bluuebunny/crossref_metadata_2025_split huggingface dataset:
Antibody-displaying extracellular vesicles for targeted cancer therapy
Authors: Wiklander Oscar P. B. , Mamand Doste R. , Mohammad Dara K. , Zheng Wenyi , Jawad Wiklander Rim , Sych Taras , Zickler Antje M. , Liang Xiuming , Sharma Heena , Lavado Andrea , Bost Jeremy , Roudi Samantha , Corso Giulia , Lennaárd Angus J. , Abedi-Valugerdi Manuchehr , Mäger Imre , Alici Evren , Sezgin Erdinc , Nordin Joel Z. , Gupta Dhanu , Görgens André , EL Andaloussi Samir 
Published: May 2024

AbstractExtracellular vesicles (EVs) function as natural delivery vectors and mediators of biological signals across tissues. Here, by leveraging these functionalities, we show that EVs decorated with an antibody-binding moiety specific for the fragment crystallizable (Fc) domain can be used as a modular delivery system for targeted cancer therapy. The Fc-EVs can be decorated with different types of immunoglobulin G antibody and thus be targeted to virtually any tissue of interest. Following optimization of the engineered EVs by screening Fc-binding and EV-sorting moieties, we show the targeting of EVs to cancer cells displaying the human epidermal receptor 2 or the programmed-death ligand 1, as well as lower tumour burden and extended survival of mice with subcutaneous melanoma tumours when systemically injected with EVs displaying an antibody for the programmed-death ligand 1 and loaded with the chemotherapeutic doxorubicin. EVs with Fc-binding domains may be adapted to display other Fc-fused proteins, bispecific antibodies and antibody–drug conjugates.
Estimation of useful-stage energy returns on investment for fossil fuels and implications for renewable energy systems
Authors: Aramendia Emmanuel , Brockway Paul E. , Taylor Peter G. , Norman Jonathan B. , Heun Matthew K. , Marshall Zeke 
Published: May 2024

AbstractThe net energy implications of the energy transition have so far been analysed at best at the final energy stage. Here we argue that expanding the analysis to the useful stage is crucial. We estimate fossil fuelsʼ useful-stage energy returns on investment (EROIs) over the period 1971–2020, globally and nationally, and disaggregate EROIs by end use. We find that fossil fuelsʼ useful-stage EROIs (~3.5:1) are considerably lower than at the final stage (~8.5:1), due to low final-to-useful efficiencies. Further, we estimate the final-stage EROI for which electricity-yielding renewable energy would deliver the same net useful energy as fossil fuels (EROI equivalent) to be approximately 4.6:1. The EROIs of electricity-yielding renewable energy systems, based on published estimations, are found to be higher than the determined EROI equivalent, even considering the effects of intermittency under a range of energy transition scenarios. Results suggest that the energy transition may happen without a decline in net useful energy, countering the view that renewable energy systems cannot replace fossil fuels without incurring a substantial energy penalty.
Testing theory of mind in large language models and humans
Authors: Strachan James W. A. , Albergo Dalila , Borghini Giulia , Pansardi Oriana , Scaliti Eugenio , Gupta Saurabh , Saxena Krati , Rufo Alessandro , Panzeri Stefano , Manzi Guido , Graziano Michael S. A. , Becchio Cristina 
Published: May 2024

AbstractAt the core of what defines us as humans is the concept of theory of mind: the ability to track other people’s mental states. The recent development of large language models (LLMs) such as ChatGPT has led to intense debate about the possibility that these models exhibit behaviour that is indistinguishable from human behaviour in theory of mind tasks. Here we compare human and LLM performance on a comprehensive battery of measurements that aim to measure different theory of mind abilities, from understanding false beliefs to interpreting indirect requests and recognizing irony and faux pas. We tested two families of LLMs (GPT and LLaMA2) repeatedly against these measures and compared their performance with those from a sample of 1,907 human participants. Across the battery of theory of mind tests, we found that GPT-4 models performed at, or even sometimes above, human levels at identifying indirect requests, false beliefs and misdirection, but struggled with detecting faux pas. Faux pas, however, was the only test where LLaMA2 outperformed humans. Follow-up manipulations of the belief likelihood revealed that the superiority of LLaMA2 was illusory, possibly reflecting a bias towards attributing ignorance. By contrast, the poor performance of GPT originated from a hyperconservative approach towards committing to conclusions rather than from a genuine failure of inference. These findings not only demonstrate that LLMs exhibit behaviour that is consistent with the outputs of mentalistic inference in humans but also highlight the importance of systematic testing to ensure a non-superficial comparison between human and artificial intelligences.
A dynamic probabilistic model of the onset and interaction of cardio-metabolic comorbidities on an ageing adult population
Authors: Roversi Chiara , Tavazzi Erica , Vettoretti Martina , Di Camillo Barbara 
Published: May 2024

