bluuebunny/crossref_metadata_2025 huggingface dataset:
Oblivious Online Monitoring for Safety LTL Specification via Fully Homomorphic Encryption
Authors: Banno Ryotaro , Matsuoka Kotaro , Matsumoto Naoki , Bian Song , Waga Masaki , Suenaga Kohei
Published: August 2022

In many Internet of Things (IoT) applications, data sensed by an IoT device are continuously sent to the server and monitored against a specification. Since the data often contain sensitive information, and the monitored specification is usually proprietary, both must be kept private from the other end. We propose a protocol to conduct oblivious online monitoring—online monitoring conducted without revealing the private information of each party to the other—against a safety LTL specification. In our protocol, we first convert a safety LTL formula into a DFA and conduct online monitoring with the DFA. Based on fully homomorphic encryption (FHE), we propose two online algorithms (Reverse and Block) to run a DFA obliviously. We prove the correctness and security of our entire protocol. We also show the scalability of our algorithms theoretically and empirically. Our case study shows that our algorithms are fast enough to monitor blood glucose levels online, demonstrating our protocol’s practical relevance.
RINO: Robust INner and Outer Approximated Reachability of Neural Networks Controlled Systems
Authors: Goubault Eric , Putot Sylvie
Published: August 2022

We present a unified approach, implemented in the RINO tool, for the computation of inner and outer-approximations of reachable sets of discrete-time and continuous-time dynamical systems, possibly controlled by neural networks with differentiable activation functions. RINO combines a zonotopic set representation with generalized mean-value AE extensions to compute under and over-approximations of the robust range of differentiable functions, and applies these techniques to the particular case of learning-enabled dynamical systems. The AE extensions require an efficient and accurate evaluation of the function and its Jacobian with respect to the inputs and initial conditions. For continuous-time systems, possibly controlled by neural networks, the function to evaluate is the solution of the dynamical system. It is over-approximated in RINO using Taylor methods in time coupled with a set-based evaluation with zonotopes. We demonstrate the good performances of RINO compared to state-of-the art tools Verisig 2.0 and ReachNN* on a set of classical benchmark examples of neural network controlled closed loop systems. For generally comparable precision to Verisig 2.0 and higher precision than ReachNN*, RINO is always at least one order of magnitude faster, while also computing the more involved inner-approximations that the other tools do not compute.
Data-Driven Invariant Learning for Probabilistic Programs
Authors: Bao Jialu , Trivedi Nitesh , Pathak Drashti , Hsu Justin , Roy Subhajit
Published: August 2022

Morgan and McIver’s weakest pre-expectation framework is one of the most well-established methods for deductive verification of probabilistic programs. Roughly, the idea is to generalize binary state assertions to real-valued expectations, which can measure expected values of probabilistic program quantities. While loop-free programs can be analyzed by mechanically transforming expectations, verifying loops usually requires finding an invariant expectation, a difficult task.

We propose a new view of invariant expectation synthesis as a regression problem: given an input state, predict the average value of the post-expectation in the output distribution. Guided by this perspective, we develop the first data-driven invariant synthesis method for probabilistic programs. Unlike prior work on probabilistic invariant inference, our approach can learn piecewise continuous invariants without relying on template expectations. We also develop a data-driven approach to learn sub-invariants from data, which can be used to upper- or lower-bound expected values. We implement our approaches and demonstrate their effectiveness on a variety of benchmarks from the probabilistic programming literature.
Sound and Complete Certificates for Quantitative Termination Analysis of Probabilistic Programs
Authors: Chatterjee Krishnendu , Goharshady Amir Kafshdar , Meggendorfer Tobias , Žikelić Đorđe
Published: August 2022

We consider the quantitative problem of obtaining lower-bounds on the probability of termination of a given non-deterministic probabilistic program. Specifically, given a non-termination threshold $$p \in [0, 1],$$

p
∈
[
0
,
1
]
,

we aim for certificates proving that the program terminates with probability at least $$1-p$$

1
-
p

. The basic idea of our approach is to find a terminating stochastic invariant, i.e. a subset $$ SI $$

SI

of program states such that (i) the probability of the program ever leaving $$ SI $$

SI

is no more than p, and (ii) almost-surely, the program either leaves $$ SI $$

SI

or terminates.

While stochastic invariants are already well-known, we provide the first proof that the idea above is not only sound, but also complete for quantitative termination analysis. We then introduce a novel sound and complete characterization of stochastic invariants that enables template-based approaches for easy synthesis of quantitative termination certificates, especially in affine or polynomial forms. Finally, by combining this idea with the existing martingale-based methods that are relatively complete for qualitative termination analysis, we obtain the first automated, sound, and relatively complete algorithm for quantitative termination analysis. Notably, our completeness guarantees for quantitative termination analysis are as strong as the best-known methods for the qualitative variant.

