bluuebunny/crossref_metadata_2025 huggingface dataset:
Total pelvic exenteration extended to pelvic bones with subsequent VRAM flap reconstruction in patient with recurrent anal squamous cell carcinoma following chemoradiotherapy
Authors: Sancho-Muriel Jorge , Guerrero-Antolino Pablo , Cholewa Hanna , Flor Lorente Blas
Published: July 2024

Anal squamous cell carcinoma, typically associated with human papillomavirus infection, remains a rare malignancy. This article outlines a case of local recurrence in a male patient with a history of HIV and hepatitis C virus infection, previously treated with chemoradiotherapy. Extensive tumour involvement called for total pelvic exenteration extended to anterior osteomuscular compartment and genitalia. The surgical approach involved multidisciplinary collaboration and detailed preoperative planning using three-dimensional reconstruction. Key surgical considerations comprised the following: achieving tumour-free margins (R0 resection), extensive osteotomies and intricate pelvic floor reconstruction with prosthetic mesh and flap reconstruction. The procedure successfully yielded an R0 resection, maintaining adequate lower limb functionality. Our case report underscores the benefits of pelvic exenteration in locally advanced or recurrent pelvic tumours, invariably following careful patient selection and exhaustive preoperative studies.
Atosiban versus placebo in the treatment of threatened preterm birth between 30 and 34 weeks gestation: study protocol of the 4-year APOSTEL 8 follow-up
Authors: van der Windt Larissa , Klumper Job , van Limburg Stirum Emilie V J , van 't Hooft Janneke , van Wely Madelon , van Wassenaer-Leemhuis Aleid G , Pajkrt Eva , Oudijk Martijn A
Published: July 2024

Introduction

Currently, the majority of women worldwide with threatened preterm birth are treated with tocolytics. Although tocolytics can effectively delay birth for 48 hours, no tocolytic drug has convincingly been shown to improve neonatal outcomes and effects on long-term child development are unknown. The aim of this follow-up study of a placebo controlled randomised trial is to investigate the long-term effects of atosiban administration in case of threatened preterm birth on child’s neurodevelopment and behaviour development, overall health and mortality.

Methods and analysis

This protocol concerns a follow-up study of the multicentre randomised double-blind placebo controlled APOSTEL 8 trial (NL61439.018.17, EudraCT-number 2017-001007-72). In this trial, women with threatened preterm birth (between 30 and 34 weeks of gestation) defined as uterine contractions with (1) a cervical length of <15 mm or (2) a cervical length of 15–30 mm and a positive fibronectin test or (3) in centres where cervical length measurement is not part of the local protocol: a positive fibronectin test or Actim-Partus test or (4) ruptured membranes, are randomised to atosiban or placebo for 48 hours. The primary outcome is a composite of perinatal mortality and severe neonatal morbidity. Children born to mothers who participated in the APOSTEL 8 study (n=760) will be eligible for follow-up at 4 years of corrected age and assessed using four parent-reported questionnaires. Primary outcomes are neurodevelopment and behaviour problems. Secondary outcomes are on child growth and general health. All outcomes will be compared between the atosiban and placebo group with OR and corresponding 95% CI. Analyses will be performed using the intention-to-treat approach.

Ethics and dissemination

The Medical Research Ethics Committee from Amsterdam UMC confirmed that de Medical Research Involving Human Subjects Act (Dutch WMO-law) did not apply to our study (W21_386 # 21.431). Results will be published in a peer-reviewed journal and shared with stakeholders and participants. This protocol is published before analysis of the results.
Burnout among care workers in long-term care institutions: a systematic review and meta-analysis protocol
Authors: Su Yinhua , Zhao Ling , Yu Juping , Chen Xia , Tan E , Liu Hui , Xie Xiaoping , Liu Fen
Published: July 2024

Introduction

Care workers play a fundamental role in delivering care services in long-term care institutions. Burnout has been found to have a negative impact on care recipients and organisations providing care. Little is known about the key factors associated with care workers’ burnout. This systematic review aims to explore the prevalence, severity and correlates of burnout among care workers before and during COVID-19 pandemic.

Methods and analysis

A five-stage framework outlined by Whittemore and Knafl will be used. The following databases will be used to identify relevant literature, including Medline (PubMed), EMBASE, Cochrane library, PsycINFO, CINAHL, Scopus and Web of Science. RevMan will be used to assist the meta-analysis. Heterogeneity of the included studies will be tested using theI2test.

Ethics and dissemination

No ethics approval is required as this study only involves secondary data analysis. The findings will be published in peer-reviewed journals and presented at relevant conferences.

PROSPERO registration number

CRD42024499178.
Acute Achilles tendon ruptures between 2002–2021: sustained increased incidence, surgical decline and prolonged delay to surgery—a nationwide study of 53 688 ruptures in Sweden
Authors: Svedman Simon , Marcano Alejandro , Ackermann Paul W , Felländer-Tsai Li , Berg Hans Erik
Published: July 2024

Introduction

Given the lack of consensus on optimal treatment strategies for acute Achilles tendon rupture (ATR), understanding temporal trends, treatment choice and demographic characteristics is important. Previous research suggests increasing incidence with declining surgical treatment. Current trends in Sweden are not known.

