bluuebunny/crossref_metadata_2025_split huggingface dataset:
Diversity of larval habitats of Anopheles mosquitoes in urban areas of Benin and influence of their physicochemical and bacteriological characteristics on larval density
Authors: Hessou-Djossou Donald , Djègbè Innocent , Ahadji-Dabla Koffi Mensah , Nonfodji Odilon M. , Tchigossou Geneviève , Djouaka Rousseau , Cornelie Sylvie , Djogbenou Luc , Akogbeto Martin , Chandre Fabrice 
Published: June 2022

Abstract
Background
The implementation of anti-larval strategies in the fight against malaria vectors requires fundamental knowledge of their oviposition sites. The aim of this study was to assess the spatial and temporal distribution of Anopheles breeding sites as well as the influence of abiotic and biotic factors on the proliferation of larvae in urban and non-urban areas of Benin.

Methods
Sampling of Anopheles larvae was carried out during the rainy and dry seasons in urbanized and non-urbanized areas of the cities of Cotonou, Bohicon, Parakou, and Natitingou in Benin. The Anopheles larval breeding sites were georeferenced and characterized by their nature, type, physicochemical (pH, temperature, dissolved oxygen, conductivity, turbidity, salinity) and biological attributes (larval density and coliform density).

Results
A total of 198 positive breeding sites for Anopheles larvae were identified, comprising 163 (82.3%) in the rainy season and 35 (17.7%) in the dry season. Out of these larval habitats, 61.9% were located in urbanized areas, and were predominantly puddles. Principal component analysis revealed a high positive correlation of larval density with temperature and dissolved oxygen, and with salinity in the coastal zone. In addition, cross-sectional analysis of the microbiological results with larval density showed a significant negative correlation between larval productivity and faecal coliform load.

Conclusions
This study indicated the presence of multiple larval habitats of Anopheles in the urban areas which were created through human activities, and associations between larval density and intrinsic factors of the habitats such as temperature, dissolved oxygen and faecal coliform load. This type of information may be useful for the implementation of appropriate control strategies in urban areas, including regulation of the human activities that lead to the creation of breeding sites, proper environmental management and targeted larvicidal use.

Graphical abstract
Qualitative analysis of very weak solutions to Dirac-harmonic equations
Authors: Shi Guannan , Zhang Ye 
Published: June 2022

AbstractIn this paper, we introduce a definition of very weak solutions to the homogenous Dirac-harmonic equations for differential forms. In this setting, applying the Gehring lemma and interpolation theorems, we establish a higher integrability of the Dirac operator based on the very weak solutions and explore the relation between weak solutions and very weak solutions.
Lignocellulose degradation in Protaetia brevitarsis larvae digestive tract: refining on a tightly designed microbial fermentation production line
Authors: Wang Kui , Gao Peiwen , Geng Lili , Liu Chunqin , Zhang Jie , Shu Changlong 
Published: June 2022

Abstract
Background
The Scarabaeidae insect Protaetia brevitarsis (PB) has recently gained increasing research interest as a resource insect because its larvae can effectively convert decaying organic matter to plant growth-promoting frass with a high humic acid content and produce healthy, nutritional insect protein sources. Lignocellulose is the main component of PB larvae (PBL) feed, but PB genome annotation shows that PBL carbohydrate-active enzymes are not able to complete the lignocellulose degradation process. Thus, the mechanism by which PBL efficiently degrade lignocellulose is worthy of further study.

Results
Herein, we used combined host genomic and gut metagenomic datasets to investigate the lignocellulose degradation activity of PBL, and a comprehensive reference catalog of gut microbial genes and host gut transcriptomic genes was first established. We characterized a gene repertoire comprising highly abundant and diversified lignocellulose-degrading enzymes and demonstrated that there was unique teamwork between PBL and their gut bacterial microbiota for efficient lignocellulose degradation. PBL selectively enriched lignocellulose-degrading microbial species, mainly from Firmicutes and Bacteroidetes, which are capable of producing a broad array of cellulases and hemicellulases, thus playing a major role in lignocellulosic biomass degradation. In addition, most of the lignocellulose degradation-related module sequences in the PBL microbiome were novel. PBL provide organic functional complementarity for lignocellulose degradation via their evolved strong mouthparts, alkaline midgut, and mild stable hindgut microenvironment to facilitate lignocellulosic biomass grinding, dissolving, and symbiotic microbial fermentation, respectively.

