bluuebunny/crossref_metadata_2025_split huggingface dataset:
The effect of estrogen on brown adipose tissue activity in male rats
Authors: Sievers W. , Kettle C. , Green R. A. , Van Schaik L. , Hale M. W. , Irving H. R. , Whelan D. R. , Rathner J. A. 
Published: February 2022

Abstract
Objective
Centrally administered estrogen can increase sympathetic nerve activity to brown adipose tissue, resulting in thermogenesis. The central thermogenic effects of estrogen have not been investigated in males. Therefore, this study sought to investigate the effects of peripherally and centrally administered estrogen on thermogenesis, heart rate and mean arterial pressure in male rats. Thermogenesis was assessed by monitoring brown adipose tissue temperature.

Results
Peripherally administered estrogen elicited no significant effect on brown adipose tissue temperature, heart rate or mean arterial pressure. Centrally administered estrogen elicited a coincident increase in both brown adipose tissue and core temperature. Centrally administered estrogen also resulted in a decrease in mean arterial pressure but had no effect on heart rate. With the present data it is not possible to elucidate whether changes in temperature were the result of thermogenic or thermoregulatory mechanisms.
Guillain–Barré/Miller Fisher overlap syndrome in a patient after coronavirus disease-2019 infection: a case report
Authors: Mohammadi Seyede Momeneh , Abdi Roghayeh , Karimi Zeinab , Mortazavi Fatemeh 
Published: February 2022

Abstract
Background
Beyond the typical respiratory symptoms associated with novel coronavirus, increasing evidence has been reported of the neurological manifestations affecting both the central and peripheral nervous systems.

Case presentation
We observed a 30-year-old Persian woman developing acute motor sensory axonal neuropathy, a variant of Guillain–Barré syndrome that overlaps Miller Fisher syndrome, 30 days after confirmed coronavirus disease-2019 infection. Our case highlight the rare occurrence of Guillain–Barré syndrome overlapping with Miller Fisher during the coronavirus disease-2019 pandemic. These neurologic manifestations may occur because of an aberrant immune response to coronavirus disease-2019.

Conclusions
The early recognition of Guillain–Barré syndrome symptoms is critical, given the associated severe motor disabilities that may seriously limit the quality of life of these patients. We may still have much to learn about the co-occurrence of Guillain–Barré syndrome and Miller Fisher to improve the quality of life of these patients requiring an accurate evaluation by neurologists.
Sequence-based prediction of protein binding regions and drug–target interactions
Authors: Lee Ingoo , Nam Hojung 
Published: February 2022

AbstractIdentifying drug–target interactions (DTIs) is important for drug discovery. However, searching all drug–target spaces poses a major bottleneck. Therefore, recently many deep learning models have been proposed to address this problem. However, the developers of these deep learning models have neglected interpretability in model construction, which is closely related to a model’s performance. We hypothesized that training a model to predict important regions on a protein sequence would increase DTI prediction performance and provide a more interpretable model. Consequently, we constructed a deep learning model, named Highlights on Target Sequences (HoTS), which predicts binding regions (BRs) between a protein sequence and a drug ligand, as well as DTIs between them. To train the model, we collected complexes of protein–ligand interactions and protein sequences of binding sites and pretrained the model to predict BRs for a given protein sequence–ligand pair via object detection employing transformers. After pretraining the BR prediction, we trained the model to predict DTIs from a compound token designed to assign attention to BRs. We confirmed that training the BRs prediction model indeed improved the DTI prediction performance. The proposed HoTS model showed good performance in BR prediction on independent test datasets even though it does not use 3D structure information in its prediction. Furthermore, the HoTS model achieved the best performance in DTI prediction on test datasets. Additional analysis confirmed the appropriate attention for BRs and the importance of transformers in BR and DTI prediction. The source code is available on GitHub (https://github.com/GIST-CSBL/HoTS).
A remote attestation mechanism using group signature for the perception layer in centralized networking
Authors: Huang Jing , Zhang Hui-Juan , He Shen , Chen Jia , Sun Zhe-Yuan 
Published: February 2022

