bluuebunny/crossref_metadata_2025 huggingface dataset:
Parameter Estimation for Several Types of Linear Partial Differential Equations Based on Gaussian Processes
Authors: Zhang Wenbo , Gu Wei
Published: August 2022

This paper mainly considers the parameter estimation problem for several types of differential equations controlled by linear operators, which may be partial differential, integro-differential and fractional order operators. Under the idea of data-driven methods, the algorithms based on Gaussian processes are constructed to solve the inverse problem, where we encode the distribution information of the data into the kernels and construct an efficient data learning machine. We then estimate the unknown parameters of the partial differential Equations (PDEs), which include high-order partial differential equations, partial integro-differential equations, fractional partial differential equations and a system of partial differential equations. Finally, several numerical tests are provided. The results of the numerical experiments prove that the data-driven methods based on Gaussian processes not only estimate the parameters of the considered PDEs with high accuracy but also approximate the latent solutions and the inhomogeneous terms of the PDEs simultaneously.
Deep Water Archaeology in Italy and in the Tyrrhenian Sea
Authors: Beltrame Carlo , Costa Elisa , Gay Guido
Published: August 2022

This paper presents an overview of the history of the research in deep water archaeology in Italy and the recent activities carried out by Fondazione Azionemare, in collaboration with the Dipartimento di Studi Umanistici of the Università Ca’ Foscari Venezia, in the Tyrrhenian Sea. Through a preliminary presentation of some shipwrecks dated to the Roman period, discovered by the Fondazione Azionemare and investigated, thanks also to photogrammetry, with the archaeologists of the Venetian university, this article analyses the characteristics and the potentialities of these contexts, which present an excellent level of conservation.
Design and Synthesis of Benzene Homologues Tethered with 1,2,4-Triazole and 1,3,4-Thiadiazole Motifs Revealing Dual MCF-7/HepG2 Cytotoxic Activity with Prominent Selectivity via Histone Demethylase LSD1 Inhibitory Effect
Authors: Alsehli Mosa , Aljuhani Ateyatallah , Ihmaid Saleh K. , El-Messery Shahenda M. , Othman Dina I. A. , El-Sayed Abdel-Aziz A. A. , Ahmed Hany E. A. , Rezki Nadjet , Aouad Mohamed R.
Published: August 2022

In this study, an efficient multistep synthesis of novel aromatic tricyclic hybrids incorporating different biological active moieties, such as 1,3,4-thiadiazole and 1,2,4-triazole, was reported. These target scaffolds are characterized by having terminal lipophilic or hydrophilic parts, and their structures are confirmed by different spectroscopic methods. Further, the cytotoxic activities of the newly synthesized compounds were evaluated using in vitro MTT cytotoxicity screening assay against three different cell lines, including HepG-2, MCF-7, and HCT-116, compared with the reference drug Taxol. The results showed variable performance against cancer cell lines, exhibiting MCF-7 and HepG-2 selectivities by active analogs. Among these derivatives, 1,2,4-triazoles 11 and 13 and 1,3,4-thiadiazole 18 were found to be the most potent compounds against MCF-7 and HepG-2 cancer cells. Moreover, structure–activity relationship (SAR) studies led to the identification of some potent LSD1 inhibitors. The tested compounds showed good LSD1 inhibitory activities, with an IC50 range of 0.04–1.5 μM. Compounds 27, 23, and 22 were found to be the most active analogs with IC50 values of 0.046, 0.065, and 0.074 μM, respectively. In addition, they exhibited prominent selectivity against a MAO target with apparent cancer cell apoptosis, resulting in DNA fragmentation. This research provides some new aromatic-centered 1,2,4-triazole-3-thione and 1,3,4-thiadiazole analogs as highly effective anticancer agents with good LSD1 target selectivity.
SIG-1451, a Novel, Non-Steroidal Anti-Inflammatory Compound, Attenuates Light-Induced Photoreceptor Degeneration by Affecting the Inflammatory Process
Authors: Kikuchi Yuki , Sugano Eriko , Yuki Shiori , Tabata Kitako , Endo Yuka , Takita Yuya , Onoguchi Reina , Ozaki Taku , Fukuda Tomokazu , Takai Yoshihiro , Kurose Takahiro , Tanaka Koichi , Honma Yoichi , Perez Eduardo , Stock Maxwell , Fernández José R. , Tamura Masanori , Voronkov Michael , Stock Jeffry B. , Tomita Hiroshi
Published: August 2022

Age-related macular degeneration is a progressive retinal disease that is associated with factors such as oxidative stress and inflammation. In this study, we evaluated the protective effects of SIG-1451, a non-steroidal anti-inflammatory compound developed for treating atopic dermatitis and known to inhibit Toll-like receptor 4, in light-induced photoreceptor degeneration. SIG-1451 was intraperitoneally injected into rats once per day before exposure to 1000 lx light for 24 h; one day later, optical coherence tomography showed a decrease in retinal thickness, and electroretinogram (ERG) amplitude was also found to have decreased 3 d after light exposure. Moreover, SIG-1451 partially protected against this decrease in retinal thickness and increase in ERG amplitude. One day after light exposure, upregulation of inflammatory response-related genes was observed, and SIG-1451 was found to inhibit this upregulation. Iba-1, a microglial marker, was suppressed in SIG-1451-injected rats. To investigate the molecular mechanism underlying these effects, we used lipopolysaccharide (LPS)-stimulated rat immortalised Müller cells. The upregulation of C-C motif chemokine 2 by LPS stimulation was significantly inhibited by SIG-1451 treatment, and Western blot analysis revealed a decrease in phosphorylated I-κB levels. These results indicate that SIG-1451 indirectly protects photoreceptor cells by attenuating light damage progression, by affecting the inflammatory responses.
The Implication of 5-HT Receptor Family Members in Aggression, Depression and Suicide: Similarity and Difference
Authors: Popova Nina K. , Tsybko Anton S. , Naumenko Vladimir S.
Published: August 2022

