bluuebunny/crossref_metadata_2025_split huggingface dataset:
COVID-19 mortality in an area of northeast Brazil: epidemiological characteristics and prospective spatiotemporal modelling
Authors: Andrade L. A. , Gomes D. S. , Lima S. V. M. A. , Duque A. M. , Melo M. S. , Góes M. A. O. , Ribeiro C. J. N. , Peixoto M. V. S. , Souza C. D. F. , Santos A. D. 
Published: December 2020

Abstract
This study aimed to analyse the spatial–temporal distribution of COVID-19 mortality in Sergipe, Northeast, Brazil. It was an ecological study utilising spatiotemporal analysis techniques that included all deaths confirmed by COVID-19 in Sergipe, from 2 April to 14 June 2020. Mortality rates were calculated per 100 000 inhabitants and the temporal trends were analysed using a segmented log-linear model. For spatial analysis, the Kernel estimator was used and the crude mortality rates were smoothed by the empirical Bayesian method. The space–time prospective scan statistics applied the Poisson's probability distribution model. There were 391 COVID-19 registered deaths, with the majority among ⩾60 years old (62%) and males (53%). The most prevalent comorbidities were hypertension (40%), diabetes (31%) and cardiovascular disease (15%). An increasing mortality trend across the state was observed, with a higher increase in the countryside. An active spatiotemporal cluster of mortality comprising the metropolitan area and neighbouring cities was identified. The trend of COVID-19 mortality in Sergipe was increasing and the spatial distribution of deaths was heterogeneous with progression towards the countryside. Therefore, the use of spatial analysis techniques may contribute to surveillance and control of COVID-19 pandemic.
Mending a broken heart: a single case study on cognitive behavioural therapy for depression after romantic relationship break-up
Authors: Gozan Muthmainah Mufidah , Menaldi Adhityawarman 
Published: December 2020

Abstract
Having and building a romantic relationship is an important aspect of development in young adulthood. However, there are some challenges in relationships, including basic principle differences such as faith and culture that lead to fights and break-ups. Going through a break-up is not easy for some people and is considered to be one of the stress sources in people’s lives that often links to depression symptoms. It is crucial to focus on intervention aiming at the cognitive aspects, including negative thoughts and bias when dealing with depression, such as cognitive behavioural therapy (CBT). However, CBT in the Indonesian context, especially with religious and ethnic problems in a romantic relationship before marriage, is limited. This study aims to explore this by presenting the case of a 26-year-old Indonesian male with depressive episodes after a painful break-up due to religious and cultural differences. The previous history of childhood abuse and problems in social adjustment and communication had also worsened his conditions. CBT was used for treatment as he developed many negative thoughts about himself and had maladaptive beliefs about his conditions and his future. This case study report shows the effectiveness of CBT intervention in decreasing depression symptoms, such as sadness and the thought of being unworthy, as well as increasing self-confidence in facing difficulties.

Key learning aims

(1)
To understand delivering CBT in a single case with mild depression triggered by relationship break-up.

(2)
To understand the applicability of CBT in the Indonesian context with a cultural and religious background.

(3)
To reflect on the challenges faced and treatment strategies in implementing an adapted CBT technique.
Molecular Distributions of Diacids, Oxoacids, and α‐Dicarbonyls in Summer‐ and Winter‐Time Fine Aerosols From Tianjin, North China: Emissions From Combustion Sources and Aqueous Phase Secondary Formation
Authors: Pavuluri C. M. , Wang S. , Fu P. Q. , Zhao W. , Xu Z. , Liu C.‐Q. 
Published: December 2020

