bluuebunny/crossref_metadata_2025_split huggingface dataset:
The effect of the COVID-19 lockdown on mental health care use in South Africa: an interrupted time-series analysis
Authors: Wettstein Anja , Tlali Mpho , Joska John A. , Cornell Morna , Skrivankova Veronika W. , Seedat Soraya , Mouton Johannes P. , van den Heuvel Leigh L. , Maxwell Nicola , Davies Mary-Ann , Maartens Gary , Egger Matthias , Haas Andreas D. 
Published: June 2022

Abstract

Aims
The coronavirus disease 2019 (COVID-19) pandemic and ensuing restrictions have negatively affected the mental health and well-being of the general population, and there is increasing evidence suggesting that lockdowns have led to a disruption of health services. In March 2020, South Africa introduced a lockdown in response to the COVID-19 pandemic, entailing the suspension of all non-essential activities and a complete ban of tobacco and alcohol sales. We studied the effect of the lockdown on mental health care utilisation rates in private-sector care in South Africa.

Methods
We conducted an interrupted time-series analysis using insurance claims from 1 January 2017 to 1 June 2020 of beneficiaries 18 years or older from a large private sector medical insurance scheme. We calculated weekly outpatient consultation and hospital admission rates for organic mental disorders, substance use disorders, serious mental disorders, depression, anxiety, other mental disorders, any mental disorder and alcohol withdrawal syndrome. We calculated adjusted odds ratios (OR) for the effect of the lockdown on weekly outpatient consultation and hospital admission rates and the weekly change in rates during the lockdown until 1 June 2020.

Results
710 367 persons were followed up for a median of 153 weeks. Hospital admission rates (OR 0.38; 95% confidence interval (CI) 0.33–0.44) and outpatient consultation rates (OR 0.74; 95% CI 0.63–0.87) for any mental disorder decreased substantially after the introduction of the lockdown and did not recover to pre-lockdown levels by 1 June 2020. Health care utilisation rates for alcohol withdrawal syndrome doubled after the introduction of the lockdown, but the statistical uncertainty around the estimates was large (OR 2.24; 95% CI 0.69–7.24).

Conclusions
Mental health care utilisation rates for inpatient and outpatient services decreased substantially after the introduction of the lockdown. Hospital admissions and outpatient consultations for alcohol withdrawal syndrome increased after the introduction of the lockdown, but statistical uncertainty precludes strong conclusions about a potential unintended effect of the alcohol sales ban. Governments should integrate strategies for ensuring access and continuity of essential mental health services during lockdowns in pandemic preparedness planning.
A comparative evaluation of convolutional neural networks, training image sizes, and deep learning optimizers for weed detection in alfalfa
Authors: Yang Jie , Bagavathiannan Muthukumar , Wang Yundi , Chen Yong , Yu Jialin 
Published: June 2022

AbstractIn this research, the deep-learning optimizers Adagrad, AdaDelta, Adaptive Moment Estimation (Adam), and Stochastic Gradient Descent (SGD) were applied to the deep convolutional neural networks AlexNet, GoogLeNet, VGGNet, and ResNet that were trained to recognize weeds among alfalfa using photographic images taken at 200×200, 400×400, 600×600, and 800×800 pixels. An increase in the image sizes reduced the classification accuracy of all neural networks. The neural networks that were trained with images of 200×200 pixels resulted in better classification accuracy than the other image sizes investigated here. The optimizers AlexNet and GoogLeNet trained with AdaDelta and SGD outperformed the Adagrad and Adam optimizers; VGGNet trained with AdaDelta outperformed Adagrad, Adam, and SGD; and ResNet trained with AdaDelta and Adagrad outperformed the Adam and SGD optimizers. When the neural networks were trained with the best-performing input image size (200×200 pixels) and the best-performing deep learning optimizer, VGGNet was the most effective neural network, with high precision and recall values (≥0.99) when validation and testing datasets were used. Alternatively, ResNet was the least effective neural network in its ability to classify images containing weeds. However, there was no difference among the different neural networks in their ability to differentiate between broadleaf and grass weeds. The neural networks discussed herein may be used for scouting weed infestations in alfalfa and further integrated into the machine vision subsystem of smart sprayers for site-specific weed control.
The Mechanism for GNSS‐Based Kinematic Positioning Degradation at High‐Latitudes Under the March 2015 Great Storm
Authors: Nie Wenfeng , Rovira‐Garcia Adrià , Li Mowen , Fang Zhenlong , Wang Yong , Zheng Dunyong , Xu Tianhe 
Published: June 2022

