bluuebunny/crossref_metadata_2025 huggingface dataset:
Impact of surgeon specialty on clinical outcomes following esophagectomy for cancer
Authors: Verma Arjun , Hadaya Joseph , Kronen Elsa , Sakowitz Sara , Chervu Nikhil , Bakhtiyar Syed Shahyan , Benharash Peyman
Published: September 2023

Background

The impact of surgeon and hospital operative volume on esophagectomy outcomes is well-described; however, studies examining the influence of surgeon specialty remain limited. Therefore, we evaluated the impact of surgeon specialty on short-term outcomes following esophagectomy for cancer.

Methods

The 2016–2019 American College of Surgeons National Surgical Quality Improvement Project (ACS NSQIP) was queried to identify all patients undergoing esophagectomy for esophageal cancer. Surgeon specialty was categorized as general (GS) or thoracic (TS). Entropy balancing was used to generate sample weights that adjust for baseline differences between GS and TS patients. Weights were subsequently applied to multivariable linear and logistic regressions, which were used to evaluate the independent association of surgeon specialty with 30-day mortality, complications, and postoperative length of stay.

Results

Of 2657 esophagectomies included for analysis, 54.1% were performed by TS. Both groups had similar distributions of age, sex, and body mass index. TS patients more frequently underwent transthoracic esophagectomy, while GS patients more commonly received minimally invasive surgery. After adjustment, surgeon specialty was not associated with altered odds of 30-day mortality (adjusted odds ratio [AOR] 1.10 p = 0.73) or anastomotic leak (AOR 0.87, p = 0.33). However, TS patients exhibited a 40-min reduction in operative duration and faced greater odds of perioperative transfusion, relative to GS.

Conclusion

Among ACS NSQIP participating centers, surgeon specialty influenced operative duration and blood product utilization, but not mortality and anastomotic leak. Our results support the relative safety of esophagectomy performed by select GS and TS.
Beitrag der Humangenetik zur Präzisionsonkologie
Authors: William Doreen , Jahn Arne , Meinhardt Andrea , Schröck Evelin
Published: September 2023

Hintergrund

Bis heute werden Personen mit einem genetischen Tumorrisikosyndrom (GENTURIS) noch zu selten in der Regelversorgung identifiziert. Die interdisziplinäre Zusammenarbeit zwischen Onkologie, innerer Medizin, Pathologie und Humangenetik kann dies ändern. In der Präzisionsonkologie werden Tumor und Keimbahn parallel molekulargenetisch zur Therapiestratifizierung und zur Aufdeckung von erblichen Tumorerkrankungen umfassend untersucht.

Ziel

Der vorliegende Artikel gibt eine Übersicht zum aktuellen sowie einen Ausblick auf den zukünftigen Beitrag der Humangenetik zur Präzisionsonkologie.

Ergebnisse

Die Genomanalyse von Tumor und Keimbahn in der Präzisionsonkologie ermöglicht neben der Empfehlung einer individuellen Therapie für bis zu 80 % der Patientinnen und Patienten auch die Identifizierung von seltenen pathogenen Varianten in der Keimbahn. Damit kann für etwa 10 % der Patientinnen und Patienten die Diagnose einer erblichen Tumorerkrankung bereits für die erste erkrankte Person in einer Familie gestellt werden. Die interdisziplinär erarbeiteten medizinisch relevanten Ergebnisse können damit das klinische Management der Patientinnen und Patienten und auch von Familienangehörigen verbessern. Die Behandlungspfade für Personen mit GENTURIS werden auf europäischer Ebene durch das Europäische Referenznetzwerk für Genetische Tumorrisikosyndrome (ERN-GENTURIS) erarbeitet.

Schlussfolgerung

Die Nutzung der Präzisionsonkologie für Tumorpatientinnen und -patienten sollte multidisziplinär erfolgen und ist essenziell für die verbesserte Betreuung, die lebensverlängernde Therapie und führt zu einer präventiv wertvollen Diagnostik von erblichen Tumorerkrankungen.
Reference values for urinary protein, albumin, beta 2-microglobulin, and the alpha 1-microglobulin-to-creatinine ratio in Japanese children
Authors: Okamoto Shojiro , Matsuyama Takeshi , Hamada Riku , Morikawa Yoshihiko , Tomotsune Masako , Kaneko Tetsuji , Abe Katsumi , Uchiyama Atsushi , Honda Masataka
Published: September 2023

Background

The importance of the ratio of creatinine to urinary protein, albumin, and low-molecular weight protein as a urinary marker in chronic kidney disease patients is widely recognized. However, no reference values have hitherto been established for these markers in Japanese children. The present study aimed to establish the reference values for these urinary markers in Japanese children.

Methods

The first morning urine was randomly collected from 1712 pupils aged ≥ 3 to < 18 years during school and kindergarten mass urinary screenings. The upper limit of the reference values was set at the 97.5th percentile of the creatinine ratio per marker.

Results

The urinary protein-to-creatinine ratio (PCR), urinary albumin-to-creatinine ratio (ACR), urinary beta 2-microglobulin-to-creatinine ratio (BMCR), and urinary alpha 1-microglobulin-to-creatinine ratio (AMCR) showed an age-related decrease at the 50th percentile reflecting an age-related increase in urinary creatinine. The appropriate reference value for the PCR and ACR was 0.12 g/gCr and 35 mg/gCr, respectively, in the entire cohort. The appropriate reference value for the BMCR was 0.5 μg /mgCr for age ≥ 3 to < 6 years and 0.35 μg/mgCr for age 6 years or older. The appropriate reference value for the AMCR was 5.0 μg/mgCr for age ≥ 3 to < 6 years and 3.5 μg /mgCr for age 6 years or older.

