bluuebunny/crossref_metadata_2025_split huggingface dataset:
Bioaccumulation and detoxification of trivalent arsenic by Achromobacter xylosoxidans BHW-15 and electrochemical detection of its transformation efficiency
Authors: Diba Farzana , Khan Md. Zaved Hossain , Uddin Salman Zahir , Istiaq Arif , Shuvo Md. Sadikur Rahman , Ul Alam A. S. M. Rubayet , Hossain M. Anwar , Sultana Munawar 
Published: October 2021

AbstractArsenotrophic bacteria play an essential role in lowering arsenic contamination by converting toxic arsenite [As (III)] to less toxic and less bio-accumulative arsenate [As (V)]. The current study focused on the qualitative and electrocatalytic detection of the arsenite oxidation potential of an arsenite-oxidizing bacteria A. xylosoxidans BHW-15 (retrieved from As-contaminated tube well water), which could significantly contribute to arsenic detoxification, accumulation, and immobilization while also providing a scientific foundation for future electrochemical sensor development. The minimum inhibitory concentration (MIC) value for the bacteria was 15 mM As (III). Scanning Electron Microscopy (SEM) investigation validated its intracellular As uptake capacity and demonstrated a substantial association with the MIC value. During the stationary phase, the strain’s As (III) transformation efficiency was 0.0224 mM/h. Molecular analysis by real-time qPCR showed arsenite oxidase (aioA) gene expression increased 1.6-fold in the presence of As (III) compared to the untreated cells. The immobilized whole-cell also showed As (III) conversion up to 18 days. To analyze the electrochemical oxidation in water, we developed a modified GCE/P-Arg/ErGO-AuNPs electrode, which successfully sensed and quantified conversion of As (III) into As (V) by accepting electrons; implying a functional As oxidase enzyme activity in the cells. To the best of our knowledge, this is the first report on the electrochemical observation of the As-transformation mechanism with Achromobactersp. Furthermore, the current work highlighted that our isolate might be employed as a promising candidate for arsenic bioremediation, and information acquired from this study may be helpful to open a new window for the development of a cost-effective, eco-friendly biosensor for arsenic species detection in the future.
Resistance status of lepidopteran soybean pests following large-scale use of MON 87701 × MON 89788 soybean in Brazil
Authors: Horikoshi Renato J. , Bernardi Oderlei , Godoy Daniela N. , Semeão Altair A. , Willse Alan , Corazza Gustavo O. , Ruthes Elderson , Fernandes Davi de S. , Sosa-Gómez Daniel R. , Bueno Adeney de F. , Omoto Celso , Berger Geraldo U. , Corrêa Alberto S. , Martinelli Samuel , Dourado Patrick M. , Head Graham 
Published: October 2021

AbstractWidespread adoption of MON 87701 × MON 89788 soybean, expressing Cry1Ac Bt protein and glyphosate tolerance, has been observed in Brazil. A proactive program was implemented to phenotypically and genotypically monitor Cry1Ac resistance in Chrysodeixis includens (Walker). Recent cases of unexpected injury in MON 87701 × MON 89788 soybean were investigated and a large-scale sampling of larvae on commercial soybean fields was performed to assess the efficacy of this technology and the distribution of lepidopteran pests in Brazil. No significant shift in C. includens susceptibility to Cry1Ac was observed eight years after commercial introduction of this technology in Brazil. F2 screen results confirmed that the frequency of Cry1Ac resistance alleles remains low and stable in C. includens. Unexpected injury caused by Rachiplusia nu (Guenée) and Crocidosema aporema (Walsingham) in MON 87701 × MON 89788 soybean was detected during the 2020/21 season, and studies confirmed a genetically based alteration in their susceptibility to Cry1Ac. MON 87701 × MON 89788 soybean remains effective against Anticarsia gemmatalis (Hübner), C. includens, Chloridea virescents (Fabricius) and Helicoverpa armigera (Hübner) in Brazil. However, there is evidence of field-evolved resistance to MON 87701 × MON 89788 soybean by the secondary soybean pests R. nu and C. aporema.
Assessment of earthquake-induced landslide inventories and susceptibility maps using slope unit-based logistic regression and geospatial statistics
Authors: Pokharel Badal , Alvioli Massimiliano , Lim Samsung 
Published: October 2021

