bluuebunny/crossref_metadata_2025 huggingface dataset:
Signal power distributions for simulated outdoor sound propagation in varying refractive conditions
Authors: Wilson D. Keith , Pettit Chris L. , Ostashev Vladimir E. , Kamrath Matthew J.
Published: June 2022

Probability distributions of acoustic signals propagating through the near-ground atmosphere are simulated by the parabolic equation method. The simulations involve propagation at four angles relative to the mean wind, with frequencies of 100, 200, 400, and 800 Hz. The environmental representation includes realistic atmospheric refractive profiles, turbulence, and ground interactions; cases are considered with and without parametric uncertainties in the wind velocity and surface heat flux. The simulated signals are found to span a broad range of scintillation indices, from near zero to exceeding ten. In the absence of uncertainties, the signal power (or intensity) is fit well by a two-parameter gamma distribution, regardless of the frequency and refractive conditions. When the uncertainties are included, three-parameter distributions, namely, the compound gamma or generalized gamma, are needed for a good fit to the simulation data. The compound gamma distribution appears preferable because its parameters have a straight forward interpretation related to the saturation and modulation of the signal by uncertainties.
Three-dimensional echo decorrelation monitoring of radiofrequency ablation in ex vivo bovine liver
Authors: Ghahramani Z E. , Grimm P. D. , Eary K. J. , Swearengen M. P. , Dayavansha E. G. Sunethra K. , Mast T. D.
Published: June 2022

Three-dimensional (3D) echo decorrelation imaging was investigated for monitoring radiofrequency ablation (RFA) in ex vivo bovine liver. RFA experiments (N = 14) were imaged by 3D ultrasound using a matrix array, with in-phase and quadrature complex echo volumes acquired about every 11 s. Tissue specimens were then frozen at −80 °C, sectioned, and semi-automatically segmented. Receiver operating characteristic (ROC) curves were constructed for assessing ablation prediction performance of 3D echo decorrelation with three potential normalization approaches, as well as 3D integrated backscatter (IBS). ROC analysis indicated that 3D echo decorrelation imaging is potentially a good predictor of local RFA, with the best prediction performance observed for globally normalized decorrelation. Tissue temperatures, recorded by four thermocouples integrated into the RFA probe, showed good correspondence with spatially averaged decorrelation and statistically significant but weak correlation with measured echo decorrelation at the same spatial locations. In tests predicting ablation zones using a weighted K-means clustering approach, echo decorrelation performed better than IBS, with smaller root mean square volume errors and higher Dice coefficients relative to measured ablation zones. These results suggest that 3D echo decorrelation and IBS imaging are capable of real-time monitoring of thermal ablation, with potential application to clinical treatment of liver tumors.
Quantum advantage in learning from experiments
Authors: Huang Hsin-Yuan , Broughton Michael , Cotler Jordan , Chen Sitan , Li Jerry , Mohseni Masoud , Neven Hartmut , Babbush Ryan , Kueng Richard , Preskill John , McClean Jarrod R.
Published: June 2022

Quantum technology promises to revolutionize how we learn about the physical world. An experiment that processes quantum data with a quantum computer could have substantial advantages over conventional experiments in which quantum states are measured and outcomes are processed with a classical computer. We proved that quantum machines could learn from exponentially fewer experiments than the number required by conventional experiments. This exponential advantage is shown for predicting properties of physical systems, performing quantum principal component analysis, and learning about physical dynamics. Furthermore, the quantum resources needed for achieving an exponential advantage are quite modest in some cases. Conducting experiments with 40 superconducting qubits and 1300 quantum gates, we demonstrated that a substantial quantum advantage is possible with today’s quantum processors.
Enantioselective hydrogen-bond-donor catalysis to access diverse stereogenic-at-P(V) compounds
Authors: Forbes Katherine C. , Jacobsen Eric N.
Published: June 2022

The stereoselective synthesis of molecules bearing stereogenic phosphorus(V) centers represents an enduring challenge in organic chemistry. Although stereospecific nucleophilic substitution at P(V) provides a general strategy for elaborating optically active P(V) compounds, existing methods for accessing the requisite chiral building blocks rely almost entirely on diastereocontrol using chiral auxiliaries. Catalytic, enantioselective methods for the synthesis of synthetically versatile stereogenic P(V) building blocks offer an alternative approach to stereogenic-at-P(V) targets without requiring stoichiometric quantities of chiral control elements. Here, we report an enantioselective hydrogen-bond-donor–catalyzed synthesis of aryl chlorophosphonamidates and the development of these products as versatile chiral P(V) building blocks. We demonstrate that the two leaving groups on these chlorophosphonamidates can be displaced sequentially and stereospecifically to access a wide variety of stereogenic-at-P(V) compounds featuring diverse substitution patterns.
Phosphorus through the looking glass
Authors: Verdaguer Xavier
Published: June 2022

A key building block enables a general synthesis of chiral phosphorus drugs
Immunolocalization of Metabolite Transporter Proteins in a Model Cnidarian-Dinoflagellate Symbiosis
Authors: Mashini Amirhossein Gheitanchi , Oakley Clinton A. , Grossman Arthur R. , Weis Virginia M. , Davy Simon K.
Published: June 2022

