bluuebunny/crossref_metadata_2025 huggingface dataset:
A proof-of-concept pipeline to guide evaluation of tumor tissue perfusion by dynamic contrast-agent imaging: Direct simulation and inverse tracer-kinetic procedures
Authors: Vignon-Clementel Irene E. , Jagiella Nick , Dichamp Jules , Kowalski Jérôme , Lederle Wiltrud , Laue Hendrik , Kiessling Fabian , Sedlaczek Oliver , Drasdo Dirk
Published: April 2023

Dynamic contrast-enhanced (DCE) perfusion imaging has shown great potential to non-invasively assess cancer development and its treatment by their characteristic tissue signatures. Different tracer kinetics models are being applied to estimate tissue and tumor perfusion parameters from DCE perfusion imaging. The goal of this work is to provide an in silico model-based pipeline to evaluate how these DCE imaging parameters may relate to the true tissue parameters. As histology data provides detailed microstructural but not functional parameters, this work can also help to better interpret such data. To this aim in silico vasculatures are constructed and the spread of contrast agent in the tissue is simulated. As a proof of principle we show the evaluation procedure of two tracer kinetic models from in silico contrast-agent perfusion data after a bolus injection. Representative microvascular arterial and venous trees are constructed in silico. Blood flow is computed in the different vessels. Contrast-agent input in the feeding artery, intra-vascular transport, intra-extravascular exchange and diffusion within the interstitial space are modeled. From this spatiotemporal model, intensity maps are computed leading to in silico dynamic perfusion images. Various tumor vascularizations (architecture and function) are studied and show spatiotemporal contrast imaging dynamics characteristic of in vivo tumor morphotypes. The Brix II also called 2CXM, and extended Tofts tracer-kinetics models common in DCE imaging are then applied to recover perfusion parameters that are compared with the ground truth parameters of the in silico spatiotemporal models. The results show that tumor features can be well identified for a certain permeability range. The simulation results in this work indicate that taking into account space explicitly to estimate perfusion parameters may lead to significant improvements in the perfusion interpretation of the current tracer-kinetics models.
Expanding the pH range of glutamate decarboxylase from L. pltarum LC84 by site-directed mutagenesis
Authors: Yang Lijuan , Zhang Xian , Chen Jing , Zhang Yao , Feng Zhiping
Published: April 2023

Introduction: Glutamate decarboxylase is a class Ⅱ amino acid decarboxylase dependent onpyridoxal-5′-phosphate (PLP), which catalyzes the decarboxylation of substrateL-glutamate (L-Glu) to synthesize γ-aminobutyric acid (GABA). The low activity ofglutamic acid decarboxylase (GAD) and its ability to catalyze only under acidicconditions limit its application in biosynthesis of GABA.

Methods: Taking glutamic acid decarboxylase from Lactobacillus plantarum, which produces GABA, as the research object, the mutation site was determined by amino acid sequence analysis of GAD, the mutation was introduced by primers, and the mutant was constructed by whole plasmid PCR and expressed in Escherichia coli. Then, the enzymatic properties of the mutant were analyzed. Finally, the three-dimensional structure of the mutant was simulated to support the experimental results.

Results and Discussion: In this case, mutants E313S and Q347H of glutamate decarboxylase from L. pltarum LC84 (LpGAD) were constructed by targeted mutagenesis. Compared with the wild-type, their enzyme activity increased by 62.4% and 12.0% at the optimum pH 4.8, respectively. In the range of pH 4.0–7.0, their enzyme activity was higher than that of the wild-type, and enzyme activity of mutant E313S was 5 times that of the wild-type at pH 6.2. Visualization software PyMOL analyzed the 3D structure of the mutant predicted by homologous modeling, and the results showed that mutant E313S may broadened the reaction pH of LpGAD through the influence of surface charge, while mutant Q347H may broadened the reaction pH of LpGAD through the stacking effect of aromatic rings. In a word, mutants E313S and Q347H were improved the enzyme activity and were broadened the reaction pH of the enzyme, which made it possible for it to be applied in food industry and laid the foundation for the industrial production of GABA.
Transfer learning-assisted 3D deep learning models for knee osteoarthritis detection: Data from the osteoarthritis initiative
Authors: Yeoh Pauline Shan Qing , Lai Khin Wee , Goh Siew Li , Hasikin Khairunnisa , Wu Xiang , Li Pei
Published: April 2023

Knee osteoarthritis is one of the most common musculoskeletal diseases and is usually diagnosed with medical imaging techniques. Conventionally, case identification using plain radiography is practiced. However, we acknowledge that knee osteoarthritis is a 3D complexity; hence, magnetic resonance imaging will be the ideal modality to reveal the hidden osteoarthritis features from a three-dimensional view. In this work, the feasibility of well-known convolutional neural network (CNN) structures (ResNet, DenseNet, VGG, and AlexNet) to distinguish knees with and without osteoarthritis (OA) is investigated. Using 3D convolutional layers, we demonstrated the potential of 3D convolutional neural networks of 13 different architectures in knee osteoarthritis diagnosis. We used transfer learning by transforming 2D pre-trained weights into 3D as initial weights for the training of the 3D models. The performance of the models was compared and evaluated based on the performance metrics [balanced accuracy, precision, F1 score, and area under receiver operating characteristic (AUC) curve]. This study suggested that transfer learning indeed enhanced the performance of the models, especially for ResNet and DenseNet models. Transfer learning-based models presented promising results, with ResNet34 achieving the best overall accuracy of 0.875 and an F1 score of 0.871. The results also showed that shallow networks yielded better performance than deeper neural networks, demonstrated by ResNet18, DenseNet121, and VGG11 with AUC values of 0.945, 0.914, and 0.928, respectively. This encourages the application of clinical diagnostic aid for knee osteoarthritis using 3DCNN even in limited hardware conditions.
Advances in nanomaterial-based targeted drug delivery systems
Authors: Cheng Xiaoxiao , Xie Qirong , Sun Yang
Published: April 2023