AbstractInventories of seismically induced landslides provide essential information about the extent and severity of ground effects after an earthquake. Rigorous assessment of the completeness of a landslide inventory and the quality of a landslide susceptibility map derived from the inventory is of paramount importance for disaster management applications. Methods and materials applied while preparing inventories influence their quality, but the criteria for generating an inventory are not standardized. This study considered five landslide inventories prepared by different authors after the 2015 Gorkha earthquake, to assess their differences, understand the implications of their use in producing landslide susceptibility maps in conjunction with standard landslide predisposing factors and logistic regression. We adopted three assessment criteria: (1) an error index to identify the mutual mismatches between the inventories; (2) statistical analysis, to study the inconsistency in predisposing factors and performance of susceptibility maps; and (3) geospatial analysis, to assess differences between the inventories and the corresponding susceptibility maps. Results show that substantial discrepancies exist among the mapped landslides. Although there is no distinct variation in the significance of landslide causative factors and the performance of susceptibility maps, a hot spot analysis and cluster/outlier analysis of the maps revealed notable differences in spatial patterns. The percentages of landslide-prone hot spots and clustered areas are directly proportional to the size of the landslide inventory. The proposed geospatial approaches provide a new perspective to the investigators for the quantitative analysis of earthquake-triggered landslide inventories and susceptibility maps.
Magnitude of low birthweight in malaria endemic settings of Nanoro, rural Burkina Faso: a secondary data analysis
Authors: Lingani Moussa , Zango Serge H. , Valéa Innocent , Valia Daniel , Sanou Maïmouna , Samandoulougou Sékou O. , Robert Annie , Tinto Halidou , Dramaix Michèle , Donnen Philippe 
Published: October 2021

AbstractLow birthweight (LBW) is a worldwide problem that particularly affects developing countries. However, limited information is available on its magnitude in rural area of Burkina Faso. This study aimed to estimate the prevalence of low birthweight and to identify its associated factors in Nanoro health district. A secondary analysis of data collected during a cross-sectional survey was conducted to assess the prevalence of low birthweight in Nanoro health and demographic surveillance system area (HDSS). Maternal characteristics extracted from antenatal care books or by interview, completed by malaria diagnosis were examined through a multi-level logistic regression to estimate odd-ratios of association with low birthweight. Significance level was set at 5%. Of the 291 neonates examined, the prevalence of low birthweight was 12%. After adjustment for socio-demographic, obstetric and malaria prevention variables, being primigravid (OR = 8.84, [95% CI: 3.72–21.01]), or multigravid with history of stillbirth (OR = 5.03, [95% CI: 1.54–16.40]), as well as the lack of long-lasting insecticide treated bed net use by the mother the night preceding the admission for delivery (OR = 2.5, [95% CI: 1.1–5.9]) were significantly associated with neonate low birthweight. The number of antenatal visits however did not confer any direct benefit on birthweight status within this study area. The prevalence of low birthweight was high in the study area and represents an important public health problem in Burkina Faso. In light of these results, a redefinition of the content of the antenatal care package is needed.
2020 COVID-19 lockdown and the impacts on air quality with emphasis on urban, suburban and rural zones
Authors: Slezakova Klara , Pereira Maria Carmo 
Published: October 2021

AbstractAir quality improvements pollution changes due to COVID-19 restrictions have been reported for many urban developments and large metropolitan areas, but the respective impacts at rural and remote zones are less frequently analysed. This study evaluated air pollution changes across all Portugal (68 stations) considering all urban, suburban and rural zones. PM10, PM2.5, NO2, SO2, ozone was analysed in pre-, during, and post-lockdown period (January–May 2020) and for a comparison also in 2019. NO2 was the most reduced pollutant in 2020, which coincided with decreased traffic. Significant drop (15–71%) of traffic related NO2 was observed specifically during lockdown period, being 55% for the largest and most populated region in country. PM was affected to a lesser degree (with substantial differences found for largely populated areas (Lisbon region ~ 30%; North region, up to 49%); during lockdown traffic-related PM dropped 10–70%. PM10 daily limit was exceeded 50% less in 2020, with 80% of exceedances before lockdown period. SO2 decreased by 35%, due to suspended industrial productions, whereas ozone concentrations slightly (though not significantly) increased (83 vs. 80 µg m–3).
Differential power of placebo across major psychiatric disorders: a preliminary meta-analysis and machine learning study
Authors: Cao Bo , Liu Yang S. , Selvitella Alessandro , Librenza-Garcia Diego , Passos Ives Cavalcante , Sawalha Jeffrey , Ballester Pedro , Chen Jianshan , Dong Shimiao , Wang Fei , Kapczinski Flavio , Dursun Serdar M. , Li Xin-Min , Greiner Russell , Greenshaw Andrew 
Published: October 2021