Conclusion

The present study was the first to determine appropriate reference values for the PCR, ACR, BMCR, and AMCR based on an analysis of the first morning urine samples of a large number of children.
Inverse identification of region-specific hyperelastic material parameters for human brain tissue
Authors: Hinrichsen Jan , Reiter Nina , Bräuer Lars , Paulsen Friedrich , Kaessmair Stefan , Budday Silvia
Published: September 2023

The identification of material parameters accurately describing the region-dependent mechanical behavior of human brain tissue is crucial for computational models used to assist, e.g., the development of safety equipment like helmets or the planning and execution of brain surgery. While the division of the human brain into different anatomical regions is well established, knowledge about regions with distinct mechanical properties remains limited. Here, we establish an inverse parameter identification scheme using a hyperelastic Ogden model and experimental data from multi-modal testing of tissue from 19 anatomical human brain regions to identify mechanically distinct regions and provide the corresponding material parameters. We assign the 19 anatomical regions to nine governing regions based on similar parameters and microstructures. Statistical analyses confirm differences between the regions and indicate that at least the corpus callosum and the corona radiata should be assigned different material parameters in computational models of the human brain. We provide a total of four parameter sets based on the two initial Poisson’s ratios of 0.45 and 0.49 as well as the pre- and unconditioned experimental responses, respectively. Our results highlight the close interrelation between the Poisson’s ratio and the remaining model parameters. The identified parameters will contribute to more precise computational models enabling spatially resolved predictions of the stress and strain states in human brains under complex mechanical loading conditions.
Artificial glial cells in artificial neuronal networks: a systematic review
Authors: Alvarez-Gonzalez Sara , Cedron Francisco , Pazos Alejandro , Porto-Pazos Ana B.
Published: September 2023

The concept of tripartite synapses has revolutionized the world of neuroscience and the way we understand how information is transmitted in the brain. Since its discovery, some research groups have incorporated into connectionist systems classically focused on the development of Artificial Neuron Networks (ANNs) as a single element, artificial astrocytes that try to optimize performance in problem solving.In this systematic review, we searched the ISI Web of Science for papers that focused on the development of such novel models and their comparison with classical ANNs. A total of 22 papers that satisfied the inclusion criteria were analyzed, showing three different ways of applying the neuromodulatory influence of artificial astrocytes on neural networks. Using Multilayer Perceptron Networks, Artificial Neuro-Glial Newtworks and Multilayer Perceptron with Self-Organizing Maps approaches, a detailed analysis of the incorporation of artificial astrocytic networks has been carried out, and the main differences between the different methods have been weighed up. Regardless of the type of inclusion performed, the greater the complexity of the problem to be solved, it has been observed that the influence of artificial astrocytes has improved the performance of classical ANNs, as occurs in the biological brain.
Dimer Algebras, Ghor Algebras, and Cyclic Contractions
Authors: Beil Charlie
Published: September 2023

A ghor algebra is the path algebra of a dimer quiver on a surface, modulo relations that come from the perfect matchings of its quiver. Such algebras arise from abelian quiver gauge theories in physics. We show that a ghor algebra $$\Lambda $$
Λ
on a torus is a dimer algebra (a quiver with potential) if and only if it is noetherian, and otherwise $$\Lambda $$
Λ
is the quotient of a dimer algebra by homotopy relations. Furthermore, we classify the simple $$\Lambda $$
Λ
-modules of maximal dimension and give an explicit description of the center of $$\Lambda $$
Λ
using a special subset of perfect matchings. In our proofs we introduce formalized notions of Higgsing and the mesonic chiral ring from quiver gauge theory.
Court Decisions and Air Pollution: Evidence from Ten Million Penal Cases in India
Authors: Sarmiento Luis , Nowakowski Adam
Published: September 2023

This study explores the relationship between air pollution and judicial rulings. Although environmental factors should not affect judicial decisions, realists contend that there is substantial room for external factors to transpire into sentencing and sway human reasoning. We hypothesize that air pollution is one of these factors. Using Poisson panel models and instrumental variable techniques, we show that exposure leads to more convictions. We posit that this effect occurs because the impact of exposure on the central nervous system changes the cognitive performance and empathy of judges. Back-of-the-envelope calculations suggest that decreasing average air pollution in India by one standard deviation would lead to up to 145,000 fewer convictions regarding currently active cases.
Influence of Place of Birth on Adult Mortality: The Case of Spain
Authors: Aldea Néstor , Ordanovich Dariya , Palloni Alberto , Ramiro Diego , Viciana Francisco
Published: September 2023

We use a unique data set from Spain and we estimate life expectancy at age 50 for males and females by place of residence and place of birth. We show that, consistent with expectations regarding the influence of early conditions on adult health and mortality, the effects of place of birth on adult mortality are very strong, irrespective of place of residence. Furthermore, we find that mortality levels observed in a place are strongly influenced by the composition of migrants by place of birth. This is reflected in a new measure of heritability of early childhood conditions that attains a value in the range 0.42–0.43, implying that as much as 43 percent of the variance in Spain’s life expectancy at age 50 is explained by place of birth. Finally, we find evidence of the healthy migrant effect, that is, positive health selection of migrants, at a regional level.
Applications of Limiters, Neural Networks and Polynomial Annihilation in Higher-Order FD/FV Schemes
Authors: Hillebrand Dorian , Klein Simon-Christian , Öffner Philipp
Published: September 2023

