bluuebunny/crossref_metadata_2025 huggingface dataset:
Efficient Real-Time Path Planning with Self-Evolving Particle Swarm Optimization in Dynamic Scenarios
Authors: Xin Jinghao , Li Zhi , Zhang Yang , Li Ning
Published: November 2023

Particle Swarm Optimization (PSO) has demonstrated efficacy in addressing static path planning problems. Nevertheless, such application on dynamic scenarios has been severely precluded by PSO’s low computational efficiency and premature convergence downsides. To address these limitations, we proposed a Tensor Operation Form (TOF) that converts particle-wise manipulations to tensor operations, thereby enhancing computational efficiency. Harnessing the computational advantage of TOF, a variant of PSO, designated as Self-Evolving Particle Swarm Optimization (SEPSO) was developed. The SEPSO is underpinned by a novel Hierarchical Self-Evolving Framework (HSEF) that enables autonomous optimization of its own hyper-parameters to evade premature convergence. Additionally, a Priori Initialization (PI) mechanism and an Auto Truncation (AT) mechanism that substantially elevates the real-time performance of SEPSO on dynamic path planning problems were introduced. Comprehensive experiments on four widely used benchmark optimization functions have been initially conducted to corroborate the validity of SEPSO. Following this, a dynamic simulation environment that encompasses moving start/target points and dynamic/static obstacles was employed to assess the effectiveness of SEPSO on the dynamic path planning problem. Simulation results exhibit that the proposed SEPSO is capable of generating superior paths with considerably better real-time performance (67 path planning computations per second in a regular desktop computer) in contrast to alternative methods. The code and video of this paper can be accessed here [Code and Video: https://github.com/XinJingHao/Real-time-Path-planning-with-SEPSO ].
Dynamic Meta-path Guided Temporal Heterogeneous Graph Neural Networks
Authors: Ji Yugang , Shi Chuan , Fang Yuan
Published: November 2023

Graph Neural Networks (GNNs) have become the de facto standard for representation learning on topological graphs, which usually derive effective node representations via message passing from neighborhoods. Although GNNs have achieved great success, previous models are mostly confined to static and homogeneous graphs. However, there are multiple dynamic interactions between different-typed nodes in real-world scenarios like academic networks and e-commerce platforms, forming temporal heterogeneous graphs (THGs). Limited work has been done for representation learning on THGs and the challenges are in two aspects. First, there are abundant dynamic semantics between nodes while traditional techniques like meta-paths can only capture static relevance. Second, different semantics on THGs have often mutually evolved with each other over time, making it more difficult than dynamic homogeneous graph modeling. To address this problem, here we propose the Dynamic Meta-path guided temporal heterogeneous Graph Neural Networks (DyMGNNs). To handle the dynamic semantics, we introduce the concept of dynamic meta-path which is a common base for temporal semantic search engines, and then adopt the temporal importance sampling to extract neighborhoods with temporal bias. Focusing on mutual evolution, we design the heterogeneous mutual evolution attention mechanism, which can model the fine-grained interplay of semanticlevel preferences for each node. Extensive experiments on three real-world datasets for node classification and temporal link prediction demonstrate that our method consistently outperforms state-of-the-art alternatives.
AirIndex: Versatile Index Tuning Through Data and Storage
Authors: Chockchowwat Supawit , Liu Wenjie , Park Yongjoo
Published: November 2023

The end-to-end lookup latency of a hierarchical index---such as a B-tree or a learned index---is determined by its structure such as the number of layers, the kinds of branching functions appearing in each layer, the amount of data we must fetch from layers, etc. Our primary observation is that by optimizing those structural parameters (or designs) specifically to a target system's I/O characteristics (e.g., latency, bandwidth), we can offer a faster lookup compared to the ones that are not optimized. Can we develop a systematic method for finding those optimal design parameters? Ideally, the method must have the potential to generate almost any existing index or a novel combination of them for the fastest possible lookup.

In this work, we present new data and an I/O-aware index builder (called AirIndex) that can find high-speed hierarchical index designs in a principled way. Specifically, AirIndex minimizes an objective function expressing the end-to-end latency in terms of various designs---the number of layers, types of layers, and more---for given data and a storage profile, using a graph-based optimization method purpose-built to address the computational challenges rising from the inter-dependencies among index layers and the exponentially many candidate parameters in a large search space. Our empirical studies confirm that AirIndex can find optimal index designs, build optimal indexes within the times comparable to existing methods, and deliver up to 4.1x faster lookup than a lightweight B-tree library (LMDB), 3.3x--46.3x faster than state-of-the-art learned indexes (RMI/CDFShop, PGM-index, ALEX/APEX, PLEX), and 2.0 faster than Data Calculator's suggestion on various dataset and storage settings.
Efficient Algorithm for Budgeted Adaptive Influence Maximization: An Incremental RR-set Update Approach
Authors: Guo Qintian , Feng Chen , Zhang Fangyuan , Wang Sibo
Published: November 2023

Given a graph G, a cost associated with each node, and a budget B, the budgeted influence maximization (BIM) aims to find the optimal set S of seed nodes that maximizes the influence among all possible sets such that the total cost of nodes in S is no larger than B. Existing solutions mainly follow the non-adaptive idea, i.e., determining all the seeds before observing any actual diffusion. Due to the absence of actual diffusion information, they may result in unsatisfactory influence spread. Motivated by the limitation of existing solutions, in this paper, we make the first attempt to solve the BIM problem under the adaptive setting, where seed nodes are iteratively selected after observing the diffusion result of the previous seeds. We design the first practical algorithm which achieves an expected approximation guarantee by probabilistically adopting a cost-aware greedy idea or a single influential node. Further, we develop an optimized version to improve its practical performance in terms of influence spread.