Being different multifactorial forms of psychopathology, aggression, depression and suicidal behavior, which is considered to be violent aggression directed against the self, have principal neurobiological links: preclinical and clinical evidence associates depression, aggression and suicidal behavior with dysregulation in central serotonergic (5-HT) neurotransmission. The implication of different types of 5-HT receptors in the genetic and epigenetic mechanisms of aggression, depression and suicidality has been well recognized. In this review, we consider and compare the orchestra of 5-HT receptors involved in these severe psychopathologies. Specifically, it concentrates on the role of 5-HT1A, 5-HT1B, 5-HT2A, 5-HT2B, 5-HT2C, 5-HT3 and 5-HT7 receptors in the mechanisms underlying the predisposition to aggression, depression and suicidal behavior. The review provides converging lines of evidence that: (1) depression-related 5-HT receptors include those receptors with pro-depressive properties (5-HT2A, 5-HT3 and 5-HT7) as well as those providing an antidepressant effect (5-HT1A, 5-HT1B, 5-HT2C subtypes). (2) Aggression-related 5-HT receptors are identical to depression-related 5-HT receptors with the exception of 5-HT7 receptors. Activation of 5-HT1A, 5-HT1B, 5-HT2A, 5-HT2C receptors attenuate aggressiveness, whereas agonists of 5-HT3 intensify aggressive behavior.
Computational Analysis of Short Linear Motifs in the Spike Protein of SARS-CoV-2 Variants Provides Possible Clues into the Immune Hijack and Evasion Mechanisms of Omicron Variant
Authors: Soorajkumar Anjana , Alakraf Ebrahim , Uddin Mohammed , Du Plessis Stefan S. , Alsheikh-Ali Alawi , Kandasamy Richard K.
Published: August 2022

Short linear motifs (SLiMs) are short linear sequences that can mediate protein–protein interaction. Mimicking eukaryotic SLiMs to compete with extra- or intracellular binding partners, or to sequester host proteins is the crucial strategy of viruses to pervert the host system. Evolved proteins in viruses facilitate minimal protein–protein interactions that significantly affect intracellular signaling networks. Unfortunately, very little information about SARS-CoV-2 SLiMs is known, especially across SARS-CoV-2 variants. Through the ELM database-based sequence analysis of spike proteins from all the major SARS-CoV-2 variants, we identified four overriding SLiMs in the SARS-CoV-2 Omicron variant, namely, LIG_TRFH_1, LIG_REV1ctd_RIR_1, LIG_CaM_NSCaTE_8, and MOD_LATS_1. These SLiMs are highly likely to interfere with various immune functions, interact with host intracellular proteins, regulate cellular pathways, and lubricate viral infection and transmission. These cellular interactions possibly serve as potential therapeutic targets for these variants, and this approach can be further exploited to combat emerging SARS-CoV-2 variants.
Micro-Computed Tomography Evaluation of Minimally Invasive Shaping Systems in Mandibular First Molars
Authors: Berutti Elio , Moccia Edoardo , Lavino Stefano , Multari Stefania , Carpegna Giorgia , Scotti Nicola , Pasqualini Damiano , Alovisi Mario
Published: August 2022

The aim of this study was to compare the shaping ability of a modified ProTaper Next technique (PTNm) with that of TruNatomy (TN) in lower molars mesial curved canals using micro-computed tomography (Micro-CT). Sixty mesial canals of first mandibular molars were randomly assigned between two groups (n = 30). After canal scouting with K-File #10, glide path and shaping were carried out with TN or PTNm systems. The PTNm sequence consists of ProGlider, followed by ProTaper Next X1 and apical finishing with NiTiFlex #25 up to working length (WL) to ensure adequate apical cleaning. Samples were scanned using micro-CT and pre- and post-shaping volumes were matched to analyse geometric parameters: the volume of removed dentin; the difference of canal surface; centroid shift, minimum and maximum root canal diameters; cross-sectional areas; the ratio of diameter ratios (RDR) and the ratio of cross-sectional areas (RA). Measurements were assessed 2 mm from the apex and in relation to the middle and coronal root canal thirds. Data were analysed using ANOVA (p < 0.05). No statistically significant differences were found between the groups for any parameter at each level of analysis, except for RA at the coronal level (p = 0.037). The PTNm system showed the tendency to enlarge more in the coronal portion with a lower centroid shift at apical level compared with TN sequence (p > 0.05). Both PTNm and TN sequences demonstrated similar maintenance of original anatomy during the shaping of lower molar mesial curved canals.
Relationship between Pachychoroid and Polypoidal Choroidal Vasculopathy
Authors: Yamashiro Kenji , Yanagi Yasuo , Koizumi Hideki , Matsumoto Hidetaka , Cheung Chui Ming Gemmy , Gomi Fumi , Iida Tomohiro , Tsujikawa Akitaka
Published: August 2022

