3QN) network combined with soft update strategy is proposed. Based on this, a time series combination forecasting model SU-D3QN-G is proposed. Firstly, based on production data, Gate Recurrent Unit (GRU) is used for prediction. Secondly, based on the model, SU-D3QN algorithm is used to learn and add bias to it, and the prediction results of GRU are corrected, so that the prediction value of each time node fits in the direction of reducing the absolute error. Thirdly, experiments were carried out on the dataset of a company. The data sets of four indicators, namely, the outlet temperature of drying silk, the loose moisture return water, the outlet temperature of feeding leaves and the inlet water of leaf silk warming and humidification, are selected, and more than 1000 real production data are divided into training set, inspection set and test set according to the ratio of 6:2:2. The experimental results show that the SU-D3QN-G combined time series prediction model has a great improvement compared with GRU, LSTM and ARIMA, and the MSE index is reduced by 0.846–23.930%, 5.132–36.920% and 10.606–70.714%, respectively. The RMSE index is reduced by 0.605–10.118%, 2.484–14.542% and 5.314–30.659%. The MAE index is reduced by 3.078–15.678%, 7.94–15.974% and 6.860–49.820%. The MAPE index is reduced by 3.098–15.700%, 7.98–16.395% and 7.143–50.000%.