Besides, the scalability issues of the adaptive IM-related problems still remain open. It is because they usually involve multiple rounds (e.g., equal to the number of seeds) and in each round, they have to construct sufficient new reverse-reachable set (RR-set) samples such that the claimed approximation guarantee can actually hold. However, this incurs prohibitive computation, imposing limitations on real applications. To solve this dilemma, we propose an incremental update approach. Specifically, it maintains extra construction information when building RR-sets, and then it can quickly correct a problematic RR-set from the very step where it is first affected. As a result, we recycle the RR-sets at a small computational cost, while still providing correctness guarantee. Finally, extensive experiments on large-scale real graphs demonstrate the superiority of our algorithms over baselines in terms of both influence spread and running time.
FedCSS: Joint Client-and-Sample Selection for Hard Sample-Aware Noise-Robust Federated Learning
Authors: Li Anran , Cao Yue , Guo Jiabao , Peng Hongyi , Guo Qing , Yu Han
Published: November 2023

Federated Learning (FL) enables a large number of data owners (a.k.a. FL clients) to jointly train a machine learning model without disclosing private local data. The importance of local data samples to the FL model vary widely. This is exacerbated by the presence of noisy data, which exhibit large losses similar to important (hard) samples. Currently, there lacks an FL approach that can effectively distinguish hard samples (which are beneficial) from noisy samples (which are harmful). To bridge this gap, we propose the Federated Client and Sample Selection (FedCSS) approach. It is a bilevel optimization approach for FL client-and-sample selection to achieve hard sample-aware noise-robust learning in a privacy preserving manner. It performs meta-learning based online approximation to iteratively update global FL models, select the most positively influential samples and deal with training data noise. Theoretical analysis shows that it is guaranteed to converge in an efficient manner. Experimental comparison against six state-of-the-art baselines on five real-world datasets in the presence of data noise and heterogeneity shows that it achieves up to 26.4% higher test accuracy, while saving communication and computation costs by at least 41.5% and 1.2%, respectively.
Memory-Efficient and Flexible Detection of Heavy Hitters in High-Speed Networks
Authors: Huang He , Yu Jiakun , Du Yang , Liu Jia , Dai Haipeng , Sun Yu-E
Published: November 2023

Heavy-hitter detection is a fundamental task in network traffic measurement and security. Existing work faces the dilemma of suffering dynamic and imbalanced traffic characteristics or lowering the detection efficiency and flexibility. In this paper, we propose a flexible sketch called SwitchSketch that embraces dynamic and skewed traffic for efficient and accurate heavy-hitter detection. The key idea of SwitchSketch is allowing the sketch to dynamically switch among different modes and take full use of each bit of the memory. We present an encoding-based switching scheme together with a flexible bucket structure to jointly achieve this goal by using a combination of design features, including variable-length cells, shrunk counters, embedded metadata, and switchable modes. We further implement SwitchSketch on the NetFPGA-1G-CML board. Experimental results based on real Internet traces show that SwitchSketch achieves a high Fβ-Score of threshold-t detection (consistently higher than 0.938) and over 99% precision rate of top-k detection under a tight memory size (e.g., 100KB). Besides, it outperforms the state-of-the-art by reducing the ARE by 30.77%\sim99.96%. All related implementations are open-sourced.
Origin-Destination Travel Time Oracle for Map-based Services
Authors: Lin Yan , Wan Huaiyu , Hu Jilin , Guo Shengnan , Yang Bin , Lin Youfang , Jensen Christian S.
Published: November 2023

Given an origin (O), a destination (D), and a departure time (T), an Origin-Destination (OD) travel time oracle~(ODT-Oracle) returns an estimate of the time it takes to travel from O to D when departing at T. ODT-Oracles serve important purposes in map-based services. To enable the construction of such oracles, we provide a travel-time estimation (TTE) solution that leverages historical trajectories to estimate time-varying travel times for OD pairs.

The problem is complicated by the fact that multiple historical trajectories with different travel times may connect an OD pair, while trajectories may vary from one another. To solve the problem, it is crucial to remove outlier trajectories when doing travel time estimation for future queries.

We propose a novel, two-stage framework called Diffusion-based Origin-destination Travel Time Estimation (DOT), that solves the problem. First, DOT employs a conditioned Pixelated Trajectories (PiT) denoiser that enables building a diffusion-based PiT inference process by learning correlations between OD pairs and historical trajectories. Specifically, given an OD pair and a departure time, we aim to infer a PiT. Next, DOT encompasses a Masked Vision Transformer~(MViT) that effectively and efficiently estimates a travel time based on the inferred PiT. We report on extensive experiments on two real-world datasets that offer evidence that DOT is capable of outperforming baseline methods in terms of accuracy, scalability, and explainability.
Causality-Based Fair Multiple Decision by Response Functions
Authors: Su Cong , Yu Guoxian , Zheng Yongqing , Wang Jun , Wu Zhengtian , Zhang Xiangliang , Domeniconi Carlotta
Published: November 2023

