Miracle-dz/UniFAR-FTUs huggingface dataset:
Pancreatic ductal adenocarcinoma chemoresistance: From metabolism reprogramming to novel treatment

Abstract As pancreatic cancer (PC) is highly malignant, its patients tend to develop metastasis at an early stage and show a poor response to conventional chemotherapies. First-line chemotherapies for PC, according to current guidelines, include fluoropyrimidine- and gemcitabine-based regimens. Accumulating research on drug resistance has shown that biochemical metabolic aberrations in PC, especially those involving glycolysis and glutamine metabolism, are highly associated with chemoresistance. Additionally, lipid metabolism is a major factor in chemoresistance. However, emerging compounds that target these key metabolic pathways have the potential to overcome chemoresistance. This review summarizes how PC develops chemoresistance through aberrations in biochemical metabolism and discusses novel critical targets and pathways within cancer metabolism for new drug research.
Inhibitory function of CDK12i combined with WEE1i on castration-resistant prostate cancer cells in vitro and in vivo

Abstract Objective The inhibitors of CDK12 and WEE1 (SR-4835 and AZD-1775) have rarely been evaluated in studies on castration-resistant prostate cancer (CRPC) treatment. The research objective of this article is to study the inhibitory effect of SR-4835 and AZD-1775 on CRPC cells and to explore the therapeutic effect of combining the two drugs in the treatment of CRPC cells in vitro and in vivo. Methods We performed Western blot, quantitative real-time PCR, Cell Counting Kit-8, colony formation, EdU, and immunofluorescence assays; cell cycle analysis, wound scratch and Transwell assays and nude mice xenograft tumor analysis to identify the mechanism and measure the therapeutic effect of SR-4835, AZD-1775 and the combination in CRPC cells. Results Compared with normal prostate cells, the expressions of CDK12 and WEE1 in prostate cancer cells, especially CRPC cells, were significantly increased at protein and mRNA levels. SR-4835 can cause DNA damage in CRPC cells by inhibiting the expression of DNA damage repair genes. AZD-1775 inhibits the G2/M phase checkpoint function. Performing in vivo and in vitro experiments, we found that SR-4835 combined with AZD-1775 significantly enhanced the inhibitory effect on CRPC cell to a greater degree than monotherapy. Conclusions In summary, SR-4835 combined with AZD-1775 can eliminate CRPC cells by inducing DNA damage and inhibiting the normal repair machinery. Therefore, we consider this combination therapy to be a promising strategy for CRPC patients.
Comparative effectiveness of salvage chemotherapy regimens and chimeric antigen T-cell receptor therapies in relapsed and refractory diffuse large B cell lymphoma: a network meta-analysis of clinical trials

Abstract The optimal salvage chemotherapy regimen (SC) for relapsed/refractory (R/R) diffuse large B-cell lymphoma (DLBCL) prior to autologous stem cell transplant remains unclear. Moreover, although chimeric antigen receptor T cell (CAR-T) therapies were recently approved for primary refractory DLBCL, head-to-head comparisons are lacking. We searched MEDLINE, EMBASE and CENTRAL to July 2022, for randomized trials that enrolled adult patients with R/R DLBCL and performed network meta-analyses (NMA) to assess the efficacy of SC and CAR-T therapies. NMA of SC (6 trials, 7 regimens, n = 1831) indicated that rituximab with gemcitabine, dexamethasone, cisplatin (R-GDP) improved OS and PFS over compared regimens. NMA of 3 CAR-T trials (n = 865) indicated that both axi-cel and liso-cel improved PFS over standard of care, with no difference in OS. Our results indicate that R-GDP may be preferred for R/R DLBCL over other SC compared. Longer follow-up is required for ongoing comparative survival analysis as data from CAR-T trials matures.
Road safety and children's television (literature review)