Previous clinical studies have suggested that pachychoroid can induce macular neovascularization (MNV) to develop pachychoroid neovasculopathy (PNV) and that PNV can progress to polypoidal choroidal vasculopathy (PCV). Recent studies based on the pachychoroid concept are now gradually revealing the true nature of, at least some part of, PCV. However, previous studies on PNV and/or PCV have used different frameworks for the classification of PNV, PCV, and neovascular age-related macular degeneration (nAMD). These have hampered the rapid overhaul of the understanding of PCV. Some investigators have assumed that all PCV is pachychoroid-driven whereas other investigators have classified PCV into “pachychoroid PCV” and “non-pachychoroid PCV”. Furthermore, since there is no consensus as to whether PNV includes PCV, some studies have included PCV with PNV, while other studies have excluded PCV from PNV. To address these gaps, we summarize previous studies on PCV and pachychoroid. Even before the proposal of the pachychoroid concept, previous studies had suggested that PCV could be divided into two subtypes, of which one was characterized by pachychoroid features. Previous studies had also provided keys to understand relationship between PCV and PNV. We here recommend a refined conceptual framework for future studies on PNV, PCV, and nAMD. Considering the current inconsistent understanding of PCV, we should be cautious about using the term PCV until we understand the true nature of PCV.
Evaluation of Baseline Characteristics and Prognostic Factors in Multisystemic Inflammatory Syndrome in Children: Is It Possible to Foresee the Prognosis in the First Step?
Authors: Cetin Benhur Sirvan , Kısaarslan Ayşenur Paç , Tekin Sedanur , Goksuluk Merve Basol , Baykan Ali , Akyıldız Başak Nur , Seçilmiş Yılmaz , Poyrazoglu Hakan ,   on behalf of the Erciyes MIS-C Study Group
Published: August 2022

Background: Two years after the first cases, critical gaps remain in identifying prognostic factors in multisystem inflammatory syndrome in children (MIS-C). Methods: This retrospective study included 99 patients with MIS-C hospitalized between August 2020 and March 2022 in a pediatric tertiary center. The patients were divided into two groups according to clinical severity (low- and high-risk). Prognostic values of baseline clinical and laboratory characteristics were evaluated with advanced statistical analysis, including machine learning. Results: Sixty-three patients were male, and the median age was 83 (3–205) months. Fifty-nine patients (59.6%) were low-risk cases. Patients aged six years and over tended to be at higher risk. Involvement of aortic or tricuspid valve or >1 valve was more frequent in the high-risk group. Mortality in previously healthy children was 3.2%. Intensive care unit admission and mortality rate in the high-risk group were 37.5% and 7.5%, respectively. At admission, high-risk patients were more likely to have reduced lymphocyte count and total protein level and increased brain natriuretic peptide (BNP), ferritin, D-dimer, and troponin concentrations. The multiple logistic regression model showed that BNP, total protein, and troponin were associated with higher risk. When the laboratory parameters were used together, BNP, total protein, ferritin, and D-dimer provided the highest contribution to the discrimination of the risk groups (100%, 89.6%, 85.6%, and 55.8%, respectively). Conclusions: Our study widely evaluates and points to some clinical and laboratory parameters that, at admission, may indicate a more severe course. Modeling studies with larger sample groups are strongly needed.
Mental Health Condition among University Students of Bangladesh during the Critical COVID-19 Period
Authors: Rahman Md Mostafizur , Asikunnaby  , Khan Saadmaan Jubayer , Arony Anuva , Mamun Zahid Al , Procheta Nawwar Fatima , Sakib Mohammed Sadman , Aryal Komal Raj , Rahman Farzana , Islam Abu Reza Md. Towfiqul
Published: August 2022

Bangladesh’s education sector has been in a state of flux since COVID-19. During the pandemic, all university campuses were closed. There was a mental health issue among the students. This study aims to examine the mental health condition and the determinants that contribute to adverse mental health conditions among university students of Bangladesh. A survey was performed online among university students in Bangladesh, in mid-June 2020 when averaging 3345 affected cases of the population daily. The convenience sampling technique was used and the survey gathered data from 365 university students. The relationship between general information and Depression, Anxiety, and Stress Scale 21 (DASS-21) subscales of university students was determined. The questionnaire was administered to respondents during the pandemic, which ensured fast replies. Linear regression models were used for statistical analysis. University students indicated normal levels of depression (30.41%), anxiety (43.29%), and stress (47.40%). However, a disproportionate number of extremely depressed, anxious, and stressed university students suggested a mental health status of concern. There were significant connections between the individual’s opinion of social satisfaction, mental health concerns, and the present location’s safety with an undesirable mental health condition. Female students were shown to be much more anxious and stressed than male students. Capital Dhaka city students were more depressed and anxious than students outside of Dhaka. Financial and psychological support for students may help mitigate the psychological impact. Authorities should make effective efforts to reduce mental health problems among these students. This research may aid organizations, health care providers, and social workers in their attempts to prepare for and respond to pandemics.
Factors Associated with and Prognosis Impact of Perceived Sleep Quality and Estimated Quantity in Patients Receiving Non-Invasive Ventilation for Acute Respiratory Failure
Authors: Lê Dinh Matthieu , Darmon Michael , Kouatchet Achille , Jaber Samir , Meziani Ferhat , Perbet Sebastien , Chanques Gerald , Azoulay Elie , Demoule Alexandre
Published: August 2022

