bluuebunny/crossref_metadata_2025_split huggingface dataset:
Induced neural progenitor cells and iPS-neurons from major depressive disorder patients show altered bioenergetics and electrophysiological properties
Authors: Triebelhorn Julian , Cardon Iseline , Kuffner Kerstin , Bader Stefanie , Jahner Tatjana , Meindl Katrin , Rothhammer-Hampl Tanja , Riemenschneider Markus J. , Drexler Konstantin , Berneburg Mark , Nothdurfter Caroline , Manook André , Brochhausen Christoph , Baghai Thomas C. , Hilbert Sven , Rupprecht Rainer , Milenkovic Vladimir M. , Wetzel Christian H. 
Published: June 2022

AbstractThe molecular pathomechanisms of major depressive disorder (MDD) are still not completely understood. Here, we follow the hypothesis, that mitochondria dysfunction which is inevitably associated with bioenergetic disbalance is a risk factor that contributes to the susceptibility of an individual to develop MDD. Thus, we investigated molecular mechanisms related to mitochondrial function in induced neuronal progenitor cells (NPCs) which were reprogrammed from fibroblasts of eight MDD patients and eight non-depressed controls. We found significantly lower maximal respiration rates, altered cytosolic basal calcium levels, and smaller soma size in NPCs derived from MDD patients. These findings are partially consistent with our earlier observations in MDD patient-derived fibroblasts. Furthermore, we differentiated MDD and control NPCs into iPS-neurons and analyzed their passive biophysical and active electrophysiological properties to investigate whether neuronal function can be related to altered mitochondrial activity and bioenergetics. Interestingly, MDD patient-derived iPS-neurons showed significantly lower membrane capacitance, a less hyperpolarized membrane potential, increased Na+ current density and increased spontaneous electrical activity. Our findings indicate that functional differences evident in fibroblasts derived from MDD patients are partially present after reprogramming to induced-NPCs, could relate to altered function of iPS-neurons and thus might be associated with the aetiology of major depressive disorder.
Bridging a diagnostic Kawasaki disease classifier from a microarray platform to a qRT-PCR assay
Authors: Kuiper Rowan , Wright Victoria J. , Habgood-Coote Dominic , Shimizu Chisato , Huigh Daphne , Tremoulet Adriana H. , van Keulen Danielle , Hoggart Clive J. , Rodriguez-Manzano Jesus , Herberg Jethro A. , Kaforou Myrsini , Tempel Dennie , Burns Jane C. , Levin Michael 
Published: June 2022

Abstract
Background
Kawasaki disease (KD) is a systemic vasculitis that mainly affects children under 5 years of age. Up to 30% of patients develop coronary artery abnormalities, which are reduced with early treatment. Timely diagnosis of KD is challenging but may become more straightforward with the recent discovery of a whole-blood host response classifier that discriminates KD patients from patients with other febrile conditions. Here, we bridged this microarray-based classifier to a clinically applicable quantitative reverse transcription-polymerase chain reaction (qRT-PCR) assay: the Kawasaki Disease Gene Expression Profiling (KiDs-GEP) classifier.

Methods
We designed and optimized a qRT-PCR assay and applied it to a subset of samples previously used for the classifier discovery to reweight the original classifier.

Results
The performance of the KiDs-GEP classifier was comparable to the original classifier with a cross-validated area under the ROC curve of 0.964 [95% CI: 0.924–1.00] vs 0.992 [95% CI: 0.978–1.00], respectively. Both classifiers demonstrated similar trends over various disease conditions, with the clearest distinction between individuals diagnosed with KD vs viral infections.

Conclusion
We successfully bridged the microarray-based classifier into the KiDs-GEP classifier, a more rapid and more cost-efficient qRT-PCR assay, bringing a diagnostic test for KD closer to the hospital clinical laboratory.

Impact

A diagnostic test is needed for Kawasaki disease and is currently not available.

We describe the development of a One-Step multiplex qRT-PCR assay and the subsequent modification (i.e., bridging) of the microarray-based host response classifier previously described by Wright et al.

The bridged KiDs-GEP classifier performs well in discriminating Kawasaki disease patients from febrile controls.