Conclusions
This work shows that PBL are a promising model to study lignocellulose degradation, which can provide highly abundant novel enzymes and relevant lignocellulose-degrading bacterial strains for biotechnological biomass conversion industries. The unique teamwork between PBL and their gut symbiotic bacterial microbiota for efficient lignocellulose degradation will expand the knowledge of holobionts and open a new beginning in the theory of holobionts.
Effectiveness and mechanism of a 4-week online self-help mindfulness intervention among individuals with emotional distress during COVID-19 in China
Authors: Ju Ruilin , Chiu Wingsze , Zang Yinyin , Hofmann Stefan G. , Liu Xinghua 
Published: June 2022

Abstract
Background
Many people suffered from emotional distress especially during the COVID-19 pandemic. In order to alleviate emotional distress, more accessible psychological intervention programs, such as online intervention programs, are needed. The study aimed to investigate the efficacy and the potential mechanism of a 4-week, online, self-help mindfulness-based intervention to manage emotional distress during the COVID-19 pandemic between February 3 and May 20, 2020.

Methods
A total of 302 individuals with high emotional distress completed a self-help mindfulness course, which lasted 30–60 min per day for 28 consecutive days. Participants who registered in the program later were included in the analyses as the control group (n = 315). Levels of mindfulness, perceived stress, emotional distress, anxiety and depression were assessed at baseline(T1), week 1(T2), week 2(T3), week 3(T4) and week 4(T5).

Results
Significant Group by Time interaction effects were found on mindfulness, perceived stress, emotional distress, anxiety and depression (p < 0.001). Compared to the control group, the intervention group had a greater increase in changes of all outcome variables (p < 0.001). Random intercept cross-lagged analyses showed that compared with control group, mindfulness at T2 and T4 negatively predicted stress at T3 and T5, and mindfulness at T2 and T4 negatively predicted depression at T3 and T5 while depression at T3 predicted mindfulness at T4 in the mindfulness group.

Conclusions
The results suggest that a 4-week self-help online mindfulness intervention improved mindfulness and reduced stress, emotional distress, anxiety and depression symptoms. Compared to the control group, changes in mindfulness preceded changes in stress, and mindfulness and depression reciprocally influenced each other during the intervention.
Trial registration Chinese Clinical Trial Registry: ChiCTR2000034539. Registered 9 July 2020—Retrospectively registered, http://www.chictr.org.cn/edit.aspx?pid=55721&htm=4.
Neural network-based classification of X-ray fluorescence spectra of artists’ pigments: an approach leveraging a synthetic dataset created using the fundamental parameters method
Authors: Jones Cerys , Daly Nathan S. , Higgitt Catherine , Rodrigues Miguel R. D. 
Published: June 2022

Abstract
X-ray fluorescence (XRF) spectroscopy is an analytical technique used to identify chemical elements that has found widespread use in the cultural heritage sector to characterise artists' materials including the pigments in paintings. It generates a spectrum with characteristic emission lines relating to the elements present, which is interpreted by an expert to understand the materials therein. Convolutional neural networks (CNNs) are an effective method for automating such classification tasks—an increasingly important feature as XRF datasets continue to grow in size—but they require large libraries that capture the natural variation of each class for training. As an alternative to having to acquire such a large library of XRF spectra of artists' materials a physical model, the Fundamental Parameters (FP) method, was used to generate a synthetic dataset of XRF spectra representative of pigments typically encountered in Renaissance paintings that could then be used to train a neural network. The synthetic spectra generated—modelled as single layers of individual pigments—had characteristic element lines closely matching those found in real XRF spectra. However, as the method did not incorporate effects from the X-ray source, the synthetic spectra lacked the continuum and Rayleigh and Compton scatter peaks. Nevertheless, the network trained on the synthetic dataset achieved 100% accuracy when tested on synthetic XRF data. Whilst this initial network only attained 55% accuracy when tested on real XRF spectra obtained from reference samples, applying transfer learning using a small quantity of such real XRF spectra increased the accuracy to 96%. Due to these promising results, the network was also tested on select data acquired during macro XRF (MA-XRF) scanning of a painting to challenge the model with noisier spectra Although only tested on spectra from relatively simple paint passages, the results obtained suggest that the FP method can be used to create accurate synthetic XRF spectra of individual artists' pigments, free from X-ray tube effects, on which a classification model could be trained for application to real XRF data and that the method has potential to be extended to deal with more complex paint mixtures and stratigraphies.
High-resolution micro-CT with 3D image analysis for porosity characterization of historic bricks
Authors: Reedy Chandra L. , Reedy Cara L. 
Published: June 2022

