Miracle-dz/UniFAR-FTUs huggingface dataset:
lipid nanobubbles as an ultrasound-triggered artesunate delivery system for imaging-guided, tumor-targeted chemotherapy

Purpose: Herein, this study is to prepare folic acid (FA)-conjugated lipid nanobubbles (NBs) that highly load artesunate (Arte; FA-ALNBs), as an ultrasound (US)-triggered Arte delivery system for imaging-guided, tumor-targeted chemotherapy. Materials and methods: The morphology, size, zeta potential, and stability of the FA-ALNBs were detected by optical microscopy and dynamic light scattering analysis. The cellular uptake of the FA-ALNBs was evaluated by confocal laser scanning microscopy and flow cytometry. Results: The FA-ALNBs showed uniform spheroidal structure, with 781.2 ± 5.3 nm in average diameter, great physiological stability, and ~ 91.9% ± 1.1% encapsulation efficiency of Arte. Using focused US, about 36.1% ± 2.5% of the entrapped Arte was trigger-released from the FA-ALNBs. Owing to the US contrast property, FA-ALNBs showed an enhanced US signal in vitro when using an ultrasonic diagnostic apparatus with a 1-MHz linear transducer. Due to the FA receptor-mediated endocytosis effect, FA-ALNBs can be efficiently internalized by cells, showing an uptake ratio of about 56.4% ± 3.1%. FA-ALNBs showed an enhanced, dose-dependent cell-killing ability, while FA-ALNBs plus US irradiation exhibited a stronger anticancer effect in vitro. Post intravenous injection into tumor-bearing mice, FA-ALNBs showed an enhanced US contrast effect with increase in time, indicating the increasing accumulation of FA-ALNBs in tumor tissue, which peaked at 4 hours post injection. Focused US irradiation was conducted on the tumor region at 4 hours post injection of FA-ALNBs, which showed a greater tumor suppression effect after 30 days of treatment compared with all other treatment groups. Moreover, FA-ALNBs showed negligible systemic toxicity in vivo. Conclusion: This versatile US-triggered drug delivery system with great anticancer efficacy was assessed both in vitro and in vivo, revealing great potential as a cancer theranostic agent for future application.
Biliary Bicarbonate, pH, and Glucose Are Suitable Biomarkers of Biliary Viability During Ex Situ Normothermic Machine Perfusion of Human Donor Livers

T he gap between the demand and availability of donor livers for transplantation has stimulated the use of extended-criteria donor livers, including steatotic, elderly, and donation after circulatory death (DCD) (BDI) Background. Ex situ normothermic machine perfusion (NMP) can be used to assess viability of suboptimal donor livers before implantation. Our aim was to assess the diagnostic accuracy of bile biochemistry for the assessment of bile duct injury (BDI). Methods. In a preclinical study, 23 human donor livers underwent 6 hours of end-ischemic NMP to determine biomarkers of BDI. Livers were divided into groups with low or high BDI, based on a clinically relevant histological grading system. During NMP, bile was analyzed biochemically and potential biomarkers were correlated with the degree of BDI. Receiver operating characteristics curves were generated to determine optimal cutoff values. For clinical validation, identified biomarkers were subsequently included as viability criteria in a clinical trial (n = 6) to identify transplantable liver grafts with low BDI. Results. Biliary bicarbonate and pH were significantly higher and biliary glucose was significantly lower in livers with low BDI, compared with high BDI. The following cutoff values were associated with low BDI: biliary bicarbonate greater than 18 mmol/L ( P = 0.002), biliary pH greater than 7.48 ( P = 0.019), biliary glucose less than 16 mmol/L ( P = 0.013), and bile/perfusate glucose ratio less than 0.67 (P = 0.013). In the clinical trial, 4 of 6 livers met these criteria and were transplanted, and none developed clinical evidence of posttransplant cholangiopathy. Conclusions. Biliary bicarbonate, pH, and glucose during ex situ NMP of liver grafts are accurate biomarkers of BDI and can be easily determined point of care, making them suitable for the pretransplant assessment of bile duct viability. This may improve graft selection and decrease the risk of posttransplant cholangiopathy.
Addenbrooke’s cognitive examination iii in the diagnosis of dementia: a critical review