This host response clinical test for Kawasaki disease can be adapted to the hospital clinical laboratory.
Decoding lymphomyeloid divergence and immune hyporesponsiveness in G-CSF-primed human bone marrow by single-cell RNA-seq
Authors: You Guoju , Zhang Man , Bian Zhilei , Guo Huidong , Xu Zhengyang , Ni Yanli , Lan Yu , Yue Wen , Gong Yandong , Chang Yingjun , Huang Xiaojun , Liu Bing 
Published: June 2022

AbstractGranulocyte colony-stimulating factor (G-CSF) has been widely used to mobilize bone marrow hematopoietic stem/progenitor cells for transplantation in the treatment of hematological malignancies for decades. Additionally, G-CSF is also accepted as an essential mediator in immune regulation, leading to reduced graft-versus-host disease following transplantation. Despite the important clinical roles of G-CSF, a comprehensive, unbiased, and high-resolution survey into the cellular and molecular ecosystem of the human G-CSF-primed bone marrow (G-BM) is lacking so far. Here, we employed single-cell RNA sequencing to profile hematopoietic cells in human bone marrow from two healthy donors before and after 5-day G-CSF administration. Through unbiased bioinformatics analysis, our data systematically showed the alterations in the transcriptional landscape of hematopoietic cells in G-BM, and revealed that G-CSF-induced myeloid-biased differentiation initiated from the stage of lymphoid-primed multipotent progenitors. We also illustrated the cellular and molecular basis of hyporesponsiveness of T cells and natural killer (NK) cells caused by G-CSF stimulation, including the potential direct mechanisms and indirect regulations mediated by ligand–receptor interactions. Taken together, our data extend the understanding of lymphomyeloid divergence and potential mechanisms involved in hyporesponsiveness of T and NK cells in human G-BM, which might provide basis for optimization of stem cell transplantation in hematological malignancy treatment.
Dynamical phase-field model of coupled electronic and structural processes
Authors: Yang Tiannan , Chen Long-Qing 
Published: June 2022

AbstractMany functional and quantum materials derive their functionality from the responses of both their electronic and lattice subsystems to thermal, electric, and mechanical stimuli or light. Here we propose a dynamical phase-field model for predicting and modeling the dynamics of simultaneous electronic and structural processes and the accompanying mesoscale pattern evolution under static or ultrafast external stimuli. As an illustrative example of application, we study the transient dynamic response of ferroelectric domain walls excited by an ultrafast above-bandgap light pulse. We discover a two-stage relaxational electronic carrier evolution and a structural evolution containing multiple oscillational and relaxational components across picosecond to nanosecond timescales. The phase-field model offers a general theoretical framework which can be applied to a wide range of functional and quantum materials with interactive electronic and lattice orders and phase transitions to understand, predict, and manipulate their ultrafast dynamics and rich mesoscale evolution dynamics of domains, domain walls, and charges.
A multi-scale time-series dataset with benchmark for machine learning in decarbonized energy grids
Authors: Zheng Xiangtian , Xu Nan , Trinh Loc , Wu Dongqi , Huang Tong , Sivaranjani S. , Liu Yan , Xie Le 
Published: June 2022

AbstractThe electric grid is a key enabling infrastructure for the ambitious transition towards carbon neutrality as we grapple with climate change. With deepening penetration of renewable resources, the reliable operation of the electric grid becomes increasingly challenging. In this paper, we present PSML, a first-of-its-kind open-access multi-scale time-series dataset, to aid in the development of data-driven machine learning (ML)-based approaches towards reliable operation of future electric grids. The dataset is synthesized from a joint transmission and distribution electric grid to capture the increasingly important interactions and uncertainties of the grid dynamics, containing power, voltage and current measurements over multiple spatio-temporal scales. Using PSML, we provide state-of-the-art ML benchmarks on three challenging use cases of critical importance to achieve: (i) early detection, accurate classification and localization of dynamic disturbances; (ii) robust hierarchical forecasting of load and renewable energy; and (iii) realistic synthetic generation of physical-law-constrained measurements. We envision that this dataset will provide use-inspired ML research in safety-critical systems, while simultaneously enabling ML researchers to contribute towards decarbonization of energy sectors.
Characterization of subtilosin gene in wild type Bacillus spp. and possible physiological role
Authors: Alajlani Muaaz Mutaz 
Published: June 2022

