bluuebunny/crossref_metadata_2025_split huggingface dataset:
Foamed glass ceramics—an upcycled scaffold for microbial biofilm development
Authors: Kugler Alex , Trivelpiece Cory , Brigmon Robin L. 
Published: December 2022

AbstractGlass, a near infinitely recyclable material, can be upcycled to create new products such as foamed glass ceramics, which are essentially a synthetic pumice-like material. This material has been demonstrated to sustain preserved biofilms which have application in various fields based on the deployability of the product and the preserved microbes. Foamed glass ceramics have increased surface area compared to typical soda-lime glass cullet. This material has been explored for variety of applications including the growth, storage and transport of biofilms and microbial colonies which can be preserved and deployed later. Here, we demonstrate the ability for microbial cultures including BioTiger™, Escherichia coli K-12, Bacillus thuringiensis, and two environmental eukaryotic cells to colonize the upcycled glass products, undergo preservation, and regrow after 84 days of storage. The growth of preserved samples is correlated to the time spent incubating prior to preservation. These results demonstrate the applicability of this novel glass-biofilm combination in which various preserved microorganisms are able to be rapidly grown after storage on an upcycled glass product.
Maternal Early Life Adversity and Infant Stress Regulation: Intergenerational Associations and Mediation by Maternal Prenatal Mental Health
Authors: Barclay Margot E , Rinne Gabrielle R , Somers Jennifer A , Lee Steve S , Coussons-Read Mary , Dunkel Schetter Christine 
Published: December 2022

AbstractEarly life adversity is a potent risk factor for poor mental health outcomes across the lifespan, including offspring vulnerability to psychopathology. Developmentally, the prenatal period is a sensitive window in which maternal early life experiences may influence offspring outcomes and demarcates a time when expectant mothers and offspring are more susceptible to stressful and salutary influences. This prenatal plasticity constituted the focus of the current study where we tested the association of maternal early life adversity with infant stress regulation through maternal prenatal internalizing symptoms and moderation by prenatal social support. Mother-infant dyads (n = 162) were followed prospectively and mothers completed assessments of social support and depressive and anxiety symptoms across pregnancy. Infants completed standardized stress paradigms at one month and six months. There were several key findings. First, maternal prenatal depressive symptoms significantly mediated predictions of infant cortisol reactivity to the heel stick at one month from maternal early life adversity: specifically, maternal early life adversity positively predicted depressive symptoms in pregnancy, which in turn predicted dampened infant cortisol reactivity. Second, prenatal social support did not significantly moderate predictions of depressive or anxiety symptoms in pregnancy from maternal early life adversity nor did it alter the associations of maternal depressive or anxiety symptoms with infant stress regulation. These results suggest that maternal prenatal mental health is a key mechanism by which maternal early life adverse experiences affect offspring risk for psychopathology. We discuss potential clinical and health implications of dysregulated infant cortisol reactivity with respect to lifespan development.
A Formalization and Proof Checker for Isabelle’s Metalogic
Authors: Roßkopf Simon , Nipkow Tobias 
Published: December 2022

AbstractIsabelle is a generic theorem prover with a fragment of higher-order logic as a metalogic for defining object logics. Isabelle also provides proof terms. We formalize this metalogic and the language of proof terms in Isabelle/HOL, define an executable (but inefficient) proof term checker and prove its correctness w.r.t. the metalogic. We integrate the proof checker with Isabelle and run it on a range of logics and theories to check the correctness of all the proofs in those theories.
A new approach for clinical translation of infrared spectroscopy: exploitation of the signature of glioblastoma for general brain tumor recognition
Authors: Steiner Gerald , Galli Roberta , Preusse Grit , Michen Susanne , Meinhardt Matthias , Temme Achim , Sobottka Stephan B. , Juratli Tareq A. , Koch Edmund , Schackert Gabriele , Kirsch Matthias , Uckermann Ortrud 
Published: December 2022

Abstract
Purpose
Infrared (IR) spectroscopy has the potential for tumor delineation in neurosurgery. Previous research showed that IR spectra of brain tumors are generally characterized by reduced lipid-related and increased protein-related bands. Therefore, we propose the exploitation of these common spectral changes for brain tumor recognition.