AbstractAs the important transmission approach of the perception data, the security and privacy of the perception layer nodes have been paid more and more attention. To evaluate the credibility of the perception nodes and protect autologous identity privacy, a remote attestation mechanism using group signature (GS-RAM) for the perception layer of centralized networking is proposed in this paper. First, a RAM, based on the computational Diffie–Hellman (CDH) problem and GS, is established for the data source of the perception nodes. Second, the specific construction of the proposed GS-RAM is given to identify the trusted state of data sources without exposing the privacy of the perception nodes. Then, a strict security certification was carried out to verify the correctness, unforgeability, anonymity, traceability, unrelatedness, non-framing, anti-joint aggression and forward security of the proposed GS-RAM. Finally, simulation experiments are carried out to verify that the proposed scheme has better security and dynamic adaptability.
Throughput fairness in cognitive backscatter networks with residual hardware impairments and a nonlinear EH model
Authors: Gao Xiaona , Shi Liqin , Lu Guangyue 
Published: February 2022

AbstractThis paper is to design a throughput fairness-aware resource allocation scheme for a cognitive backscatter network (CBN), where multiple backscatter devices (BDs) take turns to modulate information on the primary signals and backscatter the modulated signals to a cooperative receiver, while harvesting energy to sustain their operations. The nonlinear energy harvesting circuits at the BDs and the residual hardware impairments at the transceivers are considered to better reflect the properties of the practical energy harvesters and transceivers, respectively. To ensure the throughput fairness among BDs, we formulate an optimization problem to maximize the minimum throughput of BDs by jointly optimizing the transmit power of the primary transmitter, the backscattering time and reflection coefficient for each BD, subject to the primary user’s quality of service and BDs’ energy-causality constraints. We introduce the variable slack and decoupling methods to transform the formulated non-convex problem, and propose an iterative algorithm based on the block coordinate descent technique to solve the transformed problem. We also investigate a special CBN with a single BD and derive the optimal solution in the closed form to maximize the BD’s throughput. Numerical results validate the quick convergence of the proposed iterative algorithm and that the proposed scheme ensures much fairness than the existing schemes.
Random CNN structure: tool to increase generalization ability in deep learning
Authors: Swiderski Bartosz , Osowski Stanislaw , Gwardys Grzegorz , Kurek Jaroslaw , Slowinska Monika , Lugowska Iwona 
Published: February 2022

AbstractThe paper presents a novel approach for designing the CNN structure of improved generalization capability in the presence of a small population of learning data. Unlike the classical methods for building CNN, we propose to introduce some randomness in the choice of layers with a different type of nonlinear activation function. The image processing in these layers is performed using either the ReLU or the softplus function. This choice is random. The randomness introduced in the network structure can be interpreted as a special form of regularization. Experiments performed on the detection of images belonging to either melanoma or non-melanoma cases have shown a significant improvement in average quality measures such as accuracy, sensitivity, precision, and area under the ROC curve.
Disentangling land model uncertainty via Matrix-based Ensemble Model Inter-comparison Platform (MEMIP)
Authors: Liao Cuijuan , Chen Yizhao , Wang Jingmeng , Liang Yishuang , Huang Yansong , Lin Zhongyi , Lu Xingjie , Huang Yuanyuan , Tao Feng , Lombardozzi Danica , Arneth Almut , Goll Daniel S. , Jain Atul , Sitch Stephen , Lin Yanluan , Xue Wei , Huang Xiaomeng , Luo Yiqi 
Published: February 2022

Abstract
Background
Large uncertainty in modeling land carbon (C) uptake heavily impedes the accurate prediction of the global C budget. Identifying the uncertainty sources among models is crucial for model improvement yet has been difficult due to multiple feedbacks within Earth System Models (ESMs). Here we present a Matrix-based Ensemble Model Inter-comparison Platform (MEMIP) under a unified model traceability framework to evaluate multiple soil organic carbon (SOC) models. Using the MEMIP, we analyzed how the vertically resolved soil biogeochemistry structure influences SOC prediction in two soil organic matter (SOM) models. By comparing the model outputs from the C-only and CN modes, the SOC differences contributed by individual processes and N feedback between vegetation and soil were explicitly disentangled.