A recent trend of fair machine learning is to build a decision model subjected to causality-based fairness requirements, which concern with the causality between sensitive attributes and decisions. Almost all (if not all) solutions focus on a single fair decision model and assume no hidden confounder to model causal effects in a too simplified way. However, multiple interdependent decision models are actually used and discrimination may transmit among them. The hidden confounder is another inescapable fact and causal effects cannot be computed from observational data in the unidentifiable situation. To address these problems, we propose a method called CMFL (Causality-based Multiple Fairness Learning). CMFL parameterizes the causal model by response-function variables, whose distributions capture the randomness of causal models. CMFL treats each classifier as a soft intervention to infer the post-intervention distribution, and combines the fairness constraints with the classification loss to train multiple decision classifiers. In this way, all classifiers can make approximately fair decisions. Experiments on synthetic and benchmark datasets confirm its effectiveness, the response-function variables can deal with the unidentifiable issue and hidden confounders.
Pedestrian Attribute Recognition via Spatio-temporal Relationship Learning for Visual Surveillance
Authors: Liu Zhenyu , Li Da , Zhang Xinyu , Zhang Zhang , Zhang Peng , Shan Caifeng , Han Jungong
Published: November 2023

Pedestrian attribute recognition (PAR) aims at predicting the visual attributes of a pedestrian image. PAR has been used as soft biometrics for visual surveillance and IoT security. Most of the current PAR methods are developed based on discrete images. However, it is challenging for the image-based method to handle the occlusion and action-related attributes in real-world applications. Recently, video-based PAR has attracted much attention in order to exploit the temporal cues in the video sequences for better PAR. Unfortunately, existing methods usually ignore the correlations among different attributes and the relations between attributes and spatio regions. To address this problem, we propose a novel method for video-based PAR by exploring the relationships among different attributes in both the spatio and temporal domains. More specifically, a spatio-temporal saliency module (STSM) is introduced to capture the key visual patterns from the video sequences, and a module for spatio-temporal attribute relationship learning (STARL) is proposed to mine the correlations among these patterns. Meanwhile, a large-scale benchmark for video-based PAR, RAP-Video, is built by extending the image-based dataset RAP-2, which contains 83,216 tracklets with 25 scenes. To the best of our knowledge, this is the largest dataset for video-based PAR. Extensive experiments are performed on the proposed benchmark as well as on MARS Attribute and DukeMTMC-Video Attribute. The superior performance demonstrates the effectiveness of the proposed method.
Reusing Convolutional Neural Network Models through Modularization and Composition
Authors: Qi Binhang , Sun Hailong , Zhang Hongyu , Gao Xiang
Published: November 2023

With the widespread success of deep learning technologies, many trained deep neural network (DNN) models are now publicly available. However, directly reusing the public DNN models for new tasks often fails due to mismatching functionality or performance. Inspired by the notion of modularization and composition in software reuse, we investigate the possibility of improving the reusability of DNN models in a more fine-grained manner. Specifically, we propose two modularization approaches named CNNSplitter and GradSplitter, which can decompose a trained convolutional neural network (CNN) model for
N
-class classification into
N
small reusable modules. Each module recognizes one of the
N
classes and contains a part of the convolution kernels of the trained CNN model. Then, the resulting modules can be reused to patch existing CNN models or build new CNN models through composition. The main difference between CNNSplitter and GradSplitter lies in their search methods: the former relies on a genetic algorithm to explore search space, while the latter utilizes a gradient-based search method. Our experiments with three representative CNNs on three widely used public datasets demonstrate the effectiveness of the proposed approaches. Compared with CNNSplitter, GradSplitter incurs less accuracy loss, produces much smaller modules (19.88% fewer kernels), and achieves better results on patching weak models. In particular, experiments on GradSplitter show that (1) by patching weak models, the average improvement in terms of precision, recall, and F1-score is 17.13%, 4.95%, and 11.47%, respectively, and (2) for a new task, compared with the models trained from scratch, reusing modules achieves similar accuracy (the average loss of accuracy is only 2.46%) without a costly training process. Our approaches provide a viable solution to the rapid development and improvement of CNN models.
The Global Technical, Economic, and Feasible Potential of Renewable Electricity
Authors: Angliviel de La Beaumelle Nils , Blok Kornelis , de Chalendar Jacques A. , Clarke Leon , Hahmann Andrea N. , Huster Jonathan , Nemet Gregory F. , Suri Dhruv , Wild Thomas B. , Azevedo Inês M.L.
Published: November 2023