: Addenbrooke’s cognitive examination III is a screening test that is composed of tests of attention, orientation, memory, language, visual perceptual and visuospatial skills. It is useful in the detection of cognitive impairment, especially in the detection of Alzheimer’s disease and fronto-temporal dementia. The aim of this study is to do a critical review of the Addenbrooke’s cognitive examination III. The different language versions available and research about the different variables that have relationship with the performance of the subject in the ACE-III are listed. The ACE-III is a detection technique that can differentiate patients with and without cognitive impairment, is sensitive to the early stages of dementia, and is available in different languages. However, further research is needed to obtain optimal cutoffs for the different versions and to evaluate the impact of different age, gender, IQ, and education variables on the performance of the test.
Six sessions of sprint interval training increases muscle oxidative potential and cycle endurance capacity in humans.

Parra et al. (Acta Physiol. Scand 169: 157-165, 2000) showed that 2 wk of daily sprint interval training (SIT) increased citrate synthase (CS) maximal activity but did not change "anaerobic" work capacity, possibly because of chronic fatigue induced by daily training. The effect of fewer SIT sessions on muscle oxidative potential is unknown, and aside from changes in peak oxygen uptake (Vo(2 peak)), no study has examined the effect of SIT on "aerobic" exercise capacity. We tested the hypothesis that six sessions of SIT, performed over 2 wk with 1-2 days rest between sessions to promote recovery, would increase CS maximal activity and endurance capacity during cycling at approximately 80% Vo(2 peak). Eight recreationally active subjects [age = 22 +/- 1 yr; Vo(2 peak) = 45 +/- 3 ml.kg(-1). min(-1) (mean +/- SE)] were studied before and 3 days after SIT. Each training session consisted of four to seven "all-out" 30-s Wingate tests with 4 min of recovery. After SIT, CS maximal activity increased by 38% (5.5 +/- 1.0 vs. 4.0 +/- 0.7 mmol.kg protein(-1). h(-1)) and resting muscle glycogen content increased by 26% (614 +/- 39 vs. 489 +/- 57 mmol/kg dry wt) (both P < 0.05). Most strikingly, cycle endurance capacity increased by 100% after SIT (51 +/- 11 vs. 26 +/- 5 min; P < 0.05), despite no change in Vo(2 peak). The coefficient of variation for the cycle test was 12.0%, and a control group (n = 8) showed no change in performance when tested approximately 2 wk apart without SIT. We conclude that short sprint interval training (approximately 15 min of intense exercise over 2 wk) increased muscle oxidative potential and doubled endurance capacity during intense aerobic cycling in recreationally active individuals.
Large Language Models in Fault Localisation

Large Language Models (LLMs) have shown promise in multiple software engineering tasks including code generation, program repair, code summarisation, and test generation. Fault localisation is instrumental in enabling automated debugging and repair of programs and was prominently featured as a highlight during the launch event of ChatGPT-4. Nevertheless, the performance of LLMs compared to state-of-the-art methods, as well as the impact of prompt design and context length on their efficacy, remains unclear. To fill this gap, this paper presents an in-depth investigation into the capability of ChatGPT-3.5 and ChatGPT-4, the two state-of-the-art LLMs, on fault localisation. Using the widely-adopted large-scale Defects4J dataset, we compare the two LLMs with the existing fault localisation techniques. We also investigate the consistency of LLMs in fault localisation, as well as how prompt engineering and the length of code context affect the fault localisation effectiveness. Our findings demonstrate that within function-level context, ChatGPT-4 outperforms all the existing fault localisation methods. Additional error logs can further improve ChatGPT models' localisation accuracy and consistency, with an average 46.9% higher accuracy over the state-of-the-art baseline SmartFL on the Defects4J dataset in terms of TOP-1 metric. However, when the code context of the Defects4J dataset expands to the class-level, ChatGPT-4's performance suffers a significant drop, with 49.9% lower accuracy than SmartFL under TOP-1 metric. These observations indicate that although ChatGPT can effectively localise faults under specific conditions, limitations are evident. Further research is needed to fully harness the potential of LLMs like ChatGPT for practical fault localisation applications.
The electromagnetic form factors in the $N_{f}=4$ holographic QCD

