bluuebunny/crossref_metadata_2025_split huggingface dataset:
A self-supervised vision transformer to predict survival from histopathology in renal cell carcinoma
Authors: Wessels Frederik , Schmitt Max , Krieghoff-Henning Eva , Nientiedt Malin , Waldbillig Frank , Neuberger Manuel , Kriegmair Maximilian C. , Kowalewski Karl-Friedrich , Worst Thomas S. , Steeg Matthias , Popovic Zoran V. , Gaiser Timo , von Kalle Christof , Utikal Jochen S. , Fröhling Stefan , Michel Maurice S. , Nuhn Philipp , Brinker Titus J. 
Published: June 2023

AbstractPurposeTo develop and validate an interpretable deep learning model to predict overall and disease-specific survival (OS/DSS) in clear cell renal cell carcinoma (ccRCC).MethodsDigitised haematoxylin and eosin-stained slides from The Cancer Genome Atlas were used as a training set for a vision transformer (ViT) to extract image features with a self-supervised model called DINO (self-distillation with no labels). Extracted features were used in Cox regression models to prognosticate OS and DSS. Kaplan–Meier for univariable evaluation and Cox regression analyses for multivariable evaluation of the DINO-ViT risk groups were performed for prediction of OS and DSS. For validation, a cohort from a tertiary care centre was used.ResultsA significant risk stratification was achieved in univariable analysis for OS and DSS in the training (n = 443, log rank test,p < 0.01) and validation set (n = 266,p < 0.01). In multivariable analysis, including age, metastatic status, tumour size and grading, the DINO-ViT risk stratification was a significant predictor for OS (hazard ratio [HR] 3.03; 95%-confidence interval [95%-CI] 2.11–4.35;p < 0.01) and DSS (HR 4.90; 95%-CI 2.78–8.64;p < 0.01) in the training set but only for DSS in the validation set (HR 2.31; 95%-CI 1.15–4.65;p = 0.02). DINO-ViT visualisation showed that features were mainly extracted from nuclei, cytoplasm, and peritumoural stroma, demonstrating good interpretability.ConclusionThe DINO-ViT can identify high-risk patients using histological images of ccRCC. This model might improve individual risk-adapted renal cancer therapy in the future.
The unremarkable alveolar epithelial glycocalyx: a thorium dioxide-based electron microscopic comparison after heparinase or pneumolysin treatment
Authors: Timm Sara , Lettau Marie , Hegermann Jan , Rocha Maria Linda , Weidenfeld Sarah , Fatykhova Diana , Gutbier Birgitt , Nouailles Geraldine , Lopez-Rodriguez Elena , Hocke Andreas , Hippenstiel Stefan , Witzenrath Martin , Kuebler Wolfgang M. , Ochs Matthias 
Published: June 2023

AbstractRecent investigations analyzed in depth the biochemical and biophysical properties of the endothelial glycocalyx. In comparison, this complex cell-covering structure is largely understudied in alveolar epithelial cells. To better characterize the alveolar glycocalyx ultrastructure, unaffected versus injured human lung tissue explants and mouse lungs were analyzed by transmission electron microscopy. Lung tissue was treated with either heparinase (HEP), known to shed glycocalyx components, or pneumolysin (PLY), the exotoxin of Streptococcus pneumoniae not investigated for structural glycocalyx effects so far. Cationic colloidal thorium dioxide (cThO2) particles were used for glycocalyx glycosaminoglycan visualization. The level of cThO2 particles orthogonal to apical cell membranes (≙ stained glycosaminoglycan height) of alveolar epithelial type I (AEI) and type II (AEII) cells was stereologically measured. In addition, cThO2 particle density was studied by dual-axis electron tomography (≙ stained glycosaminoglycan density in three dimensions). For untreated samples, the average cThO2 particle level was ≈ 18 nm for human AEI, ≈ 17 nm for mouse AEI, ≈ 44 nm for human AEII and ≈ 35 nm for mouse AEII. Both treatments, HEP and PLY, resulted in a significant reduction of cThO2 particle levels on human and mouse AEI and AEII. Moreover, a HEP- and PLY-associated reduction in cThO2 particle density was observed. The present study provides quantitative data on the differential glycocalyx distribution on AEI and AEII based on cThO2 and demonstrates alveolar glycocalyx shedding in response to HEP or PLY resulting in a structural reduction in both glycosaminoglycan height and density. Future studies should elucidate the underlying alveolar epithelial cell type-specific distribution of glycocalyx subcomponents for better functional understanding.
Changes in diagnosis and operative treatment of insulinoma over two decades
Authors: Wiese D. , Humburg F. G. , Kann P. H. , Rinke A. , Luster M. , Mahnken A. , Bartsch D. K. 
Published: June 2023

Abstract
Purpose
Most insulinomas are small solitary, benign neoplasms. Imaging and surgical techniques improved over the last 20 years. Thus, the aim of the present study was to analyze changes in diagnosis and surgery of insulinoma patients in a referral center over two decades.

