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Associations of iron (hydr)oxides (FeOx) with organic carbon are vital in regulating the stability of soil organic carbon (SOC). Like SOC, FeOx is chemically dynamic in soils, particularly under anaerobic conditions. However, previous research has not clarified how the stability of FeOx (goethite versus ferrihydrite) and the formation pathway of FeOx-OC associations (adsorption versus coprecipitation) affect the stability of FeOx-bound OC and, subsequently, the priming effect (PE) under anaerobic conditions. With an aim to bridge this gap, we incubated paddy soils for 80 d under anaerobic conditions after adding free 13C-glucose, ferrihydrite- or goethite-bound 13C-glucose formed by either adsorption or coprecipitation. Compared with the free glucose addition, the FeOx-bound glucose addition increased 13CO2 production by 8%–21% but reduced 13C–CH4 production by 7%–10%. Ferrihydrite-bound glucose was mineralised more than goethite-bound glucose; this is consistent with its lower crystallinity facilitating reduction and, thus, higher OC bioavailability. Glucose induced a negative priming effect (PE) for CO2 but a positive PE for CH4, whereas FeOx-bound glucose showed the opposite trend. This may be because FeOx-bound glucose provides an energy source and electron acceptor for Fe-reducing bacteria; this promotes the dissimilating reduction of iron and combines with an aggravated microbial P limitation resulting from the FeOx input. The crystallinity of FeOx affected the amount of primed CH4 rather than its formation pathway. In conclusion, the crystallinity of FeOx controls the stability of FeOx-OC associations and the PE of SOC decomposition under anaerobic conditions.

Approaches to quantify stress responses typically rely on subjective surveys and questionnaires. Wearable sensors can potentially be used to continuously monitor stress-relevant biomarkers. However, the biological stress response is spread across the nervous, endocrine, and immune systems, and the capabilities of current sensors are not sufficient for condition-specific stress response evaluation. Here we report an electronic skin for stress response assessment that non-invasively monitors three vital signs (pulse waveform, galvanic skin response and skin temperature) and six molecular biomarkers in human sweat (glucose, lactate, uric acid, sodium ions, potassium ions and ammonium). We develop a general approach to prepare electrochemical sensors that relies on analogous composite materials for stabilizing and conserving sensor interfaces. The resulting sensors offer long-term sweat biomarker analysis of over 100 hours with high stability. We show that the electronic skin can provide continuous multimodal physicochemical monitoring over a 24-hour period and during different daily activities. With the help of a machine learning pipeline, we also show that the platform can differentiate three stressors with an accuracy of 98.0%, and quantify psychological stress responses with a confidence level of 98.7%.

In this paper, we consider an Internet of Things (IoT) network supporting latency-critical transmissions to multiple nodes in the finite blocklength regime. An energy efficiency maximizing design is provided via efficiently jointly allocating the power and blocklength among the transmissions to different nodes, while guaranteeing per-node constraint of reliability. To address the formulated non-convex problem, we propose a Majorization-Minimization-based approach. Specifically, we tightly approximate the problem at each local point by introducing an auxiliary constant. Then, we rigorously prove the convexity of the conducted local problem. By successively optimally solving each convex local problem, a near-optimal solution is finally achieved. Via simulations, we demonstrate the performance advantages of the proposed joint design., Part of ISBN [9798350362510] QC 20240923

The transition from conventional standalone metrology to integrated metrology has been accelerating in advanced manufacturing over the past decade. This keynote paper defines the concept of integrated metrology, which extends beyond parts inspection and encompasses processes and manufacturing equipment to enhance efficiency and productivity. The paper presents the characteristics, benefits, constraints, and future possibilities of integrated metrology for parts, processes, and equipment. It also includes a classification of the physical quantities of measurands, the corresponding measuring instruments, data and communication methods, uncertainty, and traceability. The paper also discusses future challenges and emerging trends., QC 20240903

Metal additive manufacturing (MAM) technology has experienced rapid development in recent years. As both equipment and materials progress towards increased maturity and commercialization, material metallurgy technology based on high energy sources has become a key factor influencing the future development of MAM. The calculation of phase diagrams (CALPHAD) is an essential method and tool for constructing multi-component phase diagrams by employing experimental phase diagrams and Gibbs free energy models of simple systems. By combining with the element mobility data and non-equilibrium phase transition model, it has been widely used in the analysis of traditional metal materials. The development of CALPHAD application technology for MAM is focused on the compositional design of printable materials, the reduction of metallurgical imperfections, and the control of microstructural attributes. This endeavor carries considerable theoretical and practical significance. This paper summarizes the important achievements of CALPHAD in additive manufacturing (AM) technology in recent years, including material design, process parameter optimization, microstructure evolution simulation, and properties prediction. Finally, the limitations of applying CALPHAD technology to MAM technology are discussed, along with prospective research directions., QC 20240823

Amidst the global movement for online courts and digitization of justice, many jurisdictions have to overcome concerns over new technological barriers that negatively impact on the access to justice by all. China’s implementation of the online courts since 2020 introduces a distinctive approach, setting itself apart by providing litigants with a unique freedom of choice between online and offline mechanisms for every aspect of civil and administrative litigation in any Chinese court. Several inherent characteristics or even perceived weaknesses of the Chinese political and judicial system contributed to this move that had not been tested before in China or elsewhere. This innovative model creates a new hybrid judicial framework for the digital era, potentially reshaping traditional paradigms and offering solutions to the emerging challenges faced by contemporary legal systems, as part of China’s notable efforts to influence global discussions and understanding of important topics such as law and governance

Achieving both high power conversion efficiency (PCE) and device stability is a major challenge for the practical development of organic solar cells (OSCs). Herein, three non-fully conjugated dimerized giant acceptors (named 2Y-sites, including wing-site-linked 2Y-wing, core-site-linked 2Y-core, and end-site-linked 2Y-end) are developed. They share the similar monomer precursors but have different alkyl-linked sites, offering the fine-tuned molecular absorption, packing, glass transition temperature, and carrier mobility. Among their binary active layers, D18/2Y-wing has better miscibility, leading to optimized morphology and more efficient charge transfer compared to D18/2Y-core and D18/2Y-end. Therefore, the D18/2Y-wing-based OSCs achieve a superior PCE of 17.73 %, attributed to enhanced photocurrent and fill factor. Furthermore, the D18/2Y-wing-based OSCs exhibit a balance of high PCE and improved stability, distinguishing them within the 2Y-sites. Building on the success of 2Y-wing in binary systems, we extend its application to ternary OSCs by pairing it with the near-infrared absorbing D18/BS3TSe-4F host. Thanks to the complementary absorption within 300-970 nm and further optimized morphology, ternary OSCs obtain a higher PCE of 19.13 %, setting a new efficiency benchmark for the dimer-derived OSCs. This approach of alkyl-linked site engineering for constructing dimerized giant acceptors presents a promising pathway to improve both PCE and stability of OSCs. Three new non-fully conjugated dimerized giant acceptors with different alkyl-linked sites are developed. Among them, wing-sited 2Y-wing has fine-tuned packing and better miscibility with donor, allowing to 19.13 % efficiency (which is the highest value among the devices with giant acceptors) and highly stable organic solar cells. image, Funding Agencies|NSFC [22209131, 22005121, 21875182, 52173023]; National Key Research and Development Program of China [2022YFE0132400]; Key Scientific and Technological Innovation Team Project of Shaanxi Province [2020TD-002]; The 111 project 2.0 [BP0618008]; Scientific Research Project of Education Department of Hunan Province [23B0167]; Open fund support Open Funds of the State Key Laboratory of Luminescent Materials and Devices (South China University of Technology) [2023-skllmd-13]; Scientific Research Funds of Huaqiao University [605-50Y23024]