AbstractTo understand the characteristics and sources of organic aerosols (OA) in North China, we studied diacids, oxoacids, and α‐dicarbonyls in summer‐ and winter‐time fine aerosols (PM2.5) collected from Tianjin. Oxalic (C2) acid was found to be the most abundant diacid species, followed by succinic (C4), malonic (C3), and sebacic (C8) acids, respectively. Glyoxylic (ωC2) was the most abundant oxoacids followed by pyruvic acid. Concentrations of total diacids, oxoacids, and α‐dicarbonyls in winter were 2–3 times higher than those in summer, but their mass fractions in PM2.5 were exactly the opposite. On average, total diacids carbon accounted for 2.9% in total carbon and 3.3% in organic carbon (OC) in summer and 1.8% and 2.0%, respectively, in winter. Their contributions to water‐soluble OC (WSOC) was almost the same in both seasons (5.5% and 5.3%, respectively). Molecular distributions, mass ratios of selected diacid (C3, C4, M, F, C6, Ph, and C9) species, and the linear relations among the selected species (including ∑C2–C4 and ∑C8–C12) and with inorganic markers (K+ and SO42−) implied that the diacids and related compounds are mainly originated from coal combustion and biomass burning emissions and produced in the atmosphere by both in situ photochemical reactions at local scale and aging during long‐range transport in both summer and winter. This study revealed that diacids and related compounds and WSOC are increased with increasing SO42− and they are produced in the aqueous phase, implying that the reduction in NOx and SO2 emissions could possibly control the water‐soluble OA loading over North China.
Phenology and Climatic Regime Inferred from Airborne Pollen on the Northern Slope of the Qomolangma (Everest) Region
Authors: Lü Xinmiao , Paudayal Khum N. , Uhl Dieter , Zhu Liping , Yao Tandong , Mosbrugger Volker 
Published: December 2020

AbstractWe present 3 years (2011–2013) of in situ airborne pollen monitoring data on the northern slope of the Qomolangma (Everest) region to better understand the connections among airborne pollen, vegetation and climate. The pollen assemblage dominated by local herb and shrub pollen can reflect mountain shrub steppe vegetation. Herb pollen behave differently from tree and shrub pollen, including their responses to temperature, humidity, and wind speed, the distribution in wind direction and the optimum climate range. Herb pollen are mainly influenced by temperature and humidity, but wind direction and wind speed are more important to tree and shrub pollen. The flowering phenology can be reflected by annual and seasonal pollen variations, which are influenced by temperature and precipitation. Arboreal pollen (AP) represent long‐distance transported types during the westerly period and nonarboreal pollen (NAP) locally dominate in the monsoon season; thus, the AP/NAP ratio could be an indicator of atmospheric circulation, vegetation and climate change in the semiarid Tibetan Plateau (TP). Airborne pollen, as a record of biological and climatic events, provides insights into vegetation response to climate change and has significance for interpreting fossil pollen records on the TP.
Stress and the dopaminergic reward system
Authors: Baik Ja-Hyun 
Published: December 2020

AbstractDopamine regulates reward-related behavior through the mesolimbic dopaminergic pathway. Stress affects dopamine levels and dopaminergic neuronal activity in the mesolimbic dopamine system. Changes in mesolimbic dopaminergic neurotransmission are important for coping with stress, as they allow adaption to behavioral responses to various environmental stimuli. Upon stress exposure, modulation of the dopaminergic reward system is necessary for monitoring and selecting the optimal process for coping with stressful situations. Aversive stressful events may negatively regulate the dopaminergic reward system, perturbing reward sensitivity, which is closely associated with chronic stress-induced depression. The mesolimbic dopamine system is excited not only by reward but also by aversive stressful stimuli, which adds further intriguing complexity to the relationship between stress and the reward system. This review focuses on lines of evidence related to how stress, especially chronic stress, affects the mesolimbic dopamine system, and discusses the role of the dopaminergic reward system in chronic stress-induced depression.
The role of natural killer cell in gastrointestinal cancer: killer or helper
Authors: Wang Feixue , Lau Jennie Ka Ching , Yu Jun 
Published: December 2020

AbstractGastrointestinal cancer is one of the leading health problems worldwide, with a high morbidity and mortality. To date, harnessing both the innate and adaptive immune system against cancer provides a selective and effective therapeutic strategy for patients. As a first line defense against cancer, natural killer (NK) cells can swiftly target and lyse tumor cells without prior activation. In addition to its pivotal role in innate immunity, NK cells also play unique roles in the adaptive immune system as it enhance anti-tumor adaptive immune responses through secretion of cytokines and retaining an immunological memory. All these characteristics make NK cell a promising anti-cancer agent for patients. In spite of scarce infiltration and impaired function of NK cells in tumors, and the fact that tumors easily develop resistant mechanisms to evade the attacks from endogenous NK cells, multiple strategies have been developed to boost anti-tumor effect of NK cells and abolish tumor resistance. Some examples include adoptive transfer of NK cells after ex vivo activation and expansion; restoration of NK cell function using immune checkpoint inhibitors, and monoclonal antibody or cytokine treatment. Preclinical data have shown encouraging results, suggesting that NK cells hold great potential in cancer therapy. In this review, we discuss NK cells’ cytotoxicity and modulation function in GI cancer and the current application in clinical therapy.
STAT3 phosphorylation at Ser727 and Tyr705 differentially regulates the EMT–MET switch and cancer metastasis
Authors: Lin Wei-Hsin , Chang Yi-Wen , Hong Min-Xiang , Hsu Te-Cheng , Lee Ko-Chuan , Lin Che , Lee Jia-Lin 
Published: December 2020