Hypothesis/purpose

To assess how incidence rates, treatment trends and time from injury to surgery (TTS) of ATR have changed between 2002 and 2021 in Sweden, with particular attention to changes since 2012.

Study design

Descriptive epidemiology study.

Methods

We conducted a nationwide register-based study including all inpatients and outpatients ≥18 years of age with an ATR between 2002 and 2021 in Sweden.

Results

53 688 ATRs (78.5% men) were identified during the study period. 15 045 patients (81.5% men) were surgically treated within 30 days. The long-term incidence rate for ATR injury increased by 45%, from 28.8 in 2002 to 41.7 in 2021 per 100 000 person-years (p<0.0001). In the last 5 years of the study, there was a significant, continuing increase in ATR incidence by 21%, from 34.4 in 2017 to 41.7 in 2021 per 100 000 person-years (p<0.0001). The surgical incidence rates decreased from 13.4 to 6.0 per 100 000 person-years (p<0.0001). TTS increased from 0.6 days in 2002 to 5.1 in 2021 (p<0.0001).

Conclusion

The observed increase in incidence rates and decrease in surgical treatment of ATR emphasise the need for evidence-based treatment and rehabilitation protocols for non-operated patients of all ages. A significant increase in time from injury to surgery was observed throughout the study period.
Generation of non-genetically modified, CAR-like, NK cells
Authors: Coënon Loïs , Rigal Emilie , Courot Hortense , Multrier Caroline , Zemiti Sara , Lambour Jennifer , Pugnière Martine , de Toledo Marion , Bossis Guillaume , Cartron Guillaume , Robert Bruno , Martineau Pierre , Fauvel Bénédicte , Presumey Jessy , Villalba Martin
Published: July 2024

Background

Natural killer (NK) cell therapy is considered an attractive and safe strategy for anticancer therapy. Nevertheless, when autologous or allogenic NK cells are used alone, the clinical benefit has been disappointing. This is partially due to the lack of target specificity. Recently, CD19-specific chimeric antigen receptor (CAR)-NK cells have proven to be safe and potent in patients with B-cell tumors. However, the generation of CAR-NK cells is a complicated manufacturing process. We aim at developing a targeted NK cell therapy without the need for cellular genetic modifications. We took advantage of the natural expression of the IgG Fc receptor CD16a (FcγRIIIa) to induce strong antigen-specific effector functions through antibody-dependent cell-mediated cytotoxicity (ADCC). We have generated the new technology “Pin”, which enables the arming of modified monoclonal antibodies (mAbs) onto the CD16a of ex vivo expanded NK (eNK) cells.

Methods

Ex vivo eNK were prepared from umbilical cord blood cells and expanded using interleukin (IL)-2/IL-15 and Epstein-Barr virus (EBV)-transformed B-lymphoblastoid feeder cells. mAbs were engineered with four substitutions called Pin mutations to increase their affinity to CD16a. eNK were incubated with anti-CD20 or anti-CD19 Pin-mAbs to generate “armed” eNK and were used to assess effector functions in vitro on cancer cell lines, lymphoma patient cells and in vivo.

Results

CD16a/Pin-mAb interaction is stable for several days and Pin-mAb eNK inherit the mAb specificity and exclusively induce ADCC against targets expressing the cognate antigen. Hence, Pin-mAbs confer long-term selectivity to eNK, which allows specific elimination of the target cells in several in vivo mouse models. Finally, we showed that it is possible to arm eNK with at least two Pin-mAbs simultaneously, to increase efficacy against heterogenous cancer cell populations.

Conclusions

The Pin technology provides an off-the-shelf NK cell therapy platform to generate CAR-like NK cells, without genetic modifications, that easily target multiple tumor antigens.
Study on lateral stability of distributed drive electric tractor semi-trailers under low adhesion road conditions
Authors: Gao Wei , Liao Yuang , Deng Zhaowen , Zhao Youqun , Wang Baohua
Published: July 2024

To improve the lateral stability of tractor-semitrailer under low adhesion road surface, a direct yaw torque control strategy considering longitudinal speed control was proposed based on distributed electric drive technology. A hierarchical control strategy was designed, with the upper layer using a linear quadratic regulator (LQR) controller to calculate the yaw moment. To distribute the longitudinal force and additional yaw moment, the torque distribution method was adopted based on quadratic programming in the lower controller. The co-simulation was conducted by Trucksim and Matlab/Simulink under the conditions of single lane change (SLC) of 110 km/h and double lane change (DLC) of 70 km/h. The results indicate that in comparison with the PID controller and the uncontrolled vehicle, the LQR controller improves the lateral stability of the vehicle on the low-adhesion road.
CP asymmetries in τ→KSπντ decays
Authors: Chen Feng-Zhi , Li Xin-Qiang
Published: July 2024