AbstractIn this study, we focus on the kinematic precise point positioning (PPP) solutions at high‐latitudes during the March 2015 great geomagnetic storm. We aim to discover the mechanism behind the positioning degradation from the perspective of the impacts of the storm‐induced ionospheric disturbance on the global navigation satellite system (GNSS) data processing. We observed that the phase scintillation dominated the amplitude scintillation at high‐latitudes and the variation pattern of the rate of total electron content index (ROTI) was consistent with that of the phase scintillation during the storm. The kinematic PPP errors at high‐latitudes were almost three times larger than those at the middle‐ and low‐latitude, which were accompanied by large ROTI variations. From the perspective of GNSS data processing, the large positioning errors were also found to be related to the large number of satellites experiencing cycle slips (CSs). Based on the lock time from the ionospheric scintillation monitoring receiver, we found that a large amount of the CSs was falsely detected under the conventional threshold of the CS detector. By increasing such threshold, the kinematic positioning accuracy at high‐latitudes can be improved to obtain similar magnitude as at middle‐ and low‐latitude. The improved positioning accuracy may suggest that the ionospheric disturbance induced by the geomagnetic storm at high‐latitudes has minor effects on triggering the CSs. Therefore, precise positioning can be achieved at high‐latitudes under geomagnetic storms, given that the CS problem is well addressed.
FutureStreams, a global dataset of future streamflow and water temperature
Authors: Bosmans Joyce , Wanders Niko , Bierkens Marc F. P. , Huijbregts Mark A. J. , Schipper Aafke M. , Barbarossa Valerio 
Published: June 2022

AbstractThere is growing evidence that climate change impacts ecosystems and socio-economic activities in freshwater environments. Consistent global data of projected streamflow and water temperature are key to global impact assessments, but such a dataset is currently lacking. Here we present FutureStreams, the first global dataset of projected future streamflow and water temperature for multiple climate scenarios (up to 2099) gridded at a 5 arcminute spatial resolution (~10 km at the equator), including recent past data (1976–2005) for comparison. We generated the data using global hydrological and water temperature models (PCR-GLOBWB, DynWat) forced with climate data from five general circulation models. We included four representative concentration pathways to cover multiple future greenhouse gas emission trajectories and associated changes in climate. Our dataset includes weekly streamflow and water temperature for each year as well as a set of derived indicators that are particularly relevant from an ecological perspective. FutureStreams provides a crucial starting point for large-scale assessments of the implications of changes in streamflow and water temperature for society and freshwater ecosystems.
Machine learning/molecular dynamic protein structure prediction approach to investigate the protein conformational ensemble
Authors: Audagnotto Martina , Czechtizky Werngard , De Maria Leonardo , Käck Helena , Papoian Garegin , Tornberg Lars , Tyrchan Christian , Ulander Johan 
Published: June 2022

AbstractProteins exist in several different conformations. These structural changes are often associated with fluctuations at the residue level. Recent findings show that co-evolutionary analysis coupled with machine-learning techniques improves the precision by providing quantitative distance predictions between pairs of residues. The predicted statistical distance distribution from Multi Sequence Analysis reveals the presence of different local maxima suggesting the flexibility of key residue pairs. Here we investigate the ability of the residue-residue distance prediction to provide insights into the protein conformational ensemble. We combine deep learning approaches with mechanistic modeling to a set of proteins that experimentally showed conformational changes. The predicted protein models were filtered based on energy scores, RMSD clustering, and the centroids selected as the lowest energy structure per cluster. These models were compared to the experimental-Molecular Dynamics (MD) relaxed structure by analyzing the backbone residue torsional distribution and the sidechain orientations. Our pipeline allows to retrieve the experimental structural dynamics experimentally represented by different X-ray conformations for the same sequence as well the conformational space observed with the MD simulations. We show the potential correlation between the experimental structure dynamics and the predicted model ensemble demonstrating the susceptibility of the current state-of-the-art methods in protein folding and dynamics prediction and pointing out the areas of improvement.
Potential fields and fluctuation-dissipation relations derived from human flow in urban areas modeled by a network of electric circuits
Authors: Shida Yohei , Ozaki Jun’ichi , Takayasu Hideki , Takayasu Misako 
Published: June 2022