Methods
Operated patients with a histologically proven insulinoma were retrieved from a prospective database. Clinico-pathological characteristics and outcomes were retrospectively analyzed with regard to the time periods 2000–2010 (group 1) and 2011–2020 (group 2).

Results
Sixty-one of 202 operated patients with pNEN had an insulinoma, 37 (61%) in group 1 and 24 (39%) in group 2. Of those 61 insulinomas, 49 (80%) were sporadic benign, 8 (13%) benign MEN1-associated insulinomas, and 4 (7%) sporadic malignant insulinomas. In 35 of 37 (95%) patients of group 1 and all patients of group 2, the insulinoma was preoperatively identified by imaging. The most sensitive imaging modality was endoscopic ultrasound (EUS) with correctly diagnosed and localized insulinomas in 89% of patients in group 1 and 100% in group 2. In group 1, significantly less patients were operated via minimally invasive approach compared to group 2 (19% (7/37) vs. 50% (12/24), p = 0.022). Enucleation was the most frequently performed operation (31 of 61, 51%), followed by distal resection (15 of 61, 25%) without significant differences between groups 1 and 2. The rate of relevant postoperative complications was not different between groups 1 and 2 (24% vs. 21%, p = 0.99). Two patients with benign insulinoma (1 out of each group) experienced disease recurrence and underwent a second resection. After a median follow-up of 134 (1–249) months, however, all 57 (100%) patients with benign insulinoma and 3 out of 4 patients with malignant insulinoma had no evidence of disease.

Conclusion
Insulinoma can be preoperatively localized in almost all patients, allowing for a minimally invasive, parenchyma-sparing resection in selected patients. The long-term cure rate is excellent.
COVID-19-related adolescent mortality and morbidity in nineteen European countries
Authors: Cortés Jesus , Aguiar Pedro Manuel Vargues , Ferrinho Paulo 
Published: June 2023

AbstractPrior to the COVID-19 pandemic, adolescents in most countries experienced a syndemic of malnutrition, obesity, deprivation, mental health problems, inequalities, and the effects of climate change. Today, other factors have added to this burden during the pandemic, and it is important to have an updated reflection. We aimed to assess the risk and protective factors for COVID-19-related adolescent mortality and morbidity in the European region. Three double models were fitted to analyze the relationship between different factors with the number of diagnosed cases and deaths. The 1a and 1b use a multiple Poisson regression. The 2a and 2b are optimized models that use the same variables as prior models but with backward selection with a p value < 0.05 as the limit. Finally, the 3a and 3b models (backward stepwise multivariable Poisson regression) include the variable “fully vaccinated.” All models used the at-risk population (15–19 years or total population) as a regression covariate (offset). Increased access to quality healthcare (IRR 0.68; CI 0.55–0.84), increased private sector involvement (IRR 0.86; CI 0.82–0.90), Gini coefficient (IRR 0.93; CI 0.88–0.99), and full vaccination (IRR 0.94; CI 0.90–0.99) represent protective factors of COVID-19 mortality in this population. Additionally, a positive association between pollution and mortality was found. Conclusion: Being fully vaccinated and having access to quality medical care are protective factors against COVID-19 mortality in this age group. Interestingly, the more the pollution, the greater the risk of dying from COVID-19. We stress the great importance of coordination between the public and private sectors to address crises such as the current one.

What is Known:

• Compared to other age groups, adolescents have been little studied, and most studies focused on mental health during the COVID-19 pandemic.

What is New:

• In this study, we show how in 19 European countries, different factors interact, such as socio-demographic, environmental, health system, and control measures with morbidity and mortality by COVID-19, in a very little studied age group as teenagers.
Process model analysis of parenchyma sparing laparoscopic liver surgery to recognize surgical steps and predict impact of new technologies
Authors: Gholinejad Maryam , Edwin Bjørn , Elle Ole Jakob , Dankelman Jenny , Loeve Arjo J. 
Published: June 2023

Abstract
Background
Surgical process model (SPM) analysis is a great means to predict the surgical steps in a procedure as well as to predict the potential impact of new technologies. Especially in complicated and high-volume treatments, such as parenchyma sparing laparoscopic liver resection (LLR), profound process knowledge is essential for enabling improving surgical quality and efficiency.