In 2019, Neutron star Interior Composition ExploreR (NICER) mission released its findings on the mass and radius of the isolated neutron star (INS) PSR J0030+0451, revealing a mass of approximately 1.4 solar masses ($M_{\odot}$) and a radius near 13 kilometers. However, the recent re-analysis by the NICER collaboration \citep{vinciguerra2024updated} suggests that the available data primarily yields a precise inference of the compactness for this source while the resulting mass and radius are strongly model-dependent and diverse (the 68.3\% credible regions just overlap slightly for the ST+PDT and PDT-U models). By integrating this compactness data with the equation of state (EoS) refined by our latest investigations, we have deduced the mass and radius for PSR J0030+0451, delivering estimates of $M=1.48^{+0.09}_{-0.10}~M_\odot$ and $R=12.38_{-0.70}^{+0.51}~{\rm km}$ for the compactness found in ST+PDT model, alongside $M=1.47^{+0.14}_{-0.20}~M_\odot$ and $R=12.37_{-0.69}^{+0.50}~{\rm km}$ for the compactness in PDT-U model. These two groups of results are well consistent with each other and the direct X-ray data inference within the ST+PDT model seems to be favored. Additionally, we have calculated the tidal deformability, moment of inertia, and gravitational binding energy for this NS. Furthermore, employing these refined EoS models, we have updated mass-radius estimates for three INSs with established gravitational redshifts., Comment: 10 pages,6 figures, Accepted for publication in ApJ

The measurements of the bulk properties of most isolated neutron stars (INSs) are challenging tasks. Tang et al. (2020) have developed a new method, based on the equation of state (EoS) of neutron star (NS) material constrained by the observational data, to infer the gravitational masses of a few INSs whose gravitational redshifts are available. However, in that work, the authors only considered the constraints on the EoS from nuclear experiments/theories and the gravitational wave data of GW170817; the possible phase transition has not been taken into account. In this work, we adopt three EoS models (including the one incorporates a first-order strong phase transition) that are constrained by the latest multimessenger NS data, including in particular the recent mass\textendash radius measurements of two NSs by Neutron Star Interior Composition Explorer, to update the estimation of the gravitational masses of RBS 1223, RX J0720.4-3125, and RX J1856.5-3754. In comparison to our previous approach, the new constraints are tighter, and the gravitational masses are larger by about $0.1M_\odot$. All the inferred gravitational masses are within the range of the NS masses measured in other ways. We have also calculated the radius, tidal-deformability, and moment of inertia of these sources. The inclusion of the first-order strong phase transition has little influence on modifying the results., Comment: 8pages,4figures

The mass table in the deformed relativistic Hartree-Bogoliubov theory in continuum (DRHBc) with the PC-PK1 density functional has been established for even-$Z$ nuclei with $8\le Z\le120$, extended from the previous work for even-even nuclei [Zhang $\it{et al.}$ (DRHBc Mass Table Collaboration), At. Data Nucl. Data Tables 144, 101488 (2022)]. The calculated binding energies, two-nucleon and one-neutron separation energies, root-mean-square (rms) radii of neutron, proton, matter, and charge distributions, quadrupole deformations, and neutron and proton Fermi surfaces are tabulated and compared with available experimental data. A total of 4829 even-$Z$ nuclei are predicted to be bound, with an rms deviation of 1.477 MeV from the 1244 mass data. Good agreement with the available experimental odd-even mass differences, $\alpha$ decay energies, and charge radii is also achieved. The description accuracy for nuclear masses and nucleon separation energies as well as the prediction for drip lines is compared with the results obtained from other relativistic and nonrelativistic density functional. The comparison shows that the DRHBc theory with PC-PK1 provides an excellent microscopic description for the masses of even-$Z$ nuclei. The systematics of the nucleon separation energies, odd-even mass differences, pairing energies, two-nucleon gaps, $\alpha$ decay energies, rms radii, quadrupole deformations, potential energy curves, neutron density distributions, and neutron mean-field potentials are discussed., Comment: 392 pages, 17 figures, 2 tables, data file in the TXT form is available for download under "Ancillary files"; Grant No. added

By accurately measuring composition and energy spectrum of cosmic ray, the origin problem of so called "keen" region (energy > 1 PeV) can be solved. However, up to the present, the results of the spectrum in the knee region obtained by several previous experiments have shown obvious differences, so they cannot give effective evidence for judging the theoretical models on the origin of the knee. Recently, the Large High Altitude Air Shower Observatory (LHAASO) has reported several major breakthroughs and important results in astro-particle physics field. Relying on its advantages of wide-sky survey, high altitude location and large area detector arrays, the research content of LHAASO experiment mainly includes ultra high-energy gamma-ray astronomy, measurement of cosmic ray spectra in the knee region, searching for dark matter and new phenomena of particle physics at higher energy. The electron and Thermal Neutron detector (EN-Detector) is a new scintillator detector which applies thermal neutron detection technology to measure cosmic ray extensive air shower (EAS). This technology is an extension of LHAASO. The EN-Detector Array (ENDA) can highly efficiently measure thermal neutrons generated by secondary hadrons so called "skeleton" of EAS. In this paper, we perform the optimization of ENDA configuration, and obtain expectations on the ENDA results, including thermal neutron distribution, trigger efficiency and capability of cosmic ray composition separation. The obtained real data results are consistent with those by the Monte Carlo simulation.

Li An,1,2,* Xiaomei Ren,1,* Ye Pan,2 Wei Gao,1 Liqun Ren,1 Jing Wang,3 Yao Wang2 1Department of Geriatrics, ZhongDa Hospital, Southeast University School of Medicine, Nanjing, 210009, People’s Republic of China; 2Department of Endocrine, ZhongDa Hospital, Southeast University School of Medicine, Nanjing, 210009, People’s Republic of China; 3Yizheng Hospital of Nanjing Drum Tower Hospital Group, Yizheng, 211400, People’s Republic of China*These authors contributed equally to this workCorrespondence: Jing Wang; Yao Wang, Tel/Fax +86-25- 83272111, Email wang_yao100@163.com; yz3466599@hotmail.comBackground: The impact of inflammatory factors on the risk of diabetic nephropathy (DN) is inconsistent. Two-sample Mendelian randomization (MR) analyses were used to detect the causal role of inflammatory factors in DN risk.Methods: Inflammatory factor GWAS summary data were collected from a meta-analysis including 8,293 Finnish participants, and DN information was extracted from a GWAS of 213,746 individuals from FinnGen. The MR Pleiotropy Residual Sum and Outlier (MR-PRESSO) outlier test was used for the removal of horizontal pleiotropic outliers. Multivariable MR analysis was also used to adjust for pleiotropy.Results: IFN-γ [ORIVW: 1.33; 95% CI: 1.09– 1.63; p=0.005] and SCF [ORIVW: 1.25, 1.02– 1.52; p = 0.027] were associated with an increased risk of DN. MIP1b [ORIVW: 0.92; 95% CI: 0.85– 0.98; p = 0.022] and IL-16 [ORIVW: 0.89, 0.81– 0.99; p = 0.043] showed negative associations with the risk of DN. We validated our MR results with MR-PRESSO analyses. Significant horizontal pleiotropy was not found. Moreover, in the multivariable MR analysis, the associations between cytokines and DN risk remained.Conclusion: Our MR results based on genetic data contribute to a better understanding of the pathogenesis of DN and provide evidence for a causal effect of inflammatory factors on DN. These findings support targeting specific inflammatory fa

Approaches to quantify stress responses typically rely on subjective surveys and questionnaires. Wearable sensors can potentially be used to continuously monitor stress-relevant biomarkers. However, the biological stress response is spread across the nervous, endocrine and immune systems, and the capabilities of current sensors are not sufficient for condition-specific stress response evaluation. Here we report an electronic skin for stress response assessment that non-invasively monitors three vital signs (pulse waveform, galvanic skin response and skin temperature) and six molecular biomarkers in human sweat (glucose, lactate, uric acid, sodium ions, potassium ions and ammonium). We develop a general approach to prepare electrochemical sensors that relies on analogous composite materials for stabilizing and conserving sensor interfaces. The resulting sensors offer long-term sweat biomarker analysis of more than 100 h with high stability. We show that the electronic skin can provide continuous multimodal physicochemical monitoring over a 24-hour period and during different daily activities. With the help of a machine learning pipeline, we also show that the platform can differentiate three stressors with an accuracy of 98.0% and quantify psychological stress responses with a confidence level of 98.7%. © 2024, The Author(s), under exclusive licence to Springer Nature Limited.