Nanomaterial-based drug delivery systems (NBDDS) are widely used to improve the safety and therapeutic efficacy of encapsulated drugs due to their unique physicochemical and biological properties. By combining therapeutic drugs with nanoparticles using rational targeting pathways, nano-targeted delivery systems were created to overcome the main drawbacks of conventional drug treatment, including insufficient stability and solubility, lack of transmembrane transport, short circulation time, and undesirable toxic effects. Herein, we reviewed the recent developments in different targeting design strategies and therapeutic approaches employing various nanomaterial-based systems. We also discussed the challenges and perspectives of smart systems in precisely targeting different intravascular and extravascular diseases.
The proteasome: A key modulator of nervous system function, brain aging, and neurodegenerative disease
Authors: Davidson Kanisa , Pickering Andrew M.
Published: April 2023

The proteasome is a large multi-subunit protease responsible for the degradation and removal of oxidized, misfolded, and polyubiquitinated proteins. The proteasome plays critical roles in nervous system processes. This includes maintenance of cellular homeostasis in neurons. It also includes roles in long-term potentiation via modulation of CREB signaling. The proteasome also possesses roles in promoting dendritic spine growth driven by proteasome localization to the dendritic spines in an NMDA/CaMKIIα dependent manner. Proteasome inhibition experiments in varied organisms has been shown to impact memory, consolidation, recollection and extinction. The proteasome has been further shown to impact circadian rhythm through modulation of a range of ‘clock’ genes, and glial function. Proteasome function is impaired as a consequence both of aging and neurodegenerative diseases. Many studies have demonstrated an impairment in 26S proteasome function in the brain and other tissues as a consequence of age, driven by a disassembly of 26S proteasome in favor of 20S proteasome. Some studies also show proteasome augmentation to correct age-related deficits. In amyotrophic lateral sclerosis Alzheimer’s, Parkinson’s and Huntington’s disease proteasome function is impaired through distinct mechanisms with impacts on disease susceptibility and progression. Age and neurodegenerative-related deficits in the function of the constitutive proteasome are often also accompanied by an increase in an alternative form of proteasome called the immunoproteasome. This article discusses the critical role of the proteasome in the nervous system. We then describe how proteasome dysfunction contributes to brain aging and neurodegenerative disease.
Knock-down of YME1L1 induces mitochondrial dysfunction during early porcine embryonic development
Authors: Zhou Dongjie , Sun Ming-Hong , Jiang Wen-Jie , Li Xiao-Han , Lee Song-Hee , Heo Geun , Choi Jungseok , Kim Kwan-Suk , Cui Xiang-Shun
Published: April 2023

YME1L1, a mitochondrial metalloproteinase, is an Adenosine triphosphate (ATP)-dependent metalloproteinase and locates in the mitochondrial inner membrane. The protease domain of YME1L1 is oriented towards the mitochondrial intermembrane space, which modulates the mitochondrial GTPase optic atrophy type 1 (OPA1) processing. However, during embryonic development, there is no report yet about the role of YME1L1 on mitochondrial biogenesis and function in pigs. In the current study, the mRNA level of YME1L1 was knocked down by double strand RNA microinjection to the 1-cell stage embryos. The expression patterns of YME1L1 and its related proteins were performed by immunofluorescence and western blotting. To access the biological function of YME1L1, we first counted the preimplantation development rate, diameter, and total cell number of blastocyst on day-7. First, the localization of endogenous YME1L1 was found in the punctate structures of the mitochondria, and the expression level of YME1L1 is highly expressed from the 4-cell stage. Following significant knock-down of YME1L1, blastocyst rate and quality were decreased, and mitochondrial fragmentation was induced. YME1L1 knockdown induced excessive ROS production, lower mitochondrial membrane potential, and lower ATP levels. The OPA1 cleavage induced by YME1L1 knockdown was prevented by double knock-down of YME1L1 and OMA1. Moreover, cytochrome c, a pro-apoptotic signal, was released from the mitochondria after the knock-down of YME1L1. Taken together, these results indicate that YME1L1 is essential for regulating mitochondrial fission, function, and apoptosis during porcine embryo preimplantation development.
The double-edged sword of inflammation in inherited retinal degenerations: Clinical and preclinical evidence for mechanistically and prognostically impactful but treatable complications
Authors: Sarici Kubra , Vyas Aanal , Iannaccone Alessandro
Published: April 2023

