bluuebunny/crossref_metadata_2025_split huggingface dataset:
Design principles of improving the dose-response alignment in coupled GTPase switches
Authors: Qiao Lingxia , Ghosh Pradipta , Rangamani Padmini 
Published: January 2023

Abstract“Dose-response alignment” (DoRA), where the downstream response of cellular signaling pathways closely matches the fraction of activated receptor, can improve the fidelity of dose information transmission. The negative feedback has been experimentally identified as a key component for DoRA, but numerical simulations indicate that negative feedback is not sufficient to achieve perfect DoRA, i.e., perfect match of downstream response and receptor activation level. Thus a natural question is whether there exist design principles for signaling motifs within only negative feedback loops to improve DoRA to near-perfect DoRA. Here, we investigated several model formulations of an experimentally validated circuit that couples two molecular switches—mGTPase (monomeric GTPase) and tGTPase (heterotrimeric GTPases) — with negative feedback loops. In the absence of feedback, the low and intermediate mGTPase activation levels benefit DoRA in mass action and Hill-function models, respectively. Adding negative feedback has versatile roles on DoRA: it may impair DoRA in the mass action model with low mGTPase activation level and Hill-function model with intermediate mGTPase activation level; in other cases, i.e., the mass action model with a high mGTPase activation level or the Hill-function model with a non-intermediate mGTPase activation level, it improves DoRA. Furthermore, we found that DoRA in a longer cascade (i.e., tGTPase) can be obtained using Hill-function kinetics under certain conditions. In summary, we show how ranges of activity of mGTPase, reaction kinetics, the negative feedback, and the cascade length affect DoRA. This work provides a framework for improving the DoRA performance in signaling motifs with negative feedback.
High-throughput phase elucidation of polycrystalline materials using serial rotation electron diffraction
Authors: Luo Yi , Wang Bin , Smeets Stef , Sun Junliang , Yang Weimin , Zou Xiaodong 
Published: January 2023

AbstractRapid phase elucidation of polycrystalline materials is essential for developing new materials of chemical, pharmaceutical and industrial interest. Yet, the size and quantity of many crystalline phases are too small for routine X-ray diffraction analysis. This has become a workflow bottleneck in materials development, especially in high-throughput synthesis screening. Here we demonstrate the application of serial rotation electron diffraction (SerialRED) for high-throughput phase identification of complex polycrystalline zeolite products. The products were prepared from a combination of multiple framework T atoms ([Si,Ge,Al] or [Si,Ge,B]) and a simple organic structure-directing agent. We show that using SerialRED, five zeolite phases can be identified from a highly complex mixture. This includes phases with ultra-low contents undetectable using X-ray diffraction and phases with identical crystal morphology and similar unit cell parameters. By automatically and rapidly examining hundreds of crystals, SerialRED enables high-throughput phase analysis and allows the exploration of complex synthesis systems. It provides new opportunities for rapid development of polycrystalline materials.
L-form conversion in Gram-positive bacteria enables escape from phage infection
Authors: Wohlfarth Jan C. , Feldmüller Miki , Schneller Alissa , Kilcher Samuel , Burkolter Marco , Meile Susanne , Pilhofer Martin , Schuppler Markus , Loessner Martin J. 
Published: January 2023

AbstractAt the end of a lytic bacteriophage replication cycle in Gram-positive bacteria, peptidoglycan-degrading endolysins that cause explosive cell lysis of the host can also attack non-infected bystander cells. Here we show that in osmotically stabilized environments, Listeria monocytogenes can evade phage predation by transient conversion to a cell wall-deficient L-form state. This L-form escape is triggered by endolysins disintegrating the cell wall from without, leading to turgor-driven extrusion of wall-deficient, yet viable L-form cells. Remarkably, in the absence of phage predation, we show that L-forms can quickly revert to the walled state. These findings suggest that L-form conversion represents a population-level persistence mechanism to evade complete eradication by phage attack. Importantly, we also demonstrate phage-mediated L-form switching of the urinary tract pathogen Enterococcus faecalis in human urine, which underscores that this escape route may be widespread and has important implications for phage- and endolysin-based therapeutic interventions.
Environmental ranking of European industrial facilities by toxicity and global warming potentials
Authors: Erhart Szilárd , Erhart Kornél 
Published: January 2023