The construction of high-order structure-preserving numerical schemes to solve hyperbolic conservation laws has attracted a lot of attention in the last decades and various different ansatzes exist. In this paper, we compare several completely different approaches, i.e. deep neural networks, limiters and the application of polynomial annihilation to construct high-order accurate shock capturing finite difference/volume (FD/FV) schemes. We further analyze their analytical and numerical properties. We demonstrate that all techniques can be used and yield highly efficient FD/FV methods but also come with some additional drawbacks which we point out. Our investigation of the different strategies should lead to a better understanding of those techniques and can be transferred to other numerical methods as well which use similar ideas.
A Spatial Equilibrium Problem for the European Pulp and Paper Industry Under the Emission Trading System
Authors: Allevi Elisabetta , Gnudi Adriana , Konnov Igor V. , Oggioni Giorgia , Riccardi Rossana
Published: September 2023

We propose a spatial equilibrium problem for a pulp and paper supply chain network under the Emission Trading System (ETS) and the circular economy principles. The developed closed-loop supply chain model, based on the variational inequality theory, accounts for the forest deployment and the production of paper using both virgin pulp and recyclable paper waste in an area that is subject to the ETS. In addition, it considers the exchanges of virgin pulp, paper, recyclable and not-recyclable paper waste between this regulated area and geographical zones where no environmental policies apply. The aim of this paper is to investigate the ETS impacts on the pulp and paper industries located in an environmentally regulated area in terms of operating costs, production and trading volumes of the aforementioned commodities, and emissions generated. The case study focuses on the European pulp and paper companies, which are among the energy-intensive industries deemed to be exposed to the carbon leakage risk as a consequence of the ETS application. Sensitivity analyses on the carbon price value and on the trade flexibility are conducted. Our results show that the ETS implies additional costs for the European pulp and paper companies and induces them to resort more on imports of virgin pulp and paper from not-regulated zones.
Sfnet: Faster and Accurate Semantic Segmentation Via Semantic Flow
Authors: Li Xiangtai , Zhang Jiangning , Yang Yibo , Cheng Guangliang , Yang Kuiyuan , Tong Yunhai , Tao Dacheng
Published: September 2023

In this paper, we focus on exploring effective methods for faster and accurate semantic segmentation. A common practice to improve the performance is to attain high-resolution feature maps with strong semantic representation. Two strategies are widely used: atrous convolutions and feature pyramid fusion, while both are either computationally intensive or ineffective. Inspired by the Optical Flow for motion alignment between adjacent video frames, we propose a Flow Alignment Module (FAM) to learn Semantic Flow between feature maps of adjacent levels and broadcast high-level features to high-resolution features effectively and efficiently. Furthermore, integrating our FAM to a standard feature pyramid structure exhibits superior performance over other real-time methods, even on lightweight backbone networks, such as ResNet-18 and DFNet. Then to further speed up the inference procedure, we also present a novel Gated Dual Flow Alignment Module to directly align high-resolution feature maps and low-resolution feature maps where we term the improved version network as SFNet-Lite. Extensive experiments are conducted on several challenging datasets, where results show the effectiveness of both SFNet and SFNet-Lite. In particular, when using Cityscapes test set, the SFNet-Lite series achieve 80.1 mIoU while running at 60 FPS using ResNet-18 backbone and 78.8 mIoU while running at 120 FPS using STDC backbone on RTX-3090. Moreover, we unify four challenging driving datasets (i.e., Cityscapes, Mapillary, IDD, and BDD) into one large dataset, which we named Unified Driving Segmentation (UDS) dataset. It contains diverse domain and style information. We benchmark several representative works on UDS. Both SFNet and SFNet-Lite still achieve the best speed and accuracy trade-off on UDS, which serves as a strong baseline in such a challenging setting. The code and models are publicly available at https://github.com/lxtGH/SFSegNets.
Contract-based specification of mode-dependent timing behavior
Authors: Kröger Janis , Koopmann Björn , Stierand Ingo , Fränzle Martin
Published: September 2023

The design of safety-critical systems calls for rigorous application of specification and verification methods. In this context, a comprehensive consideration of safety aspects, which inevitably include timing properties, requires explicit addressing of operating modes and their transitions in the system model as well as in the respective specifications. As a side effect, this helps to reduce verification complexity. This paper presents an extension of a framework for the specification of timing properties following the contract-based design paradigm. It provides enhancements of the underlying specification language, which enables specifying modes, mode transitions, and mode-dependent behavior. A formal semantics is given in order to enable reasoning about such specifications as well as about contract operations like refinement and composition, thus enabling to make statements about mode composition. The results are discussed using a real-world example.
Automatic detection of ictal activity in EEG using synchronization and chaos-based attributes
Authors: Mahgoub Asma , Qaraqe Marwa
Published: September 2023

Automatic seizure onset detectors (SODs) have been proposed to alert epileptic patients when a seizure is about to happen and in turn improve their quality of life. Yet, the detectors proposed in literature are complex and difficult to implement in real-time as they utilize large feature sets with redundant and irrelevant features. Hence, the aim of this work is to propose a simple and lightweight SOD that exploits two characteristics that reflect the neuronal behavior during a seizure. Namely, the synchronization between EEG channels and the chaoticity of the EEG; synchronization was measured by the condition number while the recurrence period density entropy estimated the chaoticity of an EEG signal. A support vector machine was trained and tested on 10 patients from a scalp EEG dataset and was able to detect the considered seizures with a sensitivity of 100% and a false positives rate of 0.5 per hour. The results indicate that synchronization and chaos attributes can reflect the manifestation of seizures in EEG data and can be used to develop SODs. This work emphasizes that even a single relevant feature can produce an SOD with comparable performance to SODs that use many features.