We present retrospective data from our clinical research efforts of the past several years alongside a review of past and current clinical and preclinical data independently by several investigators supporting our clinical evidence for the importance of inflammation in inherited retinal degenerations (IRDs). We show how inflammation is a complicating factor in IRDs but, if recognized and managed, also a great opportunity to mitigate disease severity immediately, improve patient prognosis and quality of life, extend the treatment windows for gene-specific and agnostic therapeutic approaches, mitigate the impact of inflammatory complications on the accurate estimate of vision changes in IRD natural history studies, improve the chances of safer outcomes following cataract surgery, and potentially reduce the likelihood of inflammatory adverse events and augment the efficacy of viral vector-based treatment approaches to IRDs. Manuscript contribution to the field. Inflammation has been suspected to be at play in IRDs since the beginning of the 1900s and became a research focus through the early 1990s but was then largely abandoned in favor of genetic-focused research. Thanks to regained cognizance, better research tools, and a more holistic approach to IRDs, the recent reappraisal of the role of inflammation in IRDs has brought back to the surface its importance. A potential confounder in natural history studies and a limiting factor in clinical trials if not accounted for, inflammation can be managed and often offers an opportunity for immediately improved prognosis and outcomes for IRD patients. We present our retrospective clinical evidence for connections with a measurable secondary autoimmune component that can develop in IRDs and contribute to vision loss but is at least in part treatable. We also present ample lines of evidence from the literature corroborating our clinical observations at the preclinical level.
KLF2 is a clinical diagnostic and treatment biomarker of breast cancer
Authors: Li Ya-Zhao , Xie Juan , Wang Rui-Qi , Gao Xiao-Qian , Liu Pei-Jun , Liu Jie
Published: April 2023

Background: As a highly prevalent malignancy among women worldwide, breast cancer, remains a critical public health issue necessitating the development of novel therapeutics and biomarkers. Kruppel Like Factor 2 (KLF2), a member of the Kruppel family of transcription factors, has been implicated in various types of cancer due to its diminished expression; however, the potential implications of KLF2 expression in relation to breast cancer progression, prognosis, and therapy remain unclear.

Methods: The present study employed the Tumor Immune Estimation Resource (TIMER) and The Human Protein Atlas databases to investigate the expression pattern of KLF2 in pan-cancer. The relationship between KLF2 expression and clinical features or immune infiltration of The Cancer Genome Atlas (TCGA) breast cancer samples was evaluated using Breast Cancer Integrative Platform (BCIP) and TIMER. The expression levels of KLF2 in breast cancer were validated via immunohistochemical staining analysis. Gene Set Enrichment Analysis (GSEA) to study the KLF2-related gene ontology. STRING database was employed to construct a protein-protein interaction (PPI) network of KLF2 in relation to vascular endothelial growth factor A (VEGFA) and hypoxia-inducible factor 1α (HIF1α). The expression of KLF2 following diverse breast cancer therapies was analyzed in the Gene Expression Omnibus (GEO) databases. The expression of KLF2 following treatment with simvastatin was validated via immunofluorescence and western blotting.

Results: Our study reveals that KLF2 displays significantly reduced expression in cancerous tissues compared to non-cancerous controls. Patients with low KLF2 expression levels exhibited poor prognosis across multiple cancer types. KLF2 expression levels were found to be reduced in advanced cancer stages and grades, while positively correlated with the expression of estrogen receptor (ER), progesterone receptor (PR), and tumor size in breast cancer. KLF2 expression is associated with diverse immune infiltration cells, and may impact the breast tumor immune microenvironment by regulating dendritic cell activation. Additionally, we observed a negative correlation between KLF2 expression levels and angiogenesis, as well as the expression of VEGFA and HIF1α. Notably, the anticancer drug simvastatin could induce KLF2 expression in both breast cancer.

Conclusion: Based on our observations, KLF2 has potential as a diagnostic, prognostic, and therapeutic biomarker for breast cancer.
Identifying lipid particle sub-types in live Caenorhabditis elegans with two-photon fluorescence lifetime imaging
Authors: Chen Wei-Wen , Tang Wenyu , Hamerton Emily K. , Kuo Penelope X. , Lemieux George A. , Ashrafi Kaveh , Cicerone Marcus T.
Published: April 2023

Fat metabolism is an important modifier of aging and longevity in Caenorhabditis elegans. Given the anatomy and hermaphroditic nature of C. elegans, a major challenge is to distinguish fats that serve the energetic needs of the parent from those that are allocated to the progeny. Broadband coherent anti-Stokes Raman scattering (BCARS) microscopy has revealed that the composition and dynamics of lipid particles are heterogeneous both within and between different tissues of this organism. Using BCARS, we have previously succeeded in distinguishing lipid-rich particles that serve as energetic reservoirs of the parent from those that are destined for the progeny. While BCARS microscopy produces high-resolution images with very high information content, it is not yet a widely available platform. Here we report a new approach combining the lipophilic vital dye Nile Red and two-photon fluorescence lifetime imaging microscopy (2p-FLIM) for the in vivo discrimination of lipid particle sub-types. While it is widely accepted that Nile Red staining yields unreliable results for detecting lipid structures in live C. elegans due to strong interference of autofluorescence and non-specific staining signals, our results show that simple FLIM phasor analysis can effectively separate those signals and is capable of differentiating the non-polar lipid-dominant (lipid-storage), polar lipid-dominant (yolk lipoprotein) particles, and the intermediates that have been observed using BCARS microscopy. An advantage of this approach is that images can be acquired using common, commercially available 2p-FLIM systems within about 10% of the time required to generate a BCARS image. Our work provides a novel, broadly accessible approach for analyzing lipid-containing structures in a complex, live whole organism context.
Towards landslide space-time forecasting through machine learning: the influence of rainfall parameters and model setting
Authors: Nocentini Nicola , Rosi Ascanio , Segoni Samuele , Fanti Riccardo
Published: April 2023