AbstractComorbidity is widespread in the ageing population, implying multiple and complex medical needs for individuals and a public health burden. Determining risk factors and predicting comorbidity development can help identify at-risk subjects and design prevention strategies. Using socio-demographic and clinical data from approximately 11,000 subjects monitored over 11 years in the English Longitudinal Study of Ageing, we develop a dynamic Bayesian network (DBN) to model the onset and interaction of three cardio-metabolic comorbidities, namely type 2 diabetes (T2D), hypertension, and heart problems. The DBN allows us to identify risk factors for developing each morbidity, simulate ageing progression over time, and stratify the population based on the risk of outcome occurrence. By applying hierarchical agglomerative clustering to the simulated, dynamic risk of experiencing morbidities, we identified patients with similar risk patterns and the variables contributing to their discrimination. The network reveals a direct joint effect of biomarkers and lifestyle on outcomes over time, such as the impact of fasting glucose, HbA1c, and BMI on T2D development. Mediated cross-relationships between comorbidities also emerge, showcasing the interconnected nature of these health issues. The model presents good calibration and discrimination ability, particularly in predicting the onset of T2D (iAUC-ROC = 0.828, iAUC-PR = 0.294) and survival (iAUC-ROC = 0.827, iAUC-PR = 0.311). Stratification analysis unveils two distinct clusters for all comorbidities, effectively discriminated by variables like HbA1c for T2D and age at baseline for heart problems. The developed DBN constitutes an effective, highly-explainable predictive risk tool for simulating and stratifying the dynamic risk of developing cardio-metabolic comorbidities. Its use could help identify the effects of risk factors and develop health policies that prevent the occurrence of comorbidities.
Fuzzy analytic hierarchy process based generation management for interconnected power system
Authors: Varshney T. , Waghmare A. V. , Singh V. P. , Meena V. P. , Anand R , Khan Baseem 
Published: May 2024

AbstractDecision makers consistently face the challenge of simultaneously assessing numerous attributes, determining their respective importance, and selecting an appropriate method for calculating their weights. This article addresses the problem of automatic generation control (AGC) in a two area power system (2-APS) by proposing fuzzy analytic hierarchy process (FAHP), an multi-attribute decision-making (MADM) technique, to determine weights for sub-objective functions. The integral-time-absolute-errors (ITAE) of tie-line power fluctuation, frequency deviations and area control errors, are defined as the sub-objectives. Each of these is given a weight by the FAHP method, which then combines them into an single final objective function. This objective function is then used to design a PID controller. To improve the optimization of the objective function, the Jaya optimization algorithm (JOA) is used in conjunction with other optimization techniques such as sine cosine algorithm (SCA), Luus–Jaakola algorithm (LJA), Nelder–Mead simplex algorithm (NMSA), symbiotic organism search algorithm (SOSA) and elephant herding optimization algorithm (EHOA). Six distinct experimental cases are conducted to evaluate the controller’s performance under various load conditions, with data plotted to show responses corresponding to fluctuations in frequency and tie-line exchange. Furthermore, statistical analysis is performed to gain a better understanding of the effectiveness of the JOA-based PID controller. For non-parametric evaluation, Friedman rank test is also used to validate the performance of the proposed JOA-based controller.
The analysis of pedestrian flow in the smart city by improved DWA with robot assistance
Authors: Hu Yingyue , Long Huizhen , Chen Min 
Published: May 2024