Graphical Abstract
Radiomics nomogram based on optimal VOI of multi-sequence MRI for predicting microvascular invasion in intrahepatic cholangiocarcinoma
Authors: Ma Xijuan , Qian Xianling , Wang Qing , Zhang Yunfei , Zong Ruilong , Zhang Jia , Qian Baoxin , Yang Chun , Lu Xin , Shi Yibing
Published: September 2023

Objective

Microvascular invasion (MVI) is a significant adverse prognostic indicator of intrahepatic cholangiocarcinoma (ICC) and affects the selection of individualized treatment regimens. This study sought to establish a radiomics nomogram based on the optimal VOI of multi-sequence MRI for predicting MVI in ICC tumors.

Methods

160 single ICC lesions with MRI scanning confirmed by postoperative pathology were randomly separated into training and validation cohorts (TC and VC). Multivariate analysis identified independent clinical and imaging MVI predictors. Radiomics features were obtained from images of 6 MRI sequences at 4 different VOIs. The least absolute shrinkage and selection operator algorithm was performed to enable the derivation of robust and effective radiomics features. Then, the best three sequences and the optimal VOI were obtained through comparison. The MVI prediction nomogram combined the independent predictors and optimal radiomics features, and its performance was evaluated via the receiver operating characteristics, calibration, and decision curves.

Results

Tumor size and intrahepatic ductal dilatation are independent MVI predictors. Radiomics features extracted from the best three sequences (T1WI-D, T1WI, DWI) with VOI10mm (including tumor and 10 mm peritumoral region) showed the best predictive performance, with AUCTC = 0.987 and AUCVC = 0.859. The MVI prediction nomogram obtained excellent prediction efficacy in both TC (AUC = 0.995, 95%CI 0.987–1.000) and VC (AUC = 0.867, 95%CI 0.798–0.921) and its clinical significance was further confirmed by the decision curves.

Conclusion

A nomogram combining tumor size, intrahepatic ductal dilatation, and the radiomics model of MRI multi-sequence fusion at VOI10mm may be a predictor of preoperative MVI status in ICC patients.
Subjektivierungsprozesse neu zugewanderter Jugendlicher im Kontext schulischer Übergänge – Potenziale eines methodenpluralen Zugangs
Authors: Fritzsche Bettina , Warkentin Stephanie
Published: September 2023

Im Sinne einer subjektivierungstheoretisch ausgerichteten Übergangsforschung werden in diesem Beitrag organisationale Praktiken schulischer Übergänge und die mit diesen verbundenen Formen der Adressierung neu zugewanderter Jugendlicher zu deren subjektiv erlebten Handlungsmöglichkeiten oder auch -beschränkungen in Beziehung gesetzt. Ziel ist es hierbei, das subjektivierende Potenzial einer organisationalen Gestaltung schulischer Übergänge zu erfassen. Untersuchungsgegenstand sind die bildungsbiographisch höchst relevanten schulischen Übergänge neu zugewanderter Jugendlicher. Unter Bezug auf eine laufende ethnographische Studie werden anhand eines Fallbeispiels Subjektivierungsprozesse nachgezeichnet, die sich im Spannungsfeld zwischen einer sozialisationsbedingt erworbenen bildungsaffinen meritokratischen Haltung und machtvollen organisationalen Zuweisungspraktiken vollziehen. Auf dieser Grundlage wird abschließend auch auf das spezifische Potenzial des gewählten methoden- und perspektivenpluralen Zuganges für eine Analyse des Zusammenspiels einer strukturellen und organisationalen Ebene und der Subjektebene bei Subjektivierungsprozessen eingegangen.
Körper-Interventionen in der psychodramatischen Paar- und Sexualtherapie
Authors: Suchanek Gilbert
Published: September 2023

Ausgehend von den Ergebnissen einer deskriptiven Studie zum Thema „Psychodrama-Körperpsychotherapie – Arbeit mit somatischen Rollen im Psychodrama“ (Suchanek 2022) wird in diesem Artikel der Zeitschrift für Psychodrama und Soziometrie die Verwendung von körperbezogenen Interventionen in der psychodramatischen Paar- und Sexualtherapie thematisiert. Vorgestellt werden ein Überblick über die häufigsten Anwendungsbereiche, sowie ein Modell zur Differenzierung zwischen Anwärmtechniken und vertiefter Prozessarbeit in der Arbeit mit somatischen Rollen.
Radicalizing Hope
Authors: Chapman Michael , Komesaroff Paul
Published: September 2023