In this study, we employ a modified soft-wall holographic model with four flavors to investigate the meson spectra, decay constants, electromagnetic form factors, and charge radius of various mesons. We obtain the spectra for vector, axial vector, and pseudoscalar mesons. Decay constants are calculated and compared with experimental and lattice QCD data. The pion and kaon electromagnetic form factors are compared with the experimental data, and a good agreement is achieved for the kaon at low $Q^{2}$. For the charmed mesons, the electromagnetic form factors of the $D$ and $D_{s}$ and electric form factors of the $D^{*}$ and $D_{s}^{*}$ are well consistent with the lattice QCD data. Moreover, the electric, magnetic, and quadrupole form factors are predicted for the $\rho$, $K^{*}$, $a_1$, $K_1$, $D_1$, and $D_{s1}$ mesons. Furthermore, the charge radius of the vector, axial vector, and pseudoscalars, including the strange and charmed mesons, are computed.
Improving Generalization in Reinforcement Learning Training Regimes for Social Robot Navigation

In order for autonomous mobile robots to navigate in human spaces, they must abide by our social norms. Reinforcement learning (RL) has emerged as an effective method to train sequential decision-making policies that are able to respect these norms. However, a large portion of existing work in the field conducts both RL training and testing in simplistic environments. This limits the generalization potential of these models to unseen environments, and the meaningfulness of their reported results. We propose a method to improve the generalization performance of RL social navigation methods using curriculum learning. By employing multiple environment types and by modeling pedestrians using multiple dynamics models, we are able to progressively diversify and escalate difficulty in training. Our results show that the use of curriculum learning in training can be used to achieve better generalization performance than previous training methods. We also show that results presented in many existing state-of-the-art RL social navigation works do not evaluate their methods outside of their training environments, and thus do not reflect their policies' failure to adequately generalize to out-of-distribution scenarios. In response, we validate our training approach on larger and more crowded testing environments than those used in training, allowing for more meaningful measurements of model performance.
A General-Purpose Self-Supervised Model for Computational Pathology

Tissue phenotyping is a fundamental computational pathology (CPath) task in learning objective characterizations of histopathologic biomarkers in anatomic pathology. However, whole-slide imaging (WSI) poses a complex computer vision problem in which the large-scale image resolutions of WSIs and the enormous diversity of morphological phenotypes preclude large-scale data annotation. Current efforts have proposed using pretrained image encoders with either transfer learning from natural image datasets or self-supervised pretraining on publicly-available histopathology datasets, but have not been extensively developed and evaluated across diverse tissue types at scale. We introduce UNI, a general-purpose self-supervised model for pathology, pretrained using over 100 million tissue patches from over 100,000 diagnostic haematoxylin and eosin-stained WSIs across 20 major tissue types, and evaluated on 33 representative CPath clinical tasks in CPath of varying diagnostic difficulties. In addition to outperforming previous state-of-the-art models, we demonstrate new modeling capabilities in CPath such as resolution-agnostic tissue classification, slide classification using few-shot class prototypes, and disease subtyping generalization in classifying up to 108 cancer types in the OncoTree code classification system. UNI advances unsupervised representation learning at scale in CPath in terms of both pretraining data and downstream evaluation, enabling data-efficient AI models that can generalize and transfer to a gamut of diagnostically-challenging tasks and clinical workflows in anatomic pathology.
A Classification-Guided Approach for Adversarial Attacks against Neural Machine Translation