Our prototype implementation demonstrates the effectiveness of our approach on various probabilistic programs. We also demonstrate that our algorithm certifies lower bounds on termination probability for probabilistic programs that are beyond the reach of previous methods.
Example Guided Synthesis of Linear Approximations for Neural Network Verification
Authors: Paulsen Brandon , Wang Chao
Published: August 2022

Linear approximations of nonlinear functions have a wide range of applications such as rigorous global optimization and, recently, verification problems involving neural networks. In the latter case, a linear approximation must be hand-crafted for the neural network’s activation functions. This hand-crafting is tedious, potentially error-prone, and requires an expert to prove the soundness of the linear approximation. Such a limitation is at odds with the rapidly advancing deep learning field – current verification tools either lack the necessary linear approximation, or perform poorly on neural networks with state-of-the-art activation functions. In this work, we consider the problem of automatically synthesizing sound linear approximations for a given neural network activation function. Our approach is example-guided: we develop a procedure to generate examples, and then we leverage machine learning techniques to learn a (static) function that outputs linear approximations. However, since the machine learning techniques we employ do not come with formal guarantees, the resulting synthesized function may produce linear approximations with violations. To remedy this, we bound the maximum violation using rigorous global optimization techniques, and then adjust the synthesized linear approximation accordingly to ensure soundness. We evaluate our approach on several neural network verification tasks. Our evaluation shows that the automatically synthesized linear approximations greatly improve the accuracy (i.e., in terms of the number of verification problems solved) compared to hand-crafted linear approximations in state-of-the-art neural network verification tools. An artifact with our code and experimental scripts is available at: https://zenodo.org/record/6525186#.Yp51L9LMIzM.
"Image missing""Image missing"
Verifying Neural Networks Against Backdoor Attacks
Authors: Pham Long H. , Sun Jun
Published: August 2022

Neural networks have achieved state-of-the-art performance in solving many problems, including many applications in safety/security-critical systems. Researchers also discovered multiple security issues associated with neural networks. One of them is backdoor attacks, i.e., a neural network may be embedded with a backdoor such that a target output is almost always generated in the presence of a trigger. Existing defense approaches mostly focus on detecting whether a neural network is ‘backdoored’ based on heuristics, e.g., activation patterns. To the best of our knowledge, the only line of work which certifies the absence of backdoor is based on randomized smoothing, which is known to significantly reduce neural network performance. In this work, we propose an approach to verify whether a given neural network is free of backdoor with a certain level of success rate. Our approach integrates statistical sampling as well as abstract interpretation. The experiment results show that our approach effectively verifies the absence of backdoor or generates backdoor triggers.
AI Support for Accelerating Histopathological Slide Examinations of Prostate Cancer in Clinical Studies
Authors: Del Rio Mauro , Lianas Luca , Aspegren Oskar , Busonera Giovanni , Versaci Francesco , Zelic Renata , Vincent Per H. , Leo Simone , Pettersson Andreas , Akre Olof , Pireddu Luca
Published: August 2022

While studies in pathology are essential for the progress in the diagnostic and prognostic techniques in the field, pathologist time is becoming an increasingly scarce resource, and can indeed become the limiting factor in the feasibility of studies to be performed. In this work, we demonstrate how the Digital Pathology platform by CRS4, for supporting research studies in digital pathology, has been augmented by the addition of AI-based features to accelerate image examination to reduce the pathologist time required for clinical studies. The platform has been extended to provide computationally generated annotations and visual cues to help the pathologist prioritize high-interest image areas. The system includes an image annotation pipeline with DeepHealth-based deep learning models for tissue identification and prostate cancer identification. Annotations are viewed through the platform’s virtual microscope and can be controlled interactively (e.g., thresholding, coloring). Moreover, the platform captures inference provenance information and archives it as RO-Crate artifacts containing data and metadata required for reproducibility. We evaluate the models and the inference pipeline, achieving AUC of 0.986 and 0.969 for tissue and cancer identification, respectively, and verifying linear dependence of execution speed on image tissue content. Finally, we describe the ongoing clinical validation of the contribution, including preliminary results, and discuss feedback from clinical professionals regarding the overall approach.
Detection of Pulmonary Conditions Using the DeepHealth Framework
Authors: Carrión Salvador , López-Chilet Álvaro , Martínez-Bernia Javier , Coll-Alonso Joan , Chorro-Juan Daniel , Gómez Jon Ander
Published: August 2022

Medical diagnosis assisted by intelligent systems is an effective strategy to increase the efficiency of healthcare systems while reducing their costs. This work is focused on detecting pulmonary conditions from X-ray images using the DeepHealth framework. Our results suggest that it is possible to discriminate pulmonary conditions compatible with the COVID-19 disease from other conditions and healthy individuals. Hence, it could be stated that the DeepHealth framework is a suitable deep-learning software with which to perform reliable medical research. However, more medical data and research are still necessary to train deep learning models that could be trusted by medical personnel.
Einleitung
Authors: Kirchner Aljoscha
Published: August 2022

Es sind die großen Technologietrends der heutigen Zeit, welche die Automobilbranche nach wie vor zu einem der großen Treiber der Innovationsentwicklung werden lässt. Dabei ist es neben der Digitalisierung und der Elektrifizierung des Automobils insbesondere die Entwicklung des autonomen Fahrens, welche die Komplexität der Anforderungen stetig steigen lässt. Aus den steigenden Anforderungen in der Automobilbranche resultieren steigende Anforderungen bei den Zulieferern. Ziel dieser Arbeit ist die Effizienzsteigerung in der Entwicklung von Automotive System-on-Chips durch die direkte oder indirekte Senkung der entstehenden Aufwände. Hierzu soll eine Methode entwickelt werden, mit dem Ziel, die bestehenden Defizite des aktuellen Entwicklungs-Flows in der Automotive-SoC-Entwicklung zu minimieren. Ein besonderer Fokus der Arbeit liegt dabei auf dem Erstellen und Pflegen der Spezifikationsdokumente.
Stand der Technik
Authors: Kirchner Aljoscha
Published: August 2022