The race against COVID-19 has been intense and painful and many of us are now looking for a way to move on. We may try to seize a degree of comfort and security by convincing ourselves that we are among the “fittest”—that is, among those who have managed to survive—who can now hope for a “new-normal” time, relatively unscathed. But this isn’t what we should be hoping for. Our world, and ourselves, will never be free of COVID-19 or its insidious effects. COVID-19, like climate change, is a threat multiplier and the challenges it has raised are now indelibly engraved in our vulnerable, interconnected lives. Rather than vainly hoping for a return to an imaginary, erstwhile “normal” what we need is something more fundamental: a new version of hope that embraces a courage to learn what we need to do, to enable us to live a future to which we aspire. Perhaps counter-intuitively, we need to accept that the COVID-19 experience has already changed us deeply and hope that we can learn from this and from the future changes that the pandemic will give rise to. We need to radicalize our responses to the challenges, enabling ourselves to learn new lessons about old but increasingly pertinent topics, such as the realities of human fragility, and inter-connection.
A causal hidden Markov model for assessing effects of multiple direct mail campaigns
Authors: Pennoni Fulvia , Paas Leonard J. , Bartolucci Francesco
Published: September 2023

We propose assessing the causal effects of a dynamic treatment in a longitudinal observational study, given observed confounders under suitable assumptions. The causal hidden Markov model is based on potential versions of discrete latent variables, and it accounts for the estimated propensity to be assigned to each treatment level over time using inverse probability weighting. Estimation of the model parameters is carried out through a weighted maximum log-likelihood approach. Standard errors for the parameter estimates are provided by nonparametric bootstrap. The proposal is validated through a simulation study aimed at comparing different model specifications. As an illustrative example, we consider a marketing campaign conducted by a large European bank over time on its customers. Findings provide straightforward managerial implications.
Brain Sagging Dementia
Authors: Lashkarivand Aslan , Eide Per Kristian
Published: September 2023

Purpose of Review

Brain sagging dementia (BSD) is a rare but devastating form of early-onset dementia characterized by intracranial hypotension and behavioral changes resembling behavioral variant frontotemporal dementia. This review aims to provide a comprehensive overview of BSD, highlighting its pathomechanism, diagnostic tools, and available treatment options.

Recent Findings

BSD exhibits a complex clinical manifestation with insidious onset and gradual progression of behavioral disinhibition, apathy, inertia, and speech alterations. Additionally, patients may exhibit brainstem and cerebellar signs such as hypersomnolence and gait disturbance. Although headaches are common, they may not always demonstrate typical orthostatic features. Recent radiological advances have improved the detection of CSF leaks, enabling targeted treatment and favorable outcomes.

Understanding the pathomechanism and available diagnostic tools for BSD is crucial for a systematic approach to timely diagnosis and treatment of this reversible form of early-onset dementia, as patients often endure a complex and lengthy clinical course.
An updated algorithm for an effective choice of continuous glucose monitoring for people with insulin-treated diabetes
Authors: Maiorino Maria Ida , Buzzetti Raffaella , Irace Concetta , Laviola Luigi , Napoli Nicola , Pitocco Dario , Esposito Katherine ,   on behalf of LIVE CGM working group
Published: September 2023

Purpose

Continuous Glucose Monitoring (CGM) is a key tool for insulin-treated people with diabetes (PwD). CGM devices include both real-time CGM (rtCGM) and intermittently scanned CGM (isCGM), which are associated with an improvement of glucose control and less hypoglycemia in clinical trials of people with type 1 and type 2 diabetes.

Methods

This is an expert position to update a previous algorithm on the most suitable choice of CGM for insulin-treated PwD in light of the recent evidence and clinical practice.

Results

We identified six different clinical scenarios, including type 1 diabetes, type 2 diabetes, pregnancy on intensive insulin therapy, regular physical exercise, new onset of diabetes, and frailty. The use of rtCGM or isCGM is suggested, on the basis of the predominant clinical issue, as suboptimal glucose control or disabling hypoglycemia, regardless of baseline HbA1c or individualized HbA1c target.

Conclusion

The present algorithm may help to select the best CGM device based on patients’ clinical characteristics, needs and clinical context, offering a further opportunity of a “tailored” therapy for people with insulin-treated diabetes.
ANCA detection with solid phase chemiluminescence assay: diagnostic and severity association in vasculitis
Authors: Renuncio-García Mónica , Calvo-Río Vanesa , Benavides-Villanueva Fabricio , Al Fazazi Salma , Rodríguez-Vidriales María , Escagedo-Cagigas Clara , Martín-Penagos Luis , Irure-Ventura Juan , López-Hoyos Marcos , Blanco Ricardo
Published: September 2023

