bluuebunny/crossref_metadata_2025_split huggingface dataset:
Characterization of oscillation modes in levitated droplets using image and non-image based techniques
Authors: Brosius Nevin , Livesay Jason , Karpinski Zachary , Singiser Robert , SanSoucie Michael , Phillips Brandon , Narayanan Ranga 
Published: January 2023

AbstractThe dynamics of levitated liquid droplets can be used to measure their thermophysical properties by correlating the frequencies at which normal modes of oscillation most strongly resonate when subject to an external oscillatory force. In two preliminary works, it was shown via electrostatic levitation and processing of various metals and alloys that (1) the resonance of the first principal mode of oscillation (mode n = 2) can be used to accurately measure surface tension and (2) that so-called “higher-order resonance” of n = 3 is observable at a predictable frequency. It was also shown, in the context of future space-based experimentation on the Electrostatic Levitation Furnace (ELF), a setup on the International Space Station (ISS) operated by Japan Aerospace Exploration Agency (JAXA), that while the shadow array method in which droplet behavior is visualized would be challenging to identify the n = 3 resonance, the normal mode n = 4 was predicted to be more easily identifiable. In this short communication, experimental evidence of the first three principal modes of oscillation is provided using molten samples of Tin and Indium and it is subsequently shown that, as predicted, an “image-less" approach can be used to identify both n = 2 and n = 4 resonances in levitated liquid droplets. This suggests that the shadow array method may be satisfactorily used to obtain a self-consistent benchmark of thermophysical properties by comparing results from two successive even-mode natural frequencies.
Magnetically mediated hole pairing in fermionic ladders of ultracold atoms
Authors: Hirthe Sarah , Chalopin Thomas , Bourgund Dominik , Bojović Petar , Bohrdt Annabelle , Demler Eugene , Grusdt Fabian , Bloch Immanuel , Hilker Timon A. 
Published: January 2023

AbstractConventional superconductivity emerges from pairing of charge carriers—electrons or holes—mediated by phonons1. In many unconventional superconductors, the pairing mechanism is conjectured to be mediated by magnetic correlations2, as captured by models of mobile charges in doped antiferromagnets3. However, a precise understanding of the underlying mechanism in real materials is still lacking and has been driving experimental and theoretical research for the past 40 years. Early theoretical studies predicted magnetic-mediated pairing of dopants in ladder systems4–8, in which idealized theoretical toy models explained how pairing can emerge despite repulsive interactions9. Here we experimentally observe this long-standing theoretical prediction, reporting hole pairing due to magnetic correlations in a quantum gas of ultracold atoms. By engineering doped antiferromagnetic ladders with mixed-dimensional couplings10, we suppress Pauli blocking of holes at short length scales. This results in a marked increase in binding energy and decrease in pair size, enabling us to observe pairs of holes predominantly occupying the same rung of the ladder. We find a hole–hole binding energy of the order of the superexchange energy and, upon increased doping, we observe spatial structures in the pair distribution, indicating repulsion between bound hole pairs. By engineering a configuration in which binding is strongly enhanced, we delineate a strategy to increase the critical temperature for superconductivity.
Coherent correlation imaging for resolving fluctuating states of matter
Authors: Klose Christopher , Büttner Felix , Hu Wen , Mazzoli Claudio , Litzius Kai , Battistelli Riccardo , Zayko Sergey , Lemesh Ivan , Bartell Jason M. , Huang Mantao , Günther Christian M. , Schneider Michael , Barbour Andi , Wilkins Stuart B. , Beach Geoffrey S. D. , Eisebitt Stefan , Pfau Bastian 
Published: January 2023