AbstractEpithelial–mesenchymal transition (EMT)/mesenchymal–epithelial transition (MET) processes are proposed to be a driving force of cancer metastasis. By studying metastasis in bone marrow-derived mesenchymal stem cell (BM-MSC)-driven lung cancer models, microarray time-series data analysis by systems biology approaches revealed BM-MSC-induced signaling triggers early dissemination of CD133+/CD83+ cancer stem cells (CSCs) from primary sites shortly after STAT3 activation but promotes proliferation towards secondary sites. The switch from migration to proliferation was regulated by BM-MSC-secreted LIF and activated LIFR/p-ERK/pS727-STAT3 signaling to promote early disseminated cancer cells MET and premetastatic niche formation. Then, tumor-tropic BM-MSCs circulated to primary sites and triggered CD151+/CD38+ cells acquiring EMT-associated CSC properties through IL6R/pY705-STAT3 signaling to promote tumor initiation and were also attracted by and migrated towards the premetastatic niche. In summary, STAT3 phosphorylation at tyrosine 705 and serine 727 differentially regulates the EMT–MET switch within the distinct molecular subtypes of CSCs to complete the metastatic process.
Protein phosphorylation associated with drought priming-enhanced heat tolerance in a temperate grass species
Authors: Zhang Xiaxiang , Zhuang Lili , Liu Yu , Yang Zhimin , Huang Bingru 
Published: December 2020

AbstractProtein phosphorylation is known to play crucial roles in plant tolerance to individual stresses, but how protein phosphorylation is associated with cross-stress tolerance, particularly drought priming-enhanced heat tolerance is largely unknown. The objectives of the present study were to identify phosphorylated proteins and phosphorylation sites that were responsive to drought priming and to determine whether drought priming-enhanced heat tolerance in temperate grass species involves changes in protein phosphorylation. Comparative analysis of phosphoproteomic profiles was performed on leaves of tall fescue (Festuca arundinacea) exposed to heat stress (38/33 °C, day/night) with or without drought priming. A total of 569 differentially regulated phosphoproteins (DRPs) with 1098 phosphorylation sites were identified in response to drought priming or heat stress individually or sequentially. Most DRPs were nuclear-localized and cytosolic proteins. Motif analysis detected [GS], [DSD], and [S..E] as major phosphorylation sites in casein kinase-II and mitogen-activated protein kinases regulated by drought priming and heat stress. Functional annotation and gene ontology analysis demonstrated that DRPs in response to drought priming and in drought-primed plants subsequently exposed to heat stress were mostly enriched in four major biological processes, including RNA splicing, transcription control, stress protection/defense, and stress perception/signaling. These results suggest the involvement of post-translational regulation of the aforementioned biological processes and signaling pathways in drought priming memory and cross-tolerance with heat stress in a temperate grass species.
Ultrasensitive antibody-aptamer plasmonic biosensor for malaria biomarker detection in whole blood
Authors: Minopoli Antonio , Della Ventura Bartolomeo , Lenyk Bohdan , Gentile Francesco , Tanner Julian A. , Offenhäusser Andreas , Mayer Dirk , Velotta Raffaele 
Published: December 2020