AbstractWe present a methodology to develop the integrated toxicity and climate change risk assessment of Europe based facilities, industries and regions. There is an increasingly important need for large scale sustainability measurement solutions for company reporting with high granularity. In this paper we measure key aspects of Sustainable Development Goals in terms of human, cancer and non-cancer toxicity, ecotoxicity together with global warming impact potentials from point source pollutant releases of more than 10,000 companies and their 33,000 facilities in Europe from 2001 to 2017, by using the European Pollutant Release and Transfer Register. For our assessment, we deploy a scientific consensus model, USEtox for characterizing human and ecotoxicological impacts of chemicals and the global warming potential values from the Intergovernmental Panel on Climate Change. We discuss water and air emissions of dozens of pollutants in urban, rural, coastal and inland areas. Companies in the electricity production sector are estimated to have the largest human toxicity impact potential (46% of total) and the largest global warming impact potential (50%), while companies in the sewerage sector have the largest ecotoxicity impact potential (50%). In the overall economy, the correlation between facilities’ global warming and toxicity impact potentials is positive, however, not very strong. Therefore, we argue that carbon footprint of industrial organizations can be only used as a climate change risk indicator, but not as an overall environmental performance indicator. We confirm impact potentials of major pollutants in previous research papers (Hg accounting for 76% of the total human toxicity and Zn accounting for 68% of total ecotoxicity), although we draw the attention to the limitations of USEtox in case of metals. From 2001 to 2017 total human toxicity dropped by 28%, although the downward trend reversed in 2016. Ecotoxicity and global warming impact potentials remained unchanged in the same period. Finally, we show that the European pollutant release monitoring data quality could be further improved, as only three quarters of the toxic releases are measured in the Member States of the European Union, and a high share of toxic pollutant releases are only estimated in some countries. Of the measured or calculated toxic releases, only one third is reported according to the most robust CEN/ISO standards and about one fifth according to the least preferred other methods, like engineering judgements.
Machine-learning-based spectral methods for partial differential equations
Authors: Meuris Brek , Qadeer Saad , Stinis Panos 
Published: January 2023

AbstractSpectral methods are an important part of scientific computing’s arsenal for solving partial differential equations (PDEs). However, their applicability and effectiveness depend crucially on the choice of basis functions used to expand the solution of a PDE. The last decade has seen the emergence of deep learning as a strong contender in providing efficient representations of complex functions. In the current work, we present an approach for combining deep neural networks with spectral methods to solve PDEs. In particular, we use a deep learning technique known as the Deep Operator Network (DeepONet) to identify candidate functions on which to expand the solution of PDEs. We have devised an approach that uses the candidate functions provided by the DeepONet as a starting point to construct a set of functions that have the following properties: (1) they constitute a basis, (2) they are orthonormal, and (3) they are hierarchical, i.e., akin to Fourier series or orthogonal polynomials. We have exploited the favorable properties of our custom-made basis functions to both study their approximation capability and use them to expand the solution of linear and nonlinear time-dependent PDEs. The proposed approach advances the state of the art and versatility of spectral methods and, more generally, promotes the synergy between traditional scientific computing and machine learning.
New C8-substituted caffeine derivatives as promising antioxidants and cytoprotective agents in human erythrocytes
Authors: Sierakowska Arleta , Jasiewicz Beata , Piosik Łukasz , Mrówczyńska Lucyna 
Published: January 2023