We propose a general method for deep learning based point cloud analysis, which is invariant to rotation on the inputs. Classical methods are vulnerable to rotation, as they usually take aligned point clouds as input. Principle Component Analysis (PCA) is a practical approach to achieve rotation invariance. However, there are still some gaps between theory and practical algorithms. In this work, we present a thorough study on designing rotation invariant algorithms for point cloud analysis. We first formulate it as a permutation invariant problem, then propose a general framework which can be combined with any backbones. Our method is beneficial for further research such as 3D pre-training and multi-modal learning. Experiments show that our method has considerable or better performance compared to state-of-the-art approaches on common benchmarks. Code is available at https://github.com/luoshuqing2001/RI_framework., Comment: 5 pages, 1 figure, accepted by ICASSP 2024

Privacy-preserving neural networks have attracted increasing attention in recent years, and various algorithms have been developed to keep the balance between accuracy, computational complexity and information security from the cryptographic view. This work takes a different view from the input data and structure of neural networks. We decompose the input data (e.g., some images) into sensitive and insensitive segments according to importance and privacy. The sensitive segment includes some important and private information such as human faces and we take strong homomorphic encryption to keep security, whereas the insensitive one contains some background and we add perturbations. We propose the bi-CryptoNets, i.e., plaintext and ciphertext branches, to deal with two segments, respectively, and ciphertext branch could utilize the information from plaintext branch by unidirectional connections. We adopt knowledge distillation for our bi-CryptoNets by transferring representations from a well-trained teacher neural network. Empirical studies show the effectiveness and decrease of inference latency for our bi-CryptoNets.

The age of social media is flooded with Internet memes, necessitating a clear grasp and effective identification of harmful ones. This task presents a significant challenge due to the implicit meaning embedded in memes, which is not explicitly conveyed through the surface text and image. However, existing harmful meme detection methods do not present readable explanations that unveil such implicit meaning to support their detection decisions. In this paper, we propose an explainable approach to detect harmful memes, achieved through reasoning over conflicting rationales from both harmless and harmful positions. Specifically, inspired by the powerful capacity of Large Language Models (LLMs) on text generation and reasoning, we first elicit multimodal debate between LLMs to generate the explanations derived from the contradictory arguments. Then we propose to fine-tune a small language model as the debate judge for harmfulness inference, to facilitate multimodal fusion between the harmfulness rationales and the intrinsic multimodal information within memes. In this way, our model is empowered to perform dialectical reasoning over intricate and implicit harm-indicative patterns, utilizing multimodal explanations originating from both harmless and harmful arguments. Extensive experiments on three public meme datasets demonstrate that our harmful meme detection approach achieves much better performance than state-of-the-art methods and exhibits a superior capacity for explaining the meme harmfulness of the model predictions., Comment: The first work towards explainable harmful meme detection by harnessing advanced LLMs

In the age of the infodemic, it is crucial to have tools for effectively monitoring the spread of rampant rumors that can quickly go viral, as well as identifying vulnerable users who may be more susceptible to spreading such misinformation. This proactive approach allows for timely preventive measures to be taken, mitigating the negative impact of false information on society. We propose a novel approach to predict viral rumors and vulnerable users using a unified graph neural network model. We pre-train network-based user embeddings and leverage a cross-attention mechanism between users and posts, together with a community-enhanced vulnerability propagation (CVP) method to improve user and propagation graph representations. Furthermore, we employ two multi-task training strategies to mitigate negative transfer effects among tasks in different settings, enhancing the overall performance of our approach. We also construct two datasets with ground-truth annotations on information virality and user vulnerability in rumor and non-rumor events, which are automatically derived from existing rumor detection datasets. Extensive evaluation results of our joint learning model confirm its superiority over strong baselines in all three tasks: rumor detection, virality prediction, and user vulnerability scoring. For instance, compared to the best baselines based on the Weibo dataset, our model makes 3.8\% and 3.0\% improvements on Accuracy and MacF1 for rumor detection, and reduces mean squared error (MSE) by 23.9\% and 16.5\% for virality prediction and user vulnerability scoring, respectively. Our findings suggest that our approach effectively captures the correlation between rumor virality and user vulnerability, leveraging this information to improve prediction performance and provide a valuable tool for infodemic surveillance., Comment: Accepted by IP&M

The evolution of compression and enhancement algorithms necessitates an accurate quality assessment for point clouds. Previous works consistently regard point cloud quality assessment (PCQA) as a MOS regression problem and devise a deterministic mapping, ignoring the stochasticity in generating MOS from subjective tests. Besides, the viewpoint switching of 3D point clouds in subjective tests reinforces the judging stochasticity of different subjects compared with traditional images. This work presents the first probabilistic architecture for no-reference PCQA, motivated by the labeling process of existing datasets. The proposed method can model the quality judging stochasticity of subjects through a tailored conditional variational autoencoder (CVAE) and produces multiple intermediate quality ratings. These intermediate ratings simulate the judgments from different subjects and are then integrated into an accurate quality prediction, mimicking the generation process of a ground truth MOS. Specifically, our method incorporates a Prior Module, a Posterior Module, and a Quality Rating Generator, where the former two modules are introduced to model the judging stochasticity in subjective tests, while the latter is developed to generate diverse quality ratings. Extensive experiments indicate that our approach outperforms previous cutting-edge methods by a large margin and exhibits gratifying cross-dataset robustness.

Justification is an explanation that supports the veracity assigned to a claim in fact-checking. However, the task of justification generation is previously oversimplified as summarization of fact-check article authored by fact-checkers. Therefore, we propose a realistic approach to generate justification based on retrieved evidence. We present a new benchmark dataset called ExClaim for \underline{Ex}plainable fact-checking of real-world \underline{Claim}s, and introduce JustiLM, a novel few-shot \underline{Justi}fication generation based on retrieval-augmented \underline{L}anguage \underline{M}odel by using fact-check articles as auxiliary resource during training only. Experiments show that JustiLM achieves promising performance in justification generation compared to strong baselines, and can also enhance veracity classification with a straightforward extension., Comment: Accepted in TACL. This is a pre-MIT Press publication version

Learning multi-task models for jointly detecting stance and verifying rumors poses challenges due to the need for training data of stance at post level and rumor veracity at claim level, which are difficult to obtain. To address this issue, we leverage large language models (LLMs) as the foundation annotators for the joint stance detection (SD) and rumor verification (RV) tasks, dubbed as JSDRV. We introduce a novel reinforcement tuning framework to enhance the joint predictive capabilities of LLM-based SD and RV components. Specifically, we devise a policy for selecting LLM-annotated data at the two levels, employing a hybrid reward mechanism to choose high-quality labels for effective LLM fine-tuning on both tasks. Results demonstrate that JSDRV improves the capabilities of LLMs in the joint tasks, not only outperforming state-of-the-art methods but also generalizing to non-LLMs accommodated as task models., Comment: ACL 2024 (Findings)