The purpose of this literature review is to summarize the various ways mass media researchers have described aspects of the relationship between children and television, with special reference to the implications for road safety content in the television programmes children watch. After briefly covering the history of mass media and persuasion campaign research, the review looks at children's viewing patterns. It goes on to describe television content, in terms of the demography of characters, their drinking and driving behaviour, the influence of aggressive and prosocial content or models on attitudes and behaviour, and how television content may cultivate for heavier viewers a picture of the real world (including the world of driving and road behaviour). Because audience predispositions help shape perceptions of content, the review then considers some of these, namely the child's age and comprehension skills, the perceived reality of television, the uses and gratifications of child audiences, the social context of television watching and the question of television literacy, discussing their implications for road safety content on television. The review goes on to look at the hostile television environment in which public information campaigns take place, the lessons to be learnt from social marketing approaches and the reasons why some public information campaigns have failed and some succeeded. Finally, the review discusses the implications of such approaches for television road safety messages directed at children (a).
THE L-BOUNDEDNESS OF WAVE OPERATORS FOR TWO DIMENSIONAL SCHRÖDINGER OPERATORS WITH THRESHOLD SINGULARITIES

We generalize the recent result of Erdoğan, Goldberg and Green on the L-boundedness of wave operators for two dimensional Schrödinger operators and prove that they are bounded in L(R) for all 1 < p < ∞ if and only if the Schrödinger operator possesses no p-wave threshold resonances, viz. Schrödinger equation (−∆ + V (x))u(x) = 0 possesses no solutions which satisfy u(x) = (a1x1 + a2x2)|x| + o(|x|−1) as |x| → ∞ for an (a1, a2) ∈ R \{(0, 0)} and, otherwise, they are bounded in L(R) for 1 < p ≤ 2 and unbounded for 2 < p < ∞. We present also a new proof for the known part of the result.
EVALUATION OF CYFRA 21-1, ANGIOPOETIN-2, PENTRAXIN-3, SRAGE, IL-6, AND IL-10 IN POLYTRAUMATIZED PATIENTS WITH CONCOMITANT THORACIC TRAUMA—HELPFUL MARKERS TO PREDICT PNEUMONIA?

ABSTRACT Background: Pneumonia is a frequent complication after polytrauma. This study aims to evaluate the ability of different serum markers to identify patients at risk of developing pneumonia after polytrauma. Methods: A retrospective analysis of prospectively collected data in polytraumatized patients with concomitant thoracic trauma (Injury Severity Score ≥16, Abbreviated Injury Scale Thorax ≥ 3) was performed. The study cohort was divided into patients with and without pneumonia during the clinical course. Serum levels of lung epithelial (CYFRA 21-1), endothelial (Ang-2), and inflammatory (PTX-3, sRAGE, IL-6, IL-10) markers were measured upon arrival in the trauma room and on days 2 and 5. Results: A total of 73 patients and 16 healthy controls were included in this study. Of these, 20 patients (27.4%) developed pneumonia. Polytraumatized patients showed significantly increased CYFRA 21-1 levels with a distinct peak after admission compared with healthy controls. Serum PTX-3 significantly increased on day 2 in polytraumatized patients compared with healthy controls. Injury Severity Score and demographic parameters were comparable between both groups (pneumonia vs. no pneumonia). No statistically significant difference could be observed for serum levels of CYFRA 21-1, Ang-2, PTX-3, sRAGE, IL-6, and IL-10 between the groups (pneumonia vs. no pneumonia) on all days. Logistic regression revealed a combination of IL-6, IL-10, sRAGE, and PTX-3 to be eventually helpful to identify patients at risk of developing pneumonia and our newly developed score was significantly higher on day 0 in patients developing pneumonia (P < 0.05). Conclusion: The investigated serum markers alone are not helpful to identify polytraumatized patients at risk of developing pneumonia, while a combination of IL-6, IL-10, PTX-3, and sRAGE might be.
A Voting-Stacking Ensemble of Inception Networks for Cervical Cytology Classification