AbstractWith the acceleration of urbanization in China, the urban population continues to grow, leading to frequent occurrences of crowded public spaces, which in turn trigger traffic congestion and even safety accidents. In order to more effectively control pedestrian flow, enhance the efficiency and safety of public spaces, this experiment conducts in-depth research and improvement on the traditional Dynamic Window Approach (DWA), and applies it to the fine control of pedestrian flow. Specifically, this study comprehensively reviews and analyzes the characteristics of pedestrian traffic flow and the working principles of traditional DWA. Based on this, the shortcomings of traditional DWA in dealing with complex pedestrian flow scenarios are identified, and targeted improvement solutions are proposed. The core of this improvement scheme lies in the introduction of a new evaluation function, enabling DWA to more accurately balance various factors in the decision-making process, including pedestrian movement speed, direction, and spatial distribution. Subsequently, the improved DWA is validated through simulation experiments. The experimental scenario is set in an area of 18 m*18 m, and compared with traditional DWA, the improved DWA shows significant advantages in trajectory length and travel time. Specifically, the trajectory length of the traditional DWA robot is 19.4 m, with a required time of 34.8 s, while the trajectory length of the improved DWA robot is shortened to 18.7 m, and the time is reduced to 18.6 s. This result fully demonstrates the effectiveness of the improved DWA in optimizing pedestrian flow control. The improved DWA proposed in this study not only has strong scientific validity but also demonstrates high efficiency in practical applications. This study has important reference value for improving the safety of urban public spaces and improving pedestrian traffic flow conditions, and provides new ideas for the further development of pedestrian flow control technology in the future.
A 3D printed esophageal atresia–tracheoesophageal fistula thorascopy simulator for young surgeons
Authors: Youn Joong Kee , Ko Dayoung , Yang Hee-Beom , Kim Hyun-Young 
Published: May 2024

AbstractWe developed a 3D-printed thoracoscopic surgery simulator for esophageal atresia with tracheoesophageal fistula (EA-TEF) and assessed its effectiveness in educating young pediatric surgeons. Prototype production and modifications were repeated five times before producing the 3-D printed final product based on a patient’s preoperative chest computed tomography. A 24-item survey was used to rate the simulator, adapted from a previous report, with 16 young surgeons with an average of 6.2 years of experience in pediatric surgery for validation. Reusable parts of the thoracic cage were printed to combine with replaceable parts. Each structure was fabricated using diverse printing materials, and subsequently affixed to a frame. In evaluating the simulator, the scores for each factor were 4.33, 4.33, 4.27, 4.31, 4.63, and 4.75 out of 5, respectively, with the highest ratings in value and relevance. The global rating was 3.38 out of 4, with ten stating that it could be used with slight improvements. The most common comment from participants was that the esophageal anastomosis was close to the actual EA-TEF surgery. The 3D-printed thoracoscopic EA-TEF surgery simulator was developed and reflected the actual surgical environment. It could become an effective method of training young pediatric surgeons.
Feasibility of decoding covert speech in ECoG with a Transformer trained on overt speech
Authors: Komeiji Shuji , Mitsuhashi Takumi , Iimura Yasushi , Suzuki Hiroharu , Sugano Hidenori , Shinoda Koichi , Tanaka Toshihisa 
Published: May 2024

AbstractSeveral attempts for speech brain–computer interfacing (BCI) have been made to decode phonemes, sub-words, words, or sentences using invasive measurements, such as the electrocorticogram (ECoG), during auditory speech perception, overt speech, or imagined (covert) speech. Decoding sentences from covert speech is a challenging task. Sixteen epilepsy patients with intracranially implanted electrodes participated in this study, and ECoGs were recorded during overt speech and covert speech of eight Japanese sentences, each consisting of three tokens. In particular, Transformer neural network model was applied to decode text sentences from covert speech, which was trained using ECoGs obtained during overt speech. We first examined the proposed Transformer model using the same task for training and testing, and then evaluated the model’s performance when trained with overt task for decoding covert speech. The Transformer model trained on covert speech achieved an average token error rate (TER) of 46.6% for decoding covert speech, whereas the model trained on overt speech achieved a TER of 46.3% $$( p > 0.05; d=0.07)$$

(
p
>
0.05
;
d
=
0.07
)