AbstractIn a designed study to screen for antimicrobial exhibiting bacteria using molecular aspects, Bacillus species were considered to investigate antibiotic biosynthesis genes. 28 bacterial strains and 3 induced mutants were screened for the presence of subtilosin gene (sbo) and subtilosin through PCR and Mass spectrometry respectively. Sbo gene was detected in 16 out of 28 Bacillus strains. The results from gene sequences deliberated by multiple sequence alignments revealed high-level homology to the sequences of the sbo-alb gene locus of B. subtilis 168 and the other limited reported strains. Hence, this report provided additional strains to support the idea of subtilosin gene predominance amongst Bacillus strains isolated from environment and to find different species containing homologous genes, furthermore the utilization of its conserved region as a means of identifying Bacillus spp. that produce subtilosin. This is the first report to confirm the detection of subtilosin production from B. amyloliquefaciens.
Post combustion CO2 capture with calcium and lithium hydroxide
Authors: Costagliola Maria Antonietta , Prati Maria Vittoria , Perretta Giuseppe 
Published: June 2022

AbstractA small-scale plant was built for measuring the ability of solid sorbents towards the capture of carbon dioxide (CO2) in exhaust flue gas from an internal combustion engine. The investigated sorbents were calcium and lithium hydroxides. Both sorbents are low cost and used in the breathing gas purification systems. The carbonation capacity of each sorbent was measured for different sorbent granulometry (pellets and powder), different temperature (from ambient up to 300 °C), gas space velocity, moisture content and chemical composition of the gaseous stream. The aim was, in fact, to expose the sorbents to a gas stream with chemical and physical parameters close to those at the exhaust of an internal combustion engine. Carbonation capacity was measured with a double technique: on-line by continuously CO2 measurement with a non-dispersive infrared analyzer and off-line by using scanning electron microscopy on carbonated sorbents. Experimental results showed good CO2 uptake capacity of calcium hydroxide at low temperature (between 20 and 150 °C). Performance improvements came from the fine granulometry due to the increased exposed surface area; moreover, the presence of the moisture in gas stream also enhanced CO2 capture. The presence of sulphur dioxide and nitric oxide, instead, greatly decreased the carbonation capacity of sorbents.
The impact of Rhodiola rosea on biomarkers of diabetes, inflammation, and microbiota in a leptin receptor-knockout mouse model
Authors: Jafari Mahtab , Juanson Arabit Jasmin Grace , Courville Robert , Kiani Dara , Chaston John M. , Nguyen Cindy Duy , Jena Nilamani , Liu Zhong-Ying , Tata Prasanthi , Van Etten Richard A. 
Published: June 2022

AbstractType 2 diabetes is the most prevalent endocrine disease in the world, and recently the gut microbiota have become a potential target for its management. Recent studies have illustrated that this disease may predispose individuals to certain microbiome compositions, and treatments like metformin have been shown to change gut microbiota and their associated metabolic pathways. However, given the limitations and side effects associated with pharmaceuticals currently being used for therapy of diabetes, there is a significant need for alternative treatments. In this study, we investigated the effects of a root extract fromRhodiola roseain a Leptin receptor knockout (db/db) mouse model of type 2 diabetes. Our previous work showed thatRhodiola roseahad anti-inflammatory and gut microbiome-modulating properties, while extending lifespan in several animal models. In this study, treatment withRhodiola roseaimproved fasting blood glucose levels, altered the response to exogenous insulin, and decreased circulating lipopolysaccharide and hepatic C-reactive protein transcript levels. We hypothesize that these changes may in part reflect the modulation of the microbiota, resulting in improved gut barrier integrity and decreasing the translocation of inflammatory biomolecules into the bloodstream. These findings indicate thatRhodiola roseais an attractive candidate for further research in the management of type 2 diabetes.
Non-classical light state transfer in su(2) resonator networks
Authors: Espinosa A. F. Muñoz , Lee R.-K. , Rodríguez-Lara B. M. 
Published: June 2022

AbstractWe use a normal mode approach to show full and partial state transfer in a class of coupled resonator networks with underlying su(2) symmetry that includes the so-called $$J_{x}$$