AbstractFluctuations and stochastic transitions are ubiquitous in nanometre-scale systems, especially in the presence of disorder. However, their direct observation has so far been impeded by a seemingly fundamental, signal-limited compromise between spatial and temporal resolution. Here we develop coherent correlation imaging (CCI) to overcome this dilemma. Our method begins by classifying recorded camera frames in Fourier space. Contrast and spatial resolution emerge by averaging selectively over same-state frames. Temporal resolution down to the acquisition time of a single frame arises independently from an exceptionally low misclassification rate, which we achieve by combining a correlation-based similarity metric1,2with a modified, iterative hierarchical clustering algorithm3,4. We apply CCI to study previously inaccessible magnetic fluctuations in a highly degenerate magnetic stripe domain state with nanometre-scale resolution. We uncover an intricate network of transitions between more than 30 discrete states. Our spatiotemporal data enable us to reconstruct the pinning energy landscape and to thereby explain the dynamics observed on a microscopic level. CCI massively expands the potential of emerging high-coherence X-ray sources and paves the way for addressing large fundamental questions such as the contribution of pinning5–8and topology9–12in phase transitions and the role of spin and charge order fluctuations in high-temperature superconductivity13,14.
Vertical organic electrochemical transistors for complementary circuits
Authors: Huang Wei , Chen Jianhua , Yao Yao , Zheng Ding , Ji Xudong , Feng Liang-Wen , Moore David , Glavin Nicholas R. , Xie Miao , Chen Yao , Pankow Robert M. , Surendran Abhijith , Wang Zhi , Xia Yu , Bai Libing , Rivnay Jonathan , Ping Jianfeng , Guo Xugang , Cheng Yuhua , Marks Tobin J. , Facchetti Antonio 
Published: January 2023

AbstractOrganic electrochemical transistors (OECTs) and OECT-based circuitry offer great potential in bioelectronics, wearable electronics and artificial neuromorphic electronics because of their exceptionally low driving voltages (<1 V), low power consumption (<1 µW), high transconductances (>10 mS) and biocompatibility1–5. However, the successful realization of critical complementary logic OECTs is currently limited by temporal and/or operational instability, slow redox processes and/or switching, incompatibility with high-density monolithic integration and inferior n-type OECT performance6–8. Here we demonstrate p- and n-type vertical OECTs with balanced and ultra-high performance by blending redox-active semiconducting polymers with a redox-inactive photocurable and/or photopatternable polymer to form an ion-permeable semiconducting channel, implemented in a simple, scalable vertical architecture that has a dense, impermeable top contact. Footprint current densities exceeding 1 kA cm−2 at less than ±0.7 V, transconductances of 0.2–0.4 S, short transient times of less than 1 ms and ultra-stable switching (>50,000 cycles) are achieved in, to our knowledge, the first vertically stacked complementary vertical OECT logic circuits. This architecture opens many possibilities for fundamental studies of organic semiconductor redox chemistry and physics in nanoscopically confined spaces, without macroscopic electrolyte contact, as well as wearable and implantable device applications.
Spontaneous behaviour is structured by reinforcement without explicit reward
Authors: Markowitz Jeffrey E. , Gillis Winthrop F. , Jay Maya , Wood Jeffrey , Harris Ryley W. , Cieszkowski Robert , Scott Rebecca , Brann David , Koveal Dorothy , Kula Tomasz , Weinreb Caleb , Osman Mohammed Abdal Monium , Pinto Sandra Romero , Uchida Naoshige , Linderman Scott W. , Sabatini Bernardo L. , Datta Sandeep Robert 
Published: January 2023

AbstractSpontaneous animal behaviour is built from action modules that are concatenated by the brain into sequences1,2. However, the neural mechanisms that guide the composition of naturalistic, self-motivated behaviour remain unknown. Here we show that dopamine systematically fluctuates in the dorsolateral striatum (DLS) as mice spontaneously express sub-second behavioural modules, despite the absence of task structure, sensory cues or exogenous reward. Photometric recordings and calibrated closed-loop optogenetic manipulations during open field behaviour demonstrate that DLS dopamine fluctuations increase sequence variation over seconds, reinforce the use of associated behavioural modules over minutes, and modulate the vigour with which modules are expressed, without directly influencing movement initiation or moment-to-moment kinematics. Although the reinforcing effects of optogenetic DLS dopamine manipulations vary across behavioural modules and individual mice, these differences are well predicted by observed variation in the relationships between endogenous dopamine and module use. Consistent with the possibility that DLS dopamine fluctuations act as a teaching signal, mice build sequences during exploration as if to maximize dopamine. Together, these findings suggest a model in which the same circuits and computations that govern action choices in structured tasks have a key role in sculpting the content of unconstrained, high-dimensional, spontaneous behaviour.
Mesolimbic dopamine adapts the rate of learning from action
Authors: Coddington Luke T. , Lindo Sarah E. , Dudman Joshua T. 
Published: January 2023