AbstractOwing to the big data the extension of physical laws on nonmaterial has seen numerous successes, and human mobility is one of the scientific frontier topics. Recent GPS technology has made it possible to trace detailed trajectories of millions of people, macroscopic approaches such as the gravity law for human flow between cities and microscopic approaches of individual origin-destination distributions are attracting much attention. However, we need a more general basic model with wide applicability to realize traffic forecasting and urban planning of metropolis fully utilizing the GPS data. Here, based on a novel idea of treating moving people as charged particles, we introduce a method to map macroscopic human flows into currents on an imaginary electric circuit defined over a metropolitan area. Conductance is found to be nearly proportional to the maximum current in each location and synchronized human flows in the morning and evening are well described by the temporal changes of electric potential. Surprisingly, the famous fluctuation-dissipation theorem holds, namely, the variances of currents are proportional to the conductivities akin to an ordinary material.
Differences in energy source storage in eye stalks between two species of stalk-eyed flies, Sphyracephala detrahens and Cyrtodiopsis dalmanni
Authors: Miki Aoi , Fukuda Risa , Takeda Koji , Moriya Ayano , Kamimura Yoshitaka , Lee Chow-Yang , Adachi-Yamada Takashi 
Published: June 2022

AbstractSome diopsid flies have sexually dimorphic eye stalks that are assumed to require considerable nutrition for growth but are advantageous in competition and courtship. According to the handicap theory, the eye span in some dimorphic species serves as a reliable signal of individual quality to an opponent. However, it is not well understood how well eye span represents energy source storage. In this study, we focused on two species:Sphyracephala detrahens, which has weak dimorphism, andCyrtodiopsis dalmanni, which has moderate dimorphism. We found that the eye stalks of the former species contained more fat bodies than those of the latter species. When the flies were starved, the fat body cells in the eye stalks underwent autophagy. A strong positive correlation was consistently found between eye span and starvation tolerance forS. detrahens, while a weak correlation was found forC. dalmanni. Furthermore, starvation decreased the contest winning rate betweenS. detrahenspairs with similar eye spans. These findings suggest that the presentation of resource holding potential may be larger than the actual storage ability and that the fidelity of nutritional storage signaling varies; the signal presented byS. detrahensis more reliable than that presented byC. dalmanni.
Socioenvironmental conflicts and social representations surrounding mining extractivism at Santurban
Authors: Zárate-Rueda Ruth , Beltrán-Villamizar Yolima Ivonne , Murallas-Sánchez Daniella 
Published: June 2022

AbstractDuring the process of delimitation of the Santurban moorland ecosystem (Colombia), a socioenvironmental conflict arose from small and large-scale mining extractivism. This study analyzes the social representations of settlers in this moorland ecosystem, regarding the meaning, practice and value of their territory and water as a vital resource, in order to interpret their perceptions on the internal dynamics of conflict and the different aspects symbolizing their complexity. An ethnographic design was implemented by NVIVO software information coding. The findings underscore the uncertainty of rural stakeholders facing the impossibility to continue to develop ancestral mining activities and the disputes arising from the urban setting for the defense of water. It was concluded that the socioenvironmental conflict presented requires ongoing participation from rural and urban settlers, in consideration to their permanent questions regarding the current situation in their regions and the permanent search for solutions.
Comparative evaluation of biomedical and phytochemical applications of zinc nanoparticles by using Fagonia cretica extracts
Authors: Kiani Bushra Hafeez , Ikram Fizza , Fatima Humaira , Alhodaib Aiyeshah , Haq Ihsan-ul- , Ur-Rehman Tofeeq , Naz Iffat 
Published: June 2022

