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IntroductionSerotonin (5-HT) is critical for neurodevelopment and the serotonin transporter (SERT) modulates serotonin levels. Perturbed prenatal and postnatal dietary exposures affect the developing offspring predisposing to neurobehavioral disorders in the adult. We hypothesized that the postnatal brain 5-HT-SERT imbalance associated with gut dysbiosis forms the contributing gut-brain axis dependent mechanism responsible for such ultimate phenotypes.MethodsEmploying maternal diet restricted (IUGR, n=8) and high fat+high fructose (HFhf, n=6) dietary modifications, rodent brain serotonin was assessed temporally by ELISA and SERT by quantitative Western blot analysis. Simultaneously, colonic microbiome studies were performed.ResultsAt early postnatal (P) day 2 no changes in the IUGR, but a ~24% reduction in serotonin (p = 0.00005) in the HFhf group occurred, particularly in the males (p = 0.000007) revealing a male versus female difference (p = 0.006). No such changes in SERT concentrations emerged. At late P21 the IUGR group reared on HFhf (IUGR/HFhf, (n = 4) diet revealed increased serotonin by ~53% in males (p = 0.0001) and 36% in females (p = 0.023). While only females demonstrated a ~40% decrease in serotonin (p = 0.010), the males only trended lower without a significant change within the HFhf group (p = 0.146). SERT on the other hand was no different in HFhf or IUGR/RC, with only the female IUGR/HFhf revealing a 28% decrease (p = 0.036). In colonic microbiome studies, serotonin-producing Bacteriodes increased with decreased Lactobacillus at P2, while the serotonin-producing Streptococcus species increased in IUGR/HFhf at P21. Sex-specific changes emerged in association with brain serotonin or SERT in the case of Alistipase, Anaeroplasma, Blautia, Doria, Lactococcus, Proteus, and Roseburia genera.DiscussionWe conclude that an imbalanced 5-HT-SERT axis during postnatal brain development is sex-specific and induced by maternal dietary modifications related to pos

Jing Sang,1,2,* Peng Liu,1,3,* Meixiang Wang,1 Fengkuo Xu,1 Ji Ma,1 Zhigang Wei,1 Xin Ye1 1Department of Oncology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Shandong Lung Cancer Institute, Shandong Key Laboratory of Rheumatic Disease and Translational Medicine, Jinan, People’s Republic of China; 2Department of Pathology, The Affiliated Taian City Central Hospital of Qingdao University, Taian, People’s Republic of China; 3Yuncheng Central Hospital Affiliated to Shanxi Medical University, Yuncheng, People’s Republic of China*These authors contributed equally to this workCorrespondence: Zhigang Wei; Xin Ye, Department of Oncology, The First Affiliated