Cervical cancer is one of the most severe diseases threatening women's health. Early detection and diagnosis can significantly reduce cancer risk, in which cervical cytology classification is indispensable. Researchers have recently designed many networks for automated cervical cancer diagnosis, but the limited accuracy and bulky size of these individual models cannot meet practical application needs. To address this issue, we propose a Voting-Stacking ensemble strategy, which employs three Inception networks as base learners and integrates their outputs through a voting ensemble. The samples misclassified by the ensemble model generate a new training set on which a linear classification model is trained as the meta-learner and performs the final predictions. In addition, a multi-level Stacking ensemble framework is designed to improve performance further. The method is evaluated on the SIPakMed, Herlev, and Mendeley datasets, achieving accuracies of 100%, 100%, and 100%, respectively. The experimental results outperform the current state-of-the-art (SOTA) methods, demonstrating its potential for reducing screening workload and helping pathologists detect cervical cancer.
Collective Animal Behavior

Acknowledgments ix Chapter 1: Introduction 1 Chapter 2: Coming Together 14 Chapter 3: Information Transfer 44 Chapter 4: Making Decisions 77 Chapter 5: Moving Together 101 Chapter 6: Synchronization 130 Chapter 7: Structures 151 Chapter 8: Regulation 173 Chapter 9: Complicated Interactions 198 Chapter 10: The Evolution of Co-operation 223 Chapter 11: Conclusions 253 References 259 Index 293
Monte Carlo approach to the evaluation of the security of device-independent quantum key distribution

We present a generic study on the information-theoretic security of multi-setting device-independent quantum key distribution (DIQKD) protocols, i.e. ones that involve more than two measurements (or inputs) for each party to perform, and yield dichotomic results (or outputs). The approach we develop, when applied in protocols with either symmetric or asymmetric Bell experiments, yields nontrivial upper bounds on the secure key rates, along with the detection efficiencies required upon the measuring devices. The results imply that increasing the number of measurements may lower the detection efficiency required by the security criterion. The improvement, however, depends on (i) the choice of multi-setting Bell inequalities chosen to be tested in a protocol, and (ii) either a symmetric or asymmetric Bell experiment is considered. Our results serve as an advance toward the quest for evaluating security and reducing efficiency requirement of applying DIQKD in scenarios without heralding.
Universally counting curves in Calabi--Yau threefolds

We show that curve enumeration invariants of complex threefolds with nef anti-canonical bundle are determined by their values on local curves. This statement and its proof are inspired by the proof of the Gopakumar--Vafa integrality conjecture by Ionel and Parker. The conjecture of Maulik, Nekrasov, Okounkov, and Pandharipande relating Gromov--Witten and Donaldson--Pandharipande-Thomas invariants is known for local curves by work of Bryan, Okounkov, and Pandharipande, hence holds for all complex threefolds with nef anti-canonical bundle (in particular, all Calabi--Yau threefolds).
A Reanalysis of the Isolated Black Hole Candidate OGLE-2011-BLG-0462/MOA-2011-BLG-191

There are expected to be ∼108 isolated black holes (BHs) in the Milky Way. OGLE-2011-BLG-0462/MOA-2011-BLG-191 (OB110462) is the only such BH with a mass measurement to date. However, its mass is disputed: Lam et al. measured a lower mass of 1.6–4.4 M ⊙, while Sahu et al. and Mróz et al. measured a higher mass of 5.8–8.7 M ⊙. We reanalyze OB110462, including new data from the Hubble Space Telescope (HST) and rereduced Optical Gravitational Lensing Experiment (OGLE) photometry. We also rereduce and reanalyze the HST data set with newly available software. We find significantly different (∼1 mas) HST astrometry than Lam et al. in the unmagnified epochs due to the amount of positional bias induced by a bright star ∼0. ″4 from OB110462. After modeling the updated photometric and astrometric data sets, we find the lens of OB110462 is a 6.0−1.0+1.2M⊙ BH. Future observations with the Nancy Grace Roman Space Telescope, which will have an astrometric precision comparable or better to HST but a field of view 100× larger, will be able to measure hundreds of isolated BH masses via microlensing. This will enable the measurement of the BH mass distribution and improve understanding of massive stellar evolution and BH formation channels.
A data-driven approach to predict decision point choice during normal and evacuation wayfinding in multi-story buildings