Renewable electricity generation will need to be rapidly scaled to address climate change and other environmental challenges. Doing so effectively will require an understanding of resource availability. We review estimates for renewable electricity of the global technical potential, defined as the amount of electricity that could be produced with current technologies when accounting for geographical and technical limitations as well as conversion efficiencies; economic potential, which also includes cost; and feasible potential, which accounts for societal and environmental constraints. We consider utility-scale and rooftop solar photovoltaics, concentrated solar power, onshore and offshore wind, hydropower, geothermal electricity, and ocean (wave, tidal, ocean thermal energy conversion, and salinity gradient energy) technologies. We find that the reported technical potential for each energy resource ranges over several orders of magnitude across and often within technologies. Therefore, we also discuss the main factors explaining why authors find such different results. According to this review and on the basis of the most robust studies, we find that technical potentials for utility-scale solar photovoltaic, concentrated solar power, onshore wind, and offshore wind are above 100 PWh/year. Hydropower, geothermal electricity, and ocean thermal energy conversion have technical potentials above 10 PWh/year. Rooftop solar photovoltaic, wave, and tidal have technical potentials above 1 PWh/year. Salinity gradient has a technical potential above 0.1 PWh/year. The literature assessing the global economic potential of renewables, which considers the cost of each renewable resource, shows that the economic potential is higher than current and near-future electricity demand. Fewer studies have calculated the global feasible potential, which considers societal and environmental constraints. While these ranges are useful for assessing the magnitude of available energy sources, they may omit challenges for large-scale renewable portfolios.
Toward Zero-Carbon Urban Transitions with Health, Climate Resilience, and Equity Co-Benefits: Assessing Nexus Linkages
Authors: Ramaswami Anu , Pandey Bhartendu , Li Qingchun , Das Kirti , Nagpure Ajay
Published: November 2023

Getting to net-zero-carbon cities while advancing well-being (W), health (H), social equity (E), and climate resilience (R) (referred to as the WHER outcomes) is critical for local and global sustainability. However, science is nascent on the linkages between zero-carbon pathways and WHER outcomes. This article presents a transboundary urban metabolism framework, rooted in seven key infrastructure and food provisioning systems, to connect urban decarbonization strategies with WHER outcomes. Applying the framework along with a literature review, we find the evidence for co-beneficial decarbonization to be strong for health; limited for well-being; uncertain for resilience; and requiring intentional design to advance equity, including distributional, procedural, and recognitional aspects. We describe the evidence base, identify key knowledge gaps, and delineate broad parameters of a new urban nexus science to enable zero-carbon urban transitions with WHER co-benefits. We highlight the need for fine-scale data encompassing all seven sectors across scales, along with multiple and multiscale climate risks, accompanied by next-generation multisector, multiscale, multioutcome nexus models.
The absorption of bovine milk small extracellular vesicles largely depends on galectin 3 and galactose ligands in human intestinal cells and C57BL/6J mice
Authors: Sukreet Sonal , Braga Camila Pereira , Adamec Jiri , Cui Juan , Zempleni Janos
Published: November 2023

This is the first paper to assess the role of unique glycans and their Galectin-3 receptor in the transport and distribution of small extracellular vesicles (“exosomes”) from milk in mammals. The research assessed milk exosome transport and distribution by using multiple approaches and platforms including cell cultures, various exosome labels, knockout and mutant mice, enzymatic removal of surface proteins and glycans, and lectin blocking of glycans.
Dose-dependent nonthermal modulation of whole body heat exchange during dynamic exercise in humans
Authors: Notley Sean R. , Akerman Ashley P. , D’Souza Andrew W. , Meade Robert D. , McCourt Emma R. , McCormick James J. , Kenny Glen P.
Published: November 2023

To maintain heat balance during exercise, humans rely on skin blood flow and sweating to facilitate dry and evaporative heat exchange. These responses are modulated by body temperatures (thermal factors) and several nonthermal factors (e.g., central command, metaboreceptors), although the way thermal and nonthermal factors interact to regulate body temperature is poorly understood. We demonstrate that nonthermal factors exert dose-dependent, opposing effects on dry and evaporative heat loss, without altering heat storage during dynamic exercise.
Glial Cells of the Central Nervous System: A Potential Target in Chronic Prostatitis/Chronic Pelvic Pain Syndrome
Authors: Lao Yongfeng , Li Zewen , Bai Yanan , Li Weijia , Wang Jian , Wang Yanan , Li Qingchao , Dong Zhilong
Published: November 2023

Chronic prostatitis/chronic pelvic pain syndrome (CP/CPPS) is one of the most common diseases of the male urological system while the etiology and treatment of CP/CPPS remain a thorny issue. Cumulative research suggested a potentially important role of glial cells in CP/CPPS. This narrative review retrospected literature and grasped the research process about glial cells and CP/CPPS. Three types of glial cells showed a crucial connection with general pain and psychosocial symptoms. Microglia might also be involved in lower urinary tract symptoms. Only microglia and astrocytes have been studied in the animal model of CP/CPPS. Activated microglia and reactive astrocytes were found to be involved in both pain and psychosocial symptoms of CP/CPPS. The possible mechanism might be to mediate the production of some inflammatory mediators and their interaction with neurons. Glial cells provide a new insight to understand the cause of complex symptoms of CP/CPPS and might become a novel target to develop new treatment options. However, the activation and action mechanism of glial cells in CP/CPPS needs to be further explored.
Semisupervised Deep Features of Time‐Frequency Maps for Multimodal Emotion Recognition
Authors: Zali-Vargahan Behrooz , Charmin Asghar , Kalbkhani Hashem , Barghandan Saeed
Published: November 2023