Coral reefs are in serious decline, in particular due to the thermally induced dysfunction of the cnidarian-dinoflagellate symbiosis that underlies their success. Yet our ability to react to this crisis is hindered by limited knowledge of how this symbiosis functions.
Differential Pneumococcal Growth Features in Severe Invasive Disease Manifestations
Authors: Arends Daan W. , Alkema Wynand , Hapsari Putri Indri , van der Gaast–de Jongh Christa E. , Eleveld Marc , Langereis Jeroen D. , de Mast Quirijn , Meis Jacques F. , de Jonge Marien I. , Cremers Amelieke J. H.
Published: June 2022

Streptococcus pneumoniaeis a leading infectious cause of deaths worldwide. To understand the course and outcome of pneumococcal infection, most research has focused on the host and its response to contain bacterial growth.
Surgical drainage of spontaneous sublingual haematoma
Authors: Hamilton Amanda , Beardsley Christian
Published: June 2022

This is a case presentation of a man in his late 80s who presented to emergency department with a sublingual haematoma secondary to warfarin usage for atrial fibrillation. His international normalised ratio was rapidly reversed, and he was taken to theatre for successful surgical drainage of the haematoma. He was kept intubated and ventilated for 48 hours postoperatively to allow swelling to settle then extubated and rehabilitated prior to discharge home.
Unusual cause of intraoperative haemorrhage: a lesson for patient counselling
Authors: Alghazawi Laith Omar Khalaf , Holtermann Entwistle Olivia , Fehervari Matyas , Spalding Duncan
Published: June 2022

Hepatocellular carcinoma (HCC) is a well-known malignant neoplasm of the liver associated with spontaneous haemorrhage in 3%–15% of cases. This complication is life threatening and has a mortality rate of 33%–100%. Despite the frequency and severity of spontaneous haemorrhage, the importance of patient education about this complication has not been highlighted before. There is currently no information available on the NHS UK website, and no publications have addressed the effect of patient education. We present this case report describing a patient who developed classical symptoms of haemorrhage the day before her elective HCC resection, but was unaware of its importance, and thus did not seek medical attention. She was subsequently found to have a large volume haemoperitoneum, anaemia and a ruptured HCC intraoperatively. This case illustrates the significant importance of counselled regarding the symptoms and risk of spontaneous rupture of HCC to prompt early presentation to medical services.
Hospitalization and mortality in patients with COVID-19 with or at risk of type 2 diabetes: data from five health systems in Pennsylvania and Maryland
Authors: Yeh Hsin-Chieh , Kraschnewski Jennifer L , Kong Lan , Lehman Erik B , Heilbrunn Emily S , Williams Pamela , Poger Jennifer M , Francis Erica , Bryce Cindy L
Published: June 2022

Objective

To identify the demographic and clinical characteristics associated with adverse COVID-19 outcomes across a 12-month period in 2020 and 2021.

Research design and methods

We conducted a retrospective cohort study using electronic health records from five academic health systems in Pennsylvania and Maryland, including patients with COVID-19 with type 2 diabetes or at risk of type 2 diabetes. Patients were classified based on 30-day outcomes: (1) no hospitalization; (2) hospitalization only; or (3) a composite measure including admission to the intensive care unit (ICU), intubation, or death. Analyses were conducted in patients with type 2 diabetes and patients at risk of type 2 diabetes separately.

Results

We included 15 725 patients with COVID-19 diagnoses between March 2020 and February 2021. Older age and higher Charlson Comorbidity Index scores were associated with higher odds of adverse outcomes, while COVID-19 diagnoses later in the study period were associated with lower odds of severe outcomes. In patients with type 2 diabetes, individuals on insulin treatment had higher odds for ICU/intubation/death (OR=1.59, 95% CI 1.27 to 1.99), whereas those on metformin had lower odds (OR=0.56, 95% CI 0.45 to 0.71). Compared with non-Hispanic White patients, Hispanic patients had higher odds of hospitalization in patients with type 2 diabetes (OR=1.73, 95% CI 1.36 to 2.19) or at risk of type 2 diabetes (OR=1.77, 95% CI 1.43 to 2.18.)

Conclusions

Adults who were older, in racial minority groups, had multiple chronic conditions or were on insulin treatment had higher risks for severe COVID-19 outcomes. This study reinforced the urgency of preventing COVID-19 and its complications in vulnerable populations.

Trial registration number

NCT02788903.
Efficacy and safety of the combined metabolic medication, containing inosine, nicotinamide, riboflavin and succinic acid, for the treatment of diabetic neuropathy: a multicenter randomized, double-blind, placebo-controlled parallel group clinical trial (CYLINDER)
Authors: Kharitonova Tatiana , Shvarts Yury G , Verbovoy Andrey F , Orlova Natalia S , Puzyreva Valentina P , Strokov Igor A
Published: June 2022

Introduction

Antioxidants may have positive impact on diabetic polyneuropathy (DPN), presumably due to alleviation of oxidative stress. We aimed to evaluate the efficacy and safety of combination of antioxidants: succinic acid, inosine, nicotinamide, and riboflavin (SINR) in the treatment of DPN.

Research design and methods

In a double-blind, placebo-controlled clinical trial, men and women aged 45–74 years with type 2 diabetes and symptomatic DPN, with initial Total Symptom Score (TSS) ˃5, were randomized into experimental (n=109) or placebo (n=107) group. Patients received study medication/placebo intravenously for 10 days, followed by oral administration for 75 days. Statistical significance was defined as a two-tailed p<0.05.