ANCA-associated vasculitis (AAV) comprises a group of necrotizing vasculitis that mainly affects small- and medium-sized vessels. Serum anti-neutrophil cytoplasmic antibodies (ANCA), mainly anti-myeloperoxidase (anti-MPO) and anti-proteinase 3 (anti-PR3), levels may correlate to severity, prognosis, and recurrence of the disease. A retrospective analysis of 101 patients with MPO-positive and 54 PR3-positive vasculitis was performed, using laboratory established cut-off value, measured by chemiluminescence. Furthermore, data of renal disease and pulmonary involvement were collected at vasculitis diagnosis, as well as the progress, requiring dialysis, transplant, or mortality. For anti-MPO antibodies with a diagnosis of vasculitis (n = 77), an area under the curve (AUC) was calculated (AUC = 0.8084), and a cut-off point of 41.5 IU/ml was determined. There were significant differences in anti-MPO levels between patients with renal or pulmonary dysfunction (n = 65) versus those without them (n = 36) (p = 0.0003), and a cut-off threshold of 60 IU/ml was established. For anti-PR3 antibodies with a diagnosis of vasculitis (n = 44), an area under the curve (AUC) was calculated (AUC = 0.7318), and a cut-off point of 20.5 IU/ml was determined. Significant differences in anti-PR3 levels were observed between those patients with renal or pulmonary dysfunction (n = 30) and those without them (n = 24) (p = 0.0048), and a cut-off threshold of 41.5 IU/ml was established. No significant differences between those patients who had a worse disease progression and those who did not were found for anti-MPO and anti-PR3. Anti-MPO and anti-PR3 levels at the moment of vasculitis diagnosis are related with disease severity but not with disease outcome or vasculitis recurrence.
Deep Reinforcement Learning for Robotic Approaching Behavior Influenced by User Activity and Disengagement
Authors: Raggioli Luca , D’Asaro Fabio Aurelio , Rossi Silvia
Published: September 2023

A robot intended to monitor human behavior must account for the user’s reactions to minimize his/her perceived discomfort. The possibility of learning user interaction preferences and changing the robot’s behavior accordingly may positively impact the perceived quality of the interaction with the robot. The robot should approach the user without causing any discomfort or interference. In this work, we contribute and implement a novel Reinforcement Learning (RL) approach for robot navigation toward a human user. Our implementation is a proof-of-concept that uses data gathered from real-world experiments to show that our algorithm works on the kind of data that it would run on in a realistic scenario. To the best of our knowledge, our work is one of the first attempts to provide an adaptive navigation algorithm that uses RL to account for non-deterministic phenomena.
Binge drinking leads to an oxidative and metabolic imbalance in skeletal muscle during adolescence in rats: endocrine repercussion
Authors: Romero-Herrera Inés , Nogales Fátima , Diaz-Castro Javier , Moreno-Fernandez Jorge , Gallego-Lopez María del Carmen , Ochoa Julio J. , Carreras Olimpia , Ojeda María Luisa
Published: September 2023

Binge drinking (BD) is an especially pro-oxidant model of alcohol consumption, mainly used by adolescents. It has recently been related to the hepatic IR-process. Skeletal muscle is known to be involved in insulin action and modulation through myokine secretion. However, there is no information on muscle metabolism and myokine secretion after BD exposure in adolescents. Two experimental groups of adolescent rats have been used: control and BD-exposed one. Oxidative balance, energy status and lipid, and protein metabolism have been analyzed in muscle, together with myokine serum levels (IL-6, myostatin, LIF, IL-5, fractalkine, FGF21, irisin, BDNF, FSTL1, apelin, FABP3, osteocrin, osteonectin (SPARC), and oncostatin). In muscle, BD affects the antioxidant enzyme balance leading to lipid and protein oxidation. Besides, it also increases the activation of AMPK and thus contributes to decrease SREBP1 and pmTOR and to increase FOXO3a expressions, promoting lipid and protein degradation. These alterations deeply affect the myokine secretion pattern. This is the first study to examine a general myokine response after exposure to BD. BD not only caused a detrimental imbalance in myokines related to muscle turnover, decreased those contributing to increase IR-process, decreased FST-1 and apelin and their cardioprotective function but also reduced the neuroprotective BDNF. Consequently, BD leads to an important metabolic and energetic disequilibrium in skeletal muscle, which contributes to exacerbate a general IR-process.
Role of land cover in Finland’s greenhouse gas emissions
Authors: Holmberg Maria , Junttila Virpi , Schulz Torsti , Grönroos Juha , Paunu Ville-Veikko , Savolahti Mikko , Minunno Francesco , Ojanen Paavo , Akujärvi Anu , Karvosenoja Niko , Kortelainen Pirkko , Mäkelä Annikki , Peltoniemi Mikko , Petäjä Jouko , Vanhala Pekka , Forsius Martin
Published: September 2023

We present regionally aggregated emissions of greenhouse gases (GHG) from five land cover categories in Finland: artificial surfaces, arable land, forest, waterbodies, and wetlands. Carbon (C) sequestration to managed forests and unmanaged wetlands was also assessed. Models FRES and ALas were applied for emissions (CO2, CH4, N2O) from artificial surfaces and agriculture, and PREBAS for forest growth and C balance. Empirical emission coefficients were used to estimate emissions from drained forested peatland (CH4, N2O), cropland (CO2), waterbodies (CH4, CO2), peat production sites and undrained mires (CH4, CO2, N2O). We calculated gross emissions of 147.2 ± 6.8 TgCO2eq yr−1 for 18 administrative units covering mainland Finland, using data representative of the period 2017–2025. Emissions from energy production, industrial processes, road traffic and other sources in artificial surfaces amounted to 45.7 ± 2.0 TgCO2eq yr−1. The loss of C in forest harvesting was the largest emission source in the LULUCF sector, in total 59.8 ± 3.3 TgCO2eq yr−1. Emissions from domestic livestock production, field cultivation and organic soils added up to 12.2 ± 3.5 TgCO2eq yr−1 from arable land. Rivers and lakes (13.4 ± 1.9 TgCO2eq yr−1) as well as undrained mires and peat production sites (14.7 ± 1.8 TgCO2eq yr−1) increased the total GHG fluxes. The C sequestration from the atmosphere was 93.2 ± 13.7 TgCO2eq yr−1. with the main sink in forest on mineral soil (79.9 ± 12.2 TgCO2eq yr−1). All sinks compensated 63% of total emissions and thus the net emissions were 53.9 ± 15.3 TgCO2eq yr−1, or a net GHG flux per capita of 9.8 MgCO2eq yr−1.
Statistics education research at the school level in Australia and New Zealand: A 30-year journey
Authors: Callingham Rosemary , Watson Jane
Published: September 2023