Text-to-image diffusion models can be fine-tuned in custom domains to adapt to specific user preferences, but such unconstrained adaptability has also been utilized for illegal purposes, such as forging public figures' portraits and duplicating copyrighted artworks. Most existing work focuses on detecting the illegally generated contents, but cannot prevent or mitigate illegal adaptations of diffusion models. Other schemes of model unlearning and reinitialization, similarly, cannot prevent users from relearning the knowledge of illegal model adaptation with custom data. In this paper, we present FreezeAsGuard, a new technique that addresses these limitations and enables irreversible mitigation of illegal adaptations of diffusion models. The basic approach is that the model publisher selectively freezes tensors in pre-trained diffusion models that are critical to illegal model adaptations, to mitigate the fine-tuned model's representation power in illegal domains but minimize the impact on legal model adaptations in other domains. Such tensor freezing can be enforced via APIs provided by the model publisher for fine-tuning, can motivate users' adoption due to its computational savings. Experiment results with datasets in multiple domains show that FreezeAsGuard provides stronger power in mitigating illegal model adaptations of generating fake public figures' portraits, while having the minimum impact on model adaptation in other legal domains. The source code is available at: https://github.com/pittisl/FreezeAsGuard, Comment: 18 pages

AI-Generated Images (AGIs) have inherent multimodal nature. Unlike traditional image quality assessment (IQA) on natural scenarios, AGIs quality assessment (AGIQA) takes the correspondence of image and its textual prompt into consideration. This is coupled in the ground truth score, which confuses the unimodal IQA methods. To solve this problem, we introduce IP-IQA (AGIs Quality Assessment via Image and Prompt), a multimodal framework for AGIQA via corresponding image and prompt incorporation. Specifically, we propose a novel incremental pretraining task named Image2Prompt for better understanding of AGIs and their corresponding textual prompts. An effective and efficient image-prompt fusion module, along with a novel special [QA] token, are also applied. Both are plug-and-play and beneficial for the cooperation of image and its corresponding prompt. Experiments demonstrate that our IP-IQA achieves the state-of-the-art on AGIQA-1k and AGIQA-3k datasets. Code will be available at https://github.com/Coobiw/IP-IQA., Comment: 6 pages, 3 figures, accepted by ICME2024

Digital trace data provide potentially valuable resources for understanding human behaviour, but their value has been limited by issues of unclear measurement. The growth of large language models provides an opportunity to address this limitation in the case of text data. Specifically, recognizing cases where their responses are a form of psychological measurement (the use of observable indicators to assess an underlying construct) allows existing measures and accuracy assessment frameworks from psychology to be re-purposed to use with large language models. Based on this, we offer four methodological recommendations for using these models to quantify text features: (1) identify the target of measurement, (2) use multiple prompts, (3) assess internal consistency, and (4) treat evaluation metrics (such as human annotations) as expected correlates rather than direct ground-truth measures. Additionally, we provide a workflow for implementing this approach., Comment: 12 pages, 2 figures, 1 table

Retrieval-augmented language models have exhibited promising performance across various areas of natural language processing (NLP), including fact-critical tasks. However, due to the black-box nature of advanced large language models (LLMs) and the non-retrieval-oriented supervision signal of specific tasks, the training of retrieval model faces significant challenges under the setting of black-box LLM. We propose an approach leveraging Fine-grained Feedback with Reinforcement Retrieval (FFRR) to enhance fact-checking on news claims by using black-box LLM. FFRR adopts a two-level strategy to gather fine-grained feedback from the LLM, which serves as a reward for optimizing the retrieval policy, by rating the retrieved documents based on the non-retrieval ground truth of the task. We evaluate our model on two public datasets for real-world news claim verification, and the results demonstrate that FFRR achieves significant improvements over strong LLM-enabled and non-LLM baselines., Comment: Accepted by COLING 2024

Active intelligent reflecting surface (IRS) is a revolutionary technique for the future 6G networks. The conventional far-field single-IRS-aided directional modulation(DM) networks have only one (no direct path) or two (existing direct path) degrees of freedom (DoFs). This means that there are only one or two streams transmitted simultaneously from base station to user and will seriously limit its rate gain achieved by IRS. How to create multiple DoFs more than two for DM? In this paper, single large-scale IRS is divided to multiple small IRSs and a novel multi-IRS-aided multi-stream DM network is proposed to achieve a point-to-point multi-stream transmission by creating $K$ ($\geq3$) DoFs, where multiple small IRSs are placed distributively via multiple unmanned aerial vehicles (UAVs). The null-space projection, zero-forcing (ZF) and phase alignment are adopted to design the transmit beamforming vector, receive beamforming vector and phase shift matrix (PSM), respectively, called NSP-ZF-PA. Here, $K$ PSMs and their corresponding beamforming vectors are independently optimized. The weighted minimum mean-square error (WMMSE) algorithm is involved in alternating iteration for the optimization variables by introducing the power constraint on IRS, named WMMSE-PC, where the majorization-minimization (MM) algorithm is used to solve the total PSM. To achieve a lower computational complexity, a maximum trace method, called Max-TR-SVD, is proposed by optimize the PSM of all IRSs. Numerical simulation results has shown that the proposed NSP-ZF-PA performs much better than Max-TR-SVD in terms of rate. In particular, the rate of NSP-ZF-PA with sixteen small IRSs is about five times that of NSP-ZF-PA with combining all small IRSs as a single large IRS. Thus, a dramatic rate enhancement may be achieved by multiple distributed IRSs.

This paper reports on the NTIRE 2024 Quality Assessment of AI-Generated Content Challenge, which will be held in conjunction with the New Trends in Image Restoration and Enhancement Workshop (NTIRE) at CVPR 2024. This challenge is to address a major challenge in the field of image and video processing, namely, Image Quality Assessment (IQA) and Video Quality Assessment (VQA) for AI-Generated Content (AIGC). The challenge is divided into the image track and the video track. The image track uses the AIGIQA-20K, which contains 20,000 AI-Generated Images (AIGIs) generated by 15 popular generative models. The image track has a total of 318 registered participants. A total of 1,646 submissions are received in the development phase, and 221 submissions are received in the test phase. Finally, 16 participating teams submitted their models and fact sheets. The video track uses the T2VQA-DB, which contains 10,000 AI-Generated Videos (AIGVs) generated by 9 popular Text-to-Video (T2V) models. A total of 196 participants have registered in the video track. A total of 991 submissions are received in the development phase, and 185 submissions are received in the test phase. Finally, 12 participating teams submitted their models and fact sheets. Some methods have achieved better results than baseline methods, and the winning methods in both tracks have demonstrated superior prediction performance on AIGC.

The recent advancements in Text-to-Video Artificial Intelligence Generated Content (AIGC) have been remarkable. Compared with traditional videos, the assessment of AIGC videos encounters various challenges: visual inconsistency that defy common sense, discrepancies between content and the textual prompt, and distribution gap between various generative models, etc. Target at these challenges, in this work, we categorize the assessment of AIGC video quality into three dimensions: visual harmony, video-text consistency, and domain distribution gap. For each dimension, we design specific modules to provide a comprehensive quality assessment of AIGC videos. Furthermore, our research identifies significant variations in visual quality, fluidity, and style among videos generated by different text-to-video models. Predicting the source generative model can make the AIGC video features more discriminative, which enhances the quality assessment performance. The proposed method was used in the third-place winner of the NTIRE 2024 Quality Assessment for AI-Generated Content - Track 2 Video, demonstrating its effectiveness. Code will be available at https://github.com/Coobiw/TriVQA., Comment: 9 pages, 3 figures, 3 tables. Accepted by CVPR2024 Workshop (3rd place winner of NTIRE2024 Quality Assessment for AI-Generated Content - Track 2 Video)

Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative efforts, scientific breakthroughs can be made sooner and capitalized for the betterment of humanity.