Results

In SINR group, mean TSS change after 12 weeks was –2.65 (±1.46) vs –1.73 (±1.51) in the placebo group (p<0.0001; t-test). Reduction of symptoms in the SINR group was achieved regardless of hemoglobin A1c levels, but better results were observed in patients with initial TSS <7.5. The analysis of TSS subscores revealed statistically significant between-group differences by dynamics of the intensity of paresthesia and of numbness starting from day 11 (p=0.035 and p=0.001, respectively; mixed model); by day 57, statistically significant between-group differences were detected also by dynamics of burning intensity (p=0.005; mixed model). Study limitations are small effect size, moderate proportion of patients with severe DPN symptoms, subjective assessment of outcomes, exclusion of participants who received injectable glucose-lowering medications other than insulins, and patients with uncontrolled and type 1 diabetes.

Conclusions

The combination of SINR effectively alleviates DPN symptoms in patients with type 2 diabetes.

Trial registration number

ClinicalTrials.gov Registry (NCT04649203; Unique Protocol ID: CTF-III-DM-2019).
Understanding transmission pathways and integrated digital surveillance potential of antimicrobial resistance in Ethiopia in a One Health approach: a mixed-method study protocol
Authors: Asaduzzaman Muhammad , Rodland Ernst Kristian , Mekonnen Zeleke , Gradmann Christoph , Winkler Andrea Sylvia
Published: June 2022

Introduction

Antimicrobial resistance (AMR) has a critical global impact, mostly affecting low- and middle-income countries. A major knowledge gap exists in understanding the transmission pathway of the gut colonisation with AMR bacteria between healthy humans and their animals in addition to the presence of those AMR bacteria in the surrounding environment. A One Health (OH) approach is necessary to address this multisectoral problem.

Methods and analysis

This cross-sectional, mixed-method OH study design will use both quantitative and qualitative methods of data collection. Quantitative methods will be carried out to assess the prevalence and risk factors associated with multidrug resistant Gram-negative bacteria and vancomycin-resistant enterococci in humans, animals (cattle) and the environment. The focus will be on cattle rearing as an exposure risk for AMR among humans. The assessment of AMR in the population of Jimma, Ethiopia with or without exposure to cattle will reinforce the importance of OH research to identify the impending exchange of resistance profile between humans and animals as well as its ultimate dissemination in the surrounding environment.

The targeted semistructured key stakeholder interviews will aid to strengthen the OH-AMR surveillance in Ethiopia by understanding the acceptability of an integrated AMR surveillance platform based on the District Health Information Software-2 and the feasibility of its context-specific establishment.

Ethics and dissemination

The study has been approved by the Regional Ethics Committee, Norway, and the Institutional Review Board of Jimma University, Ethiopia. The study’s data will be stored on a secure server known as Services for Sensitive Data hosted by the University of Oslo. In addition, the new European Union Global Data Protection Guidelines for data sharing, storage and protection will be followed. We will publish the results in peer-reviewed journals and present the findings at national and international conferences.
Large-scale evidence generation and evaluation across a network of databases for type 2 diabetes mellitus (LEGEND-T2DM): a protocol for a series of multinational, real-world comparative cardiovascular effectiveness and safety studies
Authors: Khera Rohan , Schuemie Martijn J , Lu Yuan , Ostropolets Anna , Chen RuiJun , Hripcsak George , Ryan Patrick B , Krumholz Harlan M , Suchard Marc A
Published: June 2022

Introduction

Therapeutic options for type 2 diabetes mellitus (T2DM) have expanded over the last decade with the emergence of cardioprotective novel agents, but without such data for older drugs, leaving a critical gap in our understanding of the relative effects of T2DM agents on cardiovascular risk.

Methods and analysis

The large-scale evidence generations across a network of databases for T2DM (LEGEND-T2DM) initiative is a series of systematic, large-scale, multinational, real-world comparative cardiovascular effectiveness and safety studies of all four major second-line anti-hyperglycaemic agents, including sodium–glucose co-transporter-2 inhibitor, glucagon-like peptide-1 receptor agonist, dipeptidyl peptidase-4 inhibitor and sulfonylureas. LEGEND-T2DM will leverage the Observational Health Data Sciences and Informatics (OHDSI) community that provides access to a global network of administrative claims and electronic health record data sources, representing 190 million patients in the USA and about 50 million internationally. LEGEND-T2DM will identify all adult, patients with T2DM who newly initiate a traditionally second-line T2DM agent. Using an active comparator, new-user cohort design, LEGEND-T2DM will execute all pairwise class-versus-class and drug-versus-drug comparisons in each data source, producing extensive study diagnostics that assess reliability and generalisability through cohort balance and equipoise to examine the relative risk of cardiovascular and safety outcomes. The primary cardiovascular outcomes include a composite of major adverse cardiovascular events and a series of safety outcomes. The study will pursue data-driven, large-scale propensity adjustment for measured confounding, a large set of negative control outcome experiments to address unmeasured and systematic bias.