Methods
Attenuated total reflection IR spectroscopy was performed on fresh specimens of 790 patients within minutes after resection. Using principal component analysis and linear discriminant analysis, a classification model was developed on a subset of glioblastoma (n = 135) and non-neoplastic brain (n = 27) specimens, and then applied to classify the IR spectra of several types of brain tumors.

Results
The model correctly classified 82% (517/628) of specimens as “tumor” or “non-tumor”, respectively. While the sensitivity was limited for infiltrative glioma, this approach recognized GBM (86%), other types of primary brain tumors (92%) and brain metastases (92%) with high accuracy and all non-tumor samples were correctly identified.

Conclusion
The concept of differentiation of brain tumors from non-tumor brain based on a common spectroscopic tumor signature will accelerate clinical translation of infrared spectroscopy and related technologies. The surgeon could use a single instrument to detect a variety of brain tumor types intraoperatively in future clinical settings. Our data suggests that this would be associated with some risk of missing infiltrative regions or tumors, but not with the risk of removing non-tumor brain.
Deep learning based channel estimation optimization in VLC systems
Authors: Salama Wessam M. , Aly Moustafa H. , Amer Eman S. 
Published: December 2022

AbstractThis paper aims to improve the channel estimation (CE) in the indoor visible light communication system. The proposal of this paper deals with a system that depends on a comparison between Deep Neural Network (DNN), Yolo v3, and Kalman filter (KF) algorithm, for two optical modulation techniques; asymmetrically clipped optical-orthogonal frequency-division multiplexing (ACO-OFDM) and direct current optical-orthogonal frequency division multiplexing (DCO-OFDM). The CE can be evaluated by the error rates in the received bits, where increased error means a performance decrease of the system and vice versa. Receiving less errors at the receiver indicates improved CE and system performance. Hence, the main aim of our work is to decrease the error rate by using different estimators. Furthermore, we apply automatic hyper-parameter approach and Bayesian optimization, to Yolo v3 model to improve the system performance and reduce the positioning error. The metric parameter of bit error rate (BER) aims to determine the improvement ratio in different systems. The model in this paper is based on training with OFDM samples of signal with labels which are received and are corresponding to the signals of OFDM. At a BER = 10−3 with DCO-OFDM, the DNN outperforms KF with 1.7 dB (8.09%) at the bit energy per noise $$(E_{b} {/}N_{o} )$$

(

E
b

/

N
o

)

axis. Also, for ACO-OFDM at BER = 10−3, the DNN achieves better results than KF by about 1.9 dB (11.8%) at the $$(E_{b} {/}N_{o} ){ }$$

(

E
b

/

N
o

)

axis. For different values of M in QAM, the DNN outperforms KF for ACO-OFDM by average improvement of ~ 1.2 dB (~ 13%).
HMGCL-induced β-hydroxybutyrate production attenuates hepatocellular carcinoma via DPP4-mediated ferroptosis susceptibility
Authors: Cui Xiaohan , Yun Xiao , Sun Meiling , Li Renzhi , Lyu Xiajie , Lao Yuanxiang , Qin Xihu , Yu Wenbin 
Published: December 2022

Abstract
Background
Metabolic disorder is an essential characteristic of tumor development. Ketogenesis is a heterogeneous factor in multiple cancers, but the effect of ketogenesis on hepatocellular carcinoma (HCC) is elusive.

Methods
We aimed to explain the role of ketogenesis-related hydroxy-methyl-glutaryl-CoA lyase (HMGCL) on HCC suppression. Expression pattern of HMGCL in HCC specimens was evaluated by immunohistochemistry (IHC). HMGCL was depleted or overexpressed in HCC cells to investigate the functions of HMGCL in vitro and in vivo. The anti-tumor function of HMGCL was studied in subcutaneous xenograft and Trp53Δhep/Δhep; c-Myc-driven HCC mouse models. The mechanism of HMGCL-mediated tumor suppression was studied by IHC, western blot (WB) and Cut & Tag.

Results
HMGCL depletion promoted HCC proliferation and metastasis, whereas its overexpression reversed this trend. As HMGCL catalyzes β-hydroxy-butyric acid (β-OHB) production, we discovered that HMGCL increased acetylation at histone H3K9, which further promoted the transcription of dipeptidyl peptidase 4 (DPP4), a key protein maintains intracellular lipid peroxidation and iron accumulation, leading to HCC cells vulnerability to erastin- and sorafenib-induced ferroptosis.