Traditional approaches for emotion recognition utilize unimodal physiological signals. The effectiveness of such systems is affected by some limitations. To overcome them, this paper proposes a new method based on time‐frequency maps that extract the features from multimodal biological signals. At first, the fusion of electroencephalogram (EEG) and peripheral physiological signal (PPS) is performed, and then, the two‐dimensional discrete orthonormal Stockwell transform (2D‐DOST) of the multimodal signal matrix is calculated to obtain time‐frequency maps. A convolutional neural network (CNN) is then utilized to extract the local deep features from the absolute output of the 2D‐DOST. Since there are uninformative deep features, the semisupervised dimension reduction scheme reduces them by balancing the generalization and discrimination. Finally, the classifier recognizes the emotion. The Bayesian optimizer finds the proper SSDR and classifier parameter values to maximize the recognition accuracy. The performance of the proposed method is evaluated on the DEAP dataset considering the two‐ and four‐class scenarios through extensive simulations. This dataset consists of electroencephalograph (EEG) signals in 32 channels and peripheral physiological signals (PPSs) in eight channels from 32 subjects. The proposed method reaches the accuracy of 0.953 and 0.928 for two‐ and four‐class scenarios, respectively. The results indicate the efficiency of the multimodal signals for detecting emotions compared to that of unimodal signals. Also, the results indicate that the proposed method outperforms the recently introduced ones.
The Effects of Antioxidants and Pulsed Magnetic Fields on Slow and Fast Skeletal Muscle Atrophy Induced by Streptozotocin: A Preclinical Study
Authors: Tastekin Bora , Pelit Aykut , Sapmaz Tugce , Celenk Alper , Majeed Muhammed , Mundkur Lakshmi , Nagabhushanam Kalyanam
Published: November 2023

Background and Objectives. Skeletal muscle atrophy, a condition characterized by decreased muscle mass and contractility, is commonly observed in various pathological states, including prolonged inactivity, malnutrition, sarcopenia, and type 1 diabetes mellitus (T1DM). We aimed to investigate the potential therapeutic effects of pterostilbene (PTS), resveratrol (RSV), pulsed magnetic field (PMF), and their combinations on the streptozotocin- (STZ-) induced atrophy of slow- and fast-twitch skeletal muscles in rats. Material and Methods. Biomechanical analyses were utilized to examine the contractions of soleus and extensor digitorum longus (EDL) muscles, while muscle morphology was evaluated using hematoxylin-eosin staining. Immunohistochemical methods were employed to assess atrophy-related markers, including FBXO32, TRIM63, and FoxO3a. The levels of key proteins, such as pAkt, mTOR, FoxO3a, MSTN, CAPN3, TNF-α, NF-κB, MyHC IIb, MyLC 3, and GLUT4, in both skeletal muscle tissue and serum insulin levels were determined using ELISA. Total protein concentration was measured using the Bradford protein assay, and relative gene expressions of FBXO32, TRIM63, FoxO3a, 4E-BP1, p70S6K, TRIM72, and UbC were analyzed by real-time PCR. Results. Our findings suggest that antioxidants and PMF may alleviate impaired protein synthesis and degradation pathways in skeletal muscle atrophy. PTS showed a positive effect on the anabolic pathway, while RSV and PMF demonstrated potential for ameliorating the catabolic pathway. Notably, the combination therapy of antioxidants and PMF exhibited a stronger ameliorative effect on skeletal muscle atrophy than either intervention alone. Conclusion. The present results highlight the benefits of employing a multimodal approach, involving both antioxidant and PMF therapy, for the management of muscle-wasting conditions. These treatments may have potential therapeutic implications for skeletal muscle atrophy.
A Rare 46,X,t(Y;10)(q12;p14) Balanced Translocation in Non-Obstructive Azoospermic Patient with Elevated FSH and LH Levels
Authors: Jahan Syeeda Khursheed Kousar , Rahman Kaleemullah Mohammed , Joseph Annu , Hasan Al Durazi Mohammed , Bakhiet Moiz
Published: November 2023

Structural chromosomal aberrations like translocations have been shown to cause spermatogenic failure. We report a rare 46,X,t(Y;10)(q12;p14) balanced translocation in an otherwise healthy non-obstructive azoospermic male with high follicle-stimulating hormone (26.65 IU/L) and high luteinizing hormone (13.58 IU/L). The patient was referred to us after clinical, hormonal, and histopathological investigations to identify chromosomal abnormalities by karyotyping and fluorescence in situ hybridization (FISH). Analysis of the banding pattern by karyotyping followed by FISH confirmed reciprocal translocation and identified the breakpoints at Yq heterochromatin (Yq12) and 10p14. Further molecular tests including AZF microdeletion assay were done, and the results, which showed no mutations in the analyzed genes, were provided by the referring doctor. Thus, our study points to the importance of conventional cytogenetic techniques in the preliminary evaluation of a genetic abnormality in cases of infertility and would help the patient make an informed decision before pursuing assisted reproductive technology.
The Tautomerase Activity of Tumor Exosomal MIF Promotes Pancreatic Cancer Progression by Modulating MDSC Differentiation
Authors: Jia Xuebing , Xi Jianbei , Tian Binle , Zhang Yuanyuan , Wang Zhilong , Wang Fan , Li Zheng , Long Jiang , Wang JianFei , Fan Guo-Huang , Li Qi
Published: November 2023