Abstract
The study of pores in historic bricks is important for characterizing and comparing brick materials, evaluating the degree of deterioration, predicting behavior in future weathering conditions, studying the effectiveness of protective measures, and analyzing the potential effects of cleaning treatments. High-resolution micro-CT coupled with 3D image analysis is a promising new approach for studying porosity and pore systems in bricks. In this technique, hundreds or even thousands of X-ray projection images are acquired at 360 degrees around a sample. The X-radiation passing through the sample is absorbed, with radiation attenuated to varying degrees depending on the varying densities of phases within the object. The 3D volume is reconstructed by a computer algorithm, producing images where each voxel has a grayscale intensity value associated with the component it represents. Recent new instrument designs allow fast scanning with good spatial resolution. In this research, we present a set of protocols for creating optimal images of brick pores in micro-CT scans and for conducting 3D image analysis to extract both qualitative and quantitative data from those scans. Small samples give better spatial resolution for imaging of pores, so given the typical heterogeneity of bricks, scanning multiple samples from each brick ensures that the results are more likely to be representative. Machine learning and deep learning with convolutional neural networks were found to be important tools for better distinguishing pores from the surrounding matrix in the segmentation process, especially at the very limits of spatial resolution. Statistical analyses revealed which of the many parameters that can be measured are potentially most significant for characterizing the pore systems of bricks. These significant pore variables came from a multi-staged image analysis approach and include the total volume percent occupied by pores, the percentage of those pores accessible to the surface versus isolated interior ones, a variety of statistical properties of individual pores related to their size and shape, the average number of connections that pores have to other pores, and the length, diameter, and directness of those connections.

Graphical Abstract
Plasmonic sensors based on graphene and graphene hybrid materials
Authors: Zhang Zhichao , Lee Yeageun , Haque Md Farhadul , Leem Juyoung , Hsieh Ezekiel Y. , Nam SungWoo 
Published: June 2022

AbstractThe past decade has witnessed a rapid growth of graphene plasmonics and their applications in different fields. Compared with conventional plasmonic materials, graphene enables highly confined plasmons with much longer lifetimes. Moreover, graphene plasmons work in an extended wavelength range, i.e., mid-infrared and terahertz regime, overlapping with the fingerprints of most organic and biomolecules, and have broadened their applications towards plasmonic biological and chemical sensors. In this review, we discuss intrinsic plasmonic properties of graphene and strategies both for tuning graphene plasmons as well as achieving higher performance by integrating graphene with plasmonic nanostructures. Next, we survey applications of graphene and graphene-hybrid materials in biosensors, chemical sensors, optical sensors, and sensors in other fields. Lastly, we conclude this review by providing a brief outlook and challenges of the field. Through this review, we aim to provide an overall picture of graphene plasmonic sensing and to suggest future trends of development of graphene plasmonics.
Mitigation of carbon steel biocorrosion using a green biocide enhanced by a nature-mimicking anti-biofilm peptide in a flow loop
Authors: Wang Di , Unsal Tuba , Kumseranee Sith , Punpruk Suchada , Saleh Mazen A. , Alotaibi Mohammed D. , Xu Dake , Gu Tingyue 
Published: June 2022