J
x

photonic lattice. Our approach defines an auxiliary Hermitian coupling matrix describing the network that yields the normal modes of the system and its time evolution in terms of orthogonal polynomials. Our results provide insight on the full quantum state reconstruction time in a general su(2) network of any size and the full quantum transfer time in the $$J_{x}$$

J
x

network of size $$4 n + 1$$

4
n
+
1

with $$n=1,2,3,\ldots$$

n
=
1
,
2
,
3
,
…

For any other network sizes, the Fock state probability distribution of the initial state is conserved but the amplitudes suffer a phase shift proportional to $$\pi /2$$

π
/
2

that results in partial quantum state transfer.
NTN4 as a prognostic marker and a hallmark for immune infiltration in breast cancer
Authors: Yi Lili , Lei Yongqiang , Yuan Fengjiao , Tian Conghui , Chai Jian , Gu Mingliang 
Published: June 2022

AbstractNetrin-4 (NTN4), a member of neurite guidance factor family, can promote neurite growth and elongation. This study aims to investigate if NTN4 correlates with prognosis and immune infiltration in breast cancer. The prognostic landscape of NTN4 and its relationship with immune infiltration in breast cancer were deciphered with public databases and immunohistochemistry (IHC) in tissue samples. The expression profiling and prognostic value of NTN4 were explored using UALCAN, TIMER, Kaplan–Meier Plotter and Prognoscan databases. Based on TIMER, relationships of NTN4 expression with tumor immune invasion and immune cell surface markers were evaluated. Transcription and survival analyses of NTN4 in breast cancer were investigated with cBioPortal database. The STRING database was explored to identify molecular functions and signaling pathways downstream of NTN4. NTN4 expression was significantly lower in invasive breast carcinoma compared with adjacent non-malignant tissues. Promoter methylation of NTN4 exhibited different patterns in breast cancer. Low expression of NTN4 was associated with poorer survival. NTN4 was significantly positively related to infiltration of CD8+ T cells, macrophages and neutrophils, whereas significantly negatively related to B cells and tumor purity. Association patterns varied with different subtypes. Various associations between NTN4 levels and immune cell surface markers were revealed. Different subtypes of breast cancer carried different genetic alterations. Mechanistically, NTN4 was involved in mediating multiple biological processes including morphogenesis and migration.
A novel machine learning model to predict respiratory failure and invasive mechanical ventilation in critically ill patients suffering from COVID-19
Authors: Bendavid Itai , Statlender Liran , Shvartser Leonid , Teppler Shmuel , Azullay Roy , Sapir Rotem , Singer Pierre 
Published: June 2022

AbstractIn hypoxemic patients at risk for developing respiratory failure, the decision to initiate invasive mechanical ventilation (IMV) may be extremely difficult, even more so among patients suffering from COVID-19. Delayed recognition of respiratory failure may translate into poor outcomes, emphasizing the need for stronger predictive models for IMV necessity. We developed a two-step model; the first step was to train a machine learning predictive model on a large dataset of non-COVID-19 critically ill hypoxemic patients from the United States (MIMIC-III). The second step was to apply transfer learning and adapt the model to a smaller COVID-19 cohort. An XGBoost algorithm was trained on data from the MIMIC-III database to predict if a patient would require IMV within the next 6, 12, 18 or 24 h. Patients’ datasets were used to construct the model as time series of dynamic measurements and laboratory results obtained during the previous 6 h with additional static variables, applying a sliding time-window once every hour. We validated the adaptation algorithm on a cohort of 1061 COVID-19 patients from a single center in Israel, of whom 160 later deteriorated and required IMV. The new XGBoost model for the prediction of the IMV onset was trained and tested on MIMIC-III data and proved to be predictive, with an AUC of 0.83 on a shortened set of features, excluding the clinician’s settings, and an AUC of 0.91 when the clinician settings were included. Applying these models “as is” (no adaptation applied) on the dataset of COVID-19 patients degraded the prediction results to AUCs of 0.78 and 0.80, without and with the clinician’s settings, respectively. Applying the adaptation on the COVID-19 dataset increased the prediction power to an AUC of 0.94 and 0.97, respectively. Good AUC results get worse with low overall precision. We show that precision of the prediction increased as prediction probability was higher. Our model was successfully trained on a specific dataset, and after adaptation it showed promise in predicting outcome on a completely different dataset. This two-step model successfully predicted the need for invasive mechanical ventilation 6, 12, 18 or 24 h in advance in both general ICU population and COVID-19 patients. Using the prediction probability as an indicator of the precision carries the potential to aid the decision-making process in patients with hypoxemic respiratory failure despite the low overall precision.
Design, synthesis, in vitro α-glucosidase inhibition, docking, and molecular dynamics of new phthalimide-benzenesulfonamide hybrids for targeting type 2 diabetes
Authors: Askarzadeh Mohammad , Azizian Homa , Adib Mehdi , Mohammadi-Khanaposhtani Maryam , Mojtabavi Somayeh , Faramarzi Mohammad Ali , Sajjadi-Jazi Sayed Mahmoud , Larijani Bagher , Hamedifar Haleh , Mahdavi Mohammad 
Published: June 2022