Pancreatic cancer is a deadly disease that is largely resistant to immunotherapy, in part because of the accumulation of immunosuppressive cells in the tumor microenvironment (TME). Much evidence suggests that tumor-derived exosomes (TDE) contribute to the immunosuppressive activity mediated by myeloid-derived suppressor cells (MDSC) within the pancreatic cancer TME. However, the underlying mechanisms remain elusive. Herein, we report that macrophage migration inhibitory factor (MIF) in TDEs has a key role in inducing MDSC formation in pancreatic cancer. We identified MIF in both human and murine pancreatic cancer–derived exosomes. Upon specific shRNA-mediated knockdown of MIF, the ability of pancreatic cancer–derived exosomes to promote MDSC differentiation was abrogated. This phenotype was rescued by reexpression of the wild-type form of MIF rather than a tautomerase-null mutant or a thiol-protein oxidoreductase-null mutant, indicating that both MIF enzyme activity sites play a role in exosome-induced MDSC formation in pancreatic cancer. RNA sequencing data indicated that MIF tautomerase regulated the expression of genes required for MDSC differentiation, recruitment, and activation. We therefore developed a MIF tautomerase inhibitor, IPG1576. The inhibitor effectively inhibited exosome-induced MDSC differentiation in vitro and reduced tumor growth in an orthotopic pancreatic cancer model, which was associated with decreased numbers of MDSCs and increased infiltration of CD8+ T cells in the TME. Collectively, our findings highlight a pivotal role for MIF in exosome-induced MDSC differentiation in pancreatic cancer and underscore the potential of MIF tautomerase inhibitors to reverse the immunosuppressive pancreatic cancer microenvironment, thereby augmenting anticancer immune responses.
Combination of Anti–PD-1 and Electroacupuncture Induces a Potent Antitumor Immune Response in Microsatellite-Stable Colorectal Cancer
Authors: Wang Yuan , Liu Fengyi , Du Xiaoxue , Shi Jiaqi , Yu Rui , Li Shuang , Na Ruisi , Zhao Ying , Zhou Meng , Guo Ying , Cheng Liang , Wang Guangyu , Zheng Tongsen
Published: November 2023

Programmed death receptor-1 (PD-1) inhibitors are ineffective against microsatellite-stable (MSS) colorectal cancer. Electroacupuncture (EA) has oncosuppressive and immunomodulatory properties. Here, we investigated the antitumor effects of EA and explored the feasibility of EA combined with anti–PD-1 in MSS colorectal cancer. Results showed that EA exerted its antitumor effect in an intensity-specific manner, and moderate-intensity EA (1.0 mA) induced maximal tumor inhibition. EA enhanced antitumor immune responses by increasing lymphocytes and granzyme B (GzmB) levels, as well as activating the stimulator of IFN genes (STING) pathway. EA combined with anti–PD-1 showed superior efficacy compared with either monotherapy in multiple MSS colorectal cancer mouse models. Single-cell RNA sequencing revealed that cotreatment reprogrammed the tumor immune microenvironment (TIME), as characterized by enhancement of cytotoxic functions. Mechanically, we found that the potentiated effect of EA was dependent upon the STING pathway. Collectively, EA reshapes the TIME of MSS colorectal cancer and sensitizes tumors to anti–PD-1 in a STING pathway–dependent manner. These results provide a mechanistic rationale for using EA as an immunomodulatory strategy to improve the clinical efficacy of anti–PD-1 in MSS colorectal cancer. EA is safe, well-tolerated, and feasible for clinical translation as a promising strategy for treating MSS colorectal cancer.
Statin-induced Mitochondrial Priming Sensitizes Multiple Myeloma Cells to BCL2 and MCL-1 Inhibitors
Authors: Juarez Dennis , Buono Roberta , Matulis Shannon M. , Gupta Vikas A. , Duong Madeleine , Yudiono Jacob , Paul Madhuri , Mallya Sharmila , Diep Grace , Hsin Peter , Lu Alexander , Suh Sang Mi , Dong Vy M. , Roberts Andrew W. , Leverson Joel D. , Jalaluddin Muhammad , Liu Zhuangzhuang , Bueno Orlando F. , Boise Lawrence H. , Fruman David A.
Published: November 2023

The BCL2 inhibitor venetoclax promotes apoptosis in blood cancer cells and is approved for treatment of chronic lymphocytic leukemia and acute myeloid leukemia. However, multiple myeloma cells are frequently more dependent on MCL-1 for survival, conferring resistance to venetoclax. Here we report that mevalonate pathway inhibition with statins can overcome resistance to venetoclax in multiple myeloma cell lines and primary cells. In addition, statins sensitize to apoptosis induced by MCL-1 inhibitor, S63845. In retrospective analysis of venetoclax clinical studies in multiple myeloma, background statin use was associated with a significantly enhanced rate of stringent complete response and absence of progressive disease. Statins sensitize multiple myeloma cells to venetoclax by upregulating two proapoptotic proteins: PUMA via a p53-independent mechanism and NOXA via the integrated stress response. These findings provide rationale for prospective testing of statins with venetoclax regimens in multiple myeloma.