The introduction of statistical concepts into school curricula in Australia and New Zealand in the early 1990s initiated an ongoing research program into the learning and teaching of statistics and probability in both countries. This paper reviews the contribution of Australian and New Zealand researchers to building statistical literacy at school, alongside international developments. From recognising how students develop understanding of specific statistical and probabilistic concepts, through teacher knowledge and beliefs for teaching statistics, to intervention studies and targeted teaching, the field of statistics education has grown and changed. Statistics and probability are now well established as part of the mathematics curriculum. The importance of linking statistical literacy and statistical understanding across the curriculum, as well as in STEM, has also begun to receive attention as other subjects have recognised the importance of data in their fields. Following a comprehensive review of the field in Australia and New Zealand, this paper then considers emerging areas of interest, such as new approaches to data visualisation, and suggests future research.
Head Acceleration Events in Male Community Rugby Players: An Observational Cohort Study across Four Playing Grades, from Under-13 to Senior Men
Authors: Bussey Melanie D. , Salmon Danielle , Romanchuk Janelle , Nanai Bridie , Davidson Peter , Tucker Ross , Falvey Eanna
Published: September 2023

Objectives

The aim of this study was to examine the cumulative head acceleration event (HAE) exposure in male rugby players from the Under-13 (U13) to senior club level over 4 weeks of matches and training during the 2021 community rugby season.

Methods

This prospective, observational cohort study involved 328 male rugby players. Players were representative of four playing grades: U13 (N = 60, age 12.5 ± 0.6 years), U15 (N = 100, age 14.8 ± 0.9 years), U19 (N = 78, age 16.9 ± 0.7 years) and Premier senior men (N = 97, age 22.5 ± 3.1 years). HAE exposure was tracked across 48 matches and 113 training sessions. HAEs were recorded using boil-and-bite instrumented mouthguards (iMGs). The study assessed the incidence and prevalence of HAEs by ages, playing positions, and session types (match or training).

Results

For all age grades, weekly match HAE incidence was highest at lower magnitudes (10–29 g). Proportionally, younger players experienced higher weekly incidence rates during training. The U19 players had 1.36 times the risk of high-magnitude (> 30 g) events during matches, while the U13 players had the lowest risk compared with all other grades. Tackles and rucks accounted for the largest HAE burden during matches, with forwards having 1.67 times the risk of > 30 g HAEs in rucks compared with backs.

Conclusions

This study provides novel data on head accelerations during rugby matches and training. The findings have important implications for identifying populations at greatest risk of high cumulative and acute head acceleration. Findings may guide training load management and teaching of skill execution in high-risk activities, particularly for younger players who may be exposed to proportionally more contact during training and for older players during matches.
Robust pedestrian tracking in video sequences using an improved STF module
Authors: Yang Hongtao , Tang Yuchen , Yu Weibo , Li Xiulan , Zhang Peng
Published: September 2023

Object tracking technology based on image processing has made great progress recently. Based on the track-by-detection framework, the tracking algorithms are often combined with deep neural networks to perform online target tracking. However, existing motion models assume linearity and are sensitive to sudden changes in trajectories due to occlusion, overlap, or other detection issues. In this paper, we modified the existing object tracking algorithms and introduced a strong tracking filter (STF) module to the track-by-detection framework for solving the sudden change problem of the target. The STF module is designed to have a strong ability to track sudden changes by orthogonalizing the residual sequence. When the trajectory of the target is stable, the STF module returns to the inactive state, behaving similarly to tracking algorithms that follow conventional linear models. Experimental results on a public pedestrian tracking dataset show that the proposed method improves tracking performance on various metrics, including the ability to rematch missed trajectories. Moreover, compared with existing algorithms, the proposed method exhibits strong stability under noisy conditions.
Automatic Segmentation of Head and Neck Cancer from PET-MRI Data Using Deep Learning
Authors: Liedes Joonas , Hellström Henri , Rainio Oona , Murtojärvi Sarita , Malaspina Simona , Hirvonen Jussi , Klén Riku , Kemppainen Jukka
Published: September 2023

Purpose

Head and neck squamous cell carcinoma (HNSCC) is one of the most common cancer types globally. Due to the complex anatomy of the region, diagnosis and treatment is challenging. Early diagnosis and treatment are important, because advanced and recurrent HNSCC have a poor prognosis. Robust and precise tools are needed to help diagnose HNSCC reliably in its early stages. The aim of this study was to assess the applicability of a convolutional neural network in detecting and auto-delineating HNSCC from PET-MRI data.