Background. The objectives of this study were (1) to determine factors associated with impaired sleep and (2) to evaluate the relationship between impaired sleep and the outcome. Methods. Secondary analysis of a prospective observational cohort study in 54 intensive care units in France and Belgium. Sleep quality was quantified by the patients with a semi-quantitative scale. Results. Among the 389 patients included, 40% reported poor sleep during the first night in the ICU and the median (interquartile) total sleep time was 4 h (2–5). Factors independently associated with poor sleep quality were the SOFA score (odds ratio [OR] 0.90, p = 0.037), anxiety (OR 0.43, p = 0.001) and the presence of air leaks (OR 0.52, p = 0.013). Factors independently associated with short-estimated sleep duration (<4 h) were the SOFA score (1.13, p = 0.005), dyspnea on admission (1.13, p = 0.031) and the presence of air leaks (1.92, p = 0.008). Non-invasive ventilation failure was independently associated with poor sleep quality (OR 3.02, p = 0.021) and short sleep duration (OR 0.77, p = 0.001). Sleep quality and duration were not associated with an increase in mortality or length of stay. Conclusions. The sleep of patients with ARF requiring NIV is impaired and is associated with a high rate of NIV failure.
A Case of 123I-Metaiodobenzylguanidine Scintigraphy-Negative Pheochromocytoma with a Tumor-Developing Mutation in the RET Gene
Authors: Kubo Haremaru , Tsurutani Yuya , Sunouchi Takashi , Hoshino Yoshitomo , Hirose Rei , Katsuragawa Sho , Kimura Noriko , Saito Jun , Nishikawa Tetsuo
Published: August 2022

Pheochromocytoma (PCC) is rare catecholamine-producing endocrine tumor that metastasizes in approximately 10% of cases. As a functional imaging of PCC, 123I-metaiodobenzylguanidine (MIBG) scintigraphy was established, and some cases of PCC exhibit negative accumulation on MIBG scintigraphy, indicating a high risk of metastasis. Additionally, germline genetic variants of PCC are evident in approximately 30% of cases, although the genotype-phenotype correlation in PCC, especially the association between genetic mutations and MIBG scintigraphy, remains unclear. A 33-year-old man was admitted to our hospital for further examination for hypertension. He was diagnosed with sporadic PCC, and left adrenalectomy was performed. The adrenal tumor was negative on MIBG scintigraphy. Histology of the tumor revealed a moderately differentiated PCC. Target gene testing revealed a mutation in RET (c.2071G > A). This mutation has been reported to be a tumor-developing gene involved in the pathogenesis of PCC. Moreover, the RET mutation is the only gene mutation reported in a previous study of PCC with negative results on MIBG scintigraphy, except for the SDHB gene mutation, which is a common mutation in metastatic PCC. Correctively, the present RET gene mutation may be associated to MIBG-scintigraphy negative PCC and its pathophysiology. Clinicians should follow such cases more cautiously in clinical practice.
Swin Transformer Improves the IDH Mutation Status Prediction of Gliomas Free of MRI-Based Tumor Segmentation
Authors: Wu Jiangfen , Xu Qian , Shen Yiqing , Chen Weidao , Xu Kai , Qi Xian-Rong
Published: August 2022

Background: Deep learning (DL) could predict isocitrate dehydrogenase (IDH) mutation status from MRIs. Yet, previous work focused on CNNs with refined tumor segmentation. To bridge the gap, this study aimed to evaluate the feasibility of developing a Transformer-based network to predict the IDH mutation status free of refined tumor segmentation. Methods: A total of 493 glioma patients were recruited from two independent institutions for model development (TCIA; N = 259) and external test (AHXZ; N = 234). IDH mutation status was predicted directly from T2 images with a Swin Transformer and conventional ResNet. Furthermore, to investigate the necessity of refined tumor segmentation, seven strategies for the model input image were explored: (i) whole tumor slice; (ii–iii) tumor mask and/or not edema; (iv–vii) tumor bounding box of 0.8, 1.0, 1.2, 1.5 times. Performance comparison was made among the networks of different architectures along with different image input strategies, using area under the curve (AUC) and accuracy (ACC). Finally, to further boost the performance, a hybrid model was built by incorporating the images with clinical features. Results: With the seven proposed input strategies, seven Swin Transformer models and seven ResNet models were built, respectively. Based on the seven Swin Transformer models, an averaged AUC of 0.965 (internal test) and 0.842 (external test) were achieved, outperforming 0.922 and 0.805 resulting from the seven ResNet models, respectively. When a bounding box of 1.0 times was used, Swin Transformer (AUC = 0.868, ACC = 80.7%), achieved the best results against the one that used tumor segmentation (Tumor + Edema, AUC = 0.862, ACC = 78.5%). The hybrid model that integrated age and location features into images yielded improved performance (AUC = 0.878, Accuracy = 82.0%) over the model that used images only. Conclusions: Swin Transformer outperforms the CNN-based ResNet in IDH prediction. Using bounding box input images benefits the DL networks in IDH prediction and makes the IDH prediction free of refined glioma segmentation feasible.
Pain Extent Is Not Associated with Sensory-Associated Symptoms, Cognitive or Psychological Variables in COVID-19 Survivors Suffering from Post-COVID Pain
Authors: Fernández-de-las-Peñas César , Fuensalida-Novo Stella , Ortega-Santiago Ricardo , Valera-Calero Juan , Cescon Corrado , Derboni Marco , Giuffrida Vincenzo , Barbero Marco
Published: August 2022