Die in dieser Arbeit entwickelte modellbasierte Entwicklungsmethode soll über alle Phasen der Entwicklung hinweg durch einen modellbasierten Ansatz Defizite im aktuellen Flow beseitigen und die Effizienz der SoC-Entwicklung in Gesamtheit steigern. Nach aktuellem Stand gibt es nach Wissen des Autors keine existierende Lösung für die modellbasierte Systementwicklung von Automotive SoCs, welche dabei den gesamten Entwicklungs-Flow von der Analyse der Kundenanforderungen bis zum Systementwurf berücksichtigt und dabei eine Automatisierung des Entwurfs sowie der Verifikationserstellung ermöglicht. Daher erfolgt in diesem Kapitel eine Betrachtung von Lösungsansätzen aus der Literatur, welche Teilbereiche des SoC-Entwicklungs-Flows abdecken bzw. einen Teil der gestellten Anforderungen erfüllen.
SMEFT predictions for gg → hh at full NLO QCD and truncation uncertainties
Authors: Heinrich Gudrun , Lang Jannis , Scyboz Ludovic
Published: August 2022

Abstract

We present a calculation of the NLO QCD corrections for Higgs-boson pair production in gluon fusion including effects of anomalous couplings within Standard Model Effective Field Theory (SMEFT). We study effects of different truncation options of the EFT expansion in 1/Λ and of double operator insertions, both at total cross-section level as well as for the distribution of the invariant mass of the Higgs-boson pair, at $$ \sqrt{s} $$

s

= 13 TeV. The NLO corrections are implemented in the generator ggHH_SMEFT in the Powheg-Box-V2 framework.
The geometry of gauged (super)conformal mechanics
Authors: Mirfendereski Delaram , Raeymaekers Joris , Şanlı Canberk , Van den Bleeken Dieter
Published: August 2022

Abstract

Motivated by recently explored examples, we undertake a systematic study of conformal invariance in one-dimensional sigma models where an isometry group has been gauged. Perhaps surprisingly, we uncover classes of sigma models which are only scale invariant in their ungauged form and become fully conformally invariant only after gauging. In these cases the target space of the gauged sigma model satisfies a deformation of the well-known conformal geometry constraints. We consider bosonic models as well as their $$ \mathcal{N} $$
N
= 1, 2, 4 supersymmetric extensions. We solve the quantum ordering ambiguities in implementing (super-) conformal symmetry on the physical Hilbert space. Examples of our general results are furnished by the D(2, 1; 0)-invariant Coulomb branch quiver models relevant for black hole physics.
Minimal models of field theories: SDYM and SDGR
Authors: Skvortsov Evgeny , Van Dongen Richard
Published: August 2022

Abstract

There exists a natural L∞-algebra or Q-manifold that can be associated to any (gauge) field theory. Perturbatively, it can be obtained by reducing the L∞-algebra behind the jet space BV-BRST formulation to its minimal model. We explicitly construct the minimal models of self-dual Yang-Mills and self-dual gravity theories, which also represents their equations of motion as Free Differential Algebras. The minimal model regains all relevant information about the field theory, e.g. actions, charges, anomalies, can be understood in terms of the corresponding Q-cohomology.
One-loop matching for gluon lattice TMDs
Authors: Schindler Stella T. , Stewart Iain W. , Zhao Yong
Published: August 2022

Abstract

Transverse-momentum-dependent parton distributions (TMDs) can be calculated from first principles by computing a related set of Euclidean lattice observables and connecting them via a factorization formula. This work focuses on the leading-power factorization formula connecting the lattice quasi-TMD and continuum Collins TMD for gluons. We calculate the one-loop gluon matching coefficient, which is known to be independent of spin and exhibits no mixing with quarks. We demonstrate that this coefficient satisfies Casimir scaling with respect to the quark matching coefficient at one-loop order. Our result facilitates reliable lattice QCD calculations of gluon TMDs.
Sterile neutrino portals to Majorana dark matter: effective operators and UV completions
Authors: Coito Leonardo , Faubel Carlos , Herrero-García Juan , Santamaria Arcadi , Titov Arsenii
Published: August 2022

Abstract

Stringent constraints on the interactions of dark matter with the Standard Model suggest that dark matter does not take part in gauge interactions. In this regard, the possibility of communicating between the visible and dark sectors via gauge singlets seems rather natural. We consider a framework where the dark matter talks to the Standard Model through its coupling to sterile neutrinos, which generate active neutrino masses. We focus on the case of Majorana dark matter, with its relic abundance set by thermal freeze-out through annihilations into sterile neutrinos. We use an effective field theory approach to study the possible sterile neutrino portals to dark matter. We find that both lepton-number-conserving and lepton-number-violating operators are possible, yielding an interesting connection with the Dirac/Majorana character of active neutrinos. In a second step, we open the different operators and outline the possible renormalisable models. We analyse the phenomenology of the most promising ones, including a particular case in which the Majorana mass of the sterile neutrinos is generated radiatively.
Z′-mediated Majorana dark matter: suppressed direct-detection rate and complementarity of LHC searches
Authors: Alanne T. , Bishara F. , Fiaschi J. , Fischer O. , Gorbahn M. , Moldanazarova U.
Published: August 2022