Conclusion
Our study identified a critical role of HMGCL on HCC suppression, of which HMGCL regulated H3K9 acetylation through β-OHB and modulating the expression of DPP4 in a dose-dependent manner, which led to ferroptosis in HCC cells.
Modeling of Diffusion of Metal Ions in Carbon Electrodes for Lithium-Ion Cells
Authors: Pigłowska Marita , Kurc Beata , Rymaniak Łukasz 
Published: December 2022

AbstractThe main purpose of these studies is to compare the diffusion coefficient and ionic conductivity of carbon-based anode materials (graphene and corn starch carbon, graphene nanoplatelets) at different temperatures. The tests were carried out using electrochemical impedance spectroscopy (EIS) in a temperature chamber, which allows the assessment of important parameters during the cell’s operation and optimization, as well as the thermodynamic and kinetic response of the system to various conditions, which are important parameters taking into account the cell’s safety. An interesting method was proposed to determine thermodynamic and kinetic parameters for the development of two models of lithium ion diffusion during half-cell charging. It has been shown that the lithiation process is thermodynamically endothermic and forced. Various carbon materials were used, including corn starch-based carbon as an anode material that equates to the slogans of biodegradability, lower cost and ecology.Graphical Abstract
Reactor performance estimation in microscale flow calorimeter for rapid characterization of exothermic reactions
Authors: Frede Timothy Aljoscha , Nikbin Nick , Kockmann Norbert 
Published: December 2022

Abstract
Continuous flow calorimeters are a promising tool in process development and safety engineering, especially for flow chemistry applications to characterize the heat release and kinetic parameters of rapid chemical reactions. In this study, the digital accompaniment of an isoperibolic flow calorimeter for characterization of exothermic reactions is presented. To support experimental planning and evaluation, computational fluid dynamic simulations are carried out for single-phase flow in the microreactor. The residence time distribution is obtained and used for estimation of conversion and temperature profiles along the microreactor channel. This leads to an integration of CFD simulations into the calorimeter’s software-guided workflow reducing the experimental effort regarding the determination of thermokinetic data. The approach is tested for a highly exothermic test reaction, which provides further hints for future investigations.

Article highlights
• Estimation of conversion and temperature profiles within a microscale calorimeter
• Combination of CFD simulations and reactor performance estimation
• Approach was tested for highly oxidation of sodium thiosulfate
• Estimated conversion and temperature profiles are in good agreement with experimental data
Graphical abstract
Potassium transporter KUP9 participates in K+ distribution in roots and leaves under low K+ stress
Authors: Yamanashi Taro , Uchiyama Takeshi , Saito Shunya , Higashi Taiki , Ikeda Hayato , Kikunaga Hidetoshi , Yamagami Mutsumi , Ishimaru Yasuhiro , Uozumi Nobuyuki 
Published: December 2022

AbstractPotassium (K) is a major essential element in plant cells, and KUP/HAK/KT-type K+ transporters participate in the absorption of K+ into roots and in the long-distance transport to above-ground parts. In Arabidopsis thaliana, KUP9 is involved in the transport of K+ and Cs+ in roots. In this study, we investigated KUP9 function in relation to the K+ status of the plant. The expression of KUP9 was upregulated in older leaves on K+-depleted medium, compared to the expression of the other 12 KUP genes in the KUP/HAK/KT family in Arabidopsis. When grown on low K+ medium, the kup9 mutant had reduced chlorophyll content in seedlings and chlorosis in older rosette leaves. Tissue-specific expression of KUP9 determined by KUP9 promoter:GUS assay depended on the K+ status of the plants: In K+ sufficient medium, KUP9 was expressed in the leaf blade towards the leaf tip, whereas in K+ depleted medium expression was mainly found in the petioles. In accordance with this, K+ accumulated in the roots of kup9 plants. The short-term 43K+ tracer measurement showed that 43K was transferred at a lower rate in roots and shoots of kup9, compared to the wild type. These data show that KUP9 participates in the distribution of K+ in leaves and K+ absorption in roots under low K+ conditions.
Uneven Growth in Social Capital Organizations After Disasters by Pre-Disaster Conditions in the United States 2000–2014
Authors: Michael Yvonne L. , Smiley Kevin T. , Clay Lauren , Hirsch Jana A. , Lovasi Gina S. 
Published: December 2022

Abstract

Introduction:
Community-level social capital organizations are critical pre-existing resources that can be leveraged in a disaster.