We aimed to investigate the relationship between pain extent, as a sign of sensitization, and sensory-related, cognitive and psychological variables in hospitalized COVID-19 survivors with post-COVID pain. One hundred and forty-six (67 males, 79 females) previously hospitalized COVID-19 survivors with post-COVID pain completed demographic (age, sex, height, weight), sensory-related (Central Sensitization Inventory, Self-Report Leeds Assessment of Neuropathic Symptoms), cognitive (Pain Catastrophizing Scale, Tampa Scale for Kinesiophobia) and psychological (Hospital Anxiety and Depression Scale, Pittsburgh Sleep Quality Index) variables. Pain extent and frequency maps were calculated from pain drawings using customized software. After conducting a correlation analysis to determine the relationships between variables, a stepwise linear regression model was performed to identify pain extent predictors, if available. Pain extent was significantly and weakly associated with pain intensity (r = −0.201, p = 0.014): the larger the pain extent, the lower the pain intensity. No other significant association was observed between pain extent and sensory-related, cognitive, or psychological variables in individuals with post-COVID pain. Females had higher pain intensity, more sensitization-associated symptoms, higher anxiety, lower sleep quality, and higher kinesiophobia levels than males. Sex differences correlation analyses revealed that pain extent was associated with pain intensity in males, but not in females. Pain extent was not associated with any of the measured variables and was also not related to the presence of sensitization-associated symptoms in our sample of COVID-19 survivors with long-term post-COVID pain.
Real-World Evaluation of a Population Germline Genetic Screening Initiative for Family Medicine Patients
Authors: Hutchcraft Megan Leigh , Zhang Shulin , Lin Nan , Gottschalk Ginny Lee , Keck James W. , Belcher Elizabeth A. , Sears Catherine , Wang Chi , Liu Kun , Dietz Lauren E. , Pickarski Justine C. , Wei Sainan , Cardarelli Roberto , DiPaola Robert S. , Kolesar Jill M.
Published: August 2022

Hereditary factors contribute to disease development and drug pharmacokinetics. The risk of hereditary disease development can be attenuated or eliminated by early screening or risk reducing interventions. The purpose of this study was to assess the clinical utility of germline medical exome sequencing in patients recruited from a family medicine clinic and compare the mutation frequency of hereditary predisposition genes to established general population frequencies. At the University of Kentucky, 205 family medicine patients underwent sequencing in a Clinical Laboratory Improvement Amendments of 1988-compliant laboratory to identify clinically actionable genomic findings. The study identified pathogenic or likely pathogenic genetic variants—classified according to the American College of Medical Genetics and Genomics variant classification guidelines—and actionable pharmacogenomic variants, as defined by the Clinical Pharmacogenetics Implementation Consortium. Test results for patients with pharmacogenomic variants and pathogenic or likely pathogenic variants were returned to the participant and enrolling physician. Hereditary disease predisposition gene mutations in APOB, BRCA2, MUTYH, CACNA1S, DSC2, KCNQ1, LDLR, SCN5A, or SDHB were identified in 6.3% (13/205) of the patients. Nine of 13 (69.2%) underwent subsequent clinical interventions. Pharmacogenomic variants were identified in 76.1% (156/205) of patients and included 4.9% (10/205) who were prescribed a medication that had pharmacogenomic implications. Family physicians changed medications for 1.5% (3/205) of patients to prevent toxicity. In this pilot study, we found that with systemic support, germline genetic screening initiatives were feasible and clinically beneficial in a primary care setting.
COVID-19 Mortality and Economic Losses: The Role of Policies and Structural Conditions
Authors: Wang Weichen , Gurgone Andrea , Martínez Humberto , Barbieri Góes Maria Cristina , Gallo Ettore , Kerényi Ádam , Turco Enrico Maria , Coburger Carla , Andrade Pêdra D. S.
Published: August 2022

The response of governments to the COVID-19 outbreak was foremost oriented to two objectives: saving lives and limiting economic losses. However, the effectiveness and success factors of interventions were unknown ex-ante. This study aims to shed light on the drivers of countries’ performances during the first year of the COVID-19 pandemic. We measure performances by excess mortality and GDP growth adjusted for additional fiscal stimulus. We conduct an empirical analysis in two stages: first, using hierarchical clustering, we partition countries based on their similarity in health and economic outcomes. Second, we identify the key drivers of outcomes in each country cluster by regression analysis, which include linear, least absolute shrinkage and selection operator (LASSO), and logit models. We argue that differences in countries’ performances can be traced back both to policy responses to COVID-19 and structural conditions, the latter being immutable over the pandemic. Three relevant structural conditions emerge from the results: trade reliance on services, corruption, and the size of the vulnerable population (elderly, low-income, smoking, or cardiovascular-failing). Policies such as large-scale open public testing and additional fiscal stimulus in non-health could help reduce excess mortality, which might lead to lower economic losses.
Ending Exclusion from Refugee Protection and Advancing International Justice
Authors: Simeon James C. , Rikhof Joseph
Published: August 2022

