bluuebunny/crossref_metadata_2025_split huggingface dataset:
Resolved terahertz spectroscopy of tiny molecules employing tunable spoof plasmons in an otto prism configuration
Authors: Yao Haizi , Zhang Weiwei , Liu Wenfu , Mei Hongying 
Published: February 2022

Abstract
Sensitive detection of terahertz fingerprint absorption spectrum for tiny molecules is essential for bioanalysis. However, it is extremely challenging for traditional terahertz spectroscopy measurement because of the weak spectral response caused by the large mismatch between terahertz wavelengths and biomolecular dimensions. Here, we proposed a wideband-tunable metal plasmonic terahertz biosensor to detect tiny biomolecules, employing attenuated total reflection in an Otto prism configuration and tightly confined spoof surface plasmons on the grooved metal surface. Benefitting from the plasmonic electric field enhancement, such a biosensor is able to identify the molecular terahertz fingerprints. As a proof of concept, a hypothetical molecule modeled by the Lorentz model with two vibrational modes is used as the sensing analytes. Simulation results show that the absorption of two vibrational modes of analytes can be selectively enhanced up to ten times by plasmonic resonance, and their fingerprints can be resolved by sweeping incident angle in a wide waveband. Our work provides an effective approach for the highly sensitive identification of molecular fingerprints in fields of biochemical sensing for tiny analytes.
The characterization of a nanoscale MnNi cluster in thermally aged reactor pressure vessel steels
Authors: Liu Hailong , Li Qiulin 
Published: February 2022

Abstract
The MnNi-rich clusters or precipitates (MNPs) are one of the most important nanofeatures resulting in irradiation-induced embrittlement of reactor pressure vessel (RPV) steels. In this study, the microstructure and mechanical properties of the thermally-aged RPV steel were investigated by microhardness and tensile test, x-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), and atom probe tomography (APT), revealing the presence of a novel nanoscale B2 MnNi phase. The formation of these B2 MnNi phases was dominated by the thermodynamic of the RPV-steel system, and high-density dislocations could boost their formation and growth. Meanwhile, for samples aged for 2 h at 723 K, these ordered phases result in a yield-strengthening increment of 190 MPa. Although aged-induced ordered phases have a clear difference in terms of the lattice structure, nucleation rate, and thermal stability compared to irradiation-induced MNPs, the discovery of these B2 ordered phases helps us to establish a more comprehensive understanding of the nature and formation mechanism of MNPs produced by neutron irradiation.
Mechanical, light transmittance properties and simulation study of sustainable translucent lightweight aggregate concrete
Authors: Lian Fengmei , Yin Zhixiang 
Published: February 2022

Abstract
To investigate the engineering feasibility of translucent lightweight aggregate concrete (TLAC) in sustainable buildings, this paper focuses on testing the light transmission properties and mechanical performance of TLACs, the light transmission effect in building interiors is simulated and evaluated using automated steps seamlessly integrated with REVIT software and Radiance software. The preparation process of TLACs and the mixture proportions of raw materials were designed by combining translucent concrete technology with self-compacting mortar. The results indicate that the light transmission of TLAC increases with the volume of polymethylmethacrylate optical fiber (POF) incorporated and the diameter of POF, while the compressive strength tends to increase first and then decrease with the increasing volume fraction of POF in the TLAC samples. Moreover, the building lighting simulations show that indoor illuminance will improve significantly with the use of TLACs, which further illustrates the energy savings and structural performance improvements owing to the application of TLAC as a modern construction material.
Identifying robust functional modules using three-body correlations in Escherichia coli
Authors: Chen Tianlong , Singh Pramesh , Bassler Kevin E 
Published: February 2022

Abstract
Understanding the underlying structure of a gene regulatory network is crucial to understand the biological functions of genes or groups of genes. A common strategy to investigate it is to find community structure of these networks. However, methods of finding these communities are often sensitive to noise in the gene expression data and the inherent stochasticity of the community detection algorithms. Here we introduce an approach for identifying functional groups and their hierarchical organization in gene co-expression networks from expression data. A network describing the relatedness in the expression profiles of genes is first inferred using an information theoretic approach. Community structure within the inferred network is found by using modularity maximization. This community structure is further refined using three-body structural correlations to robustly identify important functional gene communities. We apply this approach to the expression data of E. coli genes and identify 25 robust groups, many of which show key associations with important biological functions as demonstrated by gene ontology term enrichment analysis. Thus, our approach makes specific and novel predictions about the function of these genes.
SATB2 Expression in Undifferentiated Pleomorphic Sarcomas of Bone
Authors: Warmke Laura M , Maloney Nolan , Leung Cheuk Hong , Lin Heather , Lazar Alexander J , Wang Wei-Lien 
Published: February 2022