AbstractBiocorrosion, also called microbiologically influenced corrosion (MIC), is a common operational threat to many industrial processes. It threatens carbon steel, stainless steel and many other metals. In the bioprocessing industry, reactor vessels in biomass processing and bioleaching are prone to MIC. MIC is caused by biofilms. The formation and morphology of biofilms can be impacted by fluid flow. Fluid velocity affects biocide distribution and MIC. Thus, assessing the efficacy of a biocide for the mitigation of MIC under flow condition is desired before a field trial. In this work, a benchtop closed flow loop bioreactor design was used to investigate the biocide mitigation of MIC of C1018 carbon steel at 25 °C for 7 days using enriched artificial seawater. An oilfield biofilm consortium was analyzed using metagenomics. The biofilm consortium was grown anaerobically in the flow loop which had a holding vessel for the culture medium and a chamber to hold C1018 carbon steel coupons. Peptide A (codename) was a chemically synthesized cyclic 14-mer (cys-ser-val-pro-tyr-asp-tyr-asn-trp-tyr-ser-asn-trp-cys) with its core 12-mer sequence originated from a biofilm dispersing protein secreted by a sea anemone which possesses a biofilm-free exterior. It was used as a biocide enhancer. The combination of 50 ppm (w/w) THPS (tetrakis hydroxymethyl phosphonium sulfate) biocide + 100 nM (180 ppb by mass) Peptide A resulted in extra 1-log reduction in the sulfate reducing bacteria (SRB) sessile cell count and the acid producing bacteria (APB) sessile cell count compared to 50 ppm THPS alone treatment. Furthermore, with the enhancement of 100 nM Peptide A, extra 44% reduction in weight loss and 36% abatement in corrosion pit depth were achieved compared to 50 ppm THPS alone treatment.
Graphical Abstract
Ultra high speed SPECT bone imaging enabled by a deep learning enhancement method: a proof of concept
Authors: Pan Boyang , Qi Na , Meng Qingyuan , Wang Jiachen , Peng Siyue , Qi Chengxiao , Gong Nan-Jie , Zhao Jun 
Published: June 2022

AbstractBackgroundTo generate high-quality bone scan SPECT images from only 1/7 scan time SPECT images using deep learning-based enhancement method.Materials and methodsNormal-dose (925–1110 MBq) clinical technetium 99 m-methyl diphosphonate (99mTc-MDP) SPECT/CT images and corresponding SPECT/CT images with 1/7 scan time from 20 adult patients with bone disease and a phantom were collected to develop a lesion-attention weighted U2-Net (Qin et al. in Pattern Recognit 106:107404, 2020), which produces high-quality SPECT images from fast SPECT/CT images. The quality of synthesized SPECT images from different deep learning models was compared using PSNR and SSIM. Clinic evaluation on 5-point Likert scale (5 = excellent) was performed by two experienced nuclear physicians. Average score and Wilcoxon test were constructed to assess the image quality of 1/7 SPECT, DL-enhanced SPECT and the standard SPECT. SUVmax, SUVmean, SSIM and PSNR from each detectable sphere filled with imaging agent were measured and compared for different images.ResultsU2-Net-based model reached the best PSNR (40.8) and SSIM (0.788) performance compared with other advanced deep learning methods. The clinic evaluation showed the quality of the synthesized SPECT images is much higher than that of fast SPECT images (P < 0.05). Compared to the standard SPECT images, enhanced images exhibited the same general image quality (P > 0.999), similar detail of 99mTc-MDP (P = 0.125) and the same diagnostic confidence (P = 0.1875). 4, 5 and 6 spheres could be distinguished on 1/7 SPECT, DL-enhanced SPECT and the standard SPECT, respectively. The DL-enhanced phantom image outperformed 1/7 SPECT in SUVmax, SUVmean, SSIM and PSNR in quantitative assessment.ConclusionsOur proposed method can yield significant image quality improvement in the noise level, details of anatomical structure and SUV accuracy, which enabled applications of ultra fast SPECT bone imaging in real clinic settings.
Synthesis of a 2-nitroimidazole derivative N-(4-[18F]fluorobenzyl)-2-(2-nitro-1H-imidazol-1-yl)-acetamide ([18 F]FBNA) as PET radiotracer for imaging tumor hypoxia
Authors: Nario Arian Pérez , Woodfield Jenilee , dos Santos Sofia Nascimento , Bergman Cody , Wuest Melinda , Araújo Yasniel Babí , Lapolli André Luis , West Frederick G. , Wuest Frank , Bernardes Emerson Soares 
Published: June 2022

Abstract
Background
Tissue hypoxia is a pathological condition characterized by reducing oxygen supply. Hypoxia is a hallmark of tumor environment and is commonly observed in many solid tumors. Non-invasive imaging techniques like positron emission tomography (PET) are at the forefront of detecting and monitoring tissue hypoxia changes in vivo.