Landslide susceptibility assessment using machine learning models is a popular and consolidated approach worldwide. The main constraint of susceptibility maps is that they are not adequate for temporal assessments: they are generated from static predisposing factors, allowing only a spatial prediction of landslides. Recently, some methodologies have been proposed to provide spatiotemporal landslides prediction starting from machine learning algorithms (e.g., combining susceptibility maps with rainfall thresholds), but the attempt to obtain a dynamic landslide probability map directly by applying machine learning models is still in the preliminary phase. This work provides a contribution to fix this gap, combining in a Random Forest (RF) algorithm a static indicator of the spatial probability of landslide occurrence (i.e., a classical susceptibility index) and a number of dynamic variables (i.e., seasonality and the rainfall amount cumulated over different reference periods). The RF implementation used in this work allows the calculation of the Out-of-Bag Error and depicts Partial Dependence Plots, two indices that were used to quantify the variables’ importance and to comprehend if the model outcomes are consistent with the triggering mechanism observed in the case of study (Metropolitan City of Florence, Italy). The goal of this research is not to set up a landslide probability map, but to 1) understand how to populate training and test datasets with observations sampled over space and time, 2) assess which rainfall variables are statistically more relevant for the identification of the time and location of landslides, and 3) test the dynamic application of RF in a forecasting model for the spatiotemporal prediction of landslides. The proposed dynamic methodology shows encouraging results, consistent with the actual knowledge of the physical mechanism of the triggering of shallow landslides (mainly influenced by short and intense rainfalls) and identifies some benchmark configurations that represents a promising starting point for future regional-scale applications of machine learning models to dynamic landslide probability assessment and early warning.
Experimental investigation into rock burst proneness of rock materials considering strain rate and size effect
Authors: Li Xiaobin , Wang Haoteng , Zhao Ying , Xue Haifang , Li Lingyun
Published: April 2023

In deep rock engineering, evaluating the likelihood of rock burst is imperative to ensure safety. This study proposes a new metric, the post-peak dissipated energy index, which accounts for strain rate and size effects in assessment of the rock burst proneness of a rock mass. To investigate rock burst proneness, conventional compression tests were conducted on limestone and slate samples with different length to diameter (L/D) ratios (ranging from 0.3 to 1.5) at four different strain rates (0.005, 0.01, 0.5, and 1.0 s−1). Based on the testing observations, the actual rock burst proneness was classified into three categories (no risk, low risk, and high risk). A new criterion was also established using the post-peak dissipated energy index, which is the ratio of elastic energy to total dissipated energy. The impact of the strain rate and L/D ratio on rock burst proneness was analyzed. The results indicated that increased strain rates cause a strong hardening effect, leading to staged growth of rock burst proneness. However, the rock burst proneness decreases non-linearly with the increasing L/D ratio. The accuracy of the proposed criterion was validated by comparison with existing criteria, demonstrating that the energy-based index ensures a reliable evaluation of the rock burst proneness of a rock mass. The proposed method has excellent potential for practical application in deep rock engineering.
Analysis of Th and U breeding in a heat pipe cooled traveling wave reactor
Authors: Ma Kunfeng , Hu Po
Published: April 2023

Introduction: Heat pipe cooled traveling wave reactor (HPTWR) is a newly proposed heat pipe reactor. The HPTWR can achieve the low enrichment of loaded fuel, small reactivity swing, and long-term continuous operation for the power supply of decentralized electricity markets. Due to the excellent breeding capability of the HPTWR, the Th fuel is also added into the breeding fuel region of the reactor to achieve the Th-U fuel cycle in this work.

Methods: The Monte Carlo code RMC is used to obtain the reactivity swing, propagation of axial power peak, burnup, and productions of bred fissile nuclides for the HPTWR with Th and U fuels.

Results and Discussion: The results indicate that the HPTWR with 13.6% 235U enrichment of ignition fuel and 20% 235U enrichment of breeding fuel can continuously operate for 18.1 years without refueling when the mass fraction of 232Th in heavy metals of breeding fuel region is 33%. The propagation velocity of axial power peak and total burnup for the HPTWR with Th and U fuels is about 0.5525 cm/years and 24.72 GWd/THM during the 18.1 years operation respectively. The corresponding productions of bred 239Pu, 241Pu and 233U are about 212.99 kg of 239Pu, 0.19 kg of 241Pu and 81.58 kg of 233U at the end of cycle (EOC) respectively. The obtained results in this study demonstrate that the HPTWR can achieve the Th fuel breeding in the case of the low 235U enrichment loading (≤ 20%).
Digitalization, resource misallocation and low-carbon agricultural production: evidence from China
Authors: Xu Yubing , Li Cuixia , Wang Xinyao , Wang Jingjing
Published: April 2023