Aim:
The study aimed to test the hypothesis that communities with larger pre-disaster stocks of social capital organizations would maintain pre-disaster levels or experience growth.

Methodology:
An annual panel dataset of counties in the contiguous United States from 2000 to 2014 totaling 46620 county-years, including longitudinal data on disasters and social capital institutions was used to evaluate the effect of disaster on growth of social capital.

Results:
When a county experienced more months of disasters, social capital organizations increased a year later. These findings varied based on the baseline level of social capital organizations. For counties experiencing minor disaster impacts, growth in social capital organizations tends to occur in counties with more social capital organizations in 2000; this effect is a countervailing finding to that of major disasters, and effect sizes are larger.

Conclusion:
Given the growing frequency of smaller-scale disasters and the considerable number of communities that experienced these disasters, the findings suggest that small scale events create the most common and potentially broadest impact opportunity for intervention to lessen disparities in organizational growth.
-ULTRAFILTERS IN THE RATIONAL PERFECT SET MODEL
Authors: CANCINO-MANRÍQUEZ JONATHAN 
Published: December 2022

AbstractWe give a new characterization of the cardinal invariant
$\mathfrak {d}$
as the minimal cardinality of a family
$\mathcal {D}$
of tall summable ideals such that an ultrafilter is rapid if and only if it has non-empty intersection with all the ideals in the family
$\mathcal {D}$
. On the other hand, we prove that in the Miller model, given any family
$\mathcal {D}$
of analytic tall p-ideals such that
$\vert \mathcal {D}\vert <\mathfrak {d}$
, there is an ultrafilter
$\mathcal {U}$
which is an
$\mathscr {I}$
-ultrafilter for all ideals
$\mathscr {I}\in \mathcal {D}$
at the same time, yet
$\mathcal {U}$
is not a rapid ultrafilter. As a corollary, we obtain that in the Miller model, given any analytic tall p-ideal
$\mathscr {I}$
,
$\mathscr {I}$
-ultrafilters are dense in the Rudin–Blass ordering, generalizing a theorem of Bartoszyński and S. Shelah, who proved that in such model, Hausdorff ultrafilters are dense in the Rudin–Blass ordering. This theorem also shows some limitations about possible generalizations of a theorem of C. Laflamme and J. Zhu.
Vocal iconicity in nominal classification
Authors: Erben Johansson Niklas , Cronhamn Sandra 
Published: December 2022

AbstractWhile recent years have seen a substantial increase of studies investigating vocal iconicity in the lexicon of spoken languages, its presence in grammatical structures is poorly understood. This study investigates the presence of vocal iconicity in nominal classification systems by collecting nominal classification devices from the two main system types: 210 non-agreeing languages (126 families) and 151 agreeing languages (123 families). To detect overrepresentations of sound types in class meanings, the nominal classification devices were grouped according to comparable semantic categories, transcribed using comparable phonetic system, and analyzed through Bayesian mixed models. The strongest results were found for associations between nominal classification devices denoting flat and low, front, unrounded vowels, along with several weak associations relating to shape/size/quantity, function, humanness/animacy, and sex. These associations mostly correlate with previous vocal iconicity findings, but crucially, the involved nominal classification devices are mostly semantically typical for non-agreeing, for example, classifier, systems. These findings were attributed to structural differences between nominal classification system types, which result from grammaticalization processes, for example, phonetic erosion and semantic bleaching. Thus, increased formal predictability through grammatical agreement comes at a cost of semantic transparency which, in turn, dismantles the semantic prerequisites needed for vocal iconic associations to be operational.
An event-related potential study of cross-modal translation recognition in Chinese–English bilinguals: the role of cross-linguistic orthography and phonology
Authors: Zhang Er-Hu , Li Jiaxin , Li Defeng , Chen Yiqiang , Zhang Xin-Dong , Wang Xinyi , Cao Hong-Wen 
Published: December 2022