ObjectiveMarijuana, tobacco and alcohol use are prevalent among people with HIV and may adversely affect kidney function in this population. We determined the association of use of these substances with estimated glomerular filtration rate (eGFR) among women with HIV (WWH) and women without HIV.DesignWe undertook a repeated measures study of 1043 WWH and 469 women without HIV within the United States Women's Interagency HIV Study, a multicenter, prospective cohort of HIV-seropositive and HIV-seronegative women.MethodsWe quantified substance exposures using semi-annual questionnaires. Using pooled eGFR data from 2009 to 2019, we used linear regression models with multivariable generalized estimating equations to ascertain associations between current and cumulative substance use exposures with eGFR, adjusting for sociodemographics, chronic kidney disease risk factors and HIV-related factors.ResultsMarijuana use of 1-14 days/month versus 0 days/month was associated with 3.34 ml/min per 1.73 m 2 [95% confidence interval (CI) -6.63, -0.06] lower eGFR and marijuana use of >0.02-1.6 marijuana-years versus 0-0.2 marijuana-years was associated with 3.61 ml/min per 1.73 m 2 (95% CI -5.97, -1.24) lower eGFR. Tobacco use was not independently associated with eGFR. Alcohol use of seven or more drinks/week versus no drinks/week was associated with 5.41 ml/min per 1.73 m 2 (95% CI 2.34, 8.48) higher eGFR and alcohol use of >0.7-4.27 drink-years and >4.27 drink-years versus 0-0.7 drink-years were associated with 2.85 ml/min per 1.73 m 2 (95% CI 0.55, 5.15) and 2.26 ml/min per 1.73 m 2 (95% CI 0.33, 4.20) higher eGFR, respectively.ConclusionAmong a large cohort of WWH and women without HIV, marijuana use was associated with a lower eGFR while alcohol use was associated with a higher eGFR.

Li-Zhi Chen,1,2,* Han-Shu Li,1,2,* Gao-Wei Han,1,2,* Yong Su,1,3 Tian-Zhu Lu,1– 3 Hong-Hui Xie,1,3 Xiao-Chang Gong,1,3 Jin-Gao Li,1,3 Yun Xiao1,3 1NHC Key Laboratory of Personalized Diagnosis and Treatment of Nasopharyngeal Carcinoma, Jiangxi Clinical Research Center for Cancer, Jiangxi Cancer Hospital, The Second Affiliated Hospital of Nanchang Medical College, Nanchang, Jiangxi, 330029, People’s Republic of China, Nanchang, Jiangxi, 330029, People’s Republic of China; 2Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, 330029, People’s Republic of China; 3Department of Radiation Oncology, Jiangxi Clinical Research Center for Cancer, Jiangxi Cancer Hospital, The Second Affiliated Hospital of Nanchang Medical College, Nanchang, Jiangxi, 330029, People’s Republic of China*These authors contributed equally to this workCorrespondence: Jin-Gao Li; Yun Xiao, Department of Radiation Oncology, Jiangxi Clinical Research Center for Cancer, Jiangxi Cancer Hospital, The Second Affiliated Hospital of Nanchang Medical College, 519 East Beijing Road, Nanchang, Jiangxi, 330029, People’s Republic of China, Tel +86-791-88313632, Fax +86-791-8321742, Email lijingao@hotmail.com; 813898920@qq.comPurpose: This study aimed to assess the prognostic and predictive value of a circulating hematological signature (CHS) and to develop a CHS-based nomogram for predicting prognosis and guiding individualized chemotherapy in non-metastatic nasopharyngeal carcinoma (NPC) patients.Patients and Methods: NPC patients were recruited between January 2014 and December 2017 at the Jiangxi Cancer Hospital. The CHS was constructed based on a series of hematological indicators. The nomogram was developed by CHS and clinical factors.Results: A total of 779 patients were included. Three biomarkers were selected by least absolute shrinkage and selection operator regression, including prognostic nutritional index, albumin-to-fibrinogen ratio, and prealbu

Plants are sensitive to changes projected in climates, such as elevated carbon dioxide (eCO2), high temperature (T), and drought stress (DS), which affect crop growth, development, and yield. These stresses, either alone or in combination, affect all aspects of sweetpotato plant growth and development, including storage root development and yield. We tested three sweetpotato cultivars (Beauregard, Hatteras, and LA1188) responses to eight treatments (Control, DS, T, eCO2, DS + T, T + eCO2, DS + eCO2, DS + T + eCO2). All treatments were imposed 36 days after transplanting (DAP) and continued for 47 days. Treatments substantially affected gas exchange, photosynthetic pigments, growth, and storage root components. Cultivars differed considerably for many of the measured parameters. The most significant negative impact of DS was recorded for the shoot and root weights. The combination of DS + T had a significant negative effect on storage root parameters. eCO2 alleviated some of the damaging effects of DS and high T in sweetpotato. For instance, eCO2 alone or combined with DS increased the storage root weights by 22% or 42% across all three cultivars, respectively. Based on the stress response index, cultivar “Hatteras” was most tolerant to individual and interactive stresses, and “LA 1188” was sensitive. Our findings suggest that eCO2 negates the negative impact of T or DS on the growth and yield of sweetpotato. We identified a set of individual and interactive stress-tolerant traits that can help select stress cultivars or breed new lines for future environments.

Plants are sensitive to changes projected in climates, such as elevated carbon dioxide (eCO2), high temperature (T), and drought stress (DS), which affect crop growth, development, and yield. These stresses, either alone or in combination, affect all aspects of sweetpotato plant growth and development, including storage root development and yield. We tested three sweetpotato cultivars (Beauregard, Hatteras, and LA1188) responses to eight treatments (Control, DS, T, eCO2, DS + T, T + eCO2, DS + eCO2, DS + T + eCO2). All treatments were imposed 36 days after transplanting (DAP) and continued for 47 days. Treatments substantially affected gas exchange, photosynthetic pigments, growth, and storage root components. Cultivars differed considerably for many of the measured parameters. The most significant negative impact of DS was recorded for the shoot and root weights. The combination of DS + T had a significant negative effect on storage root parameters. eCO2 alleviated some of the damaging effects of DS and high T in sweetpotato. For instance, eCO2 alone or combined with DS increased the storage root weights by 22% or 42% across all three cultivars, respectively. Based on the stress response index, cultivar “Hatteras” was most tolerant to individual and interactive stresses, and “LA 1188” was sensitive. Our findings suggest that eCO2 negates the negative impact of T or DS on the growth and yield of sweetpotato. We identified a set of individual and interactive stress-tolerant traits that can help select stress cultivars or breed new lines for future environments.

Xiaoou Li,1 Wei Gao,2 Zhonglan Jia,3 Kaihu Yao,2 Jinyan Yang,1 Jingjing Tong,4 Yajuan Wang5 1Department of Neonatology, Haidian District Maternal and Child Health Care Hospital, Beijing, 100080, People’s Republic of China; 2Key Laboratory of Major Diseases in Children, Ministry of Education, National Key Discipline of Pediatrics (Capital Medical University), Laboratory of Dermatology, Beijing Pediatric Research Institute, Beijing Children’s Hospital, Capital Medical University, National Center for Children’s Health, Beijing, 100045, People’s Republic of China; 3Department of Clinical Laboratory, Haidian District Maternal and Child Health Care Hospital, Beijing, 100080, People’s Republic of China; 4Department of Neonatology, Beijing Obstetrics and Gynecology Hospital, Capital Medical University, Beijing, 100026, People’s Republic of China; 5Department of Neonatology, Children’s Hospital, Capital Institute of Pediatrics, Beijing, 100020, People’s Republic of ChinaCorrespondence: Yajuan Wang, Department of Neonatology, Children’s Hospital, Capital Institute of Pediatrics, No. 2 Yabao Road, Chaoyang District, Beijing, 100020, People’s Republic of China, Tel +86-10-85695631, Email cxswyj@vip.sina.comPurpose: We investigate the drug resistance, serotype and multilocus sequence typing (MLST) of Group B streptococcus (GBS) strains obtained from pregnant women and neonates in a hospital in Beijing.Patients and Methods: In this cross-sectional study, 1470 eligible pregnant women at a gestational age of 35– 37 weeks presented to our department between May 2015 and May 2016 were included. Vaginal and rectal samples from pregnant women together with sampling from neonatal samples were collected to screen GBS. GBS strains were subject to drug resistance and serotype analysis and MLST.Results: GBS strains were isolated from 111 pregnant women (7.6%) and 6 neonates (0.99%) from 606 matched neonates. 102 strains from pregnant women and 3 strains from neonates w