AbstractIn the present work, a new series of 14 novel phthalimide-benzenesulfonamide derivatives 4a–n were synthesized, and their inhibitory activity against yeast α-glucosidase was screened. The obtained results indicated that most of the newly synthesized compounds showed prominent inhibitory activity against α-glucosidase. Among them, 4-phenylpiperazin derivative 4m exhibited the strongest inhibition with the IC50 value of 52.2 ± 0.1 µM. Enzyme kinetic study of compound 4m proved that its inhibition mode was competitive and Ki value of this compound was calculated to be 52.7 µM. In silico induced fit docking and molecular dynamics studies were performed to further investigate the interaction, orientation, and conformation of the target compounds over the active site of α-glucosidase. Obtained date of these studies demonstrated that our new compounds interacted as well with the α-glucosidase active site with the acceptable binding energies. Furthermore, in silico druglikeness/ADME/Toxicity studies of compound 4m were performed and predicted that this compound is druglikeness and has good ADME and toxicity profiles.
Integrative transcriptome-wide analysis of atopic dermatitis for drug repositioning
Authors: Song Jaeseung , Kim Daeun , Lee Sora , Jung Junghyun , Joo Jong Wha J. , Jang Wonhee 
Published: June 2022

AbstractAtopic dermatitis (AD) is one of the most common inflammatory skin diseases, which significantly impact the quality of life. Transcriptome-wide association study (TWAS) was conducted to estimate both transcriptomic and genomic features of AD and detected significant associations between 31 expression quantitative loci and 25 genes. Our results replicated well-known genetic markers for AD, as well as 4 novel associated genes. Next, transcriptome meta-analysis was conducted with 5 studies retrieved from public databases and identified 5 additional novel susceptibility genes for AD. Applying the connectivity map to the results from TWAS and meta-analysis, robustly enriched perturbations were identified and their chemical or functional properties were analyzed. Here, we report the first research on integrative approaches for an AD, combining TWAS and transcriptome meta-analysis. Together, our findings could provide a comprehensive understanding of the pathophysiologic mechanisms of AD and suggest potential drug candidates as alternative treatment options.
Modelling systemic COVID-19 impacts in cities
Authors: Beevers Lindsay , Bedinger Melissa , McClymont Kerri , Morrison David , Aitken Gordon , Visser-Quinn Annie 
Published: June 2022

AbstractThe COVID-19 pandemic has impacted public health, the economy and society—both directly and indirectly. Few approaches exist to understand these complex impacts in a way that (1) acknowledges cross-sectoral interdependencies; (2) models how short-term shocks translate into impacts on longer-term outcomes; (3) builds in local, contextual variation; and (4) recognises a wide set of priorities. The Urban Systems Abstraction Hierarchy (USAH) is proposed as an approach with these capabilities, and applied to Edinburgh (UK) between March-October 2020 to identify city-level impacts of the pandemic and associated policy responses. Results show changing priorities in the system and suggest areas which should be targeted for future urban resilience planning in Edinburgh for both short-term shocks and long-term recovery. This makes both methodological contributions (in the form of testing a new complex systems approach) and practical contributions (in the form of city-specific results which inform different aspects of resilience) to urban science.