Results
Results showed that the multi-layer models with a vertically resolved structure predicted significantly higher SOC than the single layer models over the historical simulation (1900–2000). The SOC difference between the multi-layer models was remarkably higher than between the single-layer models. Traceability analysis indicated that over 80% of the SOC increase in the multi-layer models was contributed by the incorporation of depth-related processes, while SOC differences were similarly contributed by the processes and N feedback between models with the same soil depth representation.

Conclusions
The output suggested that feedback is a non-negligible contributor to the inter-model difference of SOC prediction, especially between models with similar process representation. Further analysis with TRENDY v7 and more extensive MEMIP outputs illustrated the potential important role of multi-layer structure to enlarge the current ensemble spread and the necessity of more detail model decomposition to fully disentangle inter-model differences. We stressed the importance of analyzing ensemble outputs from the fundamental model structures, and holding a holistic view in understanding the ensemble uncertainty.
Carbonation Model and Prediction of Polyvinyl Alcohol Fiber Concrete with Fiber Length and Content Effects
Authors: Luo Zuyun , Yang Xinhua , Ji Heli , Zhang Chuanchuan 
Published: February 2022

AbstractDurability is an important aspect of reliability of concrete structures. In order to establish the carbonation model of polyvinyl alcohol (PVA) fiber concrete including the influences of PVA fiber length and volume content, a series of accelerated carbonation experiments were carried out on the normal concrete specimens and PVA fiber concrete specimens with fiber lengths of 3 mm, 6 mm, 12 mm and 18 mm and fiber volume contents of 0.1%, 0.3%, 0.5%, 0.75%, 1.0% and 1.5%, respectively. The experimental conditions remained at temperature of 20 ℃, humidity of 70%, and carbon dioxide concentration of 20%. It was found that the addition of PVA fibers could improve the carbonation resistance of concrete considerably. Within the investigated range of fiber length and content, the carbonation resistance of concrete is first strengthened and then weakened with increasing PVA fiber length or content. The quadratic carbonation depth prediction model could characterize the influences of PVA fiber length and content better than the linear model. The carbon dioxide diffusion equation was established by introducing a PVA fiber influence factor and its parameters were determined from the carbonation depth prediction model. The carbonation process of PVA fiber concrete under the accelerated carbonation experiment condition was simulated. Both the model and the numerical method were validated by comparison between the experimental and numerical results. Finally, the influences of added PVA fibers on the carbonation life and durability of reinforced concrete components were further studied numerically. The results showed that compared with the normal concrete component, the durability of PVA fiber concrete components is significantly improved.
Stability and time-delay effect of rainfall-induced landslide considering air entrapment
Authors: Zhang Shixin , Li Li , Zhao Dongsheng , Ni Bo , Qiang Yue , Zheng Zhou 
Published: February 2022

AbstractThe pore air pressure of the soil layer has an obstructive effect on the infiltration of the rainfall-induced landslide. Therefore, the hydraulic hysteresis caused by air entrapment in the infiltration process of the slope also reduces the anti-sliding force of the slope. A method considered the air entrapment of the closed gas in soil particles’ pores is developed to study the time-delay effect and slope stability during rainfall. The Green–Ampt infiltration model is used to obtain the explicit analytical solution of the slope infiltration considering air entrapment. Moreover, the relationship between the safety factor, the rainfall duration, and the depth of the wetting front under the three rainfall conditions (qrain = 10, 26, 51 mm/h) is discussed. The results show that the air entrapment causes a significant time-delay effect of the landslide, and the hydraulic hysteresis is the strongest under the condition of heavy rainfall (qrain = 51 mm/h). The air-entrapment will have a supporting effect on the wetting front and reduce the slope normal stress. In infinite slope cases, the air entrapment under violent rainfall (qrain = 51 mm/h) will greatly reduce the slope safety factor.
‘Making the most of together time’: development of a Health Visitor–led intervention to support children’s early language and communication development at the 2–2½-year-old review
Authors: McKean Cristina , Watson Rose , Charlton Jenna , Roulstone Sue , Holme Caitlin , Gilroy Victoria , Law James 
Published: February 2022