Abstract

We study the direct-detection rate for axial-vectorial dark matter scattering off nuclei in an SU(2) × U(1) invariant effective theory and compare it against the LHC reach. Current constraints from direct detection experiments are already bounding the mediator mass to be well into the TeV range for WIMP-like scenarios. This motivates a consistent and systematic exploration of the parameter space to map out possible regions where the rates could be suppressed. We do indeed find such regions and proceed to construct consistent UV models that generate the relevant effective theory. We then discuss the corresponding constraints from both collider and direct-detection experiments on the same parameter space. We find a benchmark scenario, where even for future XENONnT experiment, LHC constraints will have a greater sensitivity to the mediator mass.
Single photon production at hadron colliders at NNLO QCD with realistic photon isolation
Authors: Chen X. , Gehrmann T. , Glover E.W.N. , Höfer M. , Huss A. , Schürmann R.
Published: August 2022

Abstract

Isolated photons at hadron colliders are defined by permitting only a limited amount of hadronic energy inside a fixed-size cone around the candidate photon direction. This isolation criterion admits contributions from collinear photon radiation off QCD partons and from parton-to-photon fragmentation processes. We compute the NNLO QCD corrections to isolated photon and photon-plus-jet production, including these two contributions. Our newly derived results allow us to reproduce the isolation prescription used in the experimental measurements, performing detailed comparisons with data from the LHC experiments. We quantify the impact of different photon isolation prescriptions, including no isolation at all, on photon-plus-jet cross sections and discuss possible measurements of the photon fragmentation functions at hadron colliders.
To digitalize or not? Navigating and merging human- and technology perspectives in production planning and control
Authors: Eriksson Kristina M. , Carlsson Linnéa , Olsson Anna Karin
Published: August 2022

Contemporary manufacturing companies are navigating industrial digitalization anticipating increased production efficiency and competitiveness in a volatile environment. This study focuses on the implementation processes of digital tools for production planning and control (PPC), i.e., advanced planning and scheduling (APS) software, in relation to the application of analog planning with physical flow boards. Digital tools can support understanding the consequences of production changes and variations, hence facilitating adaptable and resilient manufacturing. However, technological changes can be daunting, and effective implementations require dynamic capabilities to remain competitive in elusive environments. The aim is to study the implementation processes of an APS software to understand the requirements of fruitfully moving from analog planning to next-generation digital tools for decision support in PPC. The paper presents an explorative case study, at a manufacturing company within the energy sector. The interview study took place over 9 months during 2020–2021, investigating current and retrospective aspects of the case across 2019–2021. The case study comprises 17 in-depth interviews with a range of company employees, e.g., logistics managers and functions responsible for digitalization development. The results highlight the challenges of implementing and especially trusting digital tools for PPC. To realize the value of digital tools for PPC, it is argued that it is imperative to simultaneously apply a human-centric perspective in decision making to ensure trustworthy, sustainable, and resilient human-data-technology nexus implementations towards smart manufacturing.
EU-SILC and the potential for synthetic panel estimates
Authors: Colgan Brian
Published: August 2022

In the absence of panel data, researchers have devised alternative methods for estimating synthetic poverty dynamics using repeated cross section surveys. These methods are not only salient in the absence of panel data, but also in contexts where there are concerns over the quality of panel data and/or the panel data are of insufficient length to analyse medium- to long-term mobility trends. Both of these issues afflict the longitudinal element of the European Survey on Income and Living Conditions (EU-SILC) (Hérault and Jenkins, J Econ Inequ 17(1):51–76, 2019). Using the longitudinal element of EU-SILC, this paper assesses the accuracy of the synthetic panel approach put forth by Dang and Lanjouw (2021). For most conventional poverty lines, the DL approach is found to be highly accurate when the true $$\rho $$
ρ
is known. Similar to Hérault and Jenkins (J Econ Inequ 17(1):51–76, 2019) the pseudo-panel approach for estimating $$\rho $$
ρ
is found to be highly sensitive to cohort definition. The longitudinal element of EU-SILC, however, offers a unique route for overcoming this shortcoming.
The value of FDG PET/CT imaging in outcome prediction and response assessment of lymphoma patients treated with immunotherapy: a meta-analysis and systematic review
Authors: Kiamanesh Zahra , Ayati Narjess , Sadeghi Ramin , Hawkes Eliza , Lee Sze Ting , Scott Andrew M.
Published: August 2022

Purpose

Treatment strategies of lymphoid malignancies have been revolutionized by immunotherapy. Because of the inherent property of Hodgkin lymphoma and some subtypes of non-Hodgkin lymphoma as a highly FDG-avid tumor, functional 18F-FDG PET/CT imaging is already embedded in their routine care. Nevertheless, the question is whether it is still valuable in the context of these tumors being treated with immunotherapy. Herein, we will review the value of 18F-FDG PET/CT imaging lymphoid tumors treated with immunotherapy regimens.

Methods

A comprehensive literature search of the PubMed database was conducted on the value of the 18F-FDG PET/CT for immunotherapy response monitoring of patients with malignant lymphoma. The articles were considered eligible if they met all of the following inclusion criteria: (a) clinical studies on patients with different types of malignant lymphoma, (b) treatment with anti-CD20 antibodies, immune checkpoint inhibitors or immune cell therapies, (c) and incorporated PET/CT with 18F-FDG as the PET tracer.