AbstractDevelopment of plasmonic biosensors combining reliability and ease of use is still a challenge. Gold nanoparticle arrays made by block copolymer micelle nanolithography (BCMN) stand out for their scalability, cost-effectiveness and tunable plasmonic properties, making them ideal substrates for fluorescence enhancement. Here, we describe a plasmon-enhanced fluorescence immunosensor for the specific and ultrasensitive detection of Plasmodium falciparum lactate dehydrogenase (PfLDH)—a malaria marker—in whole blood. Analyte recognition is realized by oriented antibodies immobilized in a close-packed configuration via the photochemical immobilization technique (PIT), with a top bioreceptor of nucleic acid aptamers recognizing a different surface of PfLDH in a sandwich conformation. The combination of BCMN and PIT enabled maximum control over the nanoparticle size and lattice constant as well as the distance of the fluorophore from the sensing surface. The device achieved a limit of detection smaller than 1 pg/mL (<30 fM) with very high specificity without any sample pretreatment. This limit of detection is several orders of magnitude lower than that found in malaria rapid diagnostic tests or even commercial ELISA kits. Thanks to its overall dimensions, ease of use and high-throughput analysis, the device can be used as a substrate in automated multi-well plate readers and improve the efficiency of conventional fluorescence immunoassays.
Epistasis-driven identification of SLC25A51 as a regulator of human mitochondrial NAD import
Authors: Girardi Enrico , Agrimi Gennaro , Goldmann Ulrich , Fiume Giuseppe , Lindinger Sabrina , Sedlyarov Vitaly , Srndic Ismet , Gürtl Bettina , Agerer Benedikt , Kartnig Felix , Scarcia Pasquale , Di Noia Maria Antonietta , Liñeiro Eva , Rebsamen Manuele , Wiedmer Tabea , Bergthaler Andreas , Palmieri Luigi , Superti-Furga Giulio 
Published: December 2020

AbstractAbout a thousand genes in the human genome encode for membrane transporters. Among these, several solute carrier proteins (SLCs), representing the largest group of transporters, are still orphan and lack functional characterization. We reasoned that assessing genetic interactions among SLCs may be an efficient way to obtain functional information allowing their deorphanization. Here we describe a network of strong genetic interactions indicating a contribution to mitochondrial respiration and redox metabolism for SLC25A51/MCART1, an uncharacterized member of the SLC25 family of transporters. Through a combination of metabolomics, genomics and genetics approaches, we demonstrate a role for SLC25A51 as enabler of mitochondrial import of NAD, showcasing the potential of genetic interaction-driven functional gene deorphanization.
Cohesin depleted cells rebuild functional nuclear compartments after endomitosis
Authors: Cremer Marion , Brandstetter Katharina , Maiser Andreas , Rao Suhas S. P. , Schmid Volker J. , Guirao-Ortiz Miguel , Mitra Namita , Mamberti Stefania , Klein Kyle N. , Gilbert David M. , Leonhardt Heinrich , Cardoso M. Cristina , Aiden Erez Lieberman , Harz Hartmann , Cremer Thomas 
Published: December 2020

AbstractCohesin plays an essential role in chromatin loop extrusion, but its impact on a compartmentalized nuclear architecture, linked to nuclear functions, is less well understood. Using live-cell and super-resolved 3D microscopy, here we find that cohesin depletion in a human colon cancer derived cell line results in endomitosis and a single multilobulated nucleus with chromosome territories pervaded by interchromatin channels. Chromosome territories contain chromatin domain clusters with a zonal organization of repressed chromatin domains in the interior and transcriptionally competent domains located at the periphery. These clusters form microscopically defined, active and inactive compartments, which likely correspond to A/B compartments, which are detected with ensemble Hi-C. Splicing speckles are observed nearby within the lining channel system. We further observe that the multilobulated nuclei, despite continuous absence of cohesin, pass through S-phase with typical spatio-temporal patterns of replication domains. Evidence for structural changes of these domains compared to controls suggests that cohesin is required for their full integrity.
Glycoproteomics-based signatures for tumor subtyping and clinical outcome prediction of high-grade serous ovarian cancer
Authors: Pan Jianbo , Hu Yingwei , Sun Shisheng , Chen Lijun , Schnaubelt Michael , Clark David , Ao Minghui , Zhang Zhen , Chan Daniel , Qian Jiang , Zhang Hui 
Published: December 2020