With the rapid development of digital technologies such as artificial intelligence, big data and cloud computing, China’s agricultural production is entering a new era characterized by digitalization. Based on provincial panel data of China from 2013 to 2020, this paper adopts the system GMM and mediating effects model to systematically examine the impact of digitalization on low-carbon agricultural production from the perspective of resource misallocation. The results indicate that digitalization can significantly curb agricultural carbon emissions and thus promote low-carbon agricultural production, and this finding still holds after the robustness test. The heterogeneity analysis indicates that the inhibiting effect of digitalization on agricultural carbon emissions is most pronounced in the eastern region relative to the central and western regions (the regression coefficients are −0.400 and −0.126 respectively). Further mechanism analysis suggests that digitalization can reduce agricultural carbon emissions by correcting the widespread capital and labor misallocation in agricultural factor markets. The findings of this study provide significant policy implications for low-carbon agricultural production in China.
Urban waterlogging prediction and risk analysis based on rainfall time series features: A case study of Shenzhen
Authors: Zhang Zongjia , Jian Xinyao , Chen Yiye , Huang Zhejun , Liu Junguo , Yang Lili
Published: April 2023

In recent years, the frequency of extreme weather has increased, and urban waterlogging caused by sudden rainfall has occurred from time to time. With the development of urbanization, a large amount of land has been developed and the proportion of impervious area has increased, intensifying the risk of urban waterlogging. How to use the available meteorological data for accurate prediction and early warning of waterlogging hazards has become a key issue in the field of disaster prevention and risk assessment. In this paper, based on historical meteorological data, we combine domain knowledge and model parameters to experimentally extract rainfall time series related features for future waterlogging depth prediction. A novel waterlogging depth prediction model that applies only rainfall data as input is proposed by machine learning algorithms. By analyzing a large amount of historical flooding monitoring data, a “rainfall-waterlogging amplification factor” based on the geographical features of monitoring stations is constructed to quantify the mapping relationship between rainfall and waterlogging depths at different locations. After the model is trained and corrected by the measured data, the prediction error for short-time rainfall basically reaches within 2 cm. This method improves prediction performance by a factor of 2.5–3 over featureless time series methods. It effectively overcomes the limitations of small coverage of monitoring stations and insufficient historical waterlogging data, and can achieve more accurate short-term waterlogging prediction. At the same time, it can provide reference suggestions for the government to conduct waterlogging risk analysis and add new sensor stations by counting the amplification factor of other locations.
Assessment of the agronomic value of solar-dried sludge and heavy metals bioavailability based on the bioaccumulation factor and translocation index
Authors: An-nori Amal , El Mejahed Khalil , Fels Loubna El , Touhami Driss , Ezzariai Amine , El Gharous Mohamed , Hafidi Mohamed
Published: April 2023

This study aimed to assess the agronomic value of solar-dried sludge (SDS) and the transfer of Cr, Ni, Pb, and Cu to wheat (Triticum aestivum) and faba bean (Vicia faba). A greenhouse experiment was performed involving two rates of SDS (15 t/ha and 30 t/ha) from an activated sludge-based wastewater treatment plant. In addition to the single use of an SDS amendment, co-application of SDS and mineral fertilizers was also included to determine the best scenario resulting in high yields and less negative implications on the environment. Data for both wheat and faba bean showed that applying SDS at 30 t/ha led to competitive yields compared to the ones obtained previously, while 15 t/ha of SDS and mineral fertilizers were co-applied. The use of SDS increased soil organic matter, slightly decreased the pH value, and increased soil salinity. The contents of Ni, Cu, and Pb were not significantly affected by the application of SDS. Only Cr showed high soil concentrations in proportion to the increasing rates of SDS. The bioaccumulation of heavy metals in roots was more important in 30 t/ha than that in 15 t/ha amended soil. In the case of wheat, the bioconcentration factor (BCF) root values correspond to the following order: Cr (0.89) >Cu (0.85)> Ni (0.28)> Pb (0.22). In the case of faba bean, BCF roots were observed as follows: Cu (1.04 > Ni (0.37)> Cr (0.16)> Pb (0.15). Wheat excluded Cr, Ni, and Pb from the uptake by shoots, and Cu was translocated from roots to shoots with a percentage of 11% at 30 t/ha of applied SDS. Faba beans demonstrated more important values of HM’s translocation by respecting this order (Ni (37.7%) > cu (30.24%)> Cr (17.59%), while Pb was excluded from the translocation. No significant difference was observed regarding the translocation index when the sludge rate has been duplicated from 15 t/ha to 30 t/ha. Based on these outcomes, SDS used at the rate of 30 t/ha is the best scenario to amend the soil and provide nutrients to plants. Wheat is translocating less heavy metal to the edible part; it is, thus, the most suitable crop to be involved in the current context.
Could climate change and urban growth make Europeans regard urban trees as an additional source of danger?
Authors: Portoghesi Luigi , Masini Emanuela , Tomao Antonio , Agrimi Mariagrazia
Published: April 2023