Results

From the initial 1488 papers identified, 91 were ultimately included in our study. In anti-CD20 therapy, the highest pooled hazard ratios (HRs) of baseline, early, and late response monitoring parameters for progression-free survival (PFS) belong to metabolic tumor volume (MTV) (3.19 (95%CI: 2.36–4.30)), maximum standardized uptake value (SUVmax) (3.25 (95%CI: 2.08–5.08)), and Deauville score (DS) (3.73 (95%CI: 2.50–5.56)), respectively. These measurements for overall survival (OS) were MTV (4.39 (95%CI: 2.71–7.08)), DS (3.23 (95%CI: 1.87–5.58)), and DS (3.64 (95%CI: 1.40–9.43)), respectively. Early and late 18F-FDG PET/CT response assessment in immune checkpoint inhibitors (ICI) and immune cell therapy might be an effective tool for prediction of clinical outcome.

Conclusion

For anti-CD20 therapy of lymphoma, the MTV as a baseline 18F-FDG PET/CT-derived parameter has the highest HRs for PFS and OS. The DS as visual criteria in early and late response assessment has higher HRs for PFS and OS compared to the international harmonization project (IHP) visual criteria in anti-CD20 therapy. Early changes in 18F-FDG PET parameters may be predictive of response to ICIs and cell therapy in lymphoma patients.
Effect of Intraperitoneal Local Anesthetics in Laparoscopic Bariatric Surgery: A Meta‐Analysis of Randomized Controlled Trials
Authors: Dai Senjie , Fu Rongrong , Jiang Siya , He Yuanfang , Huang Tongmin , Zhou Bin , Gong Hongjun
Published: August 2022

Objective

The effectiveness of intraperitoneal local anesthesia (IPLA) has been confirmed in other fields, but its use in bariatric surgery remains debatable. This study aimed to evaluate the analgesic effect of IPLA in bariatric surgery.

Methods

PubMed, Web of Science, Embase, and the Cochrane Library were searched from inception to February 2022. All randomized controlled trials (RCTs) assessing IPLA's analgesic effect in bariatric surgery were included in this study. Pain‐related indicators were the outcome.

Results

Ten RCTs with 979 patients were included. Postoperative pain scores were significantly lower in IPLA group. Subgroup analysis demonstrated that IPLA was associated with lower pain scores in 6 h and at 24 h compared to the control group, without significant differences at 8, 12, and 48 h. Meanwhile, IPLA reduced the dose of opioids taken postoperatively. Additionally, there were no differences in adverse events between the two groups. As far as the number of postoperative analgesics used and hospital stays were concerned, our results did not show statistical differences between the two groups.

Conclusion

IPLA can reduce postoperative pain safely and effectively, particularly during the early postoperative stage.
Presence or absence of a prefrontal sulcus is linked to reasoning performance during child development
Authors: Willbrand Ethan H. , Voorhies Willa I. , Yao Jewelia K. , Weiner Kevin S. , Bunge Silvia A.
Published: August 2022

The relationship between structural variability in late-developing association cortices like the lateral prefrontal cortex (LPFC) and the development of higher-order cognitive skills is not well understood. Recent findings show that the morphology of LPFC sulci predicts reasoning performance; this work led to the observation of substantial individual variability in the morphology of one of these sulci, the para-intermediate frontal sulcus (pimfs). Here, we sought to characterize this variability and assess its behavioral significance. To this end, we identified the pimfs in a developmental cohort of 72 participants, ages 6–18. Subsequent analyses revealed that the presence or absence of the ventral component of the pimfs was associated with reasoning, even when controlling for age. This finding shows that the cortex lining the banks of sulci can support the development of complex cognitive abilities and highlights the importance of considering individual differences in local morphology when exploring the neurodevelopmental basis of cognition.
Modelling reoxygenation effects in non-small cell lung cancer cell lines and showing epithelial-mesenchymal transition
Authors: Kapeleris Joanna , Müller Bark Juliana , Ranjit Shanon , Richard Derek , Vela Ian , O’Byrne Kenneth , Punyadeera Chamindie
Published: August 2022

Purpose

Circulating tumour cells (CTCs) are a rare cell subpopulation regulated by the tumour microenvironment. In hypoxic conditions, CTCs are able to invade the lymphatic and circulatory systems leading to metastasis at distant sites.

Methods

To mimic in vivo oxygen variations and effects on CTCs, we have cultured five non-small cell lung cancer (NSCLC) cell lines under normoxic and hypoxic conditions, followed by a pulse of reoxygenation for 4 h.

Results

Proliferation, spheroid-formation and colony formation ability under varying O2 levels were investigated. Proliferation rate was not altered when cells were cultured in 2D models under hypoxic conditions. However, we observed that hypoxia enhanced in vitro formation of tumour-spheres and accelerated clonogenicity of NSCLC cell lines. In addition, cells exposed to hypoxia and reoxygenation conditions showed altered expression of epithelial-mesenchymal transition (EMT) related genes in NSCLC cell lines both at mRNA (AKT1, CAMK2NH1, DESI1, VIM, MAP1B, EGFR, ZEB1, HIF1α) and protein levels (Vimentin, Pan-cytokeratin).