We present here the CP asymmetries in the decay rate and angular distributions of [Formula: see text] decays in the Standard Model (SM) and beyond (BSM). The CP asymmetries in the SM are induced by the CP violation in [Formula: see text] mixing. To investigate the BSM CP-violating (CPV) effects, a model-independent analysis is performed by using the low-energy effective field theory (LEFT) framework at [Formula: see text] GeV. If one further assumes the BSM physics to stem from above the electroweak scale, the LEFT shall then be matched onto the SM effective field theory (SMEFT), the operators of which contributing to [Formula: see text] decays will also contribute to the neutron electric dipole moment (EDM) and [Formula: see text] mixing. The stringent bounds from the latter suggest that no remarkable CPV effects can be observed in either the decay rate or the angular distributions. The prospects for future measurements of these observables are also mentioned.
Predictive Prefetching in Client–Server Systems: A Navigational Behavior Modeling Approach
Authors: Buyuktanir Tolga , Aktas Mehmet S.
Published: July 2024

A key challenge faced in client–server systems that heavily rely on data is the fast delivery of data to end-users. To address this difficulty, this study presents a novel approach for modeling and forecasting user navigational browsing behavior, to establish an efficient prefetching mechanism. Regarding the approach for representing page visit data, we employ the Word2Vec embedding technique to encode each user’s page visit as a numerical vector. Regarding the encoding of browsing activity data, we utilize aggregation on the embedding vectors. These vectors correspond to page visits occurring sequentially and are used to describe each user’s browsing behavior using a numerical vector. In the proposed method, machine learning algorithms are employed to analyze and model the browsing behavior of all users. Machine learning models are employed to forecast the next user action during the navigation of data-intensive web and mobile application web pages. Subsequently, we employ this forecast to establish an intelligent prefetching method, which provides the capability of acquiring predicted web page data in proxy servers before it is requested. An experimental study was conducted using a large-scale open-source dataset derived from a mobile application used in a coffee shop, containing several hundred thousand sessions from tens of thousands of users over a 10-day period. The evaluation employed metrics such as prediction accuracy which can be called prefetching accuracy and cache hit/miss rates. The machine learning algorithms applied include K-Nearest Neighbor, AdaBoost, Decision Tree, Support Vector Machine, Multi-layer Perceptron, Random Forest, LightGBM, Long Short-Term Memory and Bidirectional Long Short-Term Memory. The purpose of this experimental study was to examine the efficacy of the proposed approach. The findings of the empirical investigation suggest that the proposed method has the potential to provide an efficient prefetching methodology when sufficient user navigational data is available. Hence, the method enhances the performance of data-intensive client–server-based systems.
From the Choi formalism in infinite dimensions to unique decompositions of generators of completely positive dynamical semigroups
Authors: Vom Ende Frederik
Published: July 2024

Given any separable complex Hilbert space, any trace-class operator [Formula: see text] which does not have purely imaginary trace, and any generator [Formula: see text] of a norm-continuous one-parameter semigroup of completely positive maps we prove that there exists a unique bounded operator [Formula: see text] and a unique completely positive map [Formula: see text] such that (i) [Formula: see text], (ii) the superoperator [Formula: see text] is trace class and has vanishing trace, and (iii) [Formula: see text] is a real number. Central to our proof is a modified version of the Choi formalism which relates completely positive maps to positive semi-definite operators. We characterize when this correspondence is injective and surjective, respectively, which in turn explains why the proof idea of our main result cannot extend to non-separable Hilbert spaces. In particular, we find examples of positive semi-definite operators which have empty pre-image under the Choi formalism as soon as the underlying Hilbert space is infinite-dimensional.
Analytical Modeling and Parameter Extraction of High-Performance Low-Power Memristive Device Based on Trilayer Interlinked Graphene Oxide
Authors: Atiyah Abbas Mohammed , Sedghi Hassan , Ahmadi Mohammad Taghi , Rahmani Meisam
Published: July 2024

To overcome the scaling limitations of large-scale charge-storage-based memories, the physical-based analytic model of a memristor with high-performance resistive random-access memory and low power based on Trilayer interlinked graphene oxide (TIGO) with significant charge transport as an active layer is proposed in this research. To this end, the electron transport of the proposed device is investigated with two electrode-limited conduction mechanisms based on Schottky emission (SE) and trap-assisted tunneling (TAT). In the proposed model, electrically driven reduction of oxygen groups makes the formation of [Formula: see text] islands across the TIGO layer. The TIGO-like islands operate as intermediate trap sites and help electrons to tunnel from the cathode toward the anode despite being isolated by the disordered [Formula: see text]-bonded matrix. The existence of vertically aligned trap sites leads to the formation of percolation paths which allows a steady flow of electrons. The conductive path by the redox of TIGO atoms, because of the conversion of [Formula: see text] to [Formula: see text] oxygen functionalities, is produced, which can be modeled by degenerate region as ON state with the Low resistance switching (LRS). This path is also ruptured by declining the voltage into the reset voltage, which is modeled by the nondegenerate region as OFF state with High resistance switching (HRS). In fact, the resistance state of the proposed memristor can be reversibly switched by modulating the concentration of [Formula: see text] islands. To investigate the performance of the device, the density of states, carrier concentration, electrical conductance in the degenerate and nondegenerate regions, current density and current-voltage characteristics are obtained regarding the energy band structure. In order to verify the accuracy of the research, the models of SE and TAT for two bipolar and unipolar switching modes are compared together and a rational agreement is reported in terms of trend and value. Moreover, the effects of equivalent thermal resistance, interlayer distance, temperature and conductive filament evolution on current-voltage characteristic of the device are investigated. In order to determine the accuracy of the proposed analytical method in this study, the proposed SE and TAT models are compared with each other, and an acceptable agreement is observed. Moreover, the physical-based analytic model of the proposed device in comparison with the experimental data of monolayer graphene nanoribbon and trilayer-structured graphene counterparts is investigated for analogous ambient conditions and rational results are observed. The obtained results of the proposed analytical models and figures of merit for the proposed device showed a promising performance of Trilayer graphene nanoribbon (TGN) for high-performance memristor applications.
Heuristic strategy using hybrid deep learning with transfer learning for oral cancer detection
Authors: Saraswathi T. , Murali Bhaskaran V.
Published: July 2024