Methods
Videos of thirteen parenchyma sparing LLR were analyzed to extract the duration and sequence of surgical steps according to the process model. The videos were categorized into three groups, based on the tumor locations. Next, a detailed discrete events simulation model (DESM) of LLR was built, based on the process model and the process data obtained from the endoscopic videos. Furthermore, the impact of using a navigation platform on the total duration of the LLR was studied with the simulation model by assessing three different scenarios: (i) no navigation platform, (ii) conservative positive effect, and (iii) optimistic positive effect.

Results
The possible variations of sequences of surgical steps in performing parenchyma sparing depending on the tumor locations were established. The statistically most probable chain of surgical steps was predicted, which could be used to improve parenchyma sparing surgeries. In all three categories (i–iii) the treatment phase covered the major part (~ 40%) of the total procedure duration (bottleneck). The simulation results predict that a navigation platform could decrease the total surgery duration by up to 30%.

Conclusion
This study showed a DESM based on the analysis of steps during surgical procedures can be used to predict the impact of new technology. SPMs can be used to detect, e.g., the most probable workflow paths which enables predicting next surgical steps, improving surgical training systems, and analyzing surgical performance. Moreover, it provides insight into the points for improvement and bottlenecks in the surgical process.
Estimation of coconut maturity based on fuzzy neural network and sperm whale optimization
Authors: El-Shafeiy Engy , Abohany Amr A. , Elmessery Wael M. , El-Mageed Amr A. Abd 
Published: June 2023

AbstractCoconut water is the clear liquid found inside coconuts, famous for rehydrating after exercise or while suffering from a minor sickness. The essential issue tackled in this paper is how to estimate the appropriate stage of maturity of coconut water, which is a time-consuming task in the beverage industry since, as the coconut age increases, the coconut water flavor varies. Accordingly, to handle this issue, an adaptive model based on Fuzzy Neural Network and Sperm Whale Optimization, dubbed FNN–SWO, is developed to assess coconut water maturity. The Sperm Whale Optimization (SWO) algorithm is a meta-heuristic optimization algorithm. It is embedded in this model along with neural networks and fuzzy techniques (FNN system), which can be employed as an essential building block in the beverage industry. The proposed FNN–SWO model is trained and tested utilizing fuzzy rules with an adaptive network. In contrast, the SWO algorithm is adopted to determine the optimal weights for the fuzzy rules. Three subsets of data divided according to three levels of coconut water maturity-tender, mature, and very mature, are used to validate the combined FNN–SWO model. Depending on these three subsets of data, a comparison of the proposed FNN–SWO model has been conducted against a set of the most common conventional techniques. These techniques include Support Vector Machine, Naïve Bayes, FNN, Artificial Neural Network, as well as their embedding with other meta-heuristic optimization algorithms. For various key performance indicators, such as recall, F1-score, specificity, and accuracy, the proposed FNN–SWO model provides the best prediction outcomes compared to the current time-consuming techniques. The dominance of the proposed FNN–SWO model is evident from the final findings compared to its time-consuming peers for estimating coconut water maturity on time. As a result, the proposed FNN–SWO model is an effective heuristic for locating optimal solutions to classification problems. It can thereby be reassuringly applicable to other similar prediction problems. Additionally, it would benefit the scientific community interested in evaluating coconut water.
Optimal incompatible Korn–Maxwell–Sobolev inequalities in all dimensions
Authors: Gmeineder Franz , Lewintan Peter , Neff Patrizio 
Published: June 2023

AbstractWe characterise all linear maps $${\mathscr {A}}:\mathbb R^{n\times n}\rightarrow \mathbb R^{n\times n}$$

A
:

R

n
×
n

→

R

n
×
n

such that, for $$1\le p<n$$

1
≤
p
<
n

, $$\begin{aligned} \left\Vert P\right\Vert _{{\text {L}}^{p^{*}}(\mathbb R^{n})}\le c\,\Big (\left\Vert {\mathscr {A}}[P]\right\Vert _{{\text {L}}^{p^{*}}(\mathbb R^{n})}+\left\Vert {\text {Curl}}P\right\Vert _{{\text {L}}^{p}(\mathbb R^{n})} \Big ) \end{aligned}$$