The quantification of protein biomarkers in blood at picomolar-level sensitivity requires labour-intensive incubation and washing steps. Sensing proteins in sweat, which would allow for point-of-care monitoring, is hindered by the typically large interpersonal and intrapersonal variations in its composition. Here we report the design and performance of a wearable and wireless patch for the real-time electrochemical detection of the inflammatory biomarker C-reactive (CRP) protein in sweat. The device integrates iontophoretic sweat extraction, microfluidic channels for sweat sampling and for reagent routing and replacement, and a graphene-based sensor array for quantifying CRP (via an electrode functionalized with anti-CRP capture antibodies-conjugated gold nanoparticles), ionic strength, pH and temperature for the real-time calibration of the CRP sensor. In patients with chronic obstructive pulmonary disease, with active or past infections or who had heart failure, the elevated concentrations of CRP measured via the patch correlated well with the protein’s levels in serum. Wearable biosensors for the real-time sensitive analysis of inflammatory proteins in sweat may facilitate the management of chronic diseases.

Introduction. Medication nonadherence continues to be challenging for allogeneic hematopoietic cell transplant (HCT) recipients. The risk and severity of chronic graft-versus-host disease (GVHD) are associated with low immunosuppressant concentrations (which can be improved with model-informed precision dosing (MIPD)) and with immunosuppressant nonadherence (which can be improved with acceptable interventions). Methods. With the goals of improving adherence and achieving therapeutic concentrations of immunosuppressants to eliminate GVHD, we characterized the feasibility of using the Medication Event Monitoring (MEMS®) Cap in adult HCT recipients. Results. Of the 27 participants offered the MEMS® Cap at the time of hospital discharge, 7 (25.9%) used it, which is below our a priori threshold of 70%. These data suggest the MEMS® Cap is not feasible for HCT recipients. The MEMS® Cap data were available for a median of 35 days per participant per medication (range: 7–109 days). The average daily adherence per participant ranged from 0 to 100%; four participants had an average daily adherence of over 80%. Conclusions. MIPD may be supported by MEMS® technology to provide the precise time of immunosuppressant self-administration. The MEMS® Cap was used by only a small percentage (25.9%) of HCT recipients in this pilot study. In accordance with larger studies using less accurate tools to evaluate adherence, immunosuppressant adherence varied from 0% to 100%. Future studies should establish the feasibility and clinical benefit of combining MIPD with newer technology, specifically the MEMS® Button, which can inform the oncology pharmacist of the time of immunosuppressant self-administration.

Mussel-inspired catechol-functionalization of degradable natural biomaterials has garnered significant interest as an approach to achieve bioadhesion for sutureless wound closure. However, conjugation capacity in standard coupling reactions, such as carbodiimide chemistry, is limited by low yield and lack of abundant conjugation sites. Here, a simple oxidative polymerization step before conjugation of catechol-carrying molecules (i.e., 3,4-dihydroxy-l-phenylalanine, l-DOPA) as a potential approach to amplify catechol function in bioadhesion of natural gelatin biomaterials is proposed. Solutions of gelatin modified with poly(l-DOPA) moieties (GelDOPA) are characterized by faster physical gelation and increased viscosity, providing better wound control on double-curved tissue surfaces compared to those of l-DOPA-conjugated gelatin. Physical hydrogels treated topically with low concentrations of NaIO4 solutions are crosslinked on-demand via through-thickness diffusion. Poly(l-DOPA) conjugates enhance crosslinking density compared to l-DOPA conjugated gelatin, resulting in lower swelling and enhanced cohesion in physiological conditions. Together with cohesion, more robust bioadhesion at body temperature is achieved by poly(l-DOPA) conjugates, exceeding those of commercial sealants. Further, poly(l-DOPA) motifs introduced photothermal responsiveness via near-infrared (NIR) irradiation for controlled drug release and potential applications in photothermal therapy. The above functionalities, along with antibacterial activity, render the proposed approach an effective biomaterial design strategy for wound closure applications.

Advanced technologies for the calibration of machine tools are presented. Kinematic errors independently of their causes are classified into errors within one-axis as intra-axis errors, errors between axes as inter-axis errors, and as volumetric errors. As the major technological elements of machine tool calibration, the measurement methods, modeling theories, and compensation strategies of the machine tool errors are addressed. The criteria for selecting a combination of the technological elements for machine tool calibration from the point of view of accuracy, complexity, and cost are provided. Recent applications of artificial intelligence and machine learning in machine tool calibration are introduced. Remarks are also made on future trends in machine tool calibration., QC 20230526

Imaging genetics provides an opportunity to discern associations between genetic variants and brain imaging phenotypes. Historically, the field has focused on adults and adolescents; very few imaging genetics studies have focused on brain development in infancy and early childhood (from birth to age 6 years). This is an important knowledge gap because developmental changes in the brain during the prenatal and early postnatal period are regulated by dynamic gene expression patterns that likely play an important role in establishing an individual's risk for later psychiatric illness and neurodevelopmental disabilities. In this review, we summarize findings from imaging genetics studies spanning from early infancy to early childhood, with a focus on studies examining genetic risk for neuropsychiatric disorders. We also introduce the Organization for Imaging Genomics in Infancy (ORIGINs), a working group of the ENIGMA (Enhancing NeuroImaging Genetics through Meta-Analysis) consortium, which was established to facilitate large-scale imaging genetics studies in infancy and early childhood.

Wearable sensors hold great potential in empowering personalized health monitoring, predictive analytics, and timely intervention toward personalized healthcare. Advances in flexible electronics, materials science, and electrochemistry have spurred the development of wearable sweat sensors that enable the continuous and noninvasive screening of analytes indicative of health status. Existing major challenges in wearable sensors include: improving the sweat extraction and sweat sensing capabilities, improving the form factor of the wearable device for minimal discomfort and reliable measurements when worn, and understanding the clinical value of sweat analytes toward biomarker discovery. This review provides a comprehensive review of wearable sweat sensors and outlines state-of-the-art technologies and research that strive to bridge these gaps. The physiology of sweat, materials, biosensing mechanisms and advances, and approaches for sweat induction and sampling are introduced. Additionally, design considerations for the system-level development of wearable sweat sensing devices, spanning from strategies for prolonged sweat extraction to efficient powering of wearables, are discussed. Furthermore, the applications, data analytics, commercialization efforts, challenges, and prospects of wearable sweat sensors for precision medicine are discussed.

Digital health technologies have been in use for many years in a wide spectrum of healthcare scenarios. This narrative review outlines the current use and the future strategies and significance of digital health technologies in modern healthcare applications. It covers the current state of the scientific field (delineating major strengths, limitations, and applications) and envisions the future impact of relevant emerging key technologies. Furthermore, we attempt to provide recommendations for innovative approaches that would accelerate and benefit the research, translation and utilization of digital health technologies.