AbstractRecent success in training artificial agents and robots derives from a combination of direct learning of behavioural policies and indirect learning through value functions1–3. Policy learning and value learning use distinct algorithms that optimize behavioural performance and reward prediction, respectively. In animals, behavioural learning and the role of mesolimbic dopamine signalling have been extensively evaluated with respect to reward prediction4; however, so far there has been little consideration of how direct policy learning might inform our understanding5. Here we used a comprehensive dataset of orofacial and body movements to understand how behavioural policies evolved as naive, head-restrained mice learned a trace conditioning paradigm. Individual differences in initial dopaminergic reward responses correlated with the emergence of learned behavioural policy, but not the emergence of putative value encoding for a predictive cue. Likewise, physiologically calibrated manipulations of mesolimbic dopamine produced several effects inconsistent with value learning but predicted by a neural-network-based model that used dopamine signals to set an adaptive rate, not an error signal, for behavioural policy learning. This work provides strong evidence that phasic dopamine activity can regulate direct learning of behavioural policies, expanding the explanatory power of reinforcement learning models for animal learning6.
Genetic predictors of lifelong medication-use patterns in cardiometabolic diseases
Authors: Kiiskinen Tuomo , Helkkula Pyry , Krebs Kristi , Karjalainen Juha , Saarentaus Elmo , Mars Nina , Lehisto Arto , Zhou Wei , Cordioli Mattia , Jukarainen Sakari , Rämö Joel T. , Mehtonen Juha , Veerapen Kumar , Räsänen Markus , Ruotsalainen Sanni , Maasha Mutaamba ,   FinnGen, Niiranen Teemu , Tuomi Tiinamaija , Salomaa Veikko , Kurki Mitja , Pirinen Matti , Palotie Aarno , Daly Mark , Ganna Andrea , Havulinna Aki S. , Milani Lili , Ripatti Samuli 
Published: January 2023

AbstractLittle is known about the genetic determinants of medication use in preventing cardiometabolic diseases. Using the Finnish nationwide drug purchase registry with follow-up since 1995, we performed genome-wide association analyses of longitudinal patterns of medication use in hyperlipidemia, hypertension and type 2 diabetes in up to 193,933 individuals (55% women) in the FinnGen study. In meta-analyses of up to 567,671 individuals combining FinnGen with the Estonian Biobank and the UK Biobank, we discovered 333 independent loci (P < 5 × 10–9) associated with medication use. Fine-mapping revealed 494 95% credible sets associated with the total number of medication purchases, changes in medication combinations or treatment discontinuation, including 46 credible sets in 40 loci not associated with the underlying treatment targets. The polygenic risk scores (PRS) for cardiometabolic risk factors were strongly associated with the medication-use behavior. A medication-use enhanced multitrait PRS for coronary artery disease matched the performance of a risk factor-based multitrait coronary artery disease PRS in an independent sample (UK Biobank, n = 343,676). In summary, we demonstrate medication-based strategies for identifying cardiometabolic risk loci and provide genome-wide tools for preventing cardiovascular diseases.
Real-world COVID-19 vaccine effectiveness against the Omicron BA.2 variant in a SARS-CoV-2 infection-naive population
Authors: Lau Jonathan J. , Cheng Samuel M. S. , Leung Kathy , Lee Cheuk Kwong , Hachim Asmaa , Tsang Leo C. H. , Yam Kenny W. H. , Chaothai Sara , Kwan Kelvin K. H. , Chai Zacary Y. H. , Lo Tiffany H. K. , Mori Masashi , Wu Chao , Valkenburg Sophie A. , Amarasinghe Gaya K. , Lau Eric H. Y. , Hui David S. C. , Leung Gabriel M. , Peiris Malik , Wu Joseph T. 
Published: January 2023

AbstractThe SARS-CoV-2 Omicron variant has demonstrated enhanced transmissibility and escape of vaccine-derived immunity. Although first-generation vaccines remain effective against severe disease and death, robust evidence on vaccine effectiveness (VE) against all Omicron infections, irrespective of symptoms, remains sparse. We used a community-wide serosurvey with 5,310 subjects to estimate how vaccination histories modulated risk of infection in infection-naive Hong Kong during a large wave of Omicron BA.2 epidemic in January–July 2022. We estimated that Omicron infected 45% (41–48%) of the local population. Three and four doses of BNT162b2 or CoronaVac were effective against Omicron infection 7 days after vaccination (VE of 48% (95% credible interval 34–64%) and 69% (46–98%) for three and four doses of BNT162b2, respectively; VE of 30% (1–66%) and 56% (6–97%) for three and four doses of CoronaVac, respectively). At 100 days after immunization, VE waned to 26% (7–41%) and 35% (10–71%) for three and four doses of BNT162b2, and to 6% (0–29%) and 11% (0–54%) for three and four doses of CoronaVac. The rapid waning of VE against infection conferred by first-generation vaccines and an increasingly complex viral evolutionary landscape highlight the necessity for rapidly deploying updated vaccines followed by vigilant monitoring of VE.
Ppe.CR.1 DNA test for predicting chilling requirement in peach
Authors: Demirel Gizem , Calle Alejandro , Lawton John Mark , Atagul Omer , Fu Wanfang , Gasic Ksenija 
Published: January 2023