In any utopic vision of the international refugee protection regime at least these two conditions ought to prevail: (1) all those who are genuinely in need of refugee protection will be granted international protection; (2) all those who are responsible for criminality, especially, serious international crimes, shall be held criminally liable. This presumes that the so-called “exclusion clauses” of the 1951 Refugee Convention, Article 1F, and those found in the regional refugee rights instruments (1969 OAU Convention, 1984 Cartagena Declaration, 2011 EU Qualifications Directive) are not required. No one would be excluded from refugee protection who meets the definition of refugee as found in these international refugee rights instruments. By the same token, anyone who is responsible for serious criminality, especially, serious international crimes, (as defined by the 1998 Rome Statute of the International Criminal Court) shall be held criminally liable. This serves the ideal of bringing an end to impunity for serious international criminality and ensuring everyone is held accountable for their contribution for the persecution of others. Accordingly, the first part of this article presents the thesis that serious criminality should be part of the inclusionary portions of the definition of who is a refugee and not its exclusionary portions, Article 1F of the 1951 Refugee Convention. Indeed, Article 1F, it is argued, is antiquated and no longer conforms to contemporary international norms and principles and can result in injustices to refugee applicants. Given the inherent complexity and difficulties with Article 1F and the fact it is no longer required, it can be repealed and Article 1A(2), the definition of who is a refugee, can be amended to not include anyone who is responsible for the commission of serious criminality. Moreover, when there is sufficiently reliable and trustworthy evidence that a refugee applicant is responsible for serious criminality then they can be prosecuted and by doing so both ending impunity for serious international crimes and advancing international justice can be achieved. The second part of the article is a commentary on the first part and raises a word of caution. The thesis of this part is that before adopting any radical solution with respect to the exclusion clause, it would be useful to provide a broader context to the issues raised. The commentary raises some questions regarding the underlying assumptions in the first part, specifically, in its examination of the human rights and international criminal justice framework. These questions are on three levels, namely conceptual, legal, and practical. The commentary concludes with some overarching observations in respect to the criticisms raised and the proposal submitted.
Heterologous (Over) Expression of Human SoLute Carrier (SLC) in Yeast: A Well-Recognized Tool for Human Transporter Function/Structure Studies
Authors: Pochini Lorena , Galluccio Michele
Published: August 2022

For more than 20 years, yeast has been a widely used system for the expression of human membrane transporters. Among them, more than 400 are members of the largest transporter family, the SLC superfamily. SLCs play critical roles in maintaining cellular homeostasis by transporting nutrients, ions, and waste products. Based on their involvement in drug absorption and in several human diseases, they are considered emerging therapeutic targets. Despite their critical role in human health, a large part of SLCs’ is ‘orphans’ for substrate specificity or function. Moreover, very few data are available concerning their 3D structure. On the basis of the human health benefits of filling these knowledge gaps, an understanding of protein expression in systems that allow functional production of these proteins is essential. Among the 500 known yeast species, S. cerevisiae and P. pastoris represent those most employed for this purpose. This review aims to provide a comprehensive state-of-the-art on the attempts of human SLC expression performed by exploiting yeast. The collected data will hopefully be useful for guiding new attempts in SLCs expression with the aim to reveal new fundamental data that could lead to potential effects on human health.
Graph Burning: Mathematical Formulations and Optimal Solutions
Authors: García-Díaz Jesús , Rodríguez-Henríquez Lil María Xibai , Pérez-Sansalvador Julio César , Pomares-Hernández Saúl Eduardo
Published: August 2022

The graph burning problem is an NP-hard combinatorial optimization problem that helps quantify how vulnerable a graph is to contagion. This paper introduces three mathematical formulations of the problem: an integer linear program (ILP) and two constraint satisfaction problems (CSP1 and CSP2). Thanks to off-the-shelf optimization software, these formulations can be solved optimally over arbitrary graphs; this is relevant because the only algorithms designed to date for this problem are approximation algorithms and heuristics, which do not guarantee to find optimal solutions. We empirically compared the proposed formulations using random graphs and off-the-shelf optimization software. The results show that CSP1 and CSP2 tend to reach optimal solutions in less time than the ILP. Therefore, we executed them over some benchmark graphs of order at most 5908. The previously best-known solutions for some of these graphs were improved. We draw some empirical observations from the experimental results. For instance, we find the tendency: the larger the graph’s optimal solution, the more difficult it is to find it. Finally, the resulting set of optimal solutions might be helpful as a benchmark dataset for the performance evaluation of non-exact algorithms.
Research on Hybrid Multi-Attribute Three-Way Group Decision Making Based on Improved VIKOR Model
Authors: Song Jiekun , He Zeguo , Jiang Lina , Liu Zhicheng , Leng Xueli
Published: August 2022