AbstractExtensive evidence has demonstrated that bilinguals non-selectively activate lexicons of both languages when reading or hearing words in one language. Here, we further investigated the electrophysiological roles of cross-linguistic orthography and phonology in the processing of L2 spoken words in unbalanced Chinese (L1)–English (L2) bilinguals in a cross-modal situation. Relative to unrelated control, the recognition of auditory L2 words showed behavioral interference effects when paired with orthographic or phonological neighbors of the correct translations of L2 words. Moreover, the lexical effects were also exhibited in the electrophysiological data, as reflected by marginally less positive late positive component (500–800 ms) amplitudes in the frontal region. Importantly, the orthographic rather than phonological translation neighbor condition elicited less negative N400 (300–500 ms) amplitudes in the parietal–occipital regions, suggesting that this orthographic translation neighbor condition facilitated the co-activation of spoken L2 words. Taken together, these findings indicate that cross-linguistic orthographic and phonological activation have different temporal dynamics with both bottom-up parallel cross-linguistic activation and the top-down inhibitory control mechanism governing the two-language lexical organization in L2 spoken word recognition.
Individual differences in visual word recognition: the role of epistemically unwarranted beliefs on affective processing and signal detection
Authors: Huete-Pérez Daniel , Ferré Pilar 
Published: December 2022

AbstractPrevious studies have brought conflicting results regarding the effects of valence and arousal in visual word processing. Some authors have pointed to participants’ individual differences as one of the possible explanations for these inconsistencies. The main aim of the present research was to examine whether participants’ individual differences in the level of epistemically unwarranted beliefs (EUB) contribute to these conflicting results. Therefore, participants who varied in their level of paranormal, pseudoscientific and conspiracy beliefs (assessed by self-report measures) performed a lexical decision task (LDT) and a recognition memory task. Linear mixed-effects models over LDT response times revealed that the effects of words’ emotional content (both valence and arousal) were modulated by the degree of individuals’ EUB. In addition, signal detection theory analyses showed that in the recognition task (but not in the LDT) response bias became more liberal as individuals’ EUB increased. These patterns of effects were not general to all EUB instances. The obtained results highlight the need to consider participants’ individual differences in affective word processing and signal detection. In addition, this study reveals some basic psychological mechanisms that would underlie EUB, a fact that has both theoretical and applied relevance.
Computational prediction of ω-transaminase selectivity by deep learning analysis of molecular dynamics trajectories
Authors: Ramírez-Palacios Carlos , Marrink Siewert J. 
Published: December 2022

Abstract
We previously presented a computational protocol to predict the enzymatic (enantio)selectivity of an ω-transaminase towards a set of ligands (Ramírez-Palacios et al. (2021) Journal of Chemical Information and Modeling 61(11), 5569–5580) by counting the number of binding poses present in molecular dynamics (MD) simulations that met a defined set of geometric criteria. The geometric criteria consisted of a hand-crafted set of distances, angles and dihedrals deemed to be important for the enzymatic reaction to take place. In this work, the MD trajectories are reanalysed using a deep-learning approach to predict the enantiopreference of the enzyme without the need for hand-crafted criteria. We show that a convolutional neural network is capable of classifying the trajectories as belonging to the ‘reactive’ or ‘non-reactive’ enantiomer (binary classification) with a good accuracy (>0.90). The new method reduces the computational cost of the methodology, because it does not necessitate the sampling approach from the previous work. We also show that analysing how neural networks reach specific decisions can aid hand-crafted approaches (e.g. definition of near-attack conformations, or binding poses).
Do variations in nasal irrigation recipes and storage effect the risk of bacterial contamination?
Authors: Whittaker J D , Baker E , Kumar S , Collingwood R , West M , Lee P K 
Published: December 2022

AbstractObjectiveMake-at-home nasal irrigation solutions are often recommended for treating chronic rhinosinusitis. Many patients will store pre-made solution for convenient use. This study investigated the microbiological properties of differing recipes and storage temperatures.MethodThree irrigation recipes (containing sodium chloride, sodium bicarbonate and sucrose) were stored at 5oC and 22oC. Further samples were inoculated with Staphylococcus aureus and Pseudomonas aeruginosa. Sampling and culturing were conducted at intervals from day 0–12 to examine for bacterial presence or persistence.ResultsNo significant bacterial growth was detected in any control solution stored at 5oC. Saline solutions remained relatively bacterial free, with poor survival of inoculated bacteria, which may be related to either lower pH or lower osmolality. Storing at room temperature increased the risk of contamination in control samples, particularly from pseudomonas.ConclusionIf refrigerated, pre-made nasal irrigation solutions can be stored safely for up to 12 days without risking cross-contamination to irrigation equipment or patients.