. Therefore, the challenge of collecting training data for covert speech can be addressed using overt speech. The performance of covert speech can improve by employing several overt speeches.
Novel inflammatory and insulin resistance indices provide a clue in cerebral amyloid angiopathy
Authors: Zhu Hang-hang , Wang Yun-chao , He Liu-chang , Luo Hai-yang , Zong Ce , Yang Ying-hao , Wu Jing-Hao , Song Bo , Gao Yuan , Xu Yu-ming , Li Yu-sheng 
Published: May 2024

AbstractThis study investigated the correlation of newly identified inflammatory and insulin resistance indices with cerebral amyloid angiopathy (CAA), and explored their potential to differentiate CAA from hypertensive arteriopathy (HA). We retrospectively analyzed 514 consecutive patients with cerebral small vessel disease (CSVD)-related haemorrhage, comparing the differences in novel inflammatory and insulin resistance indices between patients with CAA and HA. Univariate regression, LASSO and multivariate regression were used to screen variables and construct a classification diagnosis nomogram. Additionally, these biomarkers were explored in patients with mixed haemorrhagic CSVD. Inflammatory indices were higher in CAA patients, whereas insulin resistance indices were higher in HA patients. Further analysis identified neutrophil-to-lymphocyte ratio (NLR, OR 1.17, 95% CI 1.07–1.30, P < 0.001), and triglyceride–glucose index (TyG, OR = 0.56, 95% CI 0.36–0.83, P = 0.005) as independent factors for CAA. Therefore, we constructed a CAA prediction nomogram without haemorrhagic imaging markers. The nomogram yielded an area under the curve (AUC) of 0.811 (95% CI 0.764–0.865) in the training set and 0.830 (95% CI 0.718–0.887) in the test set, indicating an ability to identify high-risk CAA patients. These results show that CSVD patients can be phenotyped using novel inflammatory and insulin resistance indices, potentially allowing identification of high-risk CAA patients without haemorrhagic imaging markers.
Time division multiplexing based multi-spectral semantic camera for LiDAR applications
Authors: Kim Sehyeon , Jeong Tae-In , Kim San , Choi Eunji , Yang Eunju , Song Munki , Eom Tae Joong , Kim Chang-Seok , Gliserin Alexander , Kim Seungchul 
Published: May 2024

AbstractThe recent progress in the development of measurement systems for autonomous recognition had a substantial impact on emerging technology in numerous fields, especially robotics and automotive applications. In particular, time-of-flight (TOF) based light detection and ranging (LiDAR) systems enable to map the surrounding environmental information over long distances and with high accuracy. The combination of advanced LiDAR with an artificial intelligence platform allows enhanced object recognition and classification, which however still suffers from limitations of inaccuracy and misidentification. Recently, multi-spectral LiDAR systems have been employed to increase the object recognition performance by additionally providing material information in the short-wave infrared (SWIR) range where the reflection spectrum characteristics are typically very sensitive to material properties. However, previous multi-spectral LiDAR systems utilized band-pass filters or complex dispersive optical systems and even required multiple photodetectors, adding complexity and cost. In this work, we propose a time-division-multiplexing (TDM) based multi-spectral LiDAR system for semantic object inference by the simultaneous acquisition of spatial and spectral information. By utilizing the TDM method, we enable the simultaneous acquisition of spatial and spectral information as well as a TOF based distance map with minimized optical loss using only a single photodetector. Our LiDAR system utilizes nanosecond pulses of five different wavelengths in the SWIR range to acquire sufficient material information in addition to 3D spatial information. To demonstrate the recognition performance, we map the multi-spectral image from a human hand, a mannequin hand, a fabric gloved hand, a nitrile gloved hand, and a printed human hand onto an RGB-color encoded image, which clearly visualizes spectral differences as RGB color depending on the material while having a similar shape. Additionally, the classification performance of the multi-spectral image is demonstrated with a convolution neural network (CNN) model using the full multi-spectral data set. Our work presents a compact novel spectroscopic LiDAR system, which provides increased recognition performance and thus a great potential to improve safety and reliability in autonomous driving.
Addition of anterolateral ligament reconstruction to primary anterior cruciate ligament reconstruction could benefit recovery of functional outcomes
Authors: Lee Jin Hyuck , Lee Gyu Bin , Chung WooYong , Han Seung-Beom , Jang Ki-Mo 
Published: May 2024