In the era of internet connection and IOT, data-driven decision-making has become a new trend of decision-making and shows the characteristics of multi-granularity. Because three-way decision-making considers the uncertainty of decision-making for complex problems and the cost sensitivity of classification, it is becoming an important branch of modern decision-making. In practice, decision-making problems usually have the characteristics of hybrid multi-attributes, which can be expressed in the forms of real numbers, interval numbers, fuzzy numbers, intuitionistic fuzzy numbers and interval-valued intuitionistic fuzzy numbers (IVIFNs). Since other forms can be regarded as special forms of IVIFNs, transforming all forms into IVIFNs can minimize information distortion and effectively set expert weights and attribute weights. We propose a hybrid multi-attribute three-way group decision-making method and give detailed steps. Firstly, we transform all attribute values of each expert into IVIFNs. Secondly, we determine expert weights based on interval-valued intuitionistic fuzzy entropy and cross-entropy and use interval-valued intuitionistic fuzzy weighted average operator to obtain a group comprehensive evaluation matrix. Thirdly, we determine the weights of each attribute based on interval-valued intuitionistic fuzzy entropy and use the VIKOR method improved by grey correlation analysis to determine the conditional probability. Fourthly, based on the risk loss matrix expressed by IVIFNs, we use the optimization method to determine the decision threshold and give the classification rules of the three-way decisions. Finally, an example verifies the feasibility of the hybrid multi-attribute three-way group decision-making method, which provides a systematic and standard solution for this kind of decision-making problem.
Survey of Lévy Flight-Based Metaheuristics for Optimization
Authors: Li Juan , An Qing , Lei Hong , Deng Qian , Wang Gai-Ge
Published: August 2022

Lévy flight is a random walk mechanism which can make large jumps at local locations with a high probability. The probability density distribution of Lévy flight was characterized by sharp peaks, asymmetry, and trailing. Its movement pattern alternated between frequent short-distance jumps and occasional long-distance jumps, which can jump out of local optimal and expand the population search area. The metaheuristic algorithms are inspired by nature and applied to solve NP-hard problems. Lévy flight is used as an operator in the cuckoo algorithm, monarch butterfly optimization, and moth search algorithms. The superiority for the Lévy flight-based metaheuristic algorithms has been demonstrated in many benchmark problems and various application areas. A comprehensive survey of the Lévy flight-based metaheuristic algorithms is conducted in this paper. The research includes the following sections: statistical analysis about Lévy flight, metaheuristic algorithms with a Lévy flight operator, and classification of Lévy flight used in metaheuristic algorithms. The future insights and development direction in the area of Lévy flight are also discussed.
An Intelligent Athlete Signal Processing Methodology for Balance Control Ability Assessment with Multi-Headed Self-Attention Mechanism
Authors: Xu Nannan , Cui Xinze , Wang Xin , Zhang Wei , Zhao Tianyu
Published: August 2022

In different kinds of sports, the balance control ability plays an important role for every athlete. Therefore, coaches and athletes need accurate and efficient assessments of the balance control ability to improve the athletes’ training performance scientifically. With the fast growth of sport technology and training devices, intelligent and automatic assessment methods have been in high demand in the past years. This paper proposes a deep-learning-based method for a balance control ability assessment involving an analysis of the time-series signals from the athletes. The proposed method directly processes the raw data and provides the assessment results, with an end-to-end structure. This straight-forward structure facilitates its practical application. A deep learning model is employed to explore the target features with a multi-headed self-attention mechanism, which is a new approach to sports assessments. In the experiments, the real athletes’ balance control ability assessment data are utilized for the validation of the proposed method. Through comparisons with different existing methods, the accuracy rate of the proposed method is shown to be more than 95% for all four tasks, which is higher than the other compared methods for tasks containing more than one athlete of each level. The results show that the proposed method works effectively and efficiently in real scenarios for athlete balance control ability evaluations. However, reducing the proposed method’s calculation costs is an important task for future studies.
A Novel Multi-Source Domain Adaptation Method with Dempster–Shafer Evidence Theory for Cross-Domain Classification
Authors: Huang Min , Zhang Chang
Published: August 2022

In this era of big data, Multi-source Domain Adaptation (MDA) becomes more and more popular and is employed to make full use of available source data collected from several different, but related domains. Although multiple source domains provide much information, the processing of domain shifts becomes more challenging, especially in learning a common domain-invariant representation for all domains. Moreover, it is counter-intuitive to treat multiple source domains equally as most existing MDA algorithms do. Therefore, the domain-specific distribution for each source–target domain pair is aligned, respectively. Nevertheless, it is hard to combine adaptation outputs from different domain-specific classifiers effectively, because of ambiguity on the category boundary. Subjective Logic (SL) is introduced to measure the uncertainty (credibility) of each domain-specific classifier, so that MDA could be bridged with DST. Due to the advantage of information fusion, Dempster–Shafer evidence Theory (DST) is utilized to reduce the category boundary ambiguity and output reasonable decisions by combining adaptation outputs based on uncertainty. Finally, extensive comparative experiments on three popular benchmark datasets for cross-domain image classification are conducted to evaluate the performance of the proposed method via various aspects.
Updating the Landweber Iteration Method for Solving Inverse Problems
Authors: Al-Mahdawi Hassan K. Ibrahim , Alkattan Hussein , Abotaleb Mostafa , Kadi Ammar , El-kenawy El-Sayed M.
Published: August 2022