Significance:

BH3 mimetics including venetoclax hold promise for treatment of multiple myeloma but rational combinations are needed to broaden efficacy. This study presents mechanistic and clinical data to support addition of pitavastatin to venetoclax regimens in myeloma. The results open a new avenue for repurposing statins in blood cancer.
Clinical and Pathological Significance of Mesangial C1q Deposition in Kidney Transplant Recipients with Recurrent IgA Nephropathy and Patients with Native IgA Nephropathy
Authors: Hayashi Ayaka , Kawabe Mayuko , Yamamoto Izumi , Ohki Yutaro , Kobayashi Akimitsu , Ueda Hiroyuki , Tanno Yudo , Urabe Fumihiko , Miki Jun , Yamada Hiroki , Kimura Takahiro , Okido Ichiro , Tsuboi Nobuo , Yamamoto Hiroyasu , Yokoo Takashi
Published: November 2023

Introduction: In kidney transplant recipients (KTRs) whose primary disease is IgA nephropathy (IgAN), IgAN recurrence occurs in approximately half of patients by 5 years postoperatively and is associated with graft survival. Although the alternative and lectin pathways are important in the primary pathogenesis of IgAN, the significance of mesangial C1q deposition, which triggers the classical pathway, is unknown. We investigated the clinicopathological significance of mesangial C1q deposition in both recurrent IgAN in KTRs and native IgAN. Methods: Between 2000 and 2021, we conducted a 1:2 matched case-control study of 18 KTRs diagnosed with recurrent IgAN, with a group of native IgAN patients as the control. We evaluated the rate and presence/absence of mesangial C1q deposition in terms of pathological findings and kidney outcomes in each group. Results: The rate of mesangial C1q deposition was significantly higher in the recurrent IgAN patients in KTRs than in native IgAN patients (11/18 [61.1%] vs. 5/36 [13.9%], p = 0.001). In the former group, the incidence of glomerular crescents was relatively higher in C1q-positive patients. There was no significant difference in the annual rate of estimated glomerular filtration rate decline between C1q-positive and C1q-negative patients in either group. Conclusion: Mesangial C1q deposition was more frequent in KTRs with recurrent IgAN than in patients with native IgAN, but we found no difference in kidney outcomes with respect to mesangial C1q deposition. Further large-scale investigations of the importance of mesangial C1q deposition are needed in both KTRs with recurrent IgAN and patients with native IgAN.
Transcription Factor ATF3 Mediating SOCS3 Expression Aggravates Renal Ischemia-Reperfusion Injury by Activating Mitophagy
Authors: Luo Yu , Cai Zhitao , Wu Xiongfei , Liu Feng , Li Lian
Published: November 2023

Introduction: Suppressor of cytokine signaling 3 (SOCS3) is highly expressed in mice with renal ischemia/reperfusion (RI/R) injury and has the potential to regulate mitophagy. On this basis, this study further investigates the possible mechanism via which SOCS3 affects RI/R by regulating mitophagy. Method: After establishing a RI/R injury mouse model and a hypoxia/reoxygenation (H/R) cell model, the effects of silenced SOCS3 on injury and mitophagy in the above models were analyzed by ELISA, quantitative real-time polymerase chain reaction, Western blot, pathological sections, CCK-8 assay, flow cytometry, and JC-1 assay. Mechanistic studies were carried out with the help of database analysis and binding validation experiments (chromatin immunoprecipitation, dual-luciferase reporter assay, and co-immunoprecipitation). After the binding target was identified, the regulatory relationship between the target gene and SOCS3 was verified by rescue experiments. Result: The large increase in blood urea nitrogen (BUN) and creatinine (Cr) levels verified the success of the RI/R model. SOCS3 expression was up-regulated in RI/R mice. Silenced SOCS3 alleviated kidney damage and mitochondrial abnormalities in RI/R mice and inhibited mitophagy at the molecular level. Likewise, silenced SOCS3 alleviated H/R-induced cell damage and mitophagy. Finally, activating transcription factor 3 (ATF3) was determined to bind to the promoter of SOCS3, which interacted with insulin-like growth factor 1 receptor (IGF1R). Rescue experiments confirmed the effect of ATF3 on SOCS3 expression and the underlying regulatory mechanism. Conclusion: ATF3 mediates SOCS3 expression to promote the activation of mitophagy, thereby aggravating renal ischemia-reperfusion injury.
Pulmonary Injury after Radioactive Iodine Therapy in Pediatric Papillary Thyroid Cancer: A Case Report
Authors: Halada Stephen , Leftin Dobkin Shoshana , Baran Julia A. , Sisko Lindsay , Robbins Stephanie L. , Rapp Jordan B. , Young Lisa R. , Bauer Andrew J.
Published: November 2023

Introduction: Radiation-induced lung injury is a rare complication of radioactive iodine therapy (RAIT) in pediatric thyroid cancer treatment. In this case report, we describe a pediatric patient with an ERC1::RET-positive classic papillary thyroid carcinoma who developed progressive respiratory symptoms and chest imaging abnormalities following RAIT for lymph node and pulmonary disease. Case Presentation: A pediatric patient with ERC1::RET-positive classic papillary thyroid carcinoma was hospitalized for pulmonary decompensation 3 months following one empiric dose of RAIT. Testing revealed no evidence of infection or progression of pulmonary metastases, and there was no improvement with empiric antibiotic therapy for pneumonia. Despite empiric anti-inflammatory therapies, the patient remains symptomatic from a respiratory standpoint with requirement for supplemental oxygen and evidence of fibrotic changes on chest imaging. Conclusions: This patient’s pulmonary condition is consistent with radiation-induced pulmonary injury including development of pulmonary fibrosis. With the availability of RET fusion-targeted inhibitors, this case highlights a rare pulmonary side effect of radioactive iodine for clinicians to recognize. Upfront targeted therapy protocols may help avoid radioactive iodine-associated adverse reactions.
Second-Line Treatment after Failure of Immune Checkpoint Inhibitors in Hepatocellular Carcinoma: Tyrosine Kinase Inhibitor, Retrial of Immunotherapy, or Locoregional Therapy?
Authors: Hwang Sang Youn , Lee Sangjune L. , Liu Hongqun , Lee Samuel S.
Published: November 2023