Conclusion

Our data suggest that when investigating CTCs as a prognostic biomarker in NSCLC, it is also essential to take into consideration EMT status to obtain a comprehensive overview of CTCs in circulation.
Biomechanical comparison of multi-rod constructs by satellite rod configurations (in-line vs. lateral) and screw types (monoaxial vs. polyaxial) spanning a lumbar pedicle subtraction osteotomy (PSO): is there an optimal configuration?
Authors: Shekouhi Niloufar , Vosoughi Ardalan S. , Zavatsky Joseph M. , Goel Vijay K. , Theologis Alekos A.
Published: August 2022

Purpose

Multi-rod constructs are used commonly to stabilize pedicle subtraction osteotomies (PSO). This study aimed to evaluate biomechanical properties of different satellite rod configurations and effects of screw-type spanning a PSO.

Methods

A validated 3D spinopelvic finite element model with a L3 PSO (30°) was used to evaluate 5 models: (1) Control (T10–pelvis + 2 rods); (2) lateral satellite rods connected via offsets to monoaxial screws (LatSat-Mono) or (3) polyaxial screws (LatSat-Poly); (4) in-line satellite rods connected to monoaxial screws (InSat-Mono) or (4) polyaxial screws (InSat-Poly). Global and PSO range of motions (ROM) were recorded. Rods’ von Mises stresses and PSO forces were recorded and the percent differences from Control were calculated.

Results

All satellite rods (save InSat-Mono) increased PSO ROM and decreased primary rods’ von Mises stresses at the PSO. Lateral rods increased PSO forces (LatSat-Mono:347.1 N; LatSat-Poly:348.6 N; Control:336 N) and had relatively lower stresses, while in-line rods decreased PSO forces (InSat-Mono:280.1 N; InSat-Poly:330.7 N) and had relatively higher stresses. Relative to polyaxial screws, monoaxial screws further decreased PSO ROM, increased satellite rods’ stresses, and decreased PSO forces for in-line rods, but did not change PSO forces for lateral rods.

Conclusion

Multi-rod constructs using in-line and lateral satellite rods across a PSO reduced primary rods' stresses. Subtle differences in biomechanics suggest lateral satellite rods, irrespective of screw type, increase PSO forces and lower rod stresses compared to in-line satellite rods, which had a high degree of posterior instrumentation stress shielding and lower PSO forces. Clinical studies are warranted to determine if these findings influence clinical outcomes.
Association of glucose-lowering drugs with incident stroke and transient ischaemic attacks in primary care patients with type 2 diabetes: disease analyzer database
Authors: Rathmann Wolfgang , Kostev Karel
Published: August 2022

Aims

Previous observational studies on glucose-lowering drugs and risk of stroke in type 2 diabetes yielded conflicting results. The aim was to examine the association of glucose-lowering drugs with incident stroke and transient ischaemic attacks (TIA) in newly diagnosed type 2 diabetes.

Methods

We conducted a retrospective cohort analysis of the disease analyzer, which comprises a representative panel of 1248 general and internal medicine practices throughout Germany (01/2000–12/2019: 9.8 million patients). Incident non-fatal stroke/TIA was defined based on ICD-10 codes (I63, I64; G45) in newly diagnosed type 2 diabetes. Cox regression models were fitted to obtain hazard ratios (HR; 95%CI) for stroke/TIA adjusting for potential confounders (age, sex, health insurance, coronary heart disease, myocardial infarction, heart failure, polyneuropathy, blood pressure, eGFR) and anthropometric and metabolic intermediators (BMI, HbA1c, HDL- and LDL-cholesterol, triglycerides, lipid-lowering drugs).

Result

312,368 persons with newly diagnosed type 2 diabetes without previous stroke/TIA (mean age: 64 years; 52% males) were included. There were 16,701 events of non-fatal stroke/TIA corresponding to an incidence rate of 9.3 (95%CI 9.1–9.4) per 1000 person-years. Using Cox regression, adjusted HR for stroke/TIA (per 1 year of treatment) of 0.59 (0.54–0.64) for SGLT2 inhibitors and of 0.79 (0.74–0.85) for GLP-1 receptor agonists were estimated. DPP-4 inhibitors (0.84; 0.82–0.86), metformin (0.90; 0.89–0.91), insulin (0.92; 0.91–0.93) and sulfonylureas (0.98; 0.96–0.99) also showed moderately reduced HR for stroke/TIA. Sex-specific regression analyses yielded similar results (HR).

Conclusions

Treatment with SGLT2 inhibitors or GLP-1 receptor agonists might reduce non-fatal stroke/TIA in persons with newly diagnosed type 2 diabetes.
Investigating and predicting spatiotemporal variations in vegetation cover in transitional climate zone: a case study of Gansu (China)
Authors: He Qing , Chun Kwok Pan , Dieppois Bastien , Chen Liang , Fan Ping Yu , Toker Emir , Yetemen Omer , Pan Xicai
Published: August 2022