Oral cancer becomes the most disastrous ailment that affects the oral cavity parts of the mouth. Oral cancer diagnosis is the main challenge in the medical field. It becomes expensive and less capable of classifying oral cancer. In some cases, it may cause unnecessary morbidity and mortality. Recently, the detection of malignant and premalignant oral lesions has been a critical process owing to their low image resolution and lower acquisition time. Thus, a novel hybrid deep learning with meta-heuristic-based optimization is proposed. The pre-processing occurs by median filtering and Contrast Limited Adaptive Histogram Equalization (CLAHE). The CLAHE method is utilized to reduce unwanted noise. Finally, the classification is done by a proposed hybrid-based deep learning model termed as Recurrent Deep Belief Network (RDBN), in which the Deep Belief Network (DBN) is incorporated with the Recurrent Neural Network (RNN). Here, the RDBN helps to increase the performance classification. Furthermore, the hyperparameters of the RDBN model, such as learning rate, epochs and hidden neurons, are tuned using the proposed Hybrid Beetle-Barnacle Swarm Optimization (HBBSO) algorithm, where the Barnacles Mating Optimizer (BMO) is superimposed with Beetle Swarm Optimization (BSO) algorithms. In the given proposed model, the selected features are extracted by the optimization algorithms. Here, the parameters are fine-tuned to get the better optimal solution. From the experimental outcome, the developed model has acquired 5.2% better than PSO-RDBN, 5.6% improved than GWO-RDBN, 3.2% enhanced than BSO-RDBN and 3.5% superior to BMO-RDBN regarding accuracy. Thus, the proposed model achieves higher results for detecting oral cancer with enhanced classification performance than the existing approaches.
Equivalence problem for first-order and second-order ODEs with a quadratic restriction
Authors: Bayrakdar Tuna , Turhan Ayşe
Published: July 2024

We consider the equivalence problem for first-order ordinary differential equations (ODEs) and recover the well-known result that any two first-order ODEs are equivalent under a point transformation by Cartan’s equivalence method. Curvature properties of some first-order and second-order ODEs motivate us to define the equivalence problem for such equations as the metric equivalence problem which defines the [Formula: see text]-structure on the corresponding manifolds. Accordingly, we show that there are first-order ODEs and linear second-order ODEs which may not be equivalent under a class of fiber-preserving diffeomorphisms.
Research on Phase Diagram of an Improved Dual-Lane Heterogeneous Lattice Hydrodynamic Model Considering Curved Road
Authors: Xu Yuanzi , Cheng Rongjun
Published: July 2024

The actual road traffic is a heterogeneous traffic flow composed of various types of vehicles. Ensuring safe operation of different types of vehicles while improving traffic stabilization has become a very important issue. Inspired by this, taking into account the discrepancy between the safety headway and the maximal velocity of different types of vehicles, an improved dual-lane heterogeneous lattice hydrodynamic model (DLHLHM) considering curved road is put forward in this research. The improved model’s stabilization condition is inferred by reductive perturbation method. The mKdV equation is also inferred to describe the evolutionary processes of traffic density wave. Numerical examples probed into the impact of lane-changing rate, angle of curved road and different maximum speeds and safety distances on traffic stabilization. The hysteresis loop for new model also is explored to study traffic flow stabilization. Numerical simulation results accord with theoretical analysis, which indicates the effectiveness and feasibility of the DLHLHM. This study provides a new perspective on the dynamic evolution of heterogeneous traffic flow.
Scattering of Oblique Water Waves by Two Vertical Barriers Over Trench-Type Bottom
Authors: Sharma Nidhi , Aggarwal Akshita , Martha S. C.
Published: July 2024

The problem of scattering of oblique water waves by two surface-piercing vertical thin rigid barriers over a trench-like bottom is investigated within the framework of linear water wave theory. The eigenfunction expansion technique is used to obtain a system of algebraic equations, which is further solved using the algebraic Least squares method. The reflection and transmission coefficients are computed and contrasted with the prior results. Physical characteristics such as surface elevation and horizontal force on both barriers are also determined and depicted graphically against various parameters to see their behavior. It is possible to strategically construct barriers and trench-like bottoms to regulate how waves reflect and propagate. This might mitigate the wave impact on ships, improving the safety, effectiveness and longevity of ships navigating through the channels. The highlights of the given problem include comparing the impacts of different depth ratios and barrier placements, followed by a study of the modulation parameter to evaluate standing wave patterns in the trench region. The specific circumstances for the formation of the standing wave are identified. Hence, understanding this study is critical for safe navigation because standing waves may produce hazardous conditions for boats, making navigation difficult and causing substantial damage.
A novel investigation of the influence of vaccination on pneumonia disease
Authors: Shyamsunder  , Purohit S. D. , Suthar D. L.
Published: July 2024