Methods

2D U-net models were trained and tested on PET, MRI, PET-MRI and augmented PET-MRI data from 44 patients diagnosed with HNSCC. The scans were taken 12 weeks after chemoradiation therapy with a curative intention. A proportion of the patients had follow-up scans which were included in this study as well, giving a total of 62 PET-MRI scans. The scans yielded a total of 178 PET-MRI slices with cancer. A corresponding number of negative slices were chosen randomly yielding a total of 356 slices. The data was divided into training, validation and test sets (n = 247, n = 43 and n = 66 respectively). Dice score was used to evaluate the segmentation accuracy. In addition, the classification capabilities of the models were assessed.

Results

When true positive segmentations were considered, the mean Dice scores for the test set were 0.79, 0.84 and 0.87 for PET, PET-MRI and augmented PET-MRI, respectively. Classification accuracies were 0.62, 0.71 and 0.65 for PET, PET-MRI and augmented PET-MRI, respectively. The MRI based model did not yield segmentation results. A statistically significant difference was found between the PET-MRI and PET models (p = 0.008).

Conclusion

Automatic segmentation of HNSCC from the PET-MRI data with 2D U-nets was shown to give sufficiently accurate segmentations.
Laboratory demonstration of the impact of weak interfaces and layered rock properties on hydraulic fracture containment and height growth
Authors: Lu Qiao , EI-Fayoumi Amr , Adachi Jose , Zaki Karim , Bunger Andrew
Published: September 2023

Hydraulic fracturing and waterflooding are both widely applied methods for improving the recovery of oil and gas resources. These methods have increasing commonality because many waterfloods are being carried out at high enough pressure to generate hydraulic fractures. Even so, it is challenging for engineers to make an optimal wellbore pressure design for layered and otherwise complex underground formations. An overly aggressive injection pressure may lead to uncontrollable fracture height growth into non-producing layers adjacent to the reservoir. In contrast, when using classical but highly simplified height growth models, the pressure limits can be far too conservative which may lead to lower recovery rates and inefficient use of resources invested in developing producing reservoirs. Therefore, it is necessary to investigate the mechanism of fracture height growth while considering the coupling effect from multiple dominated factors. This research contributes an experimental approach to evaluating the role of stresses, weak interfaces, and mechanical properties of a three-layer system in promoting or containing hydraulic fracture height growth from a central reservoir into neighboring barrier layers. In all cases, the experiments agree that the pressure required to induce substantial height growth exceeds the stress applied to the barrier layers and is far above classical predictions. Additionally, when the reservoir layer is softer than the barriers, the containment is sustained to even higher pressures than for layers with similar material properties. Finally, the experiments show that permeability of the barrier layer can induce a more sudden transition to uncontrolled height growth when fracture reaches the bedding interfaces. Hydraulic fracture height growth is mitigated by weak interfaces between layers. Unstable height growth typically requires fluid pressure to exceed the in-situ stress in the bounding layer(s). Contrasting layer stiffness and permeability often leads to further mitigation of height growth.
New insights into the chemical activation of lignins and tannins using K2CO3—a combined thermoanalytical and structural study
Authors: Guizani Chamseddine , Widsten Petri , Siipola Virpi , Paalijärvi Riina , Berg Jonathan , Pasanen Antti , Kalliola Anna , Torvinen Katariina
Published: September 2023

Engineering of activated carbons (ACs) through chemical activation of organic precursors has been extensively studied for a wide variety of biopolymers, biomasses, wastes and other fossil-based precursors. Despite huge efforts to engineer evermore performant and sustainable ACs, “searching-for-the-best-recipe” type of studies are more the rule than the exception in the published literature. Emerging AC applications related to energy and gas storage require strict control of the AC properties and a better understanding of the fundamentals underlying their engineering. In this study, we provide new insights into the K2CO3 chemical activation of plant-based polyphenols—lignins and tannins—through careful thermoanalytical and structural analyses. We showed for the the first time that the reactivity of polyphenols during K2CO3 chemical activation depends remarkably on their purity and structural properties, such as their content of inorganics, OH functionalities and average molecular weight. We also found that the burn-off level is proportional to the K2CO3/lignin impregnation ratio (IR), but only within a certain range—high impregnation ratios are not needed, unlike often reported in the literature. Furthermore, we showed for the first time that the K2CO3 chemical activation of different carbon surfaces from lignins and tannins can be modelled using simple global solid-state decomposition kinetics. The identified activation energies lay in the range of values reported for heterogenous gas-carbon surface gasification reactions (O2-C, H2O-C, or CO2-C) in which the decomposition of C(O) surface complexes is the common rate-limiting step.
Comprehensive Behavioral (ComB) Treatment of Body-Focused Repetitive Behaviors
Authors: Mansueto Charles S. , Mouton-Odum Suzanne , Goldfinger Golomb Ruth
Published: September 2023

More common than was previously believed, body-focused repetitive behaviors (BFRBs) affect as many as 1 in 20 people. Written by the experts who created and developed the ComB Model, it provides a practical and user-friendly manual for therapists on an effective, individualized treatment approach for BFRBs. It features the most up-to-date information on BRFBs and guides clinicians from conceptualization of the problem through all aspects of clinical treatment. The internal and external influences for hair-pulling and skin-picking disorders are addressed thoroughly and from a functional analytical perspective. A valuable case example illustrates exactly how specific concepts and techniques are implemented in therapy. Chapters cover preparing the client for treatment, encouraging healthy approaches to hair/skin care, preventing relapse, dealing with co-morbidities, trouble-shooting impediments to therapy and modifying treatment for children, adolescents, and their families. This book is the best single resource available for mental health professionals for conceptualizing and treating BFRBs.