Vegetation ecosystems are sensitive to large-scale climate variability in climate transition zones. As a representative transitional climate zone in Northwest China, Gansu is characterized by a sharp climate and vegetation gradient. In this study, the spatiotemporal variations of vegetation over Gansu are characterized using the satellite-based normalized difference vegetation index (NDVI) observations during 2000–2020. Results demonstrate that a significant greening trend in vegetation over Gansu is positively linked with large-scale climate factors through modulating the water and energy dynamics. As a climate transition zone, the northern water-limited and southern energy-limited regions of Gansu are affected by water and energy dynamics, differently. In the water-limited region, a weakening Asian monsoon along with colder Central Pacific (CP) and warmer North Pacific (NP) Oceans enhances prevailing westerlies which bring more atmospheric moisture. The enhanced atmospheric moisture and rising temperature promote the local vegetation growth. In contrast, large-scale climate variations suppress the southwest monsoon moisture fluxes and reduce precipitation in southern energy-limited regions. In these energy-limited regions, temperature has more effects on vegetation growth than precipitation. Therefore, the greenness of vegetation is because of more available energy from higher temperatures despite overall drying conditions in the region. Based on the above mechanism, future scenarios for climate impacts on vegetation cover over Gansu region are developed based on the two latest generation from coupled climate models (Coupled Model Intercomparison Project Phase 5 and Phase 6; CMIP5 and CMIP6). In the near-term future (2021–2039), the vegetation is likely to increase due to rising temperature. However, the vegetation is expected to decrease in a long-term future (2080–2099) when the energy-limited regions become water-limited due to increasing regional temperatures and lowering atmospheric moisture flux. This study reveals an increasing desertification risk over Gansu. Similar investigations will be valuable in climate transition regions worldwide to explore how large-scale climate variability affects local ecological services under different future climate scenarios.
Use phase and end-of-life modeling of biobased biodegradable plastics in life cycle assessment: a review
Authors: Molina-Besch Katrin
Published: August 2022

Due to the negative environmental impacts along the life cycle of fossil-based plastics, there is an increased interest in the development of biobased and biodegradable plastics. Especially, biodegradable bioplastics possess different material characteristics than fossil-based plastics and can therefore be expected to perform differently during the product use and in end-of-life (EOL) phases. Thus, it is important to consider the material performance and behavior of biobased plastics in life cycle assessment studies. In practice, this is often a difficult task due to lack of data. The purpose of this review study is to analyze how contemporary LCA method models the use and EOL phases of biobased biodegradable plastics, with a specific focus on how the properties of these chemical novel materials are considered in LCA modeling. The first part of the review summarizes recommendations for the use phase and EOL modeling of biobased plastics. The second part analyzes 42 bioplastic LCAs in relation to these recommendations. The results of the review reveal that the use phase is commonly neglected in LCAs of bioplastics. Moreover, it is shown that EOL modeling in LCAs of biobased biodegradable plastics is often based on data that is non-specific for the analyzed material(s). The results of the review call for more research on the specific material properties of newly developed biobased biodegradable plastics, as these data are needed to decrease uncertainties in bioplastic LCAs.

Graphical abstract
Gross Fixed Capital Formation in the Euro Area During the COVID-19 Pandemic
Authors: Licchetta Mirko , Meyermans Eric
Published: August 2022

This paper examines the impact of the COVID-19 pandemic on gross fixed capital formation across the euro area. The empirical analysis suggests that the intensity of the lockdown measures to contain the spread of the virus and the country-specific structure of the economy along with other traditional drivers, in particular falling output, can explain a large part of the contraction. The bold policy response at the national and EU level mitigated the impact of COVID-19 and supported the recovery. The faster-than-expected rebound in economic activity suggests that the negative economic impact of the pandemic will be more contained than initially feared. However, uncertainty over future health developments remains high, especially given the risks of new more transmissible variants.
Sustainable Carbon Cycles: A Framework for the Ramp-up of Carbon Capture?
Authors: Wolf André
Published: August 2022

With its communication “Sustainable Carbon Cycles”, the European Commission has opened a new chapter in European climate policy. For the first time, natural and artificial CO2 capture and storage technologies are to be covered jointly in an overarching regulatory approach. This article reviews the techno-economic potentials of the application fields envisaged by the Commission’s strategy, and defines central requirements for a future funding framework. The establishment of markets for carbon credits is identified as a basis for commercialising storage solutions. However, a prerequisite for efficient trading is to create transparency about the climate impact of the technology alternatives. Efforts to improve existing measurement concepts and test procedures as well as the development of certified standards are decisive steps on this path. The time horizon of carbon sequestration should be a crucial aspect in certification and monitoring. Double funding and unnecessary subsidisation of activities that are already profitable today need to be avoided.
EU Enlargement: Expanding the Union and Its Potential
Authors: Bourguignon Jiffer , Demertzis Maria , Sprenger Ekaterina
Published: August 2022

The new EU member candidates, as well as those who have had this status for a decade, present the Union with an opportunity to decide what kind of club it should be.
Towards a New Eastern Enlargement of the EU and Beyond
Authors: Dabrowski Marek
Published: August 2022

It is not the first time in EU history that the enlargement perspective is confronted with the need for integration deepening.
Geopolitical and Security Concerns of the EU’s Enlargement to the East: The Case of Ukraine, Moldova and Georgia
Authors: Akhvlediani Tinatin
Published: August 2022

Russia’s unprovoked and unjustified full-scale invasion of Ukraine has significantly changed the geopolitical circumstances on the European continent and is also importantly reshaping the EU’s enlargement policy.
Geopolitics of the European Rule of Law — Lessons from Ukraine and the Western Balkans
Authors: Bernard Elise
Published: August 2022

Several opportunities arise for rule of law promoters: to reclaim the security discourse; to explain EU enlargement through the commitment to the rule of law; and consequently, to develop a strategy to influence opponents of enlargement.
Methanol fuel production, utilization, and techno-economy: a review
Authors: Deka Tanmay J. , Osman Ahmed I. , Baruah Debendra C. , Rooney David W.
Published: August 2022