In the entire world, pneumonia is one of the leading causes of death, which is particularly dangerous for young children (those under five years old) and the elderly (those over 65). A deterministic susceptible, vaccinated, exposed, infected, and recovered (SVEIR) model is used in this work to mathematically study the dynamics of pneumonia disease and examine stability analysis, basic reproduction numbers, and equilibrium points of dynamical systems theory models. Spatial equilibria are studied to model disease-free equilibria that are locally asymptotic stable. Numerical simulations of the model have been carried out using MATLAB21. The SVEIR flow and its variables for different parameter sets have been studied through numerical simulations. The solution to the issue is provided through the use of illustrated and explicated results. According to research findings, if vaccination rates rise over the necessary vaccination ratio, the sickness will finally vanish from the community.
Numerical threshold stability of an unconditionally positivity-preserving numerical method for a nonlinear age-structured reaction–diffusion brucellosis model
Authors: Liu X. , Yang S. Y. , Yang Z. W.
Published: July 2024

This paper deals with numerical threshold stability of a nonlinear age-structured reaction–diffusion brucellosis model. An unconditionally positivity-preserving numerical scheme is established via a linearly implicit Euler method in time integration together with a center difference scheme in space discretization. A numerical basic reproduction number is proposed and denoted by [Formula: see text], which plays the same role in threshold stability analysis as the basic reproduction number of the model and converges to it as step-sizes vanishes. Namely, the numerical disease-free equilibrium is asymptotically stable if [Formula: see text]. Moreover, a unique space-independent coexistence equilibrium exists uniquely and is locally asymptotically stable while [Formula: see text]. Some numerical experiments are given in the end to confirm the efficiency of conclusions.
Multi-class classification of pathological myopia based on fundus photography
Authors: Zhao Jiaqing , Cao Guogang , He Jiangnan , Dai Cuixia
Published: July 2024

Pathological myopia (PM) is a severe ocular disease leading to blindness. As a traditional noninvasive diagnostic method, fundus color photography (FCP) is widely used in detecting PM due to its high fidelity and precision. However, manual examination of fundus photographs for PM is time-consuming and prone to high error rates. Existing automated detection technologies have yet to study the detailed classification in diagnosing different stages of PM lesions. In this paper, we proposed an intelligent system which utilized Resnet101 technology to multi-categorically diagnose PM by classifying FCPs with different stages of lesions. The system subdivided different stages of PM into eight subcategories, aiming to enhance the precision and efficiency of the diagnostic process. It achieved an average accuracy rate of 98.86% in detection of PM, with an area under the curve (AUC) of 98.96%. For the eight subcategories of PM, the detection accuracy reached 99.63%, with an AUC of 99.98%. Compared with other widely used multi-class models such as VGG16, Vision Transformer (VIT), EfficientNet, this system demonstrates higher accuracy and AUC. This artificial intelligence system is designed to be easily integrated into existing clinical diagnostic tools, providing an efficient solution for large-scale PM screening.
In vivo fluorescence flow cytometry reveals that the nanoparticle tumor vaccine OVA@HA-PEI effectively clears circulating tumor cells
Authors: Jin Wei , Fu Yuting , Ge Sisi , Sun Han , Pang Kai , Wei Xunbin
Published: July 2024

Tumor vaccine therapy offers significant advantages over conventional treatments, including reduced toxic side effects. However, it currently functions primarily as an adjuvant treatment modality in clinical oncology due to limitations in tumor antigen selection and delivery methods. Tumor vaccines often fail to elicit a sufficiently robust immune response against progressive tumors, thereby limiting their clinical efficacy. In this study, we developed a nanoparticle-based tumor vaccine, OVA@HA-PEI, utilizing ovalbumin (OVA) as the presenting antigen and hyaluronic acid (HA) and polyethyleneimine (PEI) as adjuvants and carriers. This formulation significantly enhanced the proliferation of immune cells and cytokines, such as CD3, CD8, interferon-[Formula: see text], and tumor necrosis factor-[Formula: see text], in vivo, effectively activating an immune response against B16–F10 tumors. In vivo fluorescence flow cytometry (IVFC) has already become an effective method for monitoring circulating tumor cells (CTCs) due to its direct, noninvasive, and long-term detection capabilities. Our study utilized a laboratory-constructed IVFC system to monitor the immune processes induced by the OVA@HA-PEI tumor vaccine and an anti-programmed death-1 (PD-1) antibody. The results demonstrated that the combined treatment of OVA@HA-PEI and anti-PD-1 antibody significantly improved the survival time of mice compared to anti-PD-1 antibody treatment alone. Additionally, this combination therapy substantially reduced the number of CTCs in vivo, increased the clearance rate of CTCs by the immune system, and slowed tumor progression. These findings greatly enhance the clinical application prospects of IVFC and tumor vaccines.
A 3D semantic segmentation network for accurate neuronal soma segmentation
Authors: Ma Li , Zhong Qi , Wang Yezi , Yang Xiaoquan , Du Qian
Published: July 2024