Understanding pedestrian route choice behavior in complex buildings is important to ensure pedestrian safety. Previous studies have mostly used traditional data collection methods and discrete choice modeling to understand the influence of different factors on pedestrian route and exit choice, particularly in simple indoor environments. However, research on pedestrian route choice in complex buildings is still limited. This paper presents a data-driven approach for understanding and predicting the pedestrian decision point choice during normal and emergency wayfinding in a multi-story building. For this, we first built an indoor network representation and proposed a data mapping technique to map VR coordinates to the indoor representation. We then used a well-established machine learning algorithm, namely the random forest (RF) model to predict pedestrian decision point choice along a route during four wayfinding tasks in a multi-story building. Pedestrian behavioral data in a multi-story building was collected by a Virtual Reality experiment. The results show a much higher prediction accuracy of decision points using the RF model (i.e., 93% on average) compared to the logistic regression model. The highest prediction accuracy was 96% for task 3. Additionally, we tested the model performance combining personal characteristics and we found that personal characteristics did not affect decision point choice. This paper demonstrates the potential of applying a machine learning algorithm to study pedestrian route choice behavior in complex indoor buildings.
Deep Image Harmonization in Dual Color Spaces

Image harmonization is an essential step in image composition that adjusts the appearance of composite foreground to address the inconsistency between foreground and background. Existing methods primarily operate in correlated RGB color space, leading to entangled features and limited representation ability. In contrast, decorrelated color space (e.g., Lab) has decorrelated channels that provide disentangled color and illumination statistics. In this paper, we explore image harmonization in dual color spaces, which supplements entangled RGB features with disentangled L, a, b features to alleviate the workload in harmonization process. The network comprises a RGB harmonization backbone, an Lab encoding module, and an Lab control module. The backbone is a U-Net network translating composite image to harmonized image. Three encoders in Lab encoding module extract three control codes independently from L, a, b channels, which are used to manipulate the decoder features in harmonization backbone via Lab control module. Our code and model are available at https://github.com/bcmi/DucoNet-Image-Harmonization.
Efficient techniques to GPU Accelerations of Multi-Shot Quantum Computing Simulations

Quantum computers are becoming practical for computing numerous applications. However, simulating quantum computing on classical computers is still demanding yet useful because current quantum computers are limited because of computer resources, hardware limits, instability, and noises. Improving quantum computing simulation performance in classical computers will contribute to the development of quantum computers and their algorithms. Quantum computing simulations on classical computers require long performance times, especially for quantum circuits with a large number of qubits or when simulating a large number of shots for noise simulations or circuits with intermediate measures. Graphical processing units (GPU) are suitable to accelerate quantum computer simulations by exploiting their computational power and high bandwidth memory and they have a large advantage in simulating relatively larger qubits circuits. However, GPUs are inefficient at simulating multi-shots runs with noises because the randomness prevents highly parallelization. In addition, GPUs have a disadvantage in simulating circuits with a small number of qubits because of the large overheads in GPU kernel execution. In this paper, we introduce optimization techniques for multi-shot simulations on GPUs. We gather multiple shots of simulations into a single GPU kernel execution to reduce overheads by scheduling randomness caused by noises. In addition, we introduce shot-branching that reduces calculations and memory usage for multi-shot simulations. By using these techniques, we speed up x10 from previous implementations.
Spatially correlated classical and quantum noise in driven qubits: The good, the bad, and the ugly

Correlated noise across multiple qubits poses a significant challenge for achieving scalable and fault-tolerant quantum processors. Despite recent experimental efforts to quantify this noise in various qubit architectures, a comprehensive understanding of its role in qubit dynamics remains elusive. Here, we present an analytical study of the dynamics of driven qubits under spatially correlated noise, including both Markovian and non-Markovian noise. Surprisingly, we find that, while correlated classical noise only leads to correlated decoherence without increasing the quantum coherence in the system, the correlated quantum noise can be exploited to generate entanglement. In particular, we reveal that, in the quantum limit, pure dephasing noise induces a coherent long-range two-qubit Ising interaction that correlates distant qubits. In contrast, for purely transverse noise when qubits are subjected to coherent drives, the correlated quantum noise induces both coherent symmetric exchange and Dzyaloshinskii-Moriya interaction between the qubits, as well as correlated relaxation, both of which give rise to significant entanglement. Remarkably, in this case, we uncover that the system exhibits distinct dynamical phases in different parameter regimes. Finally, we reveal the impact of spatio-temporally correlated 1/f noise on the decoherence rate, and how its temporal correlations restore lost entanglement. Our analysis not only offers critical insights into designing effective error mitigation strategies to reduce harmful effects of correlated noise, but also enables tailored protocols to leverage and harness noise-induced correlations for quantum information processing.
From Ambiguity to Explicitness: NLP-Assisted 5G Specification Abstraction for Formal Analysis