P

L

p

∗

(

R
n

)

≤
c

(

A
[
P
]

L

p

∗

(

R
n

)

+

Curl
P

L

p

(

R
n

)

)

holds for all compactly supported $$P\in {\text {C}}_{c}^{\infty }(\mathbb R^{n};\mathbb R^{n\times n})$$

P
∈

C

c

∞

(

R
n

;

R

n
×
n

)

, where $${\text {Curl}}P$$

Curl
P

displays the matrix curl. Being applicable to incompatible, that is, non-gradient matrix fields as well, such inequalities generalise the usual Korn-type inequalities used e.g. in linear elasticity. Different from previous contributions, the results gathered in this paper are applicable to all dimensions and optimal. This particularly necessitates the distinction of different combinations between the ellipticities of $${\mathscr {A}}$$
A
, the integrability p and the underlying space dimensions n, especially requiring a finer analysis in the two-dimensional situation.
Temporal changes in the associations between diagnosed psychiatric disorders and dropping out of school early
Authors: Ringbom Ida , Suvisaari Jaana , Sourander Andre , Gissler Mika , Gyllenberg David 
Published: June 2023

AbstractPsychiatric problems are risk markers for poor educational attainment. The number of adolescents receiving treatment has increased. We investigated whether the association between psychiatric problems in early adolescence and dropping out of school had changed. We used the register-based 1987 and 1997 Finnish Birth Cohort studies, which include all live births in Finland. Hospital districts with incomplete records were excluded, leaving 25,421 participants born in 1987 and 32,025 born in 1997. The main outcome was not having applied for secondary education by the year the cohort members turned 18. Our main predictors were psychiatric and neurodevelopmental disorders diagnosed by specialized services during 1998–2003 and 2008–2013, when the cohort members were 10–16 years old. We found that 511 (2.0) of subjects born in 1987 and 499 (1.6%) born in 1997 dropped out of school. Having any diagnosis at 10–16 of age was associated with dropping out of school early in both cohorts: 3.9% in 1987 and 4.8% in 1997. The highest proportions were in the subgroup with autism spectrum disorders (ASD), 19.4% in 1987 and 16.2% in 1997. Dropping out early increased among adolescents diagnosed with any psychiatric or neurodevelopmental disorder, from 3.9 to 4.8%, with the clearest increase for learning disabilities, from 3.4 to 9.0%. Dropping out decreased for those with depression, from 4.5 to 2.1%. Adolescents with psychiatric and especially neurodevelopmental disorders, need effective interventions to prevent them dropping out of school early. Increased detection of psychopathology did not result in decreased dropout rates.
A review of natural language processing in contact centre automation
Authors: Shah Shariq , Ghomeshi Hossein , Vakaj Edlira , Cooper Emmett , Fouad Shereen 
Published: June 2023

AbstractContact centres have been highly valued by organizations for a long time. However, the COVID-19 pandemic has highlighted their critical importance in ensuring business continuity, economic activity, and quality customer support. The pandemic has led to an increase in customer inquiries related to payment extensions, cancellations, and stock inquiries, each with varying degrees of urgency. To address this challenge, organizations have taken the opportunity to re-evaluate the function of contact centres and explore innovative solutions. Next-generation platforms that incorporate machine learning techniques and natural language processing, such as self-service voice portals and chatbots, are being implemented to enhance customer service. These platforms offer robust features that equip customer agents with the necessary tools to provide exceptional customer support. Through an extensive review of existing literature, this paper aims to uncover research gaps and explore the advantages of transitioning to a contact centre that utilizes natural language solutions as the norm. Additionally, we will examine the major challenges faced by contact centre organizations and offer recommendations for overcoming them, ultimately expediting the pace of contact centre automation.
Incurring detriments of unplanned readmission to the intensive care unit following surgery for brain metastasis
Authors: Schweppe Justus August , Potthoff Anna-Laura , Heimann Muriel , Ehrentraut Stefan Felix , Borger Valeri , Lehmann Felix , Schaub Christina , Bode Christian , Putensen Christian , Herrlinger Ulrich , Vatter Hartmut , Schäfer Niklas , Schuss Patrick , Schneider Matthias 
Published: June 2023

Abstract
Object
Postoperative intensive care unit (ICU) monitoring is a common regime after neurosurgical resection of brain metastasis (BM). In comparison, unplanned secondary readmission to the ICU after initial postoperative treatment course occurs in response to adverse events and might significantly impact patient prognosis. In the present study, we analyzed the potential prognostic implications of unplanned readmission to the ICU and aimed at identifying preoperatively collectable risk factors for the development of such adverse events.