AbstractChilling requirement (CR) is an important agronomic trait controlling the floral bud break for proper flowering in peach. Even though it has been widely researched and several peach CR quantitative trait loci (QTLs) have been identified, no diagnostic DNA tests validated in the U.S. peach breeding germplasm are available for this trait. Breeders and growers need a simple DNA test to predict the CR of peach cultivars for their particular environment. Therefore, we developed a quick and reliable Kompetitive Allele Specific PCR (KASP) DNA test using haplotype information from 9K IPSC genotype data of the U.S. peach germplasm integrating four CR-associated SNP markers from the previously reported CR QTL region on linkage group 1. Four KASP assays (Ppe.CR.1-1 to -4) were developed and validated on 77 peach cultivars, and nine accessions from two F2 populations, with 96 and 74% accuracy in determining expected CR genotype (compared to SNP array) and predicting phenotype, respectively. Furthermore, the Ppe.CR.1 showed 80% accuracy in predicting the precise CR phenotype in the Clemson University peach breeding material. Only one Ppe.CR.1 KASP assay (Ppe.CR.1-1) is needed to distinguish between haplotypes with CR lower and higher than 800 chilling hours, and two Ppe.CR.1 assays (Pp.CR.1-1 and -4), are capable of distinguishing low, moderate, and high CR alleles. Coupled with the crude DNA extraction, the Ppe.CR.1 DNA test provides a low-cost option for breeders and growers to predict CR in peach material with more than 70% accuracy.
Molecular diagnosis and novel genes and phenotypes in a pediatric thoracic insufficiency cohort
Authors: Strong Alanna , Behr Meckenzie , Lott Carina , Clark Abigail J. , Mentch Frank , Da Silva Renata Pellegrino , Rux Danielle R. , Campbell Robert , Skraban Cara , Wang Xiang , Anari Jason B. , Sinder Benjamin , Cahill Patrick J. , Sleiman Patrick , Hakonarson Hakon 
Published: January 2023

AbstractThoracic insufficiency syndromes are a genetically and phenotypically heterogeneous group of disorders characterized by congenital abnormalities or progressive deformation of the chest wall and/or vertebrae that result in restrictive lung disease and compromised respiratory capacity. We performed whole exome sequencing on a cohort of 42 children with thoracic insufficiency to elucidate the underlying molecular etiologies of syndromic and non-syndromic thoracic insufficiency and predict extra-skeletal manifestations and disease progression. Molecular diagnosis was established in 24/42 probands (57%), with 18/24 (75%) probands having definitive diagnoses as defined by laboratory and clinical criteria and 6/24 (25%) probands having strong candidate genes. Gene identified in cohort patients most commonly encoded components of the primary cilium, connective tissue, and extracellular matrix. A novel association between KIF7 and USP9X variants and thoracic insufficiency was identified. We report and expand the genetic and phenotypic spectrum of a cohort of children with thoracic insufficiency, reinforce the prevalence of extra-skeletal manifestations in thoracic insufficiency syndromes, and expand the phenotype of KIF7 and USP9X-related disease to include thoracic insufficiency.
Appendiceal wall thickness and Alvarado score are predictive of acute appendicitis in the patients with equivocal computed tomography findings
Authors: Krisem Massupa , Jenjitranant Pinporn , Thampongsa Tharin , Wongwaisayawan Sirote 
Published: January 2023