Ethics and dissemination

The study ensures data safety through a federated analytic approach and follows research best practices, including prespecification and full disclosure of results. LEGEND-T2DM is dedicated to open science and transparency and will publicly share all analytic code from reproducible cohort definitions through turn-key software, enabling other research groups to leverage our methods, data and results to verify and extend our findings.
Seroprevalence of SARS-CoV-2 virus antibodies and sociodemographic features of pregnant women in Mogadishu, Somalia: a cross-sectional survey study
Authors: Sh. Nur Maryan Abdullahi , Dahie Hassan Abdullahi , Hassan Nima Abdi , Garba Bashiru , Adam Mohamed Hussein , Mohamoud Jamal Hassan , Dirie Najib Isse
Published: June 2022

Objective

Recent investigations have revealed that COVID-19 during pregnancy substantially increases the risk of harmful outcomes for mothers and neonates, including preterm death and stillbirth as well as severe maternal morbidity and mortality. Hence, the urgent need to understand the prevalence rate and level of awareness about COVID-19 (SARS-CoV-2 virus infection) and the practice of preventive measures against the disease among pregnant women in Somalia. This study aims to determine the prevalence of COVID-19 among pregnant women seeking antenatal care in the Benadir region (Mogadishu) of Somalia and to assess their knowledge and preventive practices towards COVID-19.

Setting

A hospital-based cross-sectional study involving two major referral maternity hospitals in Mogadishu, Somalia.

Participants

Pregnant women seeking antenatal services were included in our study.

Methods

A total of 477 blood samples were collected from pregnant women attending the two referral hospitals in Mogadishu and screened for COVID-19. The participants were subjected to questionnaire interviews where their detailed history and practice of prevention against COVID-19 were evaluated.

Results

The results showed that 175 (36.7%) were positive while 302 (63.3%) samples were negative for SARS-CoV-2 virus antibodies. Also, out of the 141 pregnant women who had two children or less, 19.4% were positive for IgG/IgM antibodies. Participants who had close contact with patients with COVID-19 were significantly associated for testing positive with a p value 0.0001. Students, teachers, employed people and individuals reported COVID-19 like symptoms were all associated with COVID-19 seropositivity outcomes.

Conclusion

Pregnant women and those with commorbidies should be given special preventive care and health education about COVID-19 transmission.
Development of a core measurement set for research in degenerative cervical myelopathy: a study protocol (AO Spine RECODE-DCM CMS)
Authors: Davies Benjamin M , Yanez Touzet Alvaro , Mowforth Oliver D , Lee Keng Siang , Khan Danyal , Furlan Julio C , Fehlings Michael G , Harrop James S , Zipser Carl Moritz , Rodrigues-Pinto Ricardo , Milligan James , Sarewitz Ellen , Curt Armin , Rahimi-Movaghar Vafa , Aarabi Bizhan , Boerger Timothy F , Tetreault Lindsay , Chen Robert , Guest James D , Kalsi-Ryan Sukhvinder , Sadler Iwan , Widdop Shirley , McNair Angus G K , Kwon Brian K , Kotter Mark R N
Published: June 2022

Introduction

Progress in degenerative cervical myelopathy (DCM) is hindered by inconsistent measurement and reporting. This impedes data aggregation and outcome comparison across studies. This limitation can be reversed by developing a core measurement set (CMS) for DCM research. Previously, the AO Spine Research Objectives and Common Data Elements for DCM (AO Spine RECODE-DCM) defined ‘what’ should be measured in DCM: the next step of this initiative is to determine ‘how’ to measure these features. This protocol outlines the steps necessary for the development of a CMS for DCM research and audit.

Methods and analysis

The CMS will be developed in accordance with the guidance developed by the Core Outcome Measures in Effectiveness Trials and the Consensus-based Standards for the selection of health Measurement Instruments. The process involves five phases. In phase 1, the steering committee agreed on the constructs to be measured by sourcing consensus definitions from patients, professionals and the literature. In phases 2 and 3, systematic reviews were conducted to identify tools for each construct and aggregate their evidence. Constructs with and without tools were identified, and scoping reviews were conducted for constructs without tools. Evidence on measurement properties, as well as on timing of assessments, are currently being aggregated. These will be presented in phase 4: a consensus meeting where a multi-disciplinary panel of experts will select the instruments that will form the CMS. Following selection, guidance on the implementation of the CMS will be developed and disseminated (phase 5). A preliminary CMS review scheduled at 4 years from release.

Ethics and dissemination

Ethical approval was obtained from the University of Cambridge (HBREC2019.14). Dissemination strategies will include peer-reviewed scientific publications; conference presentations; podcasts; the identification of AO Spine RECODE-DCM ambassadors; and engagement with relevant journals, funders and the DCM community.
Inhaled recombinant human IL-15 in dogs with naturally occurring pulmonary metastases from osteosarcoma or melanoma: a phase 1 study of clinical activity and correlates of response
Authors: Rebhun Robert B , York Daniel , Cruz Sylvia Margret , Judge Sean J , Razmara Aryana M , Farley Lauren E , Brady Rachel V , Johnson Eric G , Burton Jenna H , Willcox Jennifer , Wittenburg Luke A , Woolard Kevin , Dunai Cordelia , Stewart Susan L , Sparger Ellen E , Withers Sita S , Gingrich Alicia A , Skorupski Katherine A , Al-Nadaf Sami , LeJeune Amandine T , Culp William TN , Murphy William J , Kent Michael S , Canter Robert J
Published: June 2022

Purpose

Although recombinant human interleukin-15 (rhIL-15) has generated much excitement as an immunotherapeutic agent for cancer, activity in human clinical trials has been modest to date, in part due to the risks of toxicity with significant dose escalation. Since pulmonary metastases are a major site of distant failure in human and dog cancers, we sought to investigate inhaled rhIL-15 in dogs with naturally occurring lung metastases from osteosarcoma (OSA) or melanoma. We hypothesized a favorable benefit/risk profile given the concentrated delivery to the lungs with decreased systemic exposure.