Neuronal soma segmentation plays a crucial role in neuroscience applications. However, the fine structure, such as boundaries, small-volume neuronal somata and fibers, are commonly present in cell images, which pose a challenge for accurate segmentation. In this paper, we propose a 3D semantic segmentation network for neuronal soma segmentation to address this issue. Using an encoding-decoding structure, we introduce a Multi-Scale feature extraction and Adaptive Weighting fusion module (MSAW) after each encoding block. The MSAW module can not only emphasize the fine structures via an upsampling strategy, but also provide pixel-wise weights to measure the importance of the multi-scale features. Additionally, a dynamic convolution instead of normal convolution is employed to better adapt the network to input data with different distributions. The proposed MSAW-based semantic segmentation network (MSAW-Net) was evaluated on three neuronal soma images from mouse brain and one neuronal soma image from macaque brain, demonstrating the efficiency of the proposed method. It achieved an F1 score of 91.8% on Fezf2-2A-CreER dataset, 97.1% on LSL-H2B-GFP dataset, 82.8% on Thy1-EGFP-Mline dataset, and 86.9% on macaque dataset, achieving improvements over the 3D U-Net model by 3.1%, 3.3%, 3.9%, and 2.3%, respectively.
Identification of noteworthy features and data mining techniques for heart disease prediction
Authors: Kumar Parvathaneni Rajendra , Ravichandran Suban , Narayana S.
Published: July 2024

The most fatal disease on the earth is thought to be illness of the heart. There are a lot of features that change the heart’s composition or functionality. In most cases, it is hard for doctors to make a diagnosis accurately and quickly. This study’s objective is to determine critical factors and methods of data mining which can improve the accuracy for prediction of heart disease. Further, it is essential to make use of automatic technologies in diagnosing heart diseases as early as possible. To develop a new prediction technique for heart disease that comprises four phases such as “(a) Pre-processing, (b) Feature extraction, (c) Feature selection and (d) Classification”. The initial stage of pre-processing is when the incoming data is treated to the elimination of redundant and missed numbers. Then, from the initial stage of data, the higher-order statistical and statistical characteristics, chi-squared features and symmetrical uncertainty attributes are derived. However, when working with a greater number of characteristics, the curse of dimensionality was a severe issue. Hence, the characteristics of optimal features are planned from the overall set of features. A novel Hybrid Bull and Elephant Algorithm (HB-EA) is introduced for the selection of optimal features. Consequently, the selected set of features is subjected to various classifiers as an ensemble model that contains “Naïve Bayes (NB), Decision Tree (DT), Neural Network (NN), Support Vector Machine (SVM), Optimized Recurrent Neural Network (RNN) and Linear Regression (LR)”. The final step is to log off efficiency for outputs obtained from the group of classifiers and determine the outcome. The RNN weights are ideally tuned by the suggested HB-EA technique to boost the system’s accuracy. The proposed model is finally evaluated against existing techniques to determine its superiority. The suggested technique for dataset 1 achieved maximum accuracy (0.916), and it is 15.24%, 8.76%, 7.56%, 4.09% and 1.89% better than convolution schemes like Random Forest (RF), Deep Belief Network (DBN), SVM, [Formula: see text]-Nearest Neighbor (KNN) and Elephant Herding Optimization (EHO) models.
Strong incidence domination in some operations of fuzzy incidence graphs and application in security allocation
Authors: Nair Kavya R. , Sunitha M. S.
Published: July 2024

This paper explores operations on fuzzy incidence graphs (FIGs), focusing on join, Cartesian product, tensor product, and composition. Emphasizing strong fuzzy incidence graphs (SFIGs), the study examines strong incidence domination (SID) and the strong incidence domination number (SIDN) in these operations. Basic properties of FIGs resulting from these operations are studied, and bounds for the SIDN of the product of two SFIGs are established for Cartesian and tensor products. The research includes FIGs with strong join and composition, as well as complete fuzzy incidence graphs (CFIGs) and FIGs with effective pairs. Additionally, the paper discusses an application of SID in security allocation.
The influence of earthworks construction on porewater pressures in clays and mudstones of the Lias Group
Authors: Briggs Kevin M. , Trinidad González Yuderka , Meijer Gerrit J. , Ridley Andrew , Powrie William , Butler Simon , Sartain Nick
Published: July 2024

Monitoring the changes in porewater pressure associated with the construction of earthworks can yield information on the stiffness and permeability of the ground, as well as how the natural groundwater regime might be impacted. This paper presents 3 years of porewater pressure measurements in weathered Lias Group mudstone, obtained from a trial cutting and a trial embankment constructed for the UK's High Speed Two (HS2) railway. The immediate changes in porewater pressure were small in relation to the changes in total stress imposed. This can be explained by the consolidation or swelling during the period of construction, combined with the sensitivity of very stiff clays and mudstones to a very small (0.5%) reduction in the degree of saturation. In the longer term, porewater pressures reduced across the site owing to the reduction in ground level at the trial cutting. Rates of porewater pressure change were accelerated by more permeable limestone within the ground profile reducing drainage path lengths. It was concluded that construction-induced porewater pressure changes may be smaller, and their rate of dissipation more rapid, in weathered clays and mudstones such as those of the Lias Group than in younger, more compressible clay deposits.
Alignment conditions for NURBS-based design of mixed tension-compression grid shells
Authors: Miki Masaaki , Mitchell Toby
Published: July 2024