Neural Machine Translation (NMT) models have been shown to be vulnerable to adversarial attacks, wherein carefully crafted perturbations of the input can mislead the target model. In this paper, we introduce ACT, a novel adversarial attack framework against NMT systems guided by a classifier. In our attack, the adversary aims to craft meaning-preserving adversarial examples whose translations in the target language by the NMT model belong to a different class than the original translations. Unlike previous attacks, our new approach has a more substantial effect on the translation by altering the overall meaning, which then leads to a different class determined by an oracle classifier. To evaluate the robustness of NMT models to our attack, we propose enhancements to existing black-box word-replacement-based attacks by incorporating output translations of the target NMT model and the output logits of a classifier within the attack process. Extensive experiments, including a comparison with existing untargeted attacks, show that our attack is considerably more successful in altering the class of the output translation and has more effect on the translation. This new paradigm can reveal the vulnerabilities of NMT systems by focusing on the class of translation rather than the mere translation quality as studied traditionally.
Efficient DCQO Algorithm within the Impulse Regime for Portfolio Optimization

We propose a faster digital quantum algorithm for portfolio optimization using the digitized-counterdiabatic quantum optimization (DCQO) paradigm in the impulse regime, that is, where the counterdiabatic terms are dominant. Our approach notably reduces the circuit depth requirement of the algorithm and enhances the solution accuracy, making it suitable for current quantum processors. We apply this protocol to a real-case scenario of portfolio optimization with 20 assets, using purely quantum and hybrid classical-quantum paradigms. We experimentally demonstrate the advantages of our protocol using up to 20 qubits on an IonQ trapped-ion quantum computer. By benchmarking our method against the standard quantum approximate optimization algorithm and finite-time digitized-adiabatic algorithms, we obtain a significant reduction in the circuit depth by factors of 2.5 to 40, while minimizing the dependence on the classical optimization subroutine. Besides portfolio optimization, the proposed method is applicable to a large class of combinatorial optimization problems.
PointCVaR: Risk-Optimized Outlier Removal for Robust 3D Point Cloud Classification

With the growth of 3D sensing technology, the deep learning system for 3D point clouds has become increasingly important, especially in applications such as autonomous vehicles where safety is a primary concern. However, there are growing concerns about the reliability of these systems when they encounter noisy point clouds, either occurring naturally or introduced with malicious intent. This paper highlights the challenges of point cloud classification posed by various forms of noise, from simple background noise to malicious adversarial/backdoor attacks that can intentionally skew model predictions. While there's an urgent need for optimized point cloud denoising, current point outlier removal approaches, an essential step for denoising, rely heavily on handcrafted strategies and are not adapted for higher-level tasks, such as classification. To address this issue, we introduce an innovative point outlier cleansing method that harnesses the power of downstream classification models. Using gradient-based attribution analysis, we define a novel concept: point risk. Drawing inspiration from tail risk minimization in finance, we recast the outlier removal process as an optimization problem, named PointCVaR. Extensive experiments show that our proposed technique not only robustly filters diverse point cloud outliers but also consistently and significantly enhances existing robust methods for point cloud classification. A notable feature of our approach is its effectiveness in defending against the latest threat of backdoor attacks in point clouds.
A Generalized Density Dissipation for Weakly-compressible SPH

The weakly compressible Smoothed Particle Hydrodynamics (SPH) is known to suffer from the pressure oscillation, which would undermine the simulation stability and accuracy. To address this issue, we propose a generalized density dissipation scheme suitable for both single-phase and multiphase flow simulations. Our approach consists of two components. Firstly, we replace the basic density dissipation with the density increment dissipation to enable numerical dissipation crossing the interfaces of different fluids in multiphase flow. Secondly, based on the dissipation volume conservation, we utilize dissipation volume correction factor (VCF) to stabilize the simulations for multiphase flows with large density ratio. We demonstrate the accuracy, stability, and robustness of our method through four three-dimensional benchmarks, i.e., the sloshing under external excitations, the single and double bubbles rising, Rayleigh-Taylor instability, and Kelvin Helmholtz instability. Additionally, our study reveals the relationship between SPH with the density dissipation and the approximate Riemann solver.
DiffBIR: Towards Blind Image Restoration with Generative Diffusion Prior