Experimental design

We performed a phase I trial of inhaled rhIL-15 in dogs with gross pulmonary metastases using a traditional 3+3 cohort design. A starting dose of 10 µg twice daily × 14 days was used based on human, non-human primate, and murine studies. Safety, dose-limiting toxicities (DLT), and maximum tolerated dose (MTD) were the primary objectives, while response rates, progression-free and overall survival (OS), and pharmacokinetic and immune correlative analyses were secondary.

Results

From October 2018 to December 2020, we enrolled 21 dogs with 18 dogs reaching the 28-day response assessment to be evaluable. At dose level 5 (70 μg), we observed two DLTs, thereby establishing 50 µg twice daily × 14 days as the MTD and recommended phase 2 dose. Among 18 evaluable dogs, we observed one complete response >1 year, one partial response with resolution of multiple target lesions, and five stable disease for an overall clinical benefit rate of 39%. Plasma rhIL-15 quantitation revealed detectable and sustained rhIL-15 concentrations between 1-hour and 6 hour postnebulization. Decreased pretreatment lymphocyte counts were significantly associated with clinical benefit. Cytotoxicity assays of banked peripheral blood mononuclear cells revealed significant increases in peak cytotoxicity against canine melanoma and OSA targets that correlated with OS.

Conclusions

In this first-in-dog clinical trial of inhaled rhIL-15 in dogs with advanced metastatic disease, we observed promising clinical activity when administered as a monotherapy for only 14 days. These data have significant clinical and biological implications for both dogs and humans with refractory lung metastases and support exploration of combinatorial therapies using inhaled rhIL-15.
Revisiting superradiant stability of Kerr–Newman black holes under a charged massive scalar
Authors: Myung Yun Soo
Published: June 2022

We revisit the superradiant stability of Kerr–Newman black holes under a charged massive scalar perturbation. We obtain a newly suitable potential which is not singular at the outer horizon when a radial equation is expressed the Schrödinger-type equation in terms of the tortoise coordinate. From the potential analysis, we find a condition for the superradiant stability of Kerr–Newman black holes.
A pure hadronic model description of the observed neutrino emission from the tidal disruption event AT2019dsg
Authors: Banik Prabir , Bhadra Arunava
Published: June 2022

Recently, the IceCube Neutrino Observatory has detected the neutrino event IceCube-170922A from the radio-emitting tidal disruption event (TDE) named AT2019dsg, indicating to be one of the most likely sources of high-energy cosmic rays. So far, the photo-hadronic interaction is considered in the literature to interpret neutrino emission from AT2019dsg. Here, we examine whether the IceCube-170922A along with the broadband electromagnetic emission from the source can also be described by a pure hadronic emission employing the proton blazar inspired (PBI) model, which takes into account the non-relativistic protons that emerge under the charge neutrality situation of the blazar jet and thus offers sufficient target matter forppinteractions with shock-accelerated protons. Our findings show that the PBI model is able to consistently describe the IceCube observations on AT2019dsg and the broadband spectrum of the source without exceeding the observed X-ray and gamma-ray flux upper limits imposed by the XMM-Newton and Fermi-LAT telescopes.
Hypergraph Convolutional Neural Network for Fast and Accurate Diagnosis (FAT) of COVID From X-ray Images
Authors: Sasikaladevi N. , Revathi A.
Published: June 2022

Background and objective: The Covid-19 pandemic significantly affects the global population’s fitness and day-to-day life. The necessary action to fight against Covid is to have a fast, accurate and affordable diagnosis system. Most of the diagnostic systems available today have a low detection rate and are time consuming. Hence, there is a demand to design an affordable, accurate, and fast diagnostic system for Covid. The diagnostic system that uses the Convolutional Neural Network (CNN) does not consider the complex correlation of multimodal image data, thus misleading the diagnostic results. Graph Convolutional Network (GCN) provides a better solution for the complex representation of data, as it is modeled based on the pairwise relationship in the image features. Method: Diagnosis of Covid, Parasite, or Lung Tumor from X-ray images needs a more complex representative model. There is a demand to categorize the images grounded on highly complex features. To solve the issue mentioned earlier, this work proposes a Hypergraph- and convolutional neural network-based Fast and Accurate Diagnosis (FAT) system for Covid. The in-depth features are mined using a residual neural network from the X-ray images. The learning-based method optimizes a high-level correlation in the deep structures by constructing it as a hypergraph. Results: The proposed method is assessed based on the Covid dataset. The experimental outcomes show that the proposed system FAT provides the accuracy of 99.8%, sensitivity of 99.5%, and specificity of 99%. It outperforms all the current diagnosis systems for Covid. Conclusion: The proposed deep learning-based model is well suited for Covid diagnosis at the preliminary level. It allows diagnosing Covid by low radiation chest X-ray images with higher accuracy.
Current-Mode PID Controller Using Second-Generation Voltage Conveyor (VCII)
Authors: Özer Emre , Kaçar Fırat
Published: June 2022