Formal method-based analysis of the 5G Wireless Communication Protocol is crucial for identifying logical vulnerabilities and facilitating an all-encompassing security assessment, especially in the design phase. Natural Language Processing (NLP) assisted techniques and most of the tools are not widely adopted by the industry and research community. Traditional formal verification through a mathematics approach heavily relied on manual logical abstraction prone to being time-consuming, and error-prone. The reason that the NLP-assisted method did not apply in industrial research may be due to the ambiguity in the natural language of the protocol designs nature is controversial to the explicitness of formal verification. To address the challenge of adopting the formal methods in protocol designs, targeting (3GPP) protocols that are written in natural language, in this study, we propose a hybrid approach to streamline the analysis of protocols. We introduce a two-step pipeline that first uses NLP tools to construct data and then uses constructed data to extract identifiers and formal properties by using the NLP model. The identifiers and formal properties are further used for formal analysis. We implemented three models that take different dependencies between identifiers and formal properties as criteria. Our results of the optimal model reach valid accuracy of 39% for identifier extraction and 42% for formal properties predictions. Our work is proof of concept for an efficient procedure in performing formal analysis for largescale complicate specification and protocol analysis, especially for 5G and nextG communications.
On counterexamples to unique continuation for critically singular wave equations

We consider wave equations with a critically singular potential $\xi \cdot \sigma^{-2}$ diverging as an inverse square at a hypersurface $\sigma = 0$. Our aim is to construct counterexamples to unique continuation from $\sigma = 0$ for this equation, provided there exists a family of null geodesics trapped near $\sigma = 0$. This extends the classical geometric optics construction of Alinhac-Baouendi (i) to linear differential operators with singular coefficients, and (ii) over non-small portions of $\sigma = 0$ - by showing that such counterexamples can be further continued as long as this null geodesic family remains trapped and regular. As an application to relativity and holography, we construct counterexamples to unique continuation from the conformal boundaries of asymptotically Anti-de Sitter spacetimes for some Klein-Gordon equations; this complements the unique continuation results of the second author with Chatzikaleas, Holzegel, and McGill and suggests a potential mechanism for counterexamples to the AdS/CFT correspondence.
Estimation of Complex Valued Laplacian Matrices for Topology Identification in Power Systems

In this paper, we investigate the problem of estimating a complex-valued Laplacian matrix with a focus on its application in the estimation of admittance matrices in power systems. The proposed approach is based on a constrained maximum likelihood estimator (CMLE) of the complex-valued Laplacian, which is formulated as an optimization problem with Laplacian and sparsity constraints. The complex-valued Laplacian is a symmetric, non-Hermitian matrix that exhibits a joint sparsity pattern between its real and imaginary parts. Thus, we present a group-sparse-based penalized log-likelihood approach for the Laplacian estimation. Leveraging the mixed \ell 2,1 norm relaxation of the joint sparsity constraint, we develop a new alternating direction method of multipliers (ADMM) estimation algorithm for the implementation of the CMLE of the Laplacian matrix under a linear Gaussian model. Next, we apply the proposed ADMM algorithms for the problem of estimating the admittance matrix under three commonly used measurement models that stem from Kirchhoff and Ohm laws, each with different assumptions and simplifications: 1) the nonlinear alternating current (AC) model; 2) the decoupled linear power flow (DLPF) model; and 3) the direct current (DC) model. The performance of the ADMM algorithm is evaluated using data from the IEEE 33-bus power system data under different settings. The numerical experiments demonstrate that the proposed algorithm outperforms existing methods in terms of mean-squared-error (MSE) and F-score, thus providing a more accurate recovery of the admittance matrix.