In some geographical areas of North America and Southeast Asia cities are frequently hit by heavy windstorms capable of knocking down hundreds of urban trees and large branches in a few minutes. Falling trees generate a broad array of disservices that vary over time and space. In Europe and worldwide the frequency of these extreme weather events has increased in recent decades and climate change could intensify these windstorms while the effects of urban land expansion could increase the extent of damage. However, Europe’s urban populations are unprepared for extreme weather events and are unable to limit the effects that the widespread loss of trees over limited space and time can have on people, buildings and city road networks. Preparing for rare, extreme future events that could strongly affect urban green infrastructures is a demanding challenge for city dwellers and for those who should ensure the continuity of the ecosystem services provided by urban trees. In fact, the damage caused by fallen trees is combined with the loss of the benefits provided by the trees themselves. Therefore the aim of this paper is to: (a) investigate the disruptions that a windstorm can cause in an urban area full of trees using a conceptual model; (b) conduct a literature review to determine how high the risk of these disservices occurring in Europe really is, which is definitely more likely than commonly perceived and could increase as a result of climate change and; (c) indicate what kind of measures can be taken in European cities to prevent or at least reduce the risks from falling urban trees during a strong windstorm, starting from the experience gained in the geographical areas most frequently and intensely affected by this type of weather event.
Genetic predisposition may not improve prediction of cardiac surgery-associated acute kidney injury
Authors: Douville Nicholas J. , Larach Daniel B. , Lewis Adam , Bastarache Lisa , Pandit Anita , He Jing , Heung Michael , Mathis Michael , Wanderer Jonathan P. , Kheterpal Sachin , Surakka Ida , Kertai Miklos D.
Published: April 2023

Background: The recent integration of genomic data with electronic health records has enabled large scale genomic studies on a variety of perioperative complications, yet genome-wide association studies on acute kidney injury have been limited in size or confounded by composite outcomes. Genome-wide association studies can be leveraged to create a polygenic risk score which can then be integrated with traditional clinical risk factors to better predict postoperative complications, like acute kidney injury.

Methods: Using integrated genetic data from two academic biorepositories, we conduct a genome-wide association study on cardiac surgery-associated acute kidney injury. Next, we develop a polygenic risk score and test the predictive utility within regressions controlling for age, gender, principal components, preoperative serum creatinine, and a range of patient, clinical, and procedural risk factors. Finally, we estimate additive variant heritability using genetic mixed models.

Results: Among 1,014 qualifying procedures at Vanderbilt University Medical Center and 478 at Michigan Medicine, 348 (34.3%) and 121 (25.3%) developed AKI, respectively. No variants exceeded genome-wide significance (p < 5 × 10−8) threshold, however, six previously unreported variants exceeded the suggestive threshold (p < 1 × 10−6). Notable variants detected include: 1) rs74637005, located in the exonic region of NFU1 and 2) rs17438465, located between EVX1 and HIBADH. We failed to replicate variants from prior unbiased studies of post-surgical acute kidney injury. Polygenic risk was not significantly associated with post-surgical acute kidney injury in any of the models, however, case duration (aOR = 1.002, 95% CI 1.000–1.003, p = 0.013), diabetes mellitus (aOR = 2.025, 95% CI 1.320–3.103, p = 0.001), and valvular disease (aOR = 0.558, 95% CI 0.372–0.835, p = 0.005) were significant in the full model.

Conclusion: Polygenic risk score was not significantly associated with cardiac surgery-associated acute kidney injury and acute kidney injury may have a low heritability in this population. These results suggest that susceptibility is only minimally influenced by baseline genetic predisposition and that clinical risk factors, some of which are modifiable, may play a more influential role in predicting this complication. The overall impact of genetics in overall risk for cardiac surgery-associated acute kidney injury may be small compared to clinical risk factors.
Construction of lncRNA prognostic model related to cuproptosis in esophageal carcinoma
Authors: Zhang Liming , Zong Ling , Li Wenhui , Ning Lu , Zhao Yajun , Wang Shaoqiang , Wang Lina
Published: April 2023

Background: Esophageal carcinoma (ESCA) is one of the most prevalent malignant tumors in the world. The prognosis of patients has significantly improved with the development of surgery, targeted therapy and immunotherapy. But the 5-year survival rate of ESCA patients is still incredibly low. Cuproptosis is a type of mitochondrial cell death induced by copper. It is unclear how cuproptosis-related lncRNAs (CRLs) affect ESCA prognosis.

Methods: In this study, we obtained the clinical data of ESCA patients, the transcriptome data from TCGA and identified CRLs by co-expression analysis, lasso regression, and cox regression analysis, to build a prognostic model. Then we validated the prognostic model using the Kaplan-Meier curve, cox regression analysis, and ROC, to create a nomogram based on risk score to forecast the prognosis of ESCA. Next, the immune escape of the CRLs was examined using the TIDE algorithm to assess its sensitivity to possible ESCA medications.

Results: To predict the prognosis of ESCA patients, we created a predictive model using 6 CRLs (AC034199.1, AC125437.1, AC107032.2, CTBP1-DT, AL024508.1, and AC008610.1), validated by the Kaplan-Meier and ROC curves. The model has a higher diagnostic value compared to other clinical features. The 6 CRLs expressed high in TCGA and ESCA specimens. Enrichment analysis revealed CRLs largely contributed to the interaction between cytokines and their receptors as well as complement coagulation cascades. The immunity escape analysis demonstrated that immunotherapy had a worse effect in the low-risk group than in the high-risk group. Additionally, we screened out potential antineoplastic drugs according to the results of the immunoassay and obtained 5 drugs, including CP-466722, crizotinib, MS-275, KIN001-135, and CP-466722.