In architecture, shapes of surfaces that can withstand gravity with no bending action are considered ideal for
shell structures.
Those shells have special geometries through which they can stream gravitational force toward the ground via stresses strictly tangent to the surface, making them highly efficient. The process of finding these special forms is called
form-finding.
Recently, [Miki and Mitchell 2022] presented a method to reliably produce mixed tension-compression continuum shells, a type of shells known to be especially difficult to form-find. The key to this method was to use the concept of the Airy stress function to derive a valid bending-free shell shape by iterating on both the shell shape and the Airy stress function; this turns a problem that is over-constrained in general into a problem with many solutions.

In [Miki and Mitchell 2022], it was proposed that the method could also be used to design grid shells by tracing curves on a continuum shell such that the resulting grid has bars that are both bending-free and form flat panels, a property useful for construction of real grid shells made of glass and steel. However, this special type of grid is
not
guaranteed to exist in general on a mixed-tension compression shell, even when the shell is in bending-free equilibrium [Miki and Mitchell 2023]. Additional conditions must be imposed on the shell shape to guarantee the existence of simultaneously bending-free and conjugate grid directions. The current study resolves the existence issue by adding
alignment conditions.

We consider several practical curve alignment conditions: alignment with the lines of curvature of the shell, approximate alignment with a bidirectional set of user-prescribed guide curves, and exact alignment with a single direction of user-prescribed guide curves. We report that the variable projection method originally used to solve the form-finding problem in the work of [Miki and Mitchell 2022] can be successfully extended to solve the newly introduced alignment conditions, and conclude with results for several practical design examples. To our knowledge, this is the first method that can take a user-input grid and find a "nearby" grid that is both flat-panelled and in bending-free equilibrium for the general case of mixed tension-compression grid shells.
BlockFusion: Expandable 3D Scene Generation using Latent Tri-plane Extrapolation
Authors: Wu Zhennan , Li Yang , Yan Han , Shang Taizhang , Sun Weixuan , Wang Senbo , Cui Ruikai , Liu Weizhe , Sato Hiroyuki , Li Hongdong , Ji Pan
Published: July 2024

We present BlockFusion, a diffusion-based model that generates 3D scenes as unit blocks and seamlessly incorporates new blocks to extend the scene. BlockFusion is trained using datasets of 3D blocks that are randomly cropped from complete 3D scene meshes. Through per-block fitting, all training blocks are converted into the hybrid neural fields: with a tri-plane containing the geometry features, followed by a Multi-layer Perceptron (MLP) for decoding the signed distance values. A variational auto-encoder is employed to compress the tri-planes into the latent tri-plane space, on which the denoising diffusion process is performed. Diffusion applied to the latent representations allows for high-quality and diverse 3D scene generation.

To expand a scene during generation, one needs only to append empty blocks to overlap with the current scene and extrapolate existing latent tri-planes to populate new blocks. The extrapolation is done by conditioning the generation process with the feature samples from the overlapping tri-planes during the denoising iterations. Latent tri-plane extrapolation produces semantically and geometrically meaningful transitions that harmoniously blend with the existing scene. A 2D layout conditioning mechanism is used to control the placement and arrangement of scene elements. Experimental results indicate that BlockFusion is capable of generating diverse, geometrically consistent and unbounded large 3D scenes with unprecedented high-quality shapes in both indoor and outdoor scenarios.
Adaptive grid generation for discretizing implicit complexes
Authors: Ju Yiwen , Du Xingyi , Zhou Qingnan , Carr Nathan , Ju Tao
Published: July 2024

We present a method for generating a simplicial (e.g., triangular or tetrahedral) grid to enable adaptive discretization of implicit shapes defined by a vector function. Such shapes, which we call implicit complexes, are generalizations of implicit surfaces and useful for representing non-smooth and non-manifold structures. While adaptive grid generation has been extensively studied for polygonizing implicit surfaces, few methods are designed for implicit complexes. Our method can generate adaptive grids for several implicit complexes, including arrangements of implicit surfaces, CSG shapes, material interfaces, and curve networks. Importantly, our method adapts the grid to the geometry of not only the implicit surfaces but also their lower-dimensional intersections. We demonstrate how our method enables efficient and detail-preserving discretization of non-trivial implicit shapes.
X-SLAM: Scalable Dense SLAM for Task-aware Optimization using CSFD
Authors: Peng Zhexi , Yang Yin , Shao Tianjia , Jiang Chenfanfu , Zhou Kun
Published: July 2024