AbstractNew structurally diverse groups of C8-substituted caffeine derivatives were synthesized and evaluated for their chemical and biological properties. Mass spectrometry, FT-IR, and NMR characterizations of these derivatives were performed. The cytotoxic activity of the derivatives was estimated in vitro using human red blood cells (RBC) and in silico pharmacokinetic studies. The antioxidant capacity of the compounds was analyzed using a ferrous ion chelating activity assay. The ability of the derivatives to protect RBC from oxidative damage, including the oxidation of hemoglobin to methemoglobin, was assessed using a water-soluble 2,2′-azobis(2-methyl-propionamidine) dihydrochloride (AAPH) as a standard inducer of peroxyl radicals. The level of intracellular oxidative stress was assessed using the fluorescent redox probe 2′,7′-dichlorodihydrofluorescein diacetate (DCF-DA). The results indicate that all derivatives are biocompatible compounds with significant antioxidant and cytoprotective potential dependent on their chemical structure. In order to explain the antioxidant and cytoprotective activity of the derivatives, a mechanism of hydrogen atom transfer (HAT), radical adduct formation (RAF), or single electron transfer (SET), as well as the specific interactions of the derivatives with the lipid bilayer of RBC membrane, have been proposed. The results show that selected modifications of the caffeine molecule enhance its antioxidant properties, which expands our knowledge of the structure–activity relationship of caffeine-based cytoprotective compounds.
Biological and metabolic effects of the association between the microalga Galdieria sulphuraria and the fungus Penicillium citrinum
Authors: Salvatore Maria Michela , Carraturo Federica , Salbitani Giovanna , Rosati Luigi , De Risi Arianna , Andolfi Anna , Salvatore Francesco , Guida Marco , Carfagna Simona 
Published: January 2023

AbstractContamination of microalgae cultures can reduce their productivity and affect the quality of biomass and valuable bioproducts. In this article, after having isolated and identified for the first time the filamentous fungus Penicillium citrinum from heterotrophic cultures of the red polyextremophilic microalga Galdieria sulphuraria, we investigated the biological and metabolic significance of this alga-fungus association. In the same medium, both organisms grow better in each other's presence than separately. Both cell density and cell size of G. sulphuraria increase in co-cultures compared to pure alga cultures. In co-cultures, despite very severe growth conditions, the load of P. citrinum increases compared to pure fungus cultures. Optical microscope images have shown physical contact between cells of P. citrinum hyphae and G. sulphuraria which, however, retain their morphology and cell wall intact. GC–MS-based metabolomics analysis of metabolites excreted in the culture medium shows that pure cultures of the fungus and alga and co-cultures of alga plus fungus can be easily differentiated based on their metabolic products. Indeed, a richer assortment of extracellular metabolites (comprising both products of primary and secondary metabolism) is a distinct feature of co-cultures compared to both pure alga and pure fungus cultures.
Detection of abnormal extraocular muscles in small datasets of computed tomography images using a three-dimensional variational autoencoder
Authors: Chung Yeon Woong , Choi In Young 
Published: January 2023

AbstractWe sought to establish an unsupervised algorithm with a three–dimensional (3D) variational autoencoder model (VAE) for the detection of abnormal extraocular muscles in small datasets of orbital computed tomography (CT) images. 334 CT images of normal orbits and 96 of abnormal orbits diagnosed as thyroid eye disease were used for training and validation; 24 normal and 11 abnormal orbits were used for the test. A 3D VAE was developed and trained. All images were preprocessed to emphasize extraocular muscles and to suppress background noise (e.g., high signal intensity from bones). The optimal cut-off value was identified through receiver operating characteristic (ROC) curve analysis. The ability of the model to detect muscles of abnormal size was assessed by visualization. The model achieved a sensitivity of 79.2%, specificity of 72.7%, accuracy of 77.1%, F1-score of 0.667, and AUROC of 0.801. Abnormal CT images correctly identified by the model showed differences in the reconstruction of extraocular muscles. The proposed model showed potential to detect abnormalities in extraocular muscles using a small dataset, similar to the diagnostic approach used by physicians. Unsupervised learning could serve as an alternative detection method for medical imaging studies in which annotation is difficult or impossible to perform.
Rapid decline in kidney function is associated with rapid deterioration of health-related quality of life in chronic kidney disease
Authors: Kim Hyo Jin , Kim Da Woon , Rhee Harin , Song Sang Heon , Park Sue Kyung , Kim Soo Wan , Kim Yeong Hoon , Sung Suah , Oh Kook-Hwan , Seong Eun Young 
Published: January 2023