In this paper, a current-mode (CM) proportional integral derivative (PID) controller based on second-generation voltage conveyor (VCII) is presented. The proposed circuit consists of two-plus type VCIIs, two resistors, and two capacitors. There is no need of critical matching condition. Considering the parasitic impedance, the operating frequency ranges of the proposed PID network are examined. The magnitude and phase responses, Monte Carlo, temperature and input-output noise analyses have been simulated. The simulation results are obtained with the LTspice program using AD844 SPICE macro-model under [Formula: see text]9[Formula: see text]V DC supply voltages. The total power consumption of the proposed CM PID controller is 235[Formula: see text]mW.
The quantum trace as a quantum non-abelianization map
Authors: Korinman J. , Quesney A.
Published: June 2022

We prove that the balanced Chekhov–Fock algebra of a punctured triangulated surface is isomorphic to a skein algebra which is a deformation of the algebra of regular functions of some abelian character variety. We first deduce from this observation a classification of the irreducible representations of the balanced Chekhov–Fock algebra at odd roots of unity, which generalizes to open surfaces the classification of Bonahon, Liu and Wong. We re-interpret Bonahon and Wong’s quantum trace map as a non-commutative deformation of some regular morphism between this abelian character variety and the [Formula: see text]-character variety. This algebraic morphism shares many resemblances with the non-abelianization map of Gaiotto, Moore, Hollands and Neitzke. When the punctured surface is closed, we prove that this algebraic non-abelianization map induces a birational morphism between a smooth torus and the relative [Formula: see text] character variety.
Black hole in a heat bath
Authors: Mäkelä Jarmo
Published: June 2022

It was recently pointed out by Barrau et al. that the mass of the Schwarzschild black hole immersed in a heat bath tends to infinity in a finite time. We show that this singularity problem in the black hole mass may be avoided, if the stretched horizon of the black hole is constructed out of a fixed, finite number of discrete constituents, and an appropriate counting is applied to the energy eigenstates of the hole.
INTERMITTENCY AND MULTISCALING IN LIMIT THEOREMS
Authors: GRAHOVAC DANIJEL , LEONENKO NIKOLAI N. , TAQQU MURAD S.
Published: June 2022

It has been recently discovered that some random processes may satisfy limit theorems even though they exhibit intermittency, namely an unusual growth of moments. In this paper, we provide a deeper understanding of these intricate limiting phenomena. We show that intermittent processes may exhibit a multiscale behavior involving growth at different rates. To these rates correspond different scales. In addition to a dominant scale, intermittent processes may exhibit secondary scales. The probability of these scales decreases to zero as a power function of time. For the analysis, we consider large deviations of the rate of growth of the processes. Our approach is quite general and covers different possible scenarios with special focus on the so-called supOU processes.
AFFINE FRACTAL LEAST SQUARES REGRESSION MODEL
Authors: SERPA CRISTINA
Published: June 2022

This paper develops a method to find fractal curves to fit real data. With a formulation for fractal functions through a type of affine systems of iterative functional equations, we apply the procedure of minimizing the sum of square residuals that is used in the classical linear regression. We develop formulas for approximation and exact fractal functions for various fractal levels and number of equations. This method gives estimates for the parameters of the equations and corresponding functions, namely the fractal coefficients (vertical scaling factors) and directional coefficients. As a consequence, it is possible to upper estimate the Hausdorff dimension of the fitted curve. We provide worked examples, including estimating a fractal curve for a time series real data set of exchange rates between USD and EUR currencies.
On the equivalence of classical Helmholtz equation and fractional Helmholtz equation with arbitrary order
Authors: Cheng Xinyu , Li Dong , Yang Wen
Published: June 2022

We show the equivalence of the classical Helmholtz equation and the fractional Helmholtz equation with arbitrary order. This improves a recent result of Guan, Murugan and Wei [Helmholtz solutions for the Fractional Laplacian and other related operators, to appear in Comm. Contemp. Math.].
Bayesian Inference for Geometric Process with Lindley Distribution and its Applications
Authors: Yılmaz Asuman , Kara Mahmut , Kara Hasan
Published: June 2022

The geometric process (GP) plays an important role in the reliability theory and life span models. It has been used extensively as a stochastic model in many areas of application. Therefore, the parameter estimation problem is very crucial in a GP. In this study, the parameter estimation problem for GP is discussed under the assumption that [Formula: see text] has a Lindley distribution with parameter [Formula: see text]. The maximum likelihood (ML) estimators of [Formula: see text] and [Formula: see text] of the GP and their asymptotic distributions are derived. A test statistic is developed based on ML estimators for testing whether [Formula: see text] or not. The same problem is also studied by using Bayesian methods. Bayes estimators of the unknown model parameters are obtained under squared error loss function (SELF) using uniform and gamma priors on the ratio [Formula: see text] and [Formula: see text] parameters. It is not possible to obtain Bayes estimators in explicit forms. Therefore, Markov Chain Monte Carlo (MCMC), Lindley (LD), and Tierney–Kadane (T–K) methods are used to estimate the parameters [Formula: see text] and [Formula: see text] in GP. The efficiencies of the ML estimators are compared with Bayes estimators via an extensive Monte Carlo simulation study. It is seen that the Bayes estimators perform better than the ML estimators. Two real-life examples are also presented for application purposes. The first data set concerns the coal mining disaster. The second is the number of COVID-19 patients in Turkey.
THE DYNAMICS OF TASK AUTOMATION AND WORKER ADJUSTMENT IN LABOR MARKETS: AN AGENT-BASED APPROACH
Authors: UPRETI ARVIND , SRIDHAR V.
Published: June 2022