We present X-SLAM, a real-time dense differentiable SLAM system that leverages the complex-step finite difference (CSFD) method for efficient calculation of numerical derivatives, bypassing the need for a large-scale computational graph. The key to our approach is treating the SLAM process as a differentiable function, enabling the calculation of the derivatives of important SLAM parameters through Taylor series expansion within the complex domain. Our system allows for the real-time calculation of not just the gradient, but also higher-order differentiation. This facilitates the use of high-order optimizers to achieve better accuracy and faster convergence. Building on X-SLAM, we implemented end-to-end optimization frameworks for two important tasks: camera relocalization in wide outdoor scenes and active robotic scanning in complex indoor environments. Comprehensive evaluations on public benchmarks and intricate real scenes underscore the improvements in the accuracy of camera relocalization and the efficiency of robotic navigation achieved through our task-aware optimization. The code and data are available at https://gapszju.github.io/X-SLAM.
ReCollection: Creating synthetic memories with AI in an interactive art installation
Authors: Zhang Weidi , Cheng Lijiaozi , Luo Jieliang
Published: July 2024

This paper describes the conceptual background, artificial intelligent (AI) system design, and visualization strategies of an interactive AI art experience: ReCollection. In the art installation, the artwork assembles collective visual memories based on participants' language input, blurring the boundaries between remembrance and imagination through intelligent system design and experimental visualization. By providing a conceptual framework for non-linear narratives, constituting symbiotic imaginations for memories, this artwork aims to provide an inclusive art experience that inspires collective memory reproduction by providing an intimate recollection of symbiosis between beings and apparatus. Furthermore, this work provides a future prototype that cultivates empathy for the dementia community by investigating the tensions in the correlations between visual representations and narratives, as well as these between the past, present, and future.
reconFIGURE: Confronting Audiences with Digital Doppelgängers
Authors: Bruggisser Florian Christoph , Leisi Chris Elvis , Lund-Jensen Pascal , Fröhlich Martin , Salter Christopher Lloyd
Published: July 2024

This paper describes the artistic context, technical implementation, user study, evaluation and future work around reconFIGURE, a participatory installation exploring the transformation of human bodies through computational systems. The installation involves capturing visitors' images, transforming these into animated 3D doppelgängers, and projecting them on a large-screen. Unique in its rapid transformation from 2D to 3D, reconFIGURE highlights the aesthetic-social repercussions of transforming whole human body images using machine learning.
Unveiling New Artistic Dimensions in Calligraphic Arabic Script with Generative Adversarial Networks
Authors: Sobhan Arshia , Pasquier Philippe , Tindale Adam
Published: July 2024

We present an artistic exploration into calligraphic Arabic script, focusing on the nastaliq style predominant in Iran, by harnessing the affordances of Generative Adversarial Networks (GANs). Recognizing the unique challenges posed by Arabic script's cursive nature and its inadequate representation by conventional tools, our work seeks to bridge the gap between traditional calligraphy and novel technological capabilities. Two custom datasets are introduced, Nas4-60k and Nas4-60k-aug, designed to train our generative networks in producing calligraphic Arabic. Utilizing the StyleGAN2-ada architecture, our approach successfully generates stylistically coherent and high-quality calligraphic samples. These samples exhibit meaningful feature extraction and generalization of calligraphic features, extending beyond the training sets. Furthermore, our system reveals a continuous spectrum of calligraphic features through latent space interpolations, leading to the creation of dynamic, innovative artworks that blend traditional and contemporary elements of Arabic calligraphy. Drawing inspiration from the compositional form of siyah-mashq, our work culminates in multiple publicly presented artworks that exhibit a new mode of creative expression and highlight the potential of GANs in unveiling new artistic dimensions in calligraphic Arabic script.
Southern Wind Algorist: Merging Nine Algorithms with Nine Gay Stories Written in Classical Chinese
Authors: Zhou Tengchao
Published: July 2024

This paper presents an immersive interactive art installation called Southern Wind Algorist, which explores the convergence of knowledge from multiple disciplines. The installation is situated at the intersection of media arts, Chinese studies, and gay & lesbian studies. It combines nine well-known algorithms in the history of computer graphics with nine gay stories written in classical Chinese. The resulting artwork is a unique fusion that has never been assembled before. The paper delves into the related works, the cultural context of the stories, the visual effects' relationship with the underlying themes, methodology, public reception, technical aspects, and sustainability. It also addresses issues related to the artwork's linguistics and visual accessibility. The installation creates a space for people to reflect on queer desires' historical perspectives and future trajectories and promotes cultural diversity.
Critical Climate Machine: A Visual and Musical Exploration of Climate Misinformation through Machine Learning
Authors: Robillard Gaëtan , Nika Jérôme
Published: July 2024

Critical Climate Machine is a cutting-edge media art installation that critically exposes and quantifies mechanisms of climate change misinformation. Utilizing computational aesthetics across data, imagery, and sound, this work processes real-time data from X (Twitter) through a natural language processing learning model derived from cognitive sciences. It not only renders the statistical aspects of this data visually but also manifests its thermal effects. A unique audio dimension is introduced through dialogues between climate skeptics and climate advocates, processed by the generative machine learning (ML) algorithm Dicy2. These elements collectively shape the installation, each unveiling its distinctive algorithmic aesthetics and technical underpinnings. This paper concentrates on the dual application of ML algorithms: one for dissecting extensive online misinformation streams, and the other for creating climate-related dialogues. This dual approach opens a discussion on the mediation of climate, at the convergence of computational and physical realms. Our aim is to critically examine the role of ML technologies in crafting aesthetic experiences that resonate within scientific discourse and public debate on climate issues.