AbstractBackgroundEarly interventions to support young children’s language development through responsive parent–child interaction have proven efficacy but are not currently delivered universally. A potential universal delivery platform is the Health Visitor (HV)–led 2–2½-year-old review in England’s Healthy Child Programme. It is unclear if it is feasible to offer such interventions through this platform. We report an intervention development process, including extensive stakeholder consultation and co-design which aimed to develop an acceptable, feasible and equitable early language intervention for delivery in this context.MethodsThe study involved five phases including 13 stakeholder co-design workshops with 7 parents and 39 practitioners (HVs, early years practitioners and speech and language therapists): (1) Identification of existing intervention evidence, (2) qualitative review of intervention studies extracting candidate target behaviours for intervention and intervention techniques, (3) co-design workshops with parents and practitioners examining acceptability, barriers and enablers to those behaviours and techniques (particular attention was paid to diverse family circumstances and the range of barriers which might exist), (4) findings were analysed using COM-B and theoretical domains frameworks and a prototype intervention model designed, and (5) co-design workshops iteratively refined the proposed model.ResultsPractitioners were committed to offering language intervention at the 2–2½-year-old review but were not sure precisely how to do so. Parents/caregivers wanted to be proactive and to have agency in supporting their own children and to do this as soon as possible. For equitable intervention, it must beproportionate, with higher ‘intensity’ for higher levels of disadvantage, andtailored,offering differing approaches considering the specific barriers and enablers, assets and challenges in each family. The importance and potential fragility of alliances between parent/caregiver and practitioner were identified as key, and so, strategies to engender successful collaborative partnership are also embedded in intervention design.ConclusionIt is possible to develop a universal intervention which parents and practitioners judge would be acceptable, feasible and equitable for use at the 2–2½-year review to promote children’s language development. The result is one of the most explicitly developed universal interventions to promote children’s language development. Further development and piloting is required to develop materials to support successful widespread implementation.
Adapting to the algorithm: how accuracy comparisons promote the use of a decision aid
Authors: Liang Garston , Sloane Jennifer F. , Donkin Christopher , Newell Ben R. 
Published: February 2022

AbstractIn three experiments, we sought to understand when and why people use an algorithm decision aid. Distinct from recent approaches, we explicitly enumerate the algorithm’s accuracy while also providing summary feedback and training that allowed participants to assess their own skills. Our results highlight that such direct performance comparisons between the algorithm and the individual encourages a strategy of selective reliance on the decision aid; individuals ignored the algorithm when the task was easier and relied on the algorithm when the task was harder. Our systematic investigation of summary feedback, training experience, and strategy hint manipulations shows that further opportunities to learn about the algorithm encourage not only increased reliance on the algorithm but also engagement in experimentation and verification of its recommendations. Together, our findings emphasize the decision-maker’s capacity to learn about the algorithm providing insights for how we can improve the use of decision aids.
Use of an antagonist of HMGB1 in mice affected by malignant mesothelioma: a preliminary ultrasound and optical imaging study
Authors: Venturini Massimo , Mezzapelle Rosanna , La Marca Salvatore , Perani Laura , Spinelli Antonello , Crippa Luca , Colarieti Anna , Palmisano Anna , Marra Paolo , Coppola Andrea , Fontana Federico , Carcano Giulio , Tacchetti Carlo , Bianchi Marco , Esposito Antonio , Crippa Massimo P. 
Published: February 2022

Abstract
Background
Malignant mesothelioma (MM) is an aggressive tumor, with a poor prognosis, usually unresectable due to late diagnosis, mainly treated with chemotherapy. BoxA, a truncated form of “high mobility group box 1” (HMGB1), acting as an HMGB1 antagonist, might exert a defensive action against MM. We investigated the potential of BoxA for MM treatment using experimental 40-MHz ultrasound and optical imaging (OI) in a murine model.