Conclusion: The prognosis of patients with ESCA can be correctly predicted by the 6 CRLs chosen from this investigation. It lays the groundwork for more investigation into the ESCA mechanism and the identification of novel therapeutic targets.
Prevalence estimation of ATTRv in China based on genetic databases
Authors: Yongsheng Zheng , Chong Sun , Bingyou Liu , Jianian Hu , Haofeng Chen , Chongbo Zhao , Zhang Victor Wei , Jie Lin
Published: April 2023

Introduction: Amyloid transthyretin (ATTR) is divided into either hereditary (ATTRv) or sporadic (ATTRwt) and ATTRv is a rare hereditary disease transmitted as an autosomal dominant manner. Its global prevalence is traditionally estimated as 5,000 to 10,000 persons. However, it may be underestimated and the exact prevalence of ATTRv in China mainland remains unknown.

Methods: The Genome Aggregation database (gnomAD) database (containing 125,748 exomes) and two genomic sequencing databases——China Metabolic Analytics Project (ChinaMAP) (containing 10588 individuals) and Amcarelab gene database (containing 45392 exomes), were integrated to estimate the prevalence of ATTRv in the world and mainland Chinese populations. Pathogenic variants allele frequency and the prevalence of ATTRv was calculated.

Results: Six variants, counting 470 alleles, were defined as pathogenic variants in gnomAD. The prevalence of ATTRv in the world population was 57.4/100,000. Two variants (2 allele counts) and 15 variants (34 individuals) were defined as pathogenic variants in the ChinaMAP database and the Amcarelab exome database, respectively. Thus, the estimated prevalence interval of ATTRv in mainland China was 18.9/100,000-74,9/100,000.

Conclusion: The present study demonstrated that the previous prevalence was greatly underestimated using traditional methods. Therefore, raising awareness of the disease is essential for recognizing ATTRv in its early stage.
The Pseudomonas aeruginosa Secreted Protein PA3611 Promotes Bronchial Epithelial Cell Epithelial-Mesenchymal Transition via Integrin αvβ6-Mediated TGF-β1-Induced p38/NF-κB Pathway Activation
Authors: Shu Lei , Chen Sixia , Lin Shaoqing , Lin Huan , Shao Yan , Yao Jing , Qu Lili , Zhang Yunshi , Liu Xing , Du Xingran , Deng Kaili , Chen Xiaolin , Feng Ganzhu
Published: April 2023

Graphical Abstract

The role of PA3611 on EMT.
Stem Cell-Derived Exosome as Potential Therapeutics for Microbial Diseases
Authors: Keshtkar Somayeh , Kaviani Maryam , Soleimanian Saeede , Azarpira Negar , Asvar Zahra , Pakbaz Sara
Published: April 2023

Graphical Abstract

Summary of the antimicrobial effect of stem cell-derived exosomes on bacteria, viral, fungal, and parasitic diseases.
Improving Taxonomic Delimitation of Fungal Species in the Age of Genomics and Phenomics
Authors: Stengel Ashley , Stanke Kimberly M. , Quattrone Amanda C. , Herr Joshua R.
Published: April 2023

Graphical Abstract

Enhanced resolution of spices boundaries.
Integration of text mining and biological network analysis: Identification of essential genes in sulfate-reducing bacteria
Authors: Saxena Priya , Rauniyar Shailabh , Thakur Payal , Singh Ram Nageena , Bomgni Alain , Alaba Mathew O. , Tripathi Abhilash Kumar , Gnimpieba Etienne Z. , Lushbough Carol , Sani Rajesh Kumar
Published: April 2023

The growth and survival of an organism in a particular environment is highly depends on the certain indispensable genes, termed as essential genes. Sulfate-reducing bacteria (SRB) are obligate anaerobes which thrives on sulfate reduction for its energy requirements. The present study used Oleidesulfovibrio alaskensis G20 (OA G20) as a model SRB to categorize the essential genes based on their key metabolic pathways. Herein, we reported a feedback loop framework for gene of interest discovery, from bio-problem to gene set of interest, leveraging expert annotation with computational prediction. Defined bio-problem was applied to retrieve the genes of SRB from literature databases (PubMed, and PubMed Central) and annotated them to the genome of OA G20. Retrieved gene list was further used to enrich protein–protein interaction and was corroborated to the pangenome analysis, to categorize the enriched gene sets and the respective pathways under essential and non-essential. Interestingly, the sat gene (dde_2265) from the sulfur metabolism was the bridging gene between all the enriched pathways. Gene clusters involved in essential pathways were linked with the genes from seleno-compound metabolism, amino acid metabolism, secondary metabolite synthesis, and cofactor biosynthesis. Furthermore, pangenome analysis demonstrated the gene distribution, where 69.83% of the 116 enriched genes were mapped under “persistent,” inferring the essentiality of these genes. Likewise, 21.55% of the enriched genes, which involves specially the formate dehydrogenases and metallic hydrogenases, appeared under “shell.” Our methodology suggested that semi-automated text mining and network analysis may play a crucial role in deciphering the previously unexplored genes and key mechanisms which can help to generate a baseline prior to perform any experimental studies.
Different microbial genera drive methane emissions in beef cattle fed with two extreme diets
Authors: Miller Gemma A. , Auffret Marc D. , Roehe Rainer , Nisbet Holly , Martínez-Álvaro Marina
Published: April 2023