AbstractObjectivesSATB2 is a transcriptional regulator that plays an important role in osteoblastic differentiation. We examined the prevalence and potential significance of SATB2 expression in undifferentiated pleomorphic sarcoma (UPS) of bone.MethodsWe examined 38 cases of bone UPS without osteoid. The male-to-female ratio was 1:1.4, with a median age of 48 years (range, 23-83 years). Tumors occurred primarily in the femur (n = 8) and ilium (n = 8), with a median tumor size of 9.5 cm (range, 1.8-27.0 cm). The median follow-up was 24.7 months (range, 2-82 months): 11 patients developed local recurrences, and 18 patients had metastases, mainly to lung and bone.ResultsSATB2 expression (nuclear labeling ≥5%) was seen in 21 of 38 (55%) cases: 5 with focal (nuclear labeling 5%), 11 with patchy (nuclear labeling 5%-50%), and 5 with diffuse (nuclear labeling ≥50%) staining. Among this group, diffuse SATB2 expression demonstrated superior metastasis-free survival (P = .036) and event-free survival (P = .024). For comparison, 100 soft tissue UPS were stained; the majority were negative (75/100 [75%]).ConclusionsUPS of bone demonstrated more frequent SATB2 expression compared with its soft tissue counterpart. In this series, diffuse SATB2 expression in UPS of bone was associated with better outcomes. Additional studies are still needed to determine its significance.
Effective knowledge graph embeddings based on multidirectional semantics relations for polypharmacy side effects prediction
Authors: Yao Junfeng , Sun Wen , Jian Zhongquan , Wu Qingqiang , Wang Xiaoli 
Published: February 2022

Abstract

Motivation
Polypharmacy is the combined use of drugs for the treatment of diseases. However, it often shows a high risk of side effects. Due to unnecessary interactions of combined drugs, the side effects of polypharmacy increase the risk of disease and even lead to death. Thus, obtaining abundant and comprehensive information on the side effects of polypharmacy is a vital task in the healthcare industry. Early traditional methods used machine learning techniques to predict side effects. However, they often make costly efforts to extract features of drugs for prediction. Later, several methods based on knowledge graphs are proposed. They are reported to outperform traditional methods. However, they still show limited performance by failing to model complex relations of side effects among drugs.

Results
To resolve the above problems, we propose a novel model by further incorporating complex relations of side effects into knowledge graph embeddings. Our model can translate and transmit multidirectional semantics with fewer parameters, leading to better scalability in large-scale knowledge graphs. Experimental evaluation shows that our model outperforms state-of-the-art models in terms of the average area under the ROC and precision–recall curves.

Availability and implementation
Code and data are available at: https://github.com/galaxysunwen/MSTE-master.
SteadyCellPhenotype: a web-based tool for the modeling of biological networks with ternary logic
Authors: Knapp Adam C , Sordo Vieira Luis , Laubenbacher Reinhard , Chifman Julia 
Published: February 2022

Abstract

Summary
We introduce SteadyCellPhenotype, a browser-based interface for the analysis of ternary biological networks. It includes tools for deterministically finding all steady states of a network, as well as the simulation and visualization of trajectories with publication quality graphics. Simulations allow us to approximate the size of the basin for attractors and deterministic simulations of trajectories nearby specified points allow us to explore the behavior of the system in that neighborhood.

Availability and implementation
https://github.com/knappa/steadycellphenotype MIT License.
TransformerGO: predicting protein–protein interactions by modelling the attention between sets of gene ontology terms
Authors: Ieremie Ioan , Ewing Rob M , Niranjan Mahesan 
Published: February 2022

Abstract

Motivation
Protein–protein interactions (PPIs) play a key role in diverse biological processes but only a small subset of the interactions has been experimentally identified. Additionally, high-throughput experimental techniques that detect PPIs are known to suffer various limitations, such as exaggerated false positives and negatives rates. The semantic similarity derived from the Gene Ontology (GO) annotation is regarded as one of the most powerful indicators for protein interactions. However, while computational approaches for prediction of PPIs have gained popularity in recent years, most methods fail to capture the specificity of GO terms.

Results
We propose TransformerGO, a model that is capable of capturing the semantic similarity between GO sets dynamically using an attention mechanism. We generate dense graph embeddings for GO terms using an algorithmic framework for learning continuous representations of nodes in networks called node2vec. TransformerGO learns deep semantic relations between annotated terms and can distinguish between negative and positive interactions with high accuracy. TransformerGO outperforms classic semantic similarity measures on gold standard PPI datasets and state-of-the-art machine-learning-based approaches on large datasets from Saccharomyces cerevisiae and Homo sapiens. We show how the neural attention mechanism embedded in the transformer architecture detects relevant functional terms when predicting interactions.

Availability and implementation
https://github.com/Ieremie/TransformerGO.