AbstractThe use of the green approach for nanoparticle synthesis yielded noticeable concern due to its eco-friendliness, cost-effectiveness, and reduced production of toxic chemicals. The current study was designed to formulate Zinc oxide nanoparticles (ZnO NPs) by using Fagonia cretica extracts, evaluating its phytochemical content, and different biological activities. Four different solvents; methanol (MeOH), n-Hexane (n–H), aqueous (Aq), and ethyl acetate (EA), had been utilized in the extracting method. ZnO NPs were successfully synthesized and characterized by UV–vis spectroscopy and scanning electron microscopy (SEM). The UV–vis spectra showed absorbance peaks between 350–400 nm range and SEM analysis revealed spherical morphology with particle sizes ranging from 65–80 nm. In phytochemical analysis, crude extracts exhibited the highest phytochemical content as they contain enriched secondary metabolites. n-hexane extract showed the highest phenolic contents while aqueous extracts showed the highest flavonoid content. Maximum free radicle scavenging activity was observed in NPs synthesized from ethyl-acetate extract with an IC50 value of 35.10 µg/ml. Significant antibacterial activity was exhibited by NPs polar solvents against K. pneumonae, E. coli, and B. subtilis. Polar solvents showed considerable antifungal potential against A. flavus and F. solani. NPs synthesized from nH extract showed potential cytotoxic activity with an LC50 value of 42.41 µg/ml against brine shrimps. A noteworthy antidiabetic activity was exhibited by nanoparticles synthesized from methanol extract i.e., 52.61 ± 0.36%. Significant bald zones were observed in nanoparticles synthesized from methanol extract rendering protein kinase inhibition. The present study highlights the significance of F. indica as a natural source for synthesizing functional nanoparticles with substantial antioxidant, antimicrobial, cytotoxic, protein kinase inhibitory, and antidiabetic properties.
Cytotoxic effects of targeted agent alone or with chemotherapy in the treatment of adenoid cystic carcinoma: a preclinical study
Authors: Savarese Teresa , Abate Andrea , Basnet Ram Manohar , Lorini Luigi , Gurizzan Cristina , Tomasoni Michele , Lombardi Davide , Tomasini Davide , Zizioli Daniela , Memo Maurizio , Berruti Alfredo , Bonini Sara A. , Sigala Sandra , Bossi Paolo 
Published: June 2022

AbstractAdenoid cystic carcinoma (ACC) is a rare malignancy characterized by high incidence of relapse. When relapsing, ACC has an indolent but relentless behaviour, thus leading to a poor long-term prognosis. The treatment of choice of relapsing ACC remains surgery followed by radiotherapy, whenever feasible. Therapeutic weapons are limited to systemic drugs. The most widely used chemotherapy regimen is the combination of cisplatin and doxorubicin, however with low response rate and not long lasting; there is also a lack of alternatives for second line therapies in case of disease progression. Therefore, a more comprehensive strategy aimed at identifying at preclinical level the most promising drugs or combination is clearly needed. In this study, the cytotoxic effects of two standard chemotherapy drugs, cisplatin and doxorubicin, and of five targeted therapy-drugs was tested in vitro, on an h-TERT immortalized ACC cell line, and in vivo, on zebrafish embryos with ACC tumoral cell xenograft. Then, combinations of one standard chemotherapy drug plus one targeted therapy drug were also evaluated, in order to find the best treatment strategy for ACC. Data obtained demonstrated that both vorinostat and olaparib significantly increased the standard chemotherapy cytotoxic effects, suggesting new interesting therapeutic options for ACC.
Assessment of cortical inhibition depends on inter individual differences in the excitatory neural populations activated by transcranial magnetic stimulation
Authors: Cerins Andris , Corp Daniel , Opie George , Do Michael , Speranza Bridgette , He Jason , Barhoun Pamela , Fuelscher Ian , Enticott Peter , Hyde Christian 
Published: June 2022