Methods
Murine MM cells infected with a lentiviral vector expressing the luciferase gene were injected into the peritoneum of 14 BALB/c mice (7 × 104 AB1-B/c-LUC cells). These mice were randomized to treatment with BoxA (n = 7) or phosphate-buffered saline (controls, n = 7). The experiment was repeated with 40 mice divided into two groups (n = 20 + 20) and treated as above to confirm the result and achieve greater statistical power. Tumor presence was investigated by experimental ultrasound and OI; suspected peritoneal masses underwent histopathology and immunohistochemistry examination.

Results
In the first experiment, none of the 7 controls survived beyond day 27, whereas 4/7 BoxA-treated mice (57.1%) survived up to day 70. In the second experiment, 6/20 controls (30.0%) and 16/20 BoxA-treated mice (80.0%) were still alive at day 34 (p = 0.004). In both experiments, histology confirmed the malignant nature of masses detected using experimental ultrasound and OI.

Conclusion
In our preclinical experience on a murine model, BoxA seems to exert a protective role toward MM. Both experimental ultrasound and OI proved to be reliable techniques for detecting MM peritoneal masses.
Guillain-Barré syndrome and fulminant encephalomyelitis following Ad26.COV2.S vaccination: double jeopardy
Authors: Stefanou Maria Ioanna , Karachaliou Eleni , Chondrogianni Maria , Moschovos Christos , Bakola Eleni , Foska Aikaterini , Melanis Konstantinos , Andreadou Elisabeth , Voumvourakis Konstantinos , Papathanasiou Matilda , Boutati Eleni , Tsivgoulis Georgios 
Published: February 2022

AbstractThis correspondence comments on a published article presenting a case of rhombencephalitis following SARS-CoV-2-vaccination with the mRNA vaccine BNT162b2 (Pfizer/BioNTech). We also present the case of a 47-year-old man who developed Guillain-Barré-syndrome and a fulminant encephalomyelitis 28 days after immunization with Ad26.COV2.S (Janssen/Johnson & Johnson). Based on the presented cases, we underscore the importance of clinical awareness for early recognition of overlapping neuroimmunological syndromes following vaccination against SARS-CoV-2. Additionally, we propose that that role of autoantibodies against angiotensin-converting enzyme 2 (ACE2) and the cell-surface receptor neuropilin-1, which mediate neurological manifestations of SARS-CoV-2, merit further investigation in patients presenting with neurological disorders following vaccination against SARS-CoV-2.
Prevalence, diversity of diarrhoeagenic Escherichia coli and associated risk factors in well water in Ile-Ife, Southwestern Nigeria
Authors: Odetoyin Babatunde , Ogundipe Olawumi , Onanuga Adebola 
Published: February 2022

Abstract
Background
Diarrhoeagenic Escherichia coli (DEC) strains are common causes of morbidity and mortality worldwide. Waterborne DEC could pose a health risk to humans through domestic use of contaminated water. However, epidemiological studies on DEC in well water are scarce in Nigeria. This study determined the prevalence, diversity and factors associated with the presence of DEC in well water in Ile-Ife, southwestern Nigeria.

Methods
We assessed 143 wells for safety and a questionnaire was administered. Contaminating isolates were identified as E. coli by amplifying their 16S rRNA gene. Five diarrhoeagenic E. coli pathotypes were sought using multiplex polymerase chain reaction (PCR). (GTG)5 repetitive PCR and Shannon diversity index were used to determine isolates diversity. Multivariate analysis was used to reveal the factors associated with the presence of DEC in well water.

Results
Fifty-six (39.2%) wells were contaminated by diarrhoeagenic E. coli. Wells with dirty platforms, undercut by erosion and sited near septic tanks significantly harboured DEC (p <  0.05). There was a preponderance of Shiga-toxin producing E. coli among the isolates with 10 (17.9%) wells contaminated by multiple DEC. The DEC isolates showed 45 unique fingerprints and were divided into six clades, with an overall diversity index of 18.87.

Discussion
The presence of DEC in well water highlights the risk to human health associated with the use of untreated water. There was a high degree of genetic diversity among the isolates implying multiple sources of contamination. There is a need for periodic sanitation and inspection of wells for cracks to prevent seepages and possible outbreaks of waterborne diseases.