Supplementary information
Supplementary data are available at Bioinformatics online.
Semi-automatic detection of honeybee brood hygiene—an example of artificial learning to facilitate ethological studies on social insects
Authors: Batz Philipp , Ruttor Andreas , Thiel Sebastian , Wegener Jakob , Zautke Fred , Schwekendiek Christoph , Bienefeld Kaspar 
Published: February 2022

AbstractMachine-learning techniques are shifting the boundaries of feasibility in many fields of ethological research. Here, we describe an application of machine learning to the detection/measurement of hygienic behaviour, an important breeding trait in the honey bee (Apis mellifera). Hygienic worker bees are able to detect and destroy diseased brood, thereby reducing the reproduction of economically important pathogens and parasites such as the Varroa mite (Varroa destructor). Video observation of this behaviour on infested combs has many advantages over other methods of measurement, but analysing the recorded material is extremely time-consuming. We approached this problem by combining automatic tracking of bees in the video recordings, extracting relevant features, and training a multi-layer discriminator on positive and negative examples of the behaviour of interest. Including expert knowledge into the design of the features lead to an efficient model for identifying the uninteresting parts of the video which can be safely skipped. This algorithm was then used to semiautomatically identify individual worker bees involved in the behaviour. Application of the machine-learning method allowed to save 70% of the time required for manual analysis, and substantially increased the number of cell openings correctly identified. It thereby turns video-observation of individual cell opening events into an economically competitive method for selecting potentially resistant bees. This method presents an example of how machine learning can be used to boost ethological research, and how it can generate new knowledge by explaining the learned decision rule in form of meaningful parameters.
Admixture and selection patterns across the European distribution of Scots pine, Pinus sylvestris (Pinaceae)
Authors: Wachowiak Witold , Perry Annika , Zaborowska Julia , González-Martínez Santiago C , Cavers Stephen 
Published: February 2022

Abstract
We analysed data on nucleotide polymorphism at 78 nuclear genes to search for signatures of divergence and selection in populations from across the distribution of Pinus sylvestris in Europe. Data on variation at a large set of maternally inherited mitochondrial DNA markers were also analysed. Most nuclear loci showed homogeneous patterns of variation across populations from Spain, Central Europe, Nordic countries and Scotland. Mitochondrial markers did not reveal strong population structure but suggested admixture of samples carrying different mitochondrial lineages in certain parts of the distribution. At nuclear DNA loci, we found high nucleotide polymorphism, generally low population differentiation and weak signals of isolation by distance. Together with the mitochondrial DNA results, these patterns seem most likely to be associated with a process of colonization from multiple sources and efficient gene flow. The results are in line with demographic inference indicating establishment of Central European and Nordic populations before the Last Glacial Maximum and admixture of colonization fronts from mainland Europe in Scottish populations. Coalescent and outlier detection methods identified several genes showing molecular signatures of selection especially in response to temperature variation in ecologically and phenotypically divergent populations. The study provides a large set of markers for analysis of genetic diversity in populations of P. sylvestris, including candidate genes that are implicated in local adaptation and population divergence.
Surgeons, Infectious Diseases, and Twitter Hit a Home Run for Antibiotic Stewardship
Authors: Goff Debra A , Alverdy John , Gerlach Anthony T , Mayol Julio , Nwomeh Benedict 
Published: February 2022

Abstract
Many infectious diseases (ID) clinicians join Twitter to follow other ID colleagues or “like” people. While there is great value in engaging with people who have similar interests, there is equal value in engaging with “unlike” or non-ID people. Here, we describe how Twitter connected an ID pharmacist with a pediatric surgeon, a vice chair of surgery, a surgeon chief medical officer from Spain, and a surgical intensive care unit pharmacist. This Twitter collaboration resulted in several scholarly activities related to antibiotic resistance and antibiotic stewardship and served as a conduit for global collaboration.
Educational Impact of #IDJClub, a Twitter-Based Infectious Diseases Journal Club
Authors: Schwartz Ilan S , McCarty Todd , Woc-Colburn Laila E , Titanji Boghuma K , Cutrell James B , Cortes-Penfield Nicolas W 
Published: February 2022