AbstractAround 8–13% of the patients who underwent CT scan for diagnosis of appendicitis had equivocal CT results. About one-third of these patients had acute appendicitis and this caused diagnostic challenge to the clinicians. This study was conducted to identify clinical and imaging features that were predictive of acute appendicitis in patients who had equivocal CT findings. During January 2015 to June 2021, we retrospectively included 103 consecutive CT scans of adult patients (22 men and 81 women; mean age, 39.1 ± 17.5 years) who had equivocal CT findings of acute appendicitis. Two readers, blinded to the clinical data, independently assessed CT images for the relevant CT findings of appendicitis. Any disagreement between the readers was solved by consensus. The clinical parameters and CT findings were analyzed and compared between the patients who had appendicitis and patients who did not have appendicitis. Thirty-one (30.1%) patients had appendicitis, all of which were non-complicated. The appendiceal wall thickness of ≥ 2 mm and the Alvarado score of ≥ 7 were independent predictors of appendicitis with adjusted odds ratios (ORs) of 2.76 (95% CI, 1.09–7.02) and 1.47 (95% CI, 1.12–1.94), respectively. The maximal appendiceal diameter was higher in the appendicitis group (7.2 ± 1.2 mm vs. 6.5 ± 1.0 mm), but not predictive of appendicitis. The rest of the clinical parameters and CT findings, including mucosal hyperenhancement, periappendiceal fat reticulation, thickening of peritoneal reflection, appendicolith, focal cecal thickening, and content in appendiceal lumen showed no significant difference between two groups. The appendiceal wall thickness and the Alvarado score were able to predict appendicitis in patients who had equivocal CT findings.
Prognosis prediction in traumatic brain injury patients using machine learning algorithms
Authors: Khalili Hosseinali , Rismani Maziyar , Nematollahi Mohammad Ali , Masoudi Mohammad Sadegh , Asadollahi Arefeh , Taheri Reza , Pourmontaseri Hossein , Valibeygi Adib , Roshanzamir Mohamad , Alizadehsani Roohallah , Niakan Amin , Andishgar Aref , Islam Sheikh Mohammed Shariful , Acharya U. Rajendra 
Published: January 2023

AbstractPredicting treatment outcomes in traumatic brain injury (TBI) patients is challenging worldwide. The present study aimed to achieve the most accurate machine learning (ML) algorithms to predict the outcomes of TBI treatment by evaluating demographic features, laboratory data, imaging indices, and clinical features. We used data from 3347 patients admitted to a tertiary trauma centre in Iran from 2016 to 2021. After the exclusion of incomplete data, 1653 patients remained. We used ML algorithms such as random forest (RF) and decision tree (DT) with ten-fold cross-validation to develop the best prediction model. Our findings reveal that among different variables included in this study, the motor component of the Glasgow coma scale, the condition of pupils, and the condition of cisterns were the most reliable features for predicting in-hospital mortality, while the patients’ age takes the place of cisterns condition when considering the long-term survival of TBI patients. Also, we found that the RF algorithm is the best model to predict the short-term mortality of TBI patients. However, the generalized linear model (GLM) algorithm showed the best performance (with an accuracy rate of 82.03 ± 2.34) in predicting the long-term survival of patients. Our results showed that using appropriate markers and with further development, ML has the potential to predict TBI patients’ survival in the short- and long-term.
Increased lactate dehydrogenase reflects the progression of COVID-19 pneumonia on chest computed tomography and predicts subsequent severe disease
Authors: Kojima Kensuke , Yoon Hyungeun , Okishio Kyoichi , Tsuyuguchi Kazunari 
Published: January 2023

AbstractChest computed tomography (CT) is effective for assessing the severity of coronavirus disease 2019 (COVID-19). However, the clinical factors reflecting the disease progression of COVID-19 pneumonia on chest CT and predicting a subsequent exacerbation remain controversial. We conducted a retrospective cohort study of 450 COVID-19 patients. We used an automated image processing tool to quantify the COVID-19 pneumonia lesion extent on chest CT at admission. The factors associated with the lesion extent were estimated by a multiple regression analysis. After adjusting for background factors by propensity score matching, we conducted a multivariate Cox proportional hazards analysis to identify factors associated with severe disease after admission. The multiple regression analysis identified, body-mass index (BMI), lactate dehydrogenase (LDH), C-reactive protein (CRP), and albumin as continuous variables associated with the lesion extent on chest CT. The standardized partial regression coefficients for them were 1.76, 2.42, 1.54, and 0.71. The multivariate Cox proportional hazards analysis identified LDH (hazard ratio, 1.003; 95% confidence interval, 1.001–1.005) as a factor independently associated with the development of severe COVID-19 pneumonia. Increased serum LDH at admission may be useful in real-world clinical practice for the simple screening of COVID-19 patients at high risk of developing subsequent severe disease.