Mi-Mi Wan,1,* Tuo Jin,2,* Zhang-Yitian Fu,1 Si-Hua Lai,1 Wei-Ping Gao1 1Department of Ophthalmology, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, People’s Republic of China; 2Department of Ophthalmology, Kunshan Hospital of Chinese Medicine, Suzhou, People’s Republic of China*These authors contributed equally to this workCorrespondence: Wei-Ping Gao, Department of Ophthalmology, Affiliated Hospital of Nanjing University of Chinese Medicine, No. 155 Hanzhong Road, Qinhuai District, Nanjing, Jiangsu, People’s Republic of China, Tel +86 13057671767, Email 260790@njucm.edu.cnPurpose: This study aimed to examine electroacupuncture’s influence on ocular pain and its potential modulation of the TNF-ɑ mediated ERK1/2/P2X3R signaling pathway in dry eye-induced rat models.Methods: Male Sprague–Dawley rats with induced dry eye, achieved through extraorbital lacrimal gland removal, were treated with electroacupuncture. Comprehensive metrics such as the corneal mechanical perception threshold, palpebral fissure height, eyeblink frequency, eye wiping duration, behavioral changes in the open field test, and the forced swimming test were employed. Additionally, morphological changes in microglia and neurons were observed. Expression patterns of key markers, TNF-ɑ, TNFR1, p-ERK1/2, and P2X3R, in the trigeminal ganglion (TG) and spinal trigeminal nucleus caudalis (SpVc) regions, were studied with etanercept serving as a control to decipher the biochemistry of electroacupuncture’s therapeutic effects.Results: Electroacupuncture treatment demonstrated a notable decrease in the corneal mechanical perception threshold, improvement in palpebral fissure height, and significant reductions in both eyeblink frequency and eye wiping duration. Moreover, it exhibited a promising role in anxiety alleviation. Notably, the technique effectively diminished ocular pain by curbing microglial and neuronal activation in the TG and SpVc reg

Periodic boundary condition along a dislocation line is commonly used in computing activation barriers or formation energies of kink pair of screw dislocation in BCC metals. Although the effect of periodic image interactions on the computation results is obvious, there had been no comprehensive analysis on such effect. In this work, we quantify it through combined nudged elastic band (NEB) simulations and theoretical analysis based on dislocation mechanics. The NEB calculation result demonstrates a non-negligible size dependence on the activation barrier at zero and low stresses. The theoretical analysis offers a practical approach to quantify such size effect without the need of time-consuming NEB simulations. Notably, a simple relationship between kink activation barrier and dislocation line length is derived at zero stress, offering a new approach to compute kink pair formation energy based on NEB simulation results., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Team Poulumi Dey

Organic photovoltaics (OPV) are one of the most effective ways to harvest renewable solar energy, with the power conversion efficiency (PCE) of the devices soaring above 19% when processed with halogenated solvents. The superior photocurrent of OPV over other emerging photovoltaics offers more opportunities to further improve the efficiency. Tailoring the absorption band of photoactive materials is an effective way to further enhance OPV photocurrent. However, the field has mostly been focusing on improving the near-infrared region photo-response, with the absorption shoulders in short-wavelength region (SWR) usually being neglected. Herein, by developing a series of non-fullerene acceptors (NFAs) with varied side-group conjugations, we observe an enhanced SWR absorption band with increased side-group conjugation length. The underpinning factors of how molecular structures and geometries improve SWR absorption are clearly elucidated through theoretical modelling and crystallography. Moreover, a clear relationship between the enhanced SWR absorption and reduced singlet-triplet energy gap is established, both of which are favorable for the OPV performance and can be tailored by rational structure design of NFAs. Finally, the rationally designed NFA, BO-TTBr, affords a decent PCE of 18.5% when processed with a non-halogenated green solvent., Funding Agencies|B.F. and W.G. contribute equally to this work. A.K.Y.J. thanks the sponsorship of the Lee Shau-Kee Chair Professor (Materials Science), and the support from the APRC Grant of the City University of Hong Kong (9380086, 9610419, 9610492, 9610508), the TCFS G [9380086, 9610419, 9610492, 9610508]; APRC Grant of the City University of Hong Kong [GHP/018/20SZ]; TCFS Grant [MRP/040/21X]; MRP Grant; Innovation and Technology Commission of Hong Kong [202020164]; Green Tech Fund; Environment and Ecology Bureau of Hong Kong [11307621, 11316422]; GRF grant [C6023-19GF]; Research Grants Council of Hong Kong [SGDX20201103095412040]; Research Grants Council of Hong Kong, Shenzhen Science and Technology Program [2019B030302007]; Guangdong Major Project of Basic and Applied Basic Research [DE-AC02-05CH11231]; Office of Science, Office of Basic Energy Sciences, of the US Department of Energy

Xuchen Huang,1,2 Sainan Li,1,2 Wei Gao,1,2 Jiajie Shi,1,2 Meng Cheng,1,2 Yunzhe Mi,1,2 Yueping Liu,3 Meixiang Sang,4 Ziyi Li,4 Cuizhi Geng1,2 1Department of Breast Center, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, People’s Republic of China; 2Key Laboratory in Hebei Province for Molecular Medicine of Breast Cancer, Shijiazhuang, Hebei, People’s Republic of China; 3Department of Pathology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, People’s Republic of China; 4Research Center and Tumor Research Institute, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, People’s Republic of ChinaCorrespondence: Cuizhi Geng, Department of Breast Center, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei, 050000, People’s Republic of China, Tel/Fax +86-311-66696313, Email gengcuizhisci@163.comPurpose: Our aim was to verify whether KIF20A has the potential to serve as a prognostic marker for female patients with estrogen receptor (ER)-positive breast cancer (BC) and treated with tamoxifen (TAM).Patients and Methods: Online tools were used to investigate the potential correlation between KIF20A gene expression and survival of patients with ER-positive BC and TAM treatment. Furthermore, immunohistochemistry (IHC) was conducted to assess the expression levels of KIF20A in patients included from our center. The prognostic value of KIF20A for disease-free survival (DFS) and overall survival (OS) was further evaluated using Cox regression analysis.Results: According to the results obtained from online tools, it was found that patients with low KIF20A expression exhibited significantly better survival outcomes in terms of relapse-free survival (RFS), distant metastasis-free survival (DMFS), and OS compared to those with high KIF20A expression (P < 0.001, P < 0.001, and P = 0.008, respectively). Additionally, significantly lower gene expression of KIF20A was found in patients who respon

Digital health technologies have been in use for many years in a wide spectrum of healthcare scenarios. This narrative review outlines the current use and the future strategies and significance of digital health technologies in modern healthcare applications. It covers the current state of the scientific field (delineating major strengths, limitations, and applications) and envisions the future impact of relevant emerging key technologies. Furthermore, we attempt to provide recommendations for innovative approaches that would accelerate and benefit the research, translation and utilization of digital health technologies., Digital health technologies have been in use for many years in a wide spectrum of healthcare scenarios. This narrative review outlines the current use and the future strategies and significance of digital health technologies in modern healthcare applications. It covers the current state of the scientific field (delineating major strengths, limitations, and applications) and envisions the future impact of relevant emerging key technologies. Furthermore, we attempt to provide recommendations for innovative approaches that would accelerate and benefit the research, translation and utilization of digital health technologies.