AbstractBackgroundJournal clubs have been an enduring mainstay of medical education, and hosting these on social media platforms can expand accessibility and engagement. We describe the creation and impact of #IDJClub, an infectious diseases (ID) Twitter journal club.MethodsWe launched #IDJClub in October 2019. Using the account @IDJClub, an ID physician leads a 1-hour open-access Twitter discussion of a recent publication. All participants use the hashtag #IDJClub. Sessions started monthly, but increased due to demand during the coronavirus disease 2019 (COVID-19) pandemic. We used Symplur ’s Healthcare Hashtag project to track engagement of #IDJClub per 60-minute discussion plus the following 30 minutes to capture ongoing conversations. We also conducted an online anonymous survey using Likert scales and open-ended questions to assess educational impact.ResultsIn its first 20 months, 31 journal clubs were held, with medians of 42 (interquartile range [IQR], 28.5–60) participants and 312 (IQR, 205–427.5) tweets per session. 134 participants completed the survey, of whom 39% were ID physicians, 19% pharmacists, 13% ID fellows, and 10% medical residents. Most agreed or strongly agreed that #IDJClub provided clinically useful knowledge (95%), increased personal confidence in independent literature appraisal (72%), and was more educational than traditional journal clubs (72%). The format addressed several barriers to traditional journal club participation such as lack of access, subject experts, and time.Conclusions#IDJClub is an effective virtual journal club, providing an engaging, open-access tool for critical literature appraisal that overcomes several barriers to traditional journal club participations while fostering connectedness within the global ID community.
Soluble urokinase plasminogen activator receptor and decline in kidney function among patients without kidney disease
Authors: Iversen Esben , Kallemose Thomas , Hornum Mads , Bengaard Anne Kathrine , Nehlin Jan Olof , Rasmussen Line Jee Hartmann , Sandholdt Haakon , Tavenier Juliette , Feldt-Rasmussen Bo , Andersen Ove , Eugen-Olsen Jesper , Houlind Morten Baltzer 
Published: February 2022

ABSTRACT

Background
Hospitalized patients are at an increased risk of developing kidney disease after discharge, often despite the absence of any clinical indicators during hospitalization. Soluble urokinase plasminogen activator receptor (suPAR) is a marker of systemic chronic inflammation that can be measured from routine blood samples. We determined whether elevated suPAR during hospitalization is associated with a decline in estimated glomerular filtration rate (eGFR) after discharge.

Methods
This was a retrospective longitudinal cohort study of patients without detectable kidney disease presenting to the emergency department on two separate occasions during a 3-year period. The association between suPAR and a decline in eGFR was assessed by linear mixed models for repeated measures adjusting for age, sex, C-reactive protein, sodium, diabetes, hypertension and cardiovascular disease.

Results
In total, 5124 patients (median age 65.9 years, 51.0% female) were included. The median suPAR was 2.9 ng/mL, the median time to readmission was 144 days and the expected rate of eGFR decline over this period was 5.1 mL/min/1.73 m2/year. Adjusting for other risk factors, patients with suPAR &lt;3, 3–6 or ≥6 ng/mL had an expected eGFR decline of 4.3, 5.2 or 9.0 mL/min/1.73 m2/year, respectively. Similarly, patients with suPAR in the lowest (&lt;2.4 ng/mL), middle (2.4–3.6 ng/mL) or highest (≥3.6 ng/mL) tertile had an expected eGFR decline of 4.2, 4.6 or 6.5 mL/min/1.73 m2/year, respectively. In both cases, a higher suPAR level was significantly and independently associated with a higher rate of eGFR decline (P &lt; .001).

Conclusions
A higher suPAR level was associated with accelerated eGFR decline among patients presenting to the emergency department, suggesting that routine suPAR measurements may have utility for the early detection of kidney disease.
Evidence for acoustic discrimination in lemurs: A playback study on wild indrisIndri indri
Authors: Spezie Giovanni , Torti Valeria , Bonadonna Giovanna , De Gregorio Chiara , Valente Daria , Giacoma Cristina , Gamba Marco 
Published: February 2022

AbstractIndris Indri indri are group-living lemurs that occupy stable territories over several years and perform remarkable long-distance vocal displays. Vocal exchanges between long-term territory neighbors may contribute to assessing reciprocal resource-holding potentials, thus adaptively reducing the costs of territorial defense by limiting aggressive escalation. Previous work showed that indris’ songs show distinctive acoustic features at individual and group level. However, the possibility that indris use such cues for individual or group-level recognition has never been investigated experimentally. We conducted a playback experiment to test whether indris discriminate between familiar and nonfamiliar songs. Our rationale lies in the hypothesis of the dear enemy phenomenon, which predicts that territorial animals will show reduced aggression levels toward familiar neighbors compared with novel rivals. We played back stimulus recordings to wild indris from their territory boundaries and examined their responses in terms of vocal and behavioral indicators of willingness to engage in a fight. In line with our predictions, focal animals responded more rapidly and approached more often the speaker in response to playback stimuli of nonfamiliar individuals than to stimuli of neighboring groups. These results indicate that indris can discriminate between different classes of intruders based on distinctive acoustic features of their song choruses. We suggest that increased aggression directed toward unfamiliar intruders may be explained by higher threat levels associated with dispersal and group formation dynamics. We further discuss the relevance of these findings in a strepsirrhine primate model for comparative studies of vocal communication and sociality.