Artificial Intelligence and associated technologies are rapidly automating routine and non-routine tasks across industries and can severely disrupt labor markets. This paper presents an agent-based, evolutionary model of labor market dynamics where workers adjust to technology shocks induced by automation. Firms produce a homogeneous service by combining the outputs of tasks performed by workers while stochastically adapting to automation of tasks causing displacement of workers. We develop a model that includes: (i) the description of occupation mobility as a directed graph where nodes represent occupations, and the directed edges represent the mobility pathways along which displaced workers can get retrained and redeployed (ii) explicit microfoundations of the processes of job matching and wage setting between firms and heterogeneously skilled workers. The model focuses on the influence of workers’ retraining choices on the employment levels and wage inequality in the labor market. Simulation results indicate distinct tipping points for unemployment and wage inequality with changes in mobility pathways along which workers retrain and redeploy across occupations. An increase in the density of mobility pathways induces a reinforcement effect on employment. Retraining displaced workers without building dense and well-distributed mobility pathways across occupations could widen wage inequality due to excessive crowding of workers around specific tasks. Our work focuses on the finance and insurance industry dataset, where we observe that the reskilling of displaced workers along occupation mobility pathways assisted by a lower retraining cost improves the unemployment levels. Also, if the firms aggressively automate their tasks, an increase in the cost of retraining increases inequality of wages in the labor market.
DFT Quantum-chemical prediction of molecular structure of iron(VI) macrocyclic complex with phthalocyanine and two oxo ligands
Authors: Chachkov Denis V. , Mikhailov Oleg V.
Published: June 2022

The possibility of the existence of the unknown iron complex containing phthalocyanine and two oxygen atoms in the inner coordination sphere was predicted using DFT quantum chemical calculation (OPBE/TZVP and B3PW91/TZVP levels). The structural parameters, the ground state multiplicity, the NBO analysis results, and the standard thermodynamic parameters for complex formation (standard enthalpy [Formula: see text], entropy [Formula: see text] and Gibbs’s energy [Formula: see text] are represented.
A pointwise estimate for pseudo-differential operators
Authors: Wang Guangqing , Chen Wenyi
Published: June 2022

Let [Formula: see text] be a pseudo-differential operator defined by the symbol [Formula: see text] with [Formula: see text] [Formula: see text]. It is shown that if [Formula: see text], then the operator satisfies the following pointwise estimate: [Formula: see text] for all [Formula: see text] and all Schwartz function [Formula: see text] Here, [Formula: see text] denotes the John-Nirenberg sharp function of [Formula: see text] and [Formula: see text] stands for the Hardy–Littlewood maximal operator.
Graph representation learning for popularity prediction problem: A survey
Authors: Chen Tiantian , Guo Jianxiong , Wu Weili
Published: June 2022

The online social platforms, like Twitter, Facebook, LinkedIn and WeChat, have grown really fast in last decade and have been one of the most effective platforms for people to communicate and share information with each other. Due to the word-of-mouth effects, information usually can spread rapidly on these social media platforms. Therefore, it is important to study the mechanisms driving the information diffusion and quantify the consequence of information spread. A lot of efforts have been focused on this problem to help us better understand and achieve higher performance in viral marketing and advertising. On the other hand, the development of neural networks has blossomed in the last few years, leading to a large number of graph representation learning (GRL) models. Compared with traditional models, GRL methods are often shown to be more effective. In this paper, we present a comprehensive review for recent works leveraging GRL methods for popularity prediction problem, and categorize related literatures into two big classes, according to their mainly used model and techniques: embedding-based methods and deep learning methods. Deep learning method is further classified into convolutional neural networks, graph convolutional networks, graph attention networks, graph neural networks, recurrent neural networks, and reinforcement learning. We compare the performance of these different models and discuss their strengths and limitations. Finally, we outline the challenges and future chances for popularity prediction problem.
Outcomes of Spinal Accessory Nerve and Intercostal Nerve Transfers for Shoulder Stabilisation and Elbow Extension in Patients with C5,6,7 Root Avulsion Injury
Authors: MALUNGPAISHROPE Kanchai , LEECHAVENGVONGS Somsak , UERPAIROJKIT Chairoj , ANANTAVORASAKUL Navapong , KITTITHAMVONGS Piyabuth , DENSIRI-AKSORN Worawit
Published: June 2022

Background: Upper arm type brachial plexus palsy results in decreased shoulder and elbow function. Reanimation of shoulder and elbow function is beneficial in these patients. The aim of this study is to report the results of restoring the shoulder abduction and elbow extension in patients with C5,6,7 root avulsion injury by simultaneous transfer of the spinal accessory nerve for the supraspinatus muscle combined with the transferring of the sixth and seventh intercostal nerves for the serratus anterior muscle along with the third to fifth intercostal nerves to the triceps muscle.