Climate change and the unsustainability of fossil fuels are calling for cleaner energies such as methanol as a fuel. Methanol is one of the simplest molecules for energy storage and is utilized to generate a wide range of products. Since methanol can be produced from biomass, numerous countries could produce and utilize biomethanol. Here, we review methanol production processes, techno-economy, and environmental viability. Lignocellulosic biomass with a high cellulose and hemicellulose content is highly suitable for gasification-based biomethanol production. Compared to fossil fuels, the combustion of biomethanol reduces nitrogen oxide emissions by up to 80%, carbon dioxide emissions by up to 95%, and eliminates sulphur oxide emission. The cost and yield of biomethanol largely depend on feedstock characteristics, initial investment, and plant location. The use of biomethanol as complementary fuel with diesel, natural gas, and dimethyl ether is beneficial in terms of fuel economy, thermal efficiency, and reduction in greenhouse gas emissions.
First insights into migration routes and nonbreeding sites used by Red-rumped Swallows (Cecropis daurica rufula) breeding in the Iberian Peninsula
Authors: Wong Joanna B. , Turon Francina , Fernández-Tizón Mario , Hahn Steffen
Published: August 2022

Using EURING data and geolocation, we describe migration routes and the nonbreeding range of Red-rumped Swallows breeding in the Western Palearctic. One bird ringed in southern Spain and recovered in southern Morocco indicates southwestern migration; geolocator data from five birds from central and eastern Iberian Peninsula confirm migration to various nonbreeding sites in sub-Saharan west Africa between Senegal/Mauritania and Ghana. Two swallows showed non-breeding site itinerancy by using more than one nonbreeding site per season. Despite wide ranges in departure for autumn (August–October) and spring migration (February–March), all birds arrived at nonbreeding and breeding sites within ± 1-week from each other.
Genetic traces of dispersal and admixture in red deer (Cervus elaphus) populations from the Carpathian Basin
Authors: Frank Krisztián , Szepesi Kinga , Bleier Norbert , Sugár László , Kusza Szilvia , Barta Endre , Horn Péter , Orosz László , Stéger Viktor
Published: August 2022

After the last glacial, the Carpathian Basin was repopulated from either eastward or northward colonisation routes for various species; one of these was the emblematic member of the European megafauna, the red deer, Cervus elaphus. We analysed 303 red deer individuals from the middle of the region, in seven Hungarian game reserves, at ten microsatellite loci (C01, C229, T26, T108, T123, T156, T172, T193, T501, T507), to investigate the genetic diversity of these subpopulations. We discovered high levels of genetic diversity of red deer subpopulations; allelic richness values ranging 4.99–7.01, observed heterozygosity 0.729–0.800, polymorphic information content 0.722–0.806, and Shannon’s information index 1.668–2.064. Multi-locus analyses indicated population admixtures of various degrees that corresponded to geographical location, and complex genetic structures were shown by clustering. Populations in the south-western and the north-eastern parts of the region formed two highly separated groups, and the red deer from populations in between them were highly admixed (in western Pannonia/Transdanubia, where the Danube flows into the Carpathian Basin). This pattern corresponds to the distribution of mitochondrial as well as Y-chromosome lineages. Assignment tests showed that a large fraction of individuals (29.4%) are found outside of their population of origin, indicating that the dispersal of red deer is rather common, which could be expected considering the life course of the species.
Efficient and adaptive incentive selection for crowdsourcing contests
Authors: Truong Nhat Van-Quoc , Dinh Le Cong , Stein Sebastian , Tran-Thanh Long , Jennings Nicholas R.
Published: August 2022

The success of crowdsourcing projects relies critically on motivating a crowd to contribute. One particularly effective method for incentivising participants to perform tasks is to run contests where participants compete against each other for rewards. However, there are numerous ways to implement such contests in specific projects, that vary in how performance is evaluated, how participants are rewarded, and the sizes of the prizes. Also, the best way to implement contests in a particular project is still an open challenge, as the effectiveness of each contest implementation (henceforth, incentive) is unknown in advance. Hence, in a crowdsourcing project, a practical approach to maximise the overall utility of the requester (which can be measured by the total number of completed tasks or the quality of the task submissions) is to choose a set of incentives suggested by previous studies from the literature or from the requester’s experience. Then, an effective mechanism can be applied to automatically select appropriate incentives from this set over different time intervals so as to maximise the cumulative utility within a given financial budget and a time limit. To this end, we present a novel approach to this incentive selection problem. Specifically, we formalise it as an online decision making problem, where each action corresponds to offering a specific incentive. After that, we detail and evaluate a novel algorithm, , to solve the incentive selection problem efficiently and adaptively. In theory, in the case that all the estimates in (except the estimates of the effectiveness of each incentive) are correct, we show that the algorithm achieves the regret bound of $\mathcal {O}(\sqrt {B/c})$
O
(

B
/
c

)
, where B denotes the financial budget and c is the average cost of the incentives. In experiments, the performance of is about 93% (up to 98%) of the optimal solution and about 9% (up to 40%) better than state-of-the-art algorithms in a broad range of settings, which vary in budget sizes, time limits, numbers of incentives, values of the standard deviation of the incentives’ utilities, and group sizes of the contests (i.e., the numbers of participants in a contest).