AbstractThis study aimed to compare functional outcomes sequentially up to 1 year after combined anterior cruciate ligament reconstruction (ACLR) and anterolateral ligament reconstruction (ALLR) and isolated ACLR. Fifty patients who underwent ACLR with versus without ALLR were analyzed at four different time points (preoperatively and 3, 6, and 12 months postoperatively). For the functional outcomes, muscle strength and acceleration time (AT) were measured using an isokinetic dynamometer. Proprioception was evaluated using joint position sense and dynamic postural stability. Patient-reported outcomes were measured using the Tampa Scale for Kinesiophobia (TSK-11) scores. Functional performance was assessed using single-leg hop distance (SLHD) and Limb Symmetry Index. In the operated knees, quadriceps (at 6 months postoperatively, p = 0.003) and hamstring (at 6 and 12 months postoperatively, p < 0.001) strength were significantly higher in the combined ACLR and ALLR group than the isolated ACLR group. The TSK-11 (at 6 and 12 months postoperatively, p < 0.001) was significantly lower in the combined ACLR and ALLR group than the isolated ACLR group. SLHD was significantly higher in the combined ACLR and ALLR group than the isolated ACLR group (at 6 months, p = 0.022 and at 12 months, p = 0.024). The addition of ALLR to primary ACLR yielded better muscle performance, fear of movement, and functional performance than isolated ACLR.
A novel anti-loosening bolt looseness diagnosis of bolt connections using a vision-based technique
Authors: Luo Jun , Li Kaili , Xie ChengQian , Yan Zhitao , Li Feng , Jia Xiaogang , Wang Yuanlai 
Published: May 2024

AbstractBolt looseness detection is a common problem in engineering. Most vision-based detection techniques focus on diagnosing ordinary bolt looseness, i.e., the methods used for diagnosis are based only on the sidelines of nuts. These methods cannot be used for anti-loosening bolt looseness diagnosis because of the simultaneous rotation of screws and nuts. Therefore, a novel anti-loosening bolt looseness diagnosis method based on a vision-based technique is proposed in this paper. First, a regular hexagonal cap was installed on the screw, which can be used as a reference for the nut. Then, to automatically distinguish the hexagonal borders of the screw cap and nut, a new hexagonal border reconstruction algorithm is proposed. Furthermore, the relative rotation angles of the screw cap and nut hexagons can be determined using the sidelines of the reconstructed hexagonal borders of the screw cap and nut. Finally, a novel anti-loosening bolt looseness diagnosis method is established by using the relative rotation angle of the regular hexagonal borders of the screw cap and nut under initial status and loose status. A prototype flange node of the transmission tower was used for experimental verification. The results show that the proposed method can effectively detect the loosening angle of anti-loosening bolts.
Multiomics integration reveals NETosis heterogeneity and TLR2 as a prognostic biomarker in pancreatic cancer
Authors: Fu Yifan , Tao Jinxin , Gu Yani , Liu Yueze , Qiu Jiangdong , Su Dan , Wang Ruobing , Luo Wenhao , Liu Tao , Zhang Feifan , Zhang Taiping , Zhao Yupei 
Published: May 2024

AbstractPancreatic ductal adenocarcinoma (PDAC) is a highly malignant neoplasm characterized by a poor prognosis and limited therapeutic strategy. The PDAC tumor microenvironment presents a complex heterogeneity, where neutrophils emerge as the predominant constituents of the innate immune cell population. Leveraging the power of single-cell RNA-seq, spatial RNA-seq, and multi-omics approaches, we included both published datasets and our in-house patient cohorts, elucidating the inherent heterogeneity in the formation of neutrophil extracellular traps (NETs) and revealed the correlation between NETs and immune suppression. Meanwhile, we constructed a multi-omics prognostic model that suggested the patients exhibiting downregulated expression of NETs may have an unfavorable outcome. We also confirmed TLR2 as a potent prognosis factor and patients with low TLR2 expression had more effective T cells and an overall survival extension for 6 months. Targeting TLR2 might be a promising strategy to reverse immunosuppression and control tumor progression for an improved prognosis.