AbstractThe placebo effect across psychiatric disorders is still not well understood. In the present study, we conducted meta-analyses including meta-regression, and machine learning analyses to investigate whether the power of placebo effect depends on the types of psychiatric disorders. We included 108 clinical trials (32,035 participants) investigating pharmacological intervention effects on major depressive disorder (MDD), bipolar disorder (BD) and schizophrenia (SCZ). We developed measures based on clinical rating scales and Clinical Global Impression scores to compare placebo effects across these disorders. We performed meta-analysis including meta-regression using sample-size weighted bootstrapping techniques, and machine learning analysis to identify the disorder type included in a trial based on the placebo response. Consistently through multiple measures and analyses, we found differential placebo effects across the three disorders, and found lower placebo effect in SCZ compared to mood disorders. The differential placebo effects could also distinguish the condition involved in each trial between SCZ and mood disorders with machine learning. Our study indicates differential placebo effect across MDD, BD, and SCZ, which is important for future neurobiological studies of placebo effects across psychiatric disorders and may lead to potential therapeutic applications of placebo on disorders more responsive to placebo compared to other conditions.
Auto-qPCR; a python-based web app for automated and reproducible analysis of qPCR data
Authors: Maussion Gilles , Thomas Rhalena A. , Demirova Iveta , Gu Gracia , Cai Eddie , Chen Carol X.-Q. , Abdian Narges , Strauss Theodore J. P. , Kelaï Sabah , Nauleau-Javaudin Angela , Beitel Lenore K. , Ramoz Nicolas , Gorwood Philip , Durcan Thomas M. 
Published: October 2021

AbstractQuantifying changes in DNA and RNA levels is essential in numerous molecular biology protocols. Quantitative real time PCR (qPCR) techniques have evolved to become commonplace, however, data analysis includes many time-consuming and cumbersome steps, which can lead to mistakes and misinterpretation of data. To address these bottlenecks, we have developed an open-source Python software to automate processing of result spreadsheets from qPCR machines, employing calculations usually performed manually. Auto-qPCR is a tool that saves time when computing qPCR data, helping to ensure reproducibility of qPCR experiment analyses. Our web-based app (https://auto-q-pcr.com/) is easy to use and does not require programming knowledge or software installation. Using Auto-qPCR, we provide examples of data treatment, display and statistical analyses for four different data processing modes within one program: (1) DNA quantification to identify genomic deletion or duplication events; (2) assessment of gene expression levels using an absolute model, and relative quantification (3) with or (4) without a reference sample. Our open access Auto-qPCR software saves the time of manual data analysis and provides a more systematic workflow, minimizing the risk of errors. Our program constitutes a new tool that can be incorporated into bioinformatic and molecular biology pipelines in clinical and research labs.
LIFR inhibition enhances the therapeutic efficacy of HDAC inhibitors in triple negative breast cancer
Authors: Li Mengxing , Viswanadhapalli Suryavathi , Santhamma Bindu , Pratap Uday P. , Luo Yiliao , Liu Junhao , Altwegg Kristin A. , Tang Weiwei , Liu Zexuan , Li Xiaonan , Ebrahimi Behnam , Yan Hui , Zou Yi , Konda Swapna , Sareddy Gangadhara R. , Xu Zhenming , Chen Yidong , Rao Manjeet K. , Brenner Andrew J. , Kaklamani Virginia G. , Tekmal Rajeshwar R. , Ahmed Gulzar , Raj Ganesh V. , Nickisch Klaus J. , Nair Hareesh B. , Vadlamudi Ratna K. 
Published: October 2021