AbstractThis study aimed to evaluate changes in health-related quality of life (HRQOL) in patients with chronic kidney disease (CKD) according to decline in kidney function. HRQOL was assessed using the Short Form-36 questionnaire composed of a physical component summary (PCS) and mental component summary (MCS). Rapid decline in kidney function was defined as a decline in the estimated glomerular filtration rate (eGFR) of > 3 mL/min/1.73 m2/year. Rapid deterioration of HRQOL was defined a change in the HRQOL value greater than the median. Among 970 patients, 360 (37.1%) were in the rapid kidney function decline group. In 720 patients who were 1:1 propensity score-matched, the baseline eGFR was not significantly different between the non-rapid and rapid kidney function decline groups. Compared with the baseline PCS score, the 5-year PCS score decreased in the non-rapid and rapid kidney function decline groups. The 5-year MCS score significantly decreased in the rapid kidney function decline group alone. Rapid decline in kidney function was significantly associated with rapid deterioration of the PCS (odds ratio [OR]: 1.48; 95% confidence interval [CI]: 1.07–2.05; P = 0.018) and MCS (OR: 1.89; 95% CI 1.36–2.62; P < 0.001) scores. Rapid decline in kidney function was associated with rapid deterioration of HRQOL in patients with CKD.
CENPF knockdown inhibits adriamycin chemoresistance in triple-negative breast cancer via the Rb-E2F1 axis
Authors: Wang Depeng , Xu Wei , Huang Minghua , Ma Wei , Liu Yulu , Zhou Xingchen , Yang Qingrui , Mu Kun 
Published: January 2023

AbstractDrug resistance occurs frequently in triple-negative breast cancer (TNBC) and leads to early relapse and short survival. Targeting the DNA damage response (DDR) has become an effective strategy for overcoming TNBC chemoresistance. CENPF (centromere protein) is a key regulator of cell cycle progression, but its role in TNBC chemotherapy resistance remains unclear. Here, we found that CENPF, which is highly expressed in TNBC, is associated with a poor prognosis in patients receiving chemotherapy. In addition, in vitro CENPF knockdown significantly increased adriamycin (ADR)-induced cytotoxicity in MDA-MB-231 cells and ADR-resistant cells (MDA-MB-231/ADR). Then, we demonstrated that CENPF targets Chk1-mediated G2/M phase arrest and binds to Rb to compete with E2F1 in TNBC. Considering the crucial role of E2F1 in the DNA damage response and DNA repair, a novel mechanism by which CENPF regulates the Rb-E2F1 axis will provide new horizons to overcome chemotherapy resistance in TNBC.
Domain-wall magnetoelectric coupling in multiferroic hexagonal YbFeO3 films
Authors: Li Xin , Yun Yu , Thind Arashdeep Singh , Yin Yuewei , Li Qiang , Wang Wenbin , N’Diaye Alpha T. , Mellinger Corbyn , Jiang Xuanyuan , Mishra Rohan , Xu Xiaoshan 
Published: January 2023

AbstractElectrical modulation of magnetic states in single-phase multiferroic materials, using domain-wall magnetoelectric (ME) coupling, can be enhanced substantially by controlling the population density of the ferroelectric (FE) domain walls during polarization switching. In this work, we investigate the domain-wall ME coupling in multiferroic h-YbFeO3 thin films, in which the FE domain walls induce clamped antiferromagnetic (AFM) domain walls with reduced magnetization magnitude. Simulation according to the phenomenological theory indicates that the domain-wall ME effect is dramatically enhanced when the separation between the FE domain walls shrinks below the characteristic width of the clamped AFM domain walls during the ferroelectric switching. Experimentally, we show that while the magnetization magnitude remains same for both the positive and the negative saturation polarization states, there is evidence of magnetization reduction at the coercive voltages. These results suggest that the domain-wall ME effect is viable for electrical control of magnetization.
Predictive correction of serum sodium concentration with formulas derived from the Edelman equation in patients with severe hyponatremia
Authors: Nagase Koya , Watanabe Tsuyoshi , Nomura Akihiro , Nagase Fumika N. , Iwasaki Keita , Nakamura Yoshihiro , Ikai Hiroki , Yamamoto Mari , Murai Yukari , Yokoyama-Kokuryo Waka , Takizawa Naoho , Shimizu Hideaki , Fujita Yoshiro 
Published: January 2023