Abstract
Inter-tumor heterogeneity is a result of genomic, transcriptional, translational, and post-translational molecular features. To investigate the roles of protein glycosylation in the heterogeneity of high-grade serous ovarian carcinoma (HGSC), we perform mass spectrometry-based glycoproteomic characterization of 119 TCGA HGSC tissues. Cluster analysis of intact glycoproteomic profiles delineates 3 major tumor clusters and 5 groups of intact glycopeptides. It also shows a strong relationship between N-glycan structures and tumor molecular subtypes, one example of which being the association of fucosylation with mesenchymal subtype. Further survival analysis reveals that intact glycopeptide signatures of mesenchymal subtype are associated with a poor clinical outcome of HGSC. In addition, we study the expression of mRNAs, proteins, glycosites, and intact glycopeptides, as well as the expression levels of glycosylation enzymes involved in glycoprotein biosynthesis pathways in each tumor. The results show that glycoprotein levels are mainly controlled by the expression of their individual proteins, and, furthermore, that the glycoprotein-modifying glycans correspond to the protein levels of glycosylation enzymes. The variation in glycan types further shows coordination to the tumor heterogeneity. Deeper understanding of the glycosylation process and glycosylation production in different subtypes of HGSC may provide important clues for precision medicine and tumor-targeted therapy.
Machine learning predicts live-birth occurrence before in-vitro fertilization treatment
Authors: Goyal Ashish , Kuchana Maheshwar , Ayyagari Kameswari Prasada Rao 
Published: December 2020

AbstractIn-vitro fertilization (IVF) is a popular method of resolving complications such as endometriosis, poor egg quality, a genetic disease of mother or father, problems with ovulation, antibody problems that harm sperm or eggs, the inability of sperm to penetrate or survive in the cervical mucus and low sperm counts, resulting human infertility. Nevertheless, IVF does not guarantee success in the fertilization. Choosing IVF is burdensome for the reason of high cost and uncertainty in the result. As the complications and fertilization factors are numerous in the IVF process, it is a cumbersome task for fertility doctors to give an accurate prediction of a successful birth. Artificial Intelligence (AI) has been employed in this study for predicting the live-birth occurrence. This work mainly focuses on making predictions of live-birth occurrence when an embryo forms from a couple and not a donor. Here, we compare various AI algorithms, including both classical Machine Learning, deep learning architecture, and an ensemble of algorithms on the publicly available dataset provided by Human Fertilisation and Embryology Authority (HFEA). Insights on data and metrics such as confusion matrices, F1-score, precision, recall, receiver operating characteristic (ROC) curves are demonstrated in the subsequent sections. The training process has two settings Without feature selection and With feature selection to train classifier models. Machine Learning, Deep learning, ensemble models classification paradigms have been trained in both settings. The Random Forest model achieves the highest F1-score of 76.49% in without feature selection setting. For the same model, the precision, recall, and area under the ROC Curve (ROC AUC) scores are 77%, 76%, and 84.60%, respectively. The success of the pregnancy depends on both male and female traits and living conditions. This study predicts a successful pregnancy through the clinically relevant parameters in In-vitro fertilization. Thus artificial intelligence plays a promising role in decision making process to support the diagnosis, prognosis, treatment etc.
Using machine learning tools to predict outcomes for emergency department intensive care unit patients
Authors: Zhai Qiangrong , Lin Zi , Ge Hongxia , Liang Yang , Li Nan , Ma Qingbian , Ye Chuyang 
Published: December 2020

AbstractThe number of critically ill patients has increased globally along with the rise in emergency visits. Mortality prediction for critical patients is vital for emergency care, which affects the distribution of emergency resources. Traditional scoring systems are designed for all emergency patients using a classic mathematical method, but risk factors in critically ill patients have complex interactions, so traditional scoring cannot as readily apply to them. As an accurate model for predicting the mortality of emergency department critically ill patients is lacking, this study’s objective was to develop a scoring system using machine learning optimized for the unique case of critical patients in emergency departments. We conducted a retrospective cohort study in a tertiary medical center in Beijing, China. Patients over 16 years old were included if they were alive when they entered the emergency department intensive care unit system from February 2015 and December 2015. Mortality up to 7 days after admission into the emergency department was considered as the primary outcome, and 1624 cases were included to derive the models. Prospective factors included previous diseases, physiologic parameters, and laboratory results. Several machine learning tools were built for 7-day mortality using these factors, for which their predictive accuracy (sensitivity and specificity) was evaluated by area under the curve (AUC). The AUCs were 0.794, 0.840, 0.849 and 0.822 respectively, for the SVM, GBDT, XGBoost and logistic regression model. In comparison with the SAPS 3 model (AUC = 0.826), the discriminatory capability of the newer machine learning methods, XGBoost in particular, is demonstrated to be more reliable for predicting outcomes for emergency department intensive care unit patients.