Methods: All patients who underwent the above set of nerve transfers and had at least 2 years of follow-up were included in the study. The outcome measures included the Medical Research Council (MRC) grading of motor strength of shoulder abduction and elbow extension and range of motion of shoulder abduction and shoulder external rotation.

Results: The study included 10 patients with an average age of 27. The mean time from injury to surgery was 6 months and the mean follow-up period was 35 months. M4 grade shoulder abduction was restored in five patients, M3 grade in three patients and M2 grade in two. M4 grade elbow extension was achieved in four patients, M3 grade in four patients and M2 grade in two patients. The average arc of shoulder abduction and external rotation was 71° and −21°, respectively.

Conclusions: The spinal accessory nerve and the sixth and seventh intercostal nerves transfer to the supraspinatus muscle and serratus anterior muscle with the third to fifth intercostal nerves transfer to the triceps muscle provided satisfactory results for both shoulder abduction and elbow extension in C5,6,7 root avulsion injury.

Level of Evidence: Level IV (Therapeutic)
Local Correlation Ensemble with GCN Based on Attention Features for Cross-domain Person Re-ID
Authors: Zhang Yue , Zhang Fanghui , Jin Yi , Cen Yigang , Voronin Viacheslav , Wan Shaohua
Published: June 2022

Person re-identification (Re-ID) has achieved great success in single-domain. However, it remains a challenging task to adapt a Re-ID model trained on one dataset to another one. Unsupervised domain adaption (UDA) was proposed to migrate a model from a labeled source domain to an unlabeled target domain. The main difference in the cross-domain is different background styles. Although the style transfer approach effectively reduces inter-domain gaps, it ignores the reduction of intra-class differences. Clustering-based pipelines maintain state-of-the-art performance for UDA by learning domain-independent features; however, most existing models do not sufficiently exploit the rich unlabeled samples in target domains due to unsatisfactory clustering. Thus, we propose a novel local correlation ensemble model that focuses on the diversity of intra-class information and the reliability of class centers. Specifically, a pedestrian attention module is proposed to enable the encoder to pay more attention to the person’s features to relieve interference caused by the shared background style. Furthermore, we propose a priority-distance graph convolutional network (PDGCN) module that employs a graph convolutional network network to predict the priority of a node as a class center and then calculates the distance between nodes with high priority values to screen out the class center nodes. Finally, the encoder features (local) and PDGCN features (context-aware) are combined to perform person Re-ID. The results of experiments on the large-scale public Re-ID datasets verified the effectiveness of the proposed method.
Multimodal Graph for Unaligned Multimodal Sequence Analysis via Graph Convolution and Graph Pooling
Authors: Mai Sijie , Xing Songlong , He Jiaxuan , Zeng Ying , Hu Haifeng
Published: June 2022

Multimodal sequence analysis aims to draw inferences from visual, language, and acoustic sequences. A majority of existing works focus on the aligned fusion of three modalities to explore inter-modal interactions, which is impractical in real-world scenarios. To overcome this issue, we seek to focus on analyzing unaligned sequences, which is still relatively underexplored and also more challenging. We propose Multimodal Graph, whose novelty mainly lies in transforming the sequential learning problem into graph learning problem. The graph-based structure enables parallel computation in time dimension (as opposed to recurrent neural network) and can effectively learn longer intra- and inter-modal temporal dependency in unaligned sequences. First, we propose multiple ways to construct the adjacency matrix for sequence to perform sequence to graph transformation. To learn intra-modal dynamics, a graph convolution network is employed for each modality based on the defined adjacency matrix. To learn inter-modal dynamics, given that the unimodal sequences are unaligned, the commonly considered word-level fusion does not pertain. To this end, we innovatively devise graph pooling algorithms to automatically explore the associations between various time slices from different modalities and learn high-level graph representation hierarchically. Multimodal Graph outperforms state-of-the-art models on three datasets under the same experimental setting.
The Genetics of Autophagy in Multicellular Organisms
Authors: Zhang Hong
Published: June 2022

Autophagy, a lysosome-mediated degradation process evolutionarily conserved from yeast to mammals, is essential for maintaining cellular homeostasis and combating diverse cellular stresses. Autophagy involves de novo synthesis of a double-membrane autophagosome, sequestration of selected cellular contents, and subsequent delivery of sequestrated contents to the vacuole (in yeasts and plants) or to lysosomes (in animal cells) for degradation and recycling. Genetic studies in unicellular and multicellular model organisms have systematically revealed the molecular machinery, regulation, and function of autophagy in physiological settings. I review genetic studies in model organisms—from yeast to worm to fly—that enable us to not only identify autophagy genes, including ATG genes and the metazoan-specific EPG genes, but also uncover variants of autophagy in developmental contexts, novel regulatory mechanisms, and signaling events involved in mediating systemic autophagy response. Genetic analysis also helps us understand the liquid–liquid phase separation and transition that control autophagic degradation of protein aggregates. The emerging role of autophagy in zebrafish tissue regeneration is also discussed.