AbstractSevere hyponatremia can cause life-threatening cerebral edema. Treatment comprises rapid elevation of serum sodium concentration; however, overcorrection can result in osmotic demyelination. This study investigated potential factors, including predictive correction based on the Edelman equation, associated with appropriate correction in 221 patients with a serum sodium concentration ≤ 120 mEq/L who were admitted to a hospital in Nagoya, Japan. Appropriate correction was defined as an elevation in serum sodium concentration in the range of 4–10 mEq/L in the first 24 h and within 18 mEq/L in the first 48 h after the start of the correction. Appropriate corrections were made in 132 (59.7%) of the 221 patients. Multivariate analysis revealed that predictive correction with an infusate and fluid loss formula derived from the Edelman equation was associated with appropriate correction of serum sodium concentration (adjusted odds ratio, 7.84; 95% confidence interval, 2.97–20.64). Relative without its use, the predictive equation results in a lower proportion of undercorrection (14.3% vs. 48.0%, respectively) and overcorrection (1.0% vs. 12.2%, respectively). These results suggest that predictive correction of serum sodium concentrations using the formula derived from the Edelman equation can play an essential role in the appropriate management of patients with severe hyponatremia.
Data-driven nonstationary signal decomposition approaches: a comparative analysis
Authors: Eriksen Thomas , Rehman Naveed ur 
Published: January 2023

AbstractSignal decomposition (SD) approaches aim to decompose non-stationary signals into their constituent amplitude- and frequency-modulated components. This represents an important preprocessing step in many practical signal processing pipelines, providing useful knowledge and insight into the data and relevant underlying system(s) while also facilitating tasks such as noise or artefact removal and feature extraction. The popular SD methods are mostly data-driven, striving to obtain inherent well-behaved signal components without making many prior assumptions on input data. Among those methods include empirical mode decomposition and variants, variational mode decomposition and variants, synchrosqueezed transform and variants and sliding singular spectrum analysis. With the increasing popularity and utility of these methods in wide-ranging applications, it is imperative to gain a better understanding and insight into the operation of these algorithms, evaluate their accuracy with and without noise in input data and gauge their sensitivity against algorithmic parameter changes. In this work, we achieve those tasks through extensive experiments involving carefully designed synthetic and real-life signals. Based on our experimental observations, we comment on the pros and cons of the considered SD algorithms as well as highlighting the best practices, in terms of parameter selection, for the their successful operation. The SD algorithms for both single- and multi-channel (multivariate) data fall within the scope of our work. For multivariate signals, we evaluate the performance of the popular algorithms in terms of fulfilling the mode-alignment property, especially in the presence of noise.
Explainable artificial intelligence as a reliable annotator of archaeal promoter regions
Authors: Sganzerla Martinez Gustavo , Perez-Rueda Ernesto , Kumar Aditya , Sarkar Sharmilee , de Avila e Silva Scheila 
Published: January 2023

AbstractArchaea are a vast and unexplored cellular domain that thrive in a high diversity of environments, having central roles in processes mediating global carbon and nutrient fluxes. For these organisms to balance their metabolism, the appropriate regulation of their gene expression is essential. A key momentum in regulating genes responsible for the life maintenance of archaea is when transcription factor proteins bind to the promoter element. This DNA segment is conserved, which enables its exploration by machine learning techniques. Here, we trained and tested a support vector machine with 3935 known archaeal promoter sequences. All promoter sequences were coded into DNA Duplex Stability. After, we performed a model interpretation task to map the decision pattern of the classification procedure. We also used a dataset of known-promoter sequences for validation. Our results showed that an AT rich region around position − 27 upstream (relative to the start TSS) is the most conserved in the analyzed organisms. In addition, we were able to identify the BRE element (− 33), the PPE (at − 10) and a position at + 3, that provides a more understandable picture of how promoters are organized in all the archaeal organisms. Finally, we used the interpreted model to identify potential promoter sequences of 135 unannotated organisms, delivering regulatory regions annotation of archaea in a scale never accomplished before (https://pcyt.unam.mx/gene-regulation/). We consider that this approach will be useful to understand how gene regulation is achieved in other organisms apart from the already established transcription factor binding sites.