AbstractTranscranial magnetic stimulation (TMS) is used to probe inhibitory intracortical neurotransmission and has been used to infer the neurobiological dysfunction that may underly several neurological disorders. One technique, short-interval intracortical inhibition (SICI), indexes gamma-aminobutyric acid (GABA) mediated inhibitory activity and is a promising biomarker. However emerging evidence suggests SICI does not exclusively represent GABAergic activity because it may be influenced by inter-individual differences in the specific excitatory neural populations activated by TMS. Here we used the latency of TMS motor evoked potentials (MEPs) to index these inter-individual differences, and found that a significant proportion of the observed variability in SICI magnitude was accounted for by MEP latency, r = − 0.57, r2 = 0.33, p = .014. We conclude that SICI is influenced by inter-individual differences in the excitatory neural populations activated by TMS, reducing the precision of this GABAergic probe. Interpreting SICI measures in the context of MEP latency may facilitate a more precise assessment of GABAergic intracortical inhibition. The reduced cortical inhibition observed in some neuropathologies could be influenced by reduced activity in specific excitatory neural populations. Including MEP latency assessment in research investigating SICI in clinical groups could assist in differentiating the cortical circuits impacted by neurological disorders.
Selection of start codon during mRNA scanning in eukaryotic translation initiation
Authors: Basu Ipsita , Gorai Biswajit , Chandran Thyageshwar , Maiti Prabal K. , Hussain Tanweer 
Published: June 2022

AbstractAccurate and high-speed scanning and subsequent selection of the correct start codon are important events in protein synthesis. Eukaryotic mRNAs have long 5′ UTRs that are inspected for the presence of a start codon by the ribosomal 48S pre-initiation complex (PIC). However, the conformational state of the 48S PIC required for inspecting every codon is not clearly understood. Here, atomistic molecular dynamics (MD) simulations and energy calculations suggest that the scanning conformation of 48S PIC may reject all but 4 (GUG, CUG, UUG and ACG) of the 63 non-AUG codons, and initiation factor eIF1 is crucial for this discrimination. We provide insights into the possible role of initiation factors eIF1, eIF1A, eIF2α and eIF2β in scanning. Overall, the study highlights how the scanning conformation of ribosomal 48S PIC acts as a coarse selectivity checkpoint for start codon selection and scans long 5′ UTRs in eukaryotic mRNAs with accuracy and high speed.
Reinforcement‐learning‐based load shedding and intentional voltage manipulation approach in a microgrid considering load dynamics
Authors: Horri Ramin , Mahdinia Roudsari Hossein 
Published: June 2022

AbstractIn the case of a power mismatch in an island microgrid (MG), sharp variations occur in the frequency and voltages due to the low inertia and stochastic behaviour of distributed generation units and loads. Load shedding (LS) is the last corrective measure to achieve frequency regulation. A Q‐learning‐based under‐frequency LS (QLLS) approach is proposed, in which the MG model is considered a grey box. Hence, the dependence of the proposed method on the grid model is minimized. The presented approach considers four main characteristics of the loads, including priority, dynamics, outage cost, and daily variations. In addition, to reduce the amount of the shed load, a supplementary intentional voltage manipulation (IVM) algorithm is presented that exploits the voltage dependency of loads as leverage to help frequency regulation. The proposed QLLS/IVM uses the observable MG states to find a corrective course of action to achieve frequency and voltage stability while keeping the under‐frequency load shedding price as low as possible. The performance of the proposed QLLS/IVM approach is tested on the modified IEEE 37‐node MG, and the results show feasibility, fast and optimal performance of the proposed algorithm.
High voltage direct current transmission cables to help decarbonisation in Europe: Recent achievements and issues
Authors: Mazzanti Giovanni 
Published: June 2022

AbstractHigh Voltage Direct Current (HVDC) underground and submarine cables constitute an essential technology for the long‐distance transmission of renewable electrical power with low losses, thereby fostering decarbonisation. In the past 2 decades, all this yielded a quasi‐exponential rise of HVDC cable systems installed worldwide, but has also made the working conditions of HVDC cables harsher. In this paper, the contribution of HVDC cables to decarbonisation is tackled, with particular reference to the situation in Europe, and a few simple calculations showing in general terms the quantitative contribution provided by HVDC cable systems to decarbonisation are given. Then, the major issues towards long‐lasting and reliable HVDC cable systems are summarised briefly, focussing—for the sake of brevity—on the influence of the main HVDC cable technologies, of cable laying environment and of duty service. Some hints are also provided at the chances given by multiterminal HVDC systems.