The ratio of forage to concentrate in cattle feeding has a major influence on the composition of the microbiota in the rumen and on the mass of methane produced. Using methane measurements and microbiota data from 26 cattle we aimed to investigate the relationships between microbial relative abundances and methane emissions, and identify potential biomarkers, in animals fed two extreme diets - a poor quality fresh cut grass diet (GRASS) or a high concentrate total mixed ration (TMR). Direct comparisons of the effects of such extreme diets on the composition of rumen microbiota have rarely been studied. Data were analyzed considering their multivariate and compositional nature. Diet had a relevant effect on methane yield of +10.6 g of methane/kg of dry matter intake for GRASS with respect to TMR, and on the centered log-ratio transformed abundance of 22 microbial genera. When predicting methane yield based on the abundance of 28 and 25 selected microbial genera in GRASS and TMR, respectively, we achieved cross-validation prediction accuracies of 66.5 ± 9% and 85 ± 8%. Only the abundance of Fibrobacter had a consistent negative association with methane yield in both diets, whereas most microbial genera were associated with methane yield in only one of the two diets. This study highlights the stark contrast in the microbiota controlling methane yield between animals fed a high concentrate diet, such as that found on intensive finishing units, and a low-quality grass forage that is often found in extensive grazing systems. This contrast must be taken into consideration when developing strategies to reduce methane emissions by manipulation of the rumen microbial composition.
Fusarium diversity from the Golden Gate Highlands National Park
Authors: Steyn Carla , Jacobs Adriaana , Summerell Brett , Venter Eduard
Published: April 2023

Members from the genus Fusarium can infect a broad range of plants and threaten agricultural and horticultural production. Studies on the diversity of Fusarium occurring in natural ecosystems have received less attention than the better known phytopathogenic members of the genus. This study identified Fusarium species from soils with low anthropogenic disturbance found in the Golden Gate Highlands National Park (GGHNP), a part of the Drakensberg system in South Africa. Selective techniques were implemented to obtain 257 individual isolates from the selected soil samples for which the translation elongation factor 1α (tef-1α) gene region was sequenced and compared against the Fusarium MLST and FUSARIUM-ID databases. Phylogenetic analyses, based on maximum likelihood and Bayesian inference, were used to determine species diversity in relation to reference isolates. Species level identifications were made within three of the seven species complexes and identified F. brachygibbosum, F. sporotrichioides, F. andiyazi, and F. gaditjirri based on the FUSARIUM-ID database, with F. transvaalense and F. lyarnte identified against the Fusarium MLST database. This indicated highly diverse populations of Fusarium from soils with low anthropogenic disturbance from the Afromontane grassland region found in mountain ranges.
A multi-center validation study on the discrimination of Legionella pneumophila sg.1, Legionella pneumophila sg. 2-15 and Legionella non-pneumophila isolates from water by FT-IR spectroscopy
Authors: Tata Alessandra , Marzoli Filippo , Cordovana Miriam , Tiengo Alessia , Zacometti Carmela , Massaro Andrea , Barco Lisa , Belluco Simone , Piro Roberto
Published: April 2023

This study developed and validated a method, based on the coupling of Fourier-transform infrared spectroscopy (FT-IR) and machine learning, for the automated serotyping of Legionella pneumophila serogroup 1, Legionella pneumophila serogroups 2-15 as well as their successful discrimination from Legionella non-pneumophila. As Legionella presents significant intra- and inter-species heterogeneities, careful data validation strategies were applied to minimize late-stage performance variations of the method across a large microbial population. A total of 244 isolates were analyzed. In details, the method was validated with a multi-centric approach with isolates from Italian thermal and drinking water (n = 82) as well as with samples from German, Italian, French, and British collections (n = 162). Specifically, robustness of the method was verified over the time-span of 1 year with multiple operators and two different FT-IR instruments located in Italy and Germany. Moreover, different production procedures for the solid culture medium (in-house or commercial) and different culture conditions (with and without 2.5% CO2) were tested. The method achieved an overall accuracy of 100, 98.5, and 93.9% on the Italian test set of Legionella, an independent batch of Legionella from multiple European culture collections, and an extra set of rare Legionella non-pneumophila, respectively.
Synergistic interactions in multispecies biofilm combinations of bacterial isolates recovered from diverse food processing industries
Authors: Sadiq Faizan Ahmed , De Reu Koen , Burmølle Mette , Maes Sharon , Heyndrickx Marc
Published: April 2023

Most biofilms within the food industry are formed by multiple bacterial species which co-exist on surfaces as a result of interspecies interactions. These ecological interactions often make these communities tolerant against antimicrobials. Our previous work led to the identification of a large number (327) of highly diverse bacterial species on food contact surfaces of the dairy, meat, and egg industries after routine cleaning and disinfection (C&D) regimes. In the current study, biofilm-forming ability of 92 bacterial strains belonging to 26 genera and 42 species was assessed and synergistic interactions in biofilm formation were investigated by coculturing species in all possible four-species combinations. Out of the total 455 four-species biofilm combinations, greater biofilm mass production, compared to the sum of biofilm masses of individual species in monoculture, was observed in 34 combinations. Around half of the combinations showed synergy in biofilm mass > 1.5-fold and most of the combinations belonged to dairy strains. The highest synergy (3.13-fold) was shown by a combination of dairy strains comprising Stenotrophomonas rhizophila, Bacillus licheniformis, Microbacterium lacticum, and Calidifontibacter indicus. The observed synergy in mixed biofilms turned out to be strain-specific rather than species-dependent. All biofilm combinations showing remarkable synergy appeared to have certain common species in all combinations which shows there are keystone industry-specific bacterial species which stimulate synergy or antagonism and this may have implication for biofilm control in the concerned food industries.