Organic photovoltaics (OPVs) have achieved great progress in recent years due to delicately designed non-fullerene acceptors (NFAs). Compared with tailoring of the aromatic heterocycles on the NFA backbone, the incorporation of conjugated side-groups is a cost-effective way to improve the photoelectrical properties of NFAs. However, the modifications of side-groups also need to consider their effects on device stability since the molecular planarity changes induced by side-groups are related to the NFA aggregation and the evolution of the blend morphology under stresses. Herein, a new class of NFAs with localisomerized conjugated side-groups are developed and the impact of local isomerization on their geometries and device performance/stability are systematically investigated. The device based on one of the isomers with balanced side- and terminal-group torsion angles can deliver an impressive power conversion efficiency (PCE) of 18.5%, with a low energy loss (0.528 V) and an excellent photo- and thermal stability. A similar approach can also be applied to another polymer donor to achieve an even higher PCE of 18.8%, which is among the highest efficiencies obtained for binary OPVs. This work demonstrates the effectiveness of applying local isomerization to fine-tune the side-group steric effect and non-covalent interactions between side-group and backbone, therefore improving both photovoltaic performance and stability of fused ring NFA-based OPVs., Funding Agencies|APRC Grant of the City University of Hong Kong; Innovation and Technology Commission of Hong Kong; Environment and Ecology Bureau of Hong Kong; Research Grants Council of Hong Kong [9380086, 9610508]; US Office of Naval Research [GHP/018/20SZ, MRP/040/21X]; Shen zhen Science and Technology Program [202020164]; Guangdong Major Project of Basic and Applied Basic Research [11307621, 11316422, N00014-20-1-2191]; Guangdong-Hong Kong-Macao Joint Laboratory of Optoelectronic and Magnetic Functional Materials [C6023-19GF]; Office of Science, Office of Basic Energy Sciences, of the US Department of Energy [SGDX20201103095412040]; [2019B030302007]; [2019B121205002]; [DE-AC02-05CH11231]

Kennedy B Nkhoma,1 Lindsay Farrant,2 Olona Mzimkulu,2 Joy Hunter,2 Irene Higginson,1 Wei Gao,2 Matthew Maddocks,1 Liz Gwyther,2 Richard Harding1 1Cicely Saunders Institute of Palliative Care Policy and Rehabilitation, King’s College London, London, UK; 2Division of Interdisciplinary Palliative Care & Medicine, Department of Public Health and Family Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South AfricaCorrespondence: Kennedy B Nkhoma, Email kennedy.nkhoma@kcl.ac.ukBackground: Many deaths globally are attributable to non-communicable disease, and four-fifths of these deaths are in low- and middle-income countries. Globally, COPD is currently the third leading cause of mortality.Research Question: 1) To determine the prevalence and burden of symptoms and concerns, and 2) determine predicting factors of symptom burden among patients with COPD.Methods: A cross-sectional survey was conducted at eight primary care sites in Western Cape. We collected socio-demographic data (age, gender, smoking status, number of missed doses of prescribed medication in the last seven days) and clinical data (PEF and KPS). The Memorial Symptom Assessment Scale (MSAS), the Medical Outcomes Study, Social Support Survey (MOS-SSS), the London Chest Activity of Daily Living Scale (LCADLS) and the COPD Assessment Test (CAT) (impairment on person’s life) were administered to patients. We conducted ordered logistic regression analysis to assess factors associated with the burden of symptoms. MSAS subscales: 1) Global symptom distress index, 2) physical symptom distress and 3) psychological symptom distress were dependent outcomes. We constructed three ordinal logistic regression models for each of the three subscales. Covariates were MOS-SSS, LCADLS, CAT, demographic and clinical variables.Results: We recruited n=387 patients, mean age 59.5 years, 53.0% female. In multivariate analysis, each of the three models (ie, global, psychological and physical symptom

Cuiping Xu, Feng Yang, Qimin Wang, Wei Gao Department of Respiratory and Critical Care Medicine, China Rehabilitation Research Center, Rehabilitation School of Capital Medical University, Beijing, People’s Republic of ChinaCorrespondence: Wei Gao, Tel +86 10-87569070, Fax +86 10-67573428, Email rhhuxi@163.comPurpose: We aimed to evaluate whether high flow nasal cannula (HFNC) is an effective and safe method for adult patients with acute hypercapnic respiratory failure (AHRF).Methods: We searched the Cochrane Library, Embase, and PubMed databases from inception to August 2022 to obtain randomized controlled trials (RCTs) that compared HFNC with conventional oxygen treatment (COT) or non-invasive ventilation (NIV) in patients with AHRF, and then performed a meta-analysis.Results: A total of ten parallel RCTs with 1265 individuals were identified. Of them, two studies compared HFNC with COT and eight studies compared HFNC with NIV. In terms of intubation rate, mortality, and arterial blood gas (ABG) improvement, HFNC showed comparable effects to NIV and COT. However, HFNC was more comfortable (mean difference [MD] − 1.87, 95% confidence interval [CI] =− 2.59, − 1.15, P < 0.00001, I2 =0%) and resulted in fewer adverse events (odds ratio [OR] 0.12, 95% CI=0.06, 0.28, P< 0.00001, I2 = 0%), compared with NIV. In comparison to NIV, HFNC could significantly lower heart rate (HR) (MD − 4.66, 95% CI=− 6.82, − 2.50, P < 0.0001, I2 =0%), respiratory rate (RR) (MD − 1.17, 95% CI=− 2.03, − 0.31, P =0.008, I2 =0%), and hospital stay length (MD − 0.80, 95% CI=− 1.44, − 0.16, P =0.01, I2 =0%). NIV showed a decreased frequency in the treatment crossover rate, compared with HFNC in patients with pH< 7.30 (OR 5.78, 95% CI=1.50, 22.31, P = 0.01, I2: not applicable). Contrary to COT, HFNC could considerably reduce the need for NIV (OR 0.57, 95% CI=0.35, 0.91, P=0.02, I2=0%).Conclusion: HFNC was effective and safe in patients with AHRF. However, in pat

Shuo Zhang,1,&ast; Yishu Wang,1,&ast; Shuai Zhang,1 Chengqi Huang,1 Qiyang Ding,2 Ji Xia,2 Daocheng Wu,1 Wei Gao2 1The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an, 710049, People’s Republic of China; 2Department of Anesthesiology & Center for Brain Science & Center for Translational Medicine, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, 710049, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Daocheng Wu, The Key Laboratory of Biomedical Information Engineering of Ministry of Education, School of Life Science and Technology, Xi’an Jiaotong University, Xi’an, 710049, People’s Republic of China, Tel/Fax +86-29-82663941, Email wudaocheng@mail.xjtu.edu.cn Wei Gao, Department of Anesthesiology & Center for Brain Science & Center for Translational Medicine, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, 710049, People’s Republic of China, Tel +86 15209225834, Email gaowei2906@xjtufh.edu.cnAbstract: Anesthetics, which include both local and general varieties, are a unique class of drugs widely utilized in clinical surgery to alleviate pain and promote relaxation in patients. Although numerous anesthetics and their traditional formulations are available in the market, only a select few exhibit excellent anesthetic properties that meet clinical requirements. The main challenges are the potential toxic and adverse effects of anesthetics, as well as the presence of the blood-brain barrier (BBB), which makes it difficult for most general anesthetics to effectively penetrate to the brain. Loading anesthetics onto nanocarriers as anesthetic nanomedicines might address these challenges and improve anesthesia effectiveness, reduce toxic and adverse effects, while significantly enhance the efficiency of general anesthetics passing through the

Few-shot or zero-shot fact verification only relies on a few or no labeled training examples. In this paper, we propose a novel method called ProToCo, to \underline{Pro}mpt pre-trained language models (PLMs) \underline{To} be \underline{Co}nsistent, for improving the factuality assessment capability of PLMs in the few-shot and zero-shot settings. Given a claim-evidence pair, ProToCo generates multiple variants of the claim with different relations and frames a simple consistency mechanism as constraints for making compatible predictions across these variants. We update PLMs by using parameter-efficient fine-tuning (PEFT), leading to more accurate predictions in few-shot and zero-shot fact verification tasks. Our experiments on three public verification datasets show that ProToCo significantly outperforms state-of-the-art few-shot fact verification baselines. With a small number of unlabeled instances, ProToCo also outperforms the strong zero-shot learner T0 on zero-shot verification. Compared to large PLMs using in-context learning (ICL) method, ProToCo outperforms OPT-30B and the Self-Consistency-enabled OPT-6.7B model in both few- and zero-shot settings., Comment: Accepted as ACL 2023 Findings