We present DiffBIR, which leverages pretrained text-to-image diffusion models for blind image restoration problem. Our framework adopts a two-stage pipeline. In the first stage, we pretrain a restoration module across diversified degradations to improve generalization capability in real-world scenarios. The second stage leverages the generative ability of latent diffusion models, to achieve realistic image restoration. Specifically, we introduce an injective modulation sub-network -- LAControlNet for finetuning, while the pre-trained Stable Diffusion is to maintain its generative ability. Finally, we introduce a controllable module that allows users to balance quality and fidelity by introducing the latent image guidance in the denoising process during inference. Extensive experiments have demonstrated its superiority over state-of-the-art approaches for both blind image super-resolution and blind face restoration tasks on synthetic and real-world datasets. The code is available at https://github.com/XPixelGroup/DiffBIR.
Revista Española De Cirugía Ortopédica Y Traumatología Hip Joint Phylogenesis. Phenotypic Plasticity. Lamarckian or Darwinian Paradigm? Part Ii Palabras Clave

Objective: The aim of this work is to analyse the origin of phenotypic plastic changes into a biologic structure, in this case the hip. As a hypothesis of the work, the possibility that changes could be explained following the Lamarckian paradigm, opposed to the Darwinian paradigm, is shown. The section ''Material and methods'' of this work have been published in part I. Studies in plants and fish have been added. Discussion: Results showed that the ball-and-socket design of the hip joint remains unchanged. Phenotype in the elements that form the hip joint tissues showed significant plastic changes. Conclusion: Interpretation of our results suggest that changes in phenotype plasticity of the hip joint are immanent to phenotype and cannot be explained by following Lamarck's or Darwin's paradigm.
Psychological treatments for depression and anxiety in dementia and mild cognitive impairment: systematic review and meta-analysis†

Background Anxiety and depression are common in people with dementia and mild cognitive impairment (MCI), but there is uncertainty about the effectiveness of both pharmacological and psychological therapies. Aims To evaluate the evidence of effectiveness of psychological treatments in treating depression and anxiety in people with dementia and MCI. Method We carried out a systematic review and meta-analysis of randomised controlled trials (RCTs) of psychological treatment versus usual care in people with dementia and MCI. Primary outcomes were symptoms of anxiety and depression. Secondary outcomes were quality of life, ability to perform daily activities, neuropsychiatric symptoms, cognition and caregivers' self-rated depressive symptoms. Results We included six RCTs, involving 439 participants with dementia, which used cognitive–behavioural therapy, interpersonal therapy, counselling or multimodal interventions including a specific psychological therapy. We found beneficial effects for both depression and anxiety. Overall, the quality of the evidence was moderate for depression and low for anxiety, due to the methodological limitations of the studies we identified and the limited number of trials. Conclusions The evidence from six RCTs suggests that psychological treatments are effective in reducing symptoms of depression and anxiety for people with dementia. There is a need for high-quality, multicentre trials including standardised, well-defined interventions. Declarations of interest A.Q. , A.S. and M.O. are investigators in one of the included studies.
A Quantitative Approach for Adopting Disaggregated Memory in HPC Systems

Memory disaggregation has recently been adopted in data centers to improve resource utilization, motivated by cost and sustainability. Recent studies on large-scale HPC facilities have also highlighted memory underutilization. A promising and non-disruptive option for memory disaggregation is rack-scale memory pooling, where node-local memory is supplemented by shared memory pools. This work outlines the prospects and requirements for adoption and clarifies several misconceptions. We propose a quantitative method for dissecting application requirements on the memory system from the top down in three levels, moving from general, to multi-tier memory systems, and then to memory pooling. We provide a multi-level profiling tool and LBench to facilitate the quantitative approach. We evaluate a set of representative HPC workloads on an emulated platform. Our results show that prefetching activities can significantly influence memory traffic profiles. Interference in memory pooling has varied impacts on applications, depending on their access ratios to memory tiers and arithmetic intensities. Finally, in two case studies, we show the benefits of our findings at the application and system levels, achieving 50% reduction in remote access and 13% speedup in BFS, and reducing performance variation of co-located workloads in interference-aware job scheduling.