Background: Immune checkpoint inhibitor (ICI)-based therapy such as atezolizumab plus bevacizumab or durvalumab plus tremelimumab became mainstream first-line systemic treatment in advanced hepatocellular carcinoma (HCC) patients since remarkably superior efficacy of ICI-based therapy compared to tyrosine kinase inhibitors (TKIs) was reported in two recent randomized controlled trials (RCTs) (IMbrave150, HIMALAYA). However, the optimal second-line therapy after treatment failure of first-line ICI-based therapy remains unknown as no RCT has examined this issue. Summary: Therefore, at present, most clinicians are empirically treating patients with TKIs or retrial of ICI or locoregional treatment (LRT) modality such as transarterial therapy, radiofrequency ablation, and radiation therapy in this clinical setting without solid evidence. In this review, we will discuss current optimal strategies for second-line treatment after the failure of first-line ICI-based therapy by reviewing published studies and ongoing prospective trials. Key Messages: Clinicians should consider carefully whether to treat the patients with TKI, other ICI-based therapy, or LRT in this situation by considering several factors including liver function reserve, performance status, adverse events of previous therapy, and presence of lesion that can consider LRT such as oligoprogression and vascular invasion. In the meantime, we await the results of ongoing prospective trials to elucidate the best management options.
A Novel Variant in AKAP9 Gene, a Controversial Gene, in Long QT Syndrome
Authors: Erdogan Murat , Sunkak Suleyman , Bahadır Oguzhan , Doğan Muhammet Ensar , Ada Yasin , Balta Burhan
Published: November 2023

Introduction: Long QT syndrome (LQTS) is a common congenital cause of fatal cardiac arrhythmia. Characteristic clinical findings are prolonged QT interval and ventricular arrhythmia on electrocardiogram (ECG), syncope, seizure, and sudden death. It is a genetically heterogeneous disease. To date, disease-causing variant have been reported in seventeen genes. The AKAP9 is still considered controversial among those genes. Case Report: We report the case of a 10-year-old female who was born from a non-consanguineous Turkish couple. She visited pediatrics cardiology clinic presenting with dyspnea and tachycardia. Prolongation of the QT interval was detected in her ECG. Panel test associated with LQTS genes was performed. She was diagnosed with long QTS type 11 due to a heterozygous variant in AKAP9:c.11487_11489 delTACinsCGTA, p.(Thr3830ValfsTer12), that was revealed through next-generation sequencing test. The variant was also found in her mother and brother. Discussion and Conclusion: Novel heterozygous frameshift variant in the AKAP9 gene was considered as “Uncertain Significance (VUS)” in the ACMG classification. The novel variant is absent from population databases (PM2); it is a null variant (PVS1_moderate). AKAP9 gene has the lowest known rate among the causes of LQTS. Information is limited on genotype-phenotype correlation. Yet it is still among the candidate genes. Although the relationship of the AKAP9 gene with LQTS has not yet been fully indicated, individuals with a pathogenic variant in AKAP9 gene and silent carriers may be at risk for fatal cardiac events. Improvements of the genetic tests in the near future may contribute to the literature and clinical research about AKAP9 gene.
Evaluation of the Characteristics of Ovarian Endometriomas in Patients with Endometriosis: Efficacy of a Low-Dose Estrogen/Progestogen Combination
Authors: Komatsu Hiroaki , Sunada Hiroshi , Endo Yusuke , Noma Hisashi , Taniguchi Fuminori , Harada Tasuku
Published: November 2023

Objectives: Approximately 17–44% of women diagnosed with endometriosis have ovarian endometriomas (cysts). Although ovarian endometriomas may adversely affect quality of life and work performance, the associations among patient characteristics, cyst size, and pain in women with endometriosis have not yet been reported. Thus, the objective of this study was to assess the association among age, cyst size, and pain in women with ovarian endometriomas. Design: This was a retrospective secondary analysis of pooled data from six randomized clinical trials on the use of low-dose estrogen/progestogen drugs for endometriosis. Participants/Materials, Setting, and Methods: Data on 491 patients enrolled in four randomized and two nonrandomized trials between 2003 and 2017 were pooled. None of the participants had undergone surgical treatment before trial participation. We examined differences in dysmenorrhea score, menstrual pain score, analgesic score, and pelvic pain, as measured using a visual analog scale (VAS), by age and endometrioma size. Results: The mean dysmenorrhea, menstrual pain, and analgesic scores were 4.2, 2.2, and 2.0, respectively. The mean VAS for pelvic pain was 55, which decreased significantly with an increase in age. Age was not associated with endometrioma size, including volume and maximum diameter, or dysmenorrhea score. Additionally, endometrioma volume and maximum diameter were not associated with menstrual pain, analgesic score, or pelvic pain. Limitations: The details of past treatment history were not available; therefore, these could not be considered in the analysis. Additionally, the assessment of pain is heavily influenced by psychological factors, making it difficult to assess the true extent of pain. Conclusions: Endometrioma size was not associated with dysmenorrhea or pelvic pain measured using the VAS.