AbstractHistone deacetylase inhibitors (HDACi) are identified as novel therapeutic agents, however, recent clinical studies suggested that they are marginally effective in treating triple negative breast cancer (TNBC). Here, we show that first-in-class Leukemia Inhibitory Factor Receptor (LIFRα) inhibitor EC359 could enhance the therapeutic efficacy of HDACi against TNBC. We observed that both targeted knockdown of LIFR with CRISPR or treatment with EC359 enhanced the potency of four different HDACi in reducing cell viability, cell survival, and enhanced apoptosis compared to monotherapy in TNBC cells. RNA-seq studies demonstrated oncogenic/survival signaling pathways activated by HDACi were attenuated by the EC359 + HDACi therapy. Importantly, combination therapy potently inhibited the growth of TNBC patient derived explants, cell derived xenografts and patient-derived xenografts in vivo. Collectively, our results suggest that targeted inhibition of LIFR can enhance the therapeutic efficacy of HDACi in TNBC.
An intrinsically disordered nascent protein interacts with specific regions of the ribosomal surface near the exit tunnel
Authors: Guzman-Luna Valeria , Fuchs Andrew M. , Allen Anna J. , Staikos Alexios , Cavagnero Silvia 
Published: October 2021

AbstractThe influence of the ribosome on nascent chains is poorly understood, especially in the case of proteins devoid of signal or arrest sequences. Here, we provide explicit evidence for the interaction of specific ribosomal proteins with ribosome-bound nascent chains (RNCs). We target RNCs pertaining to the intrinsically disordered protein PIR and a number of mutants bearing a variable net charge. All the constructs analyzed in this work lack N-terminal signal sequences. By a combination chemical crosslinking and Western-blotting, we find that all RNCs interact with ribosomal protein L23 and that longer nascent chains also weakly interact with L29. The interacting proteins are spatially clustered on a specific region of the large ribosomal subunit, close to the exit tunnel. Based on chain-length-dependence and mutational studies, we find that the interactions with L23 persist despite drastic variations in RNC sequence. Importantly, we also find that the interactions are highly Mg+2-concentration-dependent. This work is significant because it unravels a novel role of the ribosome, which is shown to engage with the nascent protein chain even in the absence of signal or arrest sequences.
Statin-mediated disruption of Rho GTPase prenylation and activity inhibits respiratory syncytial virus infection
Authors: Malhi Manpreet , Norris Michael J. , Duan Wenming , Moraes Theo J. , Maynes Jason T. 
Published: October 2021

AbstractRespiratory syncytial virus (RSV) is a leading cause of severe respiratory tract infections in children. To uncover new antiviral therapies, we developed a live cell-based high content screening approach for rapid identification of RSV inhibitors and characterized five drug classes which inhibit the virus. Among the molecular targets for each hit, there was a strong functional enrichment in lipid metabolic pathways. Modulation of lipid metabolites by statins, a key hit from our screen, decreases the production of infectious virus through a combination of cholesterol and isoprenoid-mediated effects. Notably, RSV infection globally upregulates host protein prenylation, including the prenylation of Rho GTPases. Treatment by statins or perillyl alcohol, a geranylgeranyltransferase inhibitor, reduces infection in vitro. Of the Rho GTPases assayed in our study, a loss in Rac1 activity strongly inhibits the virus through a decrease in F protein surface expression. Our findings provide new insight into the importance of host lipid metabolism to RSV infection and highlight geranylgeranyltransferases as an antiviral target for therapeutic development.
Geothermal heating and episodic cold-seawater intrusions into an isolated ridge-flank basin near the Mid-Atlantic Ridge
Authors: Becker Keir , Thomson Richard E. , Davis Earl E. , Villinger Heinrich , Wheat C. Geoffrey 
Published: October 2021