The Landweber iteration method is one of the most popular methods for the solution of linear discrete ill-posed problems. The diversity of physical problems and the diversity of operators that result from them leads us to think about updating the main methods and algorithms to achieve the best results. We considered in this work the linear operator equation and the use of a new version of the Landweber iterative method as an iterative solver. The main goal of updating the Landweber iteration method is to make the iteration process fast and more accurate. We used a polar decomposition to achieve a symmetric positive definite operator instead of an identity operator in the classical Landweber method. We carried out the convergence and other necessary analyses to prove the usability of the new iteration method. The residual method was used as an analysis method to rate the convergence of the iteration. The modified iterative method was compared to the classical Landweber method. A numerical experiment illustrates the effectiveness of this method by applying it to solve the inverse boundary value problem of the heat equation (IBVP).
Statistical Inference on a Finite Mixture of Exponentiated Kumaraswamy-G Distributions with Progressive Type II Censoring Using Bladder Cancer Data
Authors: Alotaibi Refah , Baharith Lamya A. , Almetwally Ehab M. , Khalifa Mervat , Ghosh Indranil , Rezk Hoda
Published: August 2022

A new family of distributions called the mixture of the exponentiated Kumaraswamy-G (henceforth, in short, ExpKum-G) class is developed. We consider Weibull distribution as the baseline (G) distribution to propose and study this special sub-model, which we call the exponentiated Kumaraswamy Weibull distribution. Several useful statistical properties of the proposed ExpKum-G distribution are derived. Under the classical paradigm, we consider the maximum likelihood estimation under progressive type II censoring to estimate the model parameters. Under the Bayesian paradigm, independent gamma priors are proposed to estimate the model parameters under progressive type II censored samples, assuming several loss functions. A simulation study is carried out to illustrate the efficiency of the proposed estimation strategies under both classical and Bayesian paradigms, based on progressively type II censoring models. For illustrative purposes, a real data set is considered that exhibits that the proposed model in the new class provides a better fit than other types of finite mixtures of exponentiated Kumaraswamy-type models.
Portfolio Selection Problem Using CVaR Risk Measures Equipped with DEA, PSO, and ICA Algorithms
Authors: Hamdi Abdelouahed , Karimi Arezou , Mehrdoust Farshid , Belhaouari Samir
Published: August 2022

Investors always pay attention to the two factors of return and risk in portfolio optimization. There are different metrics for the calculation of the risk factor, among which the most important one is the Conditional Value at Risk (CVaR). On the other hand, Data Envelopment Analysis (DEA) can be used to form the optimal portfolio and evaluate its efficiency. In these models, the optimal portfolio is created by stocks or companies with high efficiency. Since the search space is vast in actual markets and there are limitations such as the number of assets and their weight, the optimization problem becomes difficult. Evolutionary algorithms are a powerful tool to deal with these difficulties. The automotive industry in Iran involves international automotive manufacturers. Hence, it is essential to investigate the market related to this industry and invest in it. Therefore, in this study we examined this market based on the price index of the automotive group, then optimized a portfolio of automotive companies using two methods. In the first method, the CVaR measurement was modeled by means of DEA, then Particle Swarm Optimization (PSO) and the Imperial Competitive Algorithm (ICA) were used to solve the proposed model. In the second method, PSO and ICA were applied to solve the CVaR model, and the efficiency of the portfolios of the automotive companies was analyzed. Then, these methods were compared with the classic Mean-CVaR model. The results showed that the automotive price index was skewed to the right, and there was a possibility of an increase in return. Most companies showed favorable efficiency. This was displayed the return of the portfolio produced using the DEA-Mean-CVaR model increased because the investment proposal was basedon the stock with the highest expected return and was effective at three risk levels. It was found that when solving the Mean-CVaR model with evolutionary algorithms, the risk decreased. The efficient boundary of the PSO algorithm was higher than that of the ICA algorithm, and it displayed more efficient portfolios.Therefore, this algorithm was more successful in optimizing the portfolio.
Solving Nonlinear Second-Order Differential Equations through the Attached Flow Method
Authors: Ionescu Carmen , Constantinescu Radu
Published: August 2022

The paper considers a simple and well-known method for reducing the differentiability order of an ordinary differential equation, defining the first derivative as a function that will become the new variable. Practically, we attach to the initial equation a supplementary one, very similar to the flow equation from the dynamical systems. This is why we name it as the “attached flow equation”. Despite its apparent simplicity, the approach asks for a closer investigation because the reduced equation in the flow variable could be difficult to integrate. To overcome this difficulty, the paper considers a class of second-order differential equations, proposing a decomposition of the free term in two parts and formulating rules, based on a specific balancing procedure, on how to choose the flow. These are the main novelties of the approach that will be illustrated by solving important equations from the theory of solitons as those arising in the Chafee–Infante, Fisher, or Benjamin–Bona–Mahony models.
Performance Comparison of Numerical Methods in a Predictive Controller for an AC–DC Power Converter
Authors: Ramirez-Hernandez Jazmin , Juarez-Sandoval Oswaldo Ulises , Hernandez-Gonzalez Leobardo , Cortes Domingo , Sanchez-Garcia Juan C. , Guevara-Lopez Pedro
Published: August 2022

The use of model-based predictive control in power converters has substantially increased in recent years. This control technique always needs a discrete system model to be implemented. There are several methods to obtain a discrete model; in this paper, all common methods are examined from a practical point of view. Their precision, simplicity, and implementation requirements are analyzed to establish their advantages and disadvantages. From this analysis, it is shown that different discretization methods result in different closed-loop converter performance. A model-based predictive control AC–DC converter is used to show that different discretization procedures result in different total harmonic distortion. For this evaluation, a simulation of a 1 kW three-phase active rectifier was performed in Matlab-Simulink.