Results
We have developed a novel 18F-labeled radiotracer for hypoxia PET imaging based on cytotoxic agent benznidazole. Radiotracer N-(4-[18F]fluorobenzyl)-2-(2-nitro-1H-imidazol-1-yl)acetamide ([18F]FBNA) was synthesized through acylation chemistry with readily available 4-[18F]fluorobenzyl amine. Radiotracer [18F]FBNA was obtained in good radiochemical yields (47.4 ± 5.3%) and high radiochemical purity (> 95%). The total synthesis time was 100 min, including HPLC purification and the molar activity was greater than 40 GBq/µmol. Radiotracer [18F]FBNA was stable in saline and mouse serum for 6 h. [18F]FBNA partition coefficient (logP = 1.05) was found to be more lipophilic than [18F]EF-5 (logP = 0.75), [18F]FMISO (logP = 0.4) and [18F]FAZA (logP =  − 0.4). In vitro studies showed that [18F]FBNA accumulates in gastric cancer cell lines AGS and MKN45 under hypoxic conditions.

Conclusions
Hence, [18F]FBNA represents a novel and easy-to-prepare PET radioligand for imaging hypoxia.
Current status on cyclotron facilities and related infrastructure supporting PET applications in Latin America and the Caribbean
Authors: Avila-Rodriguez Miguel A. , Jalilian Amir R. , Korde Aruna , Schlyer David , Haji-Saeid Mohammad , Paez Jose , Perez-Pijuan Saul 
Published: June 2022

AbstractThis review presents the results of a survey conducted by the International Atomic Energy Agency on cyclotrons and related infrastructure used for radionuclide and radiopharmaceutical production which are supporting PET imaging applications in Latin America and the Caribbean region.
Added value of patient-reported outcome measures (PROMs) after an acute stroke and early predictors of 90 days PROMs
Authors: Sanchez-Gavilan Ester , Montiel Estefania , Baladas Maria , Lallanas Sofia , Aurin Eva , Watson Carolina , Gutierrez Maria , Cossio Yolima , Ribo Marc , Molina Carlos A. , Rubiera Marta 
Published: June 2022

Abstract
Introduction
Value-based health care represents a patient-centered approach by valuing Patient-Reported Outcome Measures (PROMs). Our aim was to describe the additional value of PROMs after an acute stroke over conventional outcome measures and to identify early predictors of poor PROMs.

Methods
Acute stroke patients discharged from a tertiary care hospital followed by a web/phone-based PROMs collection program in the post hospitalization phase. Main PROMs involve anxiety and depression (HADS) (each defined by HADS ≥ 10) and global physical (PHY-) and mental (M-) health (PROMIS-10). PROMIS cut-off raw values of normality were: PHY-PROMIS ≥ 13 and M-PROMIS ≥ 11. An overall health status (OHS) from 0 to 100 was also determined. PROMs related to the different modified Rankin Scale (mRS) grades were defined. Early predictors of PROMs were evaluated.

Results
We included 1321 stroke patients, mean age 75 (± 8.6) and 55.7% male; 77.7% returned home. Despite a favorable mRS at 3 months (< 3), a relevant rate of patients considered without symptoms or with mild disability showed unfavorable results in the measured PROMs (8% unfavorable OHS, 15% HAD-depression, 12.1% HAD-anxiety, 28.7% unfavorable M-PROMIS and 33.1% unfavorable PHY-PROMIS results). Along follow-up, only PHY-PROMIS and OHS showed significant improvement (p < 0.01 and 0.03, respectively). The multivariate analysis including discharge variables showed that female sex, higher discharge mRS and discharge to socio-rehabilitation-center (SRC) were independent predictors of unfavorable results in PROMs (p < 0.01). When adding 7 days PROMs results, they emerged as the strongest predictors of 3 months PROMs.

Conclusions
A high proportion of stroke patients show unfavorable results in PROMs at 3 months, even those with favorable mRS, and most results obtained by PROMs during follow-up continued to indicate alterations. Female sex, mRS and discharge to SRC predicted unfavorable results in PROMs, but the strongest predictors of 3 months PROMs were the results of the 7 days PROMs.