AbstractSix-year records of ocean bottom water temperatures at two locations in an isolated, sedimented deep-water (∼4500 m) basin on the western flank of the mid-Atlantic Ridge reveal long periods (months to >1 year) of slow temperature rises punctuated by more rapid (∼1 month) cooling events. The temperature rises are consistent with a combination of gradual heating by the geothermal flux through the basin and by diapycnal mixing, while the sharper cooling events indicate displacement of heated bottom waters by incursions of cold, dense bottom water over the deepest part of the sill bounding the basin. Profiles of bottom water temperature, salinity, and oxygen content collected just before and after a cooling event show a distinct change in the water mass suggestive of an incursion of diluted Antarctic Bottom Water from the west. Our results reveal details of a mechanism for the transfer of geothermal heat and bottom water renewal that may be common on mid-ocean ridge flanks.
Reliability‐constrained robust expansion planning of active distribution networks
Authors: Pinto Rafael S. , Unsihuay‐Vila Clodomiro , Tabarro Fabricio H. 
Published: October 2021

AbstractPower distribution systems have become more complex in recent years due to the integration of new technologies. Motivated by these challenges, this paper describes a model to solve the multi‐period expansion planning problem of active distribution systems, considering uncertainty, reliability, distributed generation, self‐healing, reactive power support, switches, and energy storage devices. The objective is to determine the best installation times and locations of new components, minimizing the total cost, and ensuring desired reliability levels. The approach consists of a three‐level decomposition. The first level identifies expansion proposals, the second level finds the worst‐case scenario using adaptive robust optimization, and the third level performs a Monte Carlo Simulation to compute reliability indexes. The main contributions are the introduction of a novel reliability sensitivity matrix to improve computational performance and the representation of the hours of the day inside the expansion planning formulation. The proposed method is illustrated using the IEEE 123‐bus test system. The analyses show high computational efficiency as compared with similar works. The impacts on the number of expansion components placed in the system and on the total cost are presented and discussed using cases varying the uncertainty budget and not considering some of these components.
Detection of bird species related to transmission line faults based on lightweight convolutional neural network
Authors: Qiu Zhibin , Zhu Xuan , Liao Caibo , Shi Dazhai , Kuang Yanjun , Li Yanglin , Zhang Yu 
Published: October 2021

AbstractEfficient bird damage prevention of transmission lines is a long‐term challenge for power grid operation and maintenance. An approach combined lightweight convolutional neural network (CNN), image processing and object detection is presented in this paper to detect typical bird species related to transmission line faults. An image dataset of 20 bird species that threaten transmission line security is constructed. The YOLOv4‐tiny algorithm model is constructed and trained combining stage‐wise training, mosaic data enhancement, cosine annealing, and label smoothing. The mean average precision (mAP) can reach 92.04% on the test set by adjusting the parameters of the training process. Then, the validity of the proposed method is verified according to the test results and performance indexes by comparing with other methods, including Faster RCNN, SSD, YOLOv4 etc. Some image pre‐processing methods such as motion blur, defocus blur, contrast and brightness adjustment are used to simulate the scenarios in practical engineering applications. The proposed method can detect bird species perched around transmission lines with high‐efficiency, which is useful for differential prevention of bird‐related outages of power grids.
A compound control strategy of three‐phase Vienna rectifier under unbalanced grid voltage
Authors: Feng Xingtian , Sun Yongming , Cui Xiao , Ma Wenzhong , Wang Yubin 
Published: October 2021

AbstractWhen traditional finite‐set predictive power control is applied to Vienna rectifier, the input current will produce a large number of third harmonics under unbalanced power network, which will seriously affect the normal operation of Vienna rectifier. This paper proposes a compound control strategy for three‐phase Vienna rectifier based on grid imbalance. And the new finite control set‐model predictive direct power control (N‐FCS‐MPDPC) is used in the inner current loop. By introducing a redefined reactive power, this method can effectively control the second harmonic of instantaneous active power and the second harmonic of instantaneous reactive power at the same time, and realizes the normal operation of Vienna rectifier under the condition of unbalanced power grid. Linear auto disturbance rejection control (LADRC) is adopted in the outer voltage loop, which overcomes the problem of voltage overshoot and slow dynamic response in PI control. Furthermore, in order to solve the difficulty of grid‐side voltage acquisition when the grid is unbalanced, a virtual flux based no‐grid voltage sensor is introduced to measure the grid voltage. Simulation, along with experimental results, is presented to show that the proposed control strategy has good robustness and fast tracking performance under the condition of power grid imbalance.