Methods
Between 2013 and 2018, 353 patients with BM had undergone BM resection at the authors’ institution. Secondary ICU admission was defined as any unplanned admission to the ICU during the initial hospital stay. A multivariable logistic regression analysis was performed to identify preoperatively identifiable risk factors for unplanned ICU readmission.

Results
A total of 19 patients (5%) were readmitted to the ICU. Median overall survival (mOS) of patients with unplanned ICU readmission was 2 months (mo) compared to 13 mo for patients without secondary ICU admission (p<0.0001). Multivariable analysis identified “multiple BM” (p=0.02) and “preoperative CRP levels > 10 mg/dl” (p=0.01) as significant and independent predictors of secondary ICU admission.

Conclusions
Unplanned ICU readmission following surgical therapy for BM is significantly related to poor OS. Furthermore, the present study identifies routinely collectable risk factors indicating patients that are at a high risk for unplanned ICU readmission after BM surgery.
Past and future rainfall change in sub-regions of Victoria, Australia
Authors: Rauniyar Surendra P. , Power Scott B. 
Published: June 2023

AbstractWe examine rainfall variability and change in three sub-regions of the state of Victoria in Australia: the Murray Basin Victoria (MBVic), southeast Victoria (SEVic), and southwest Victoria (SWVic). These sub-regions represent three different hydrological super-catchments over Victoria and received average cool season rainfall for the 1997–2018 period, about 15%, 11%, and 8% less, respectively, than the 1900–1959 average. All three observed declines are shown to be very unusual in terms of historical variability. On analysing CMIP5 models under different forcing conditions (preindustrial, historical-all, historical-GHGs-only, historical-natural-only, RCP2.6, RCP4.5, and RCP8.5), we estimate that external forcing caused 30% of the observed drying in SWVic, 18% in MBVic, and 17% in SEVic. The external forcing contributions to the observed trend for the 1900–2018 period are estimated to be 56%, 17%, and 24% for SWVic, MBVic, and SEVic, respectively. Taken at face value, these figures suggest that only the 1900–2018 trend in SWVic was dominated by external forcing. Nearly all models underestimate the magnitude of the observed drying. This arises because models underestimate the magnitude of decadal variability, and because models might also underestimate externally forced drying, and/or the contribution of internal variability in the real world to the observed event was unusually large. By 2037, approximately 90% of the models simulate drying in SWVic, even under a low emissions scenario. Under a high emission scenario, the anthropogenically forced drying towards the late twenty-first century is so large in all three sub-regions that internal variability appears too small to offset it.
A combinatorial multi-armed bandit approach to correlation clustering
Authors: Gullo F. , Mandaglio D. , Tagarelli A. 
Published: June 2023

AbstractGiven a graph whose edges are assigned positive-type and negative-type weights, the problem of correlation clustering aims at grouping the graph vertices so as to minimize (resp. maximize) the sum of negative-type (resp. positive-type) intra-cluster weights plus the sum of positive-type (resp. negative-type) inter-cluster weights. In correlation clustering, it is typically assumed that the weights are readily available. This is a rather strong hypothesis, which is unrealistic in several scenarios. To overcome this limitation, in this work we focus on the setting where edge weights of a correlation-clustering instance are unknown, and they have to be estimated in multiple rounds, while performing the clustering. The clustering solutions produced in the various rounds provide a feedback to properly adjust the weight estimates, and the goal is to maximize the cumulative quality of the clusterings. We tackle this problem by resorting to the reinforcement-learning paradigm, and, specifically, we design for the first time a Combinatorial Multi-Armed Bandit (CMAB) framework for correlation clustering. We provide a variety of contributions, namely (1) formulations of the minimization and maximization variants of correlation clustering in a CMAB setting; (2) adaptation of well-established CMAB algorithms to the correlation-clustering context; (3) regret analyses to theoretically bound the accuracy of these algorithms; (4) design of further (heuristic) algorithms to have the probability constraint satisfied at every round (key condition to soundly adopt efficient yet effective algorithms for correlation clustering as CMAB oracles); (5) extensive experimental comparison among a variety of both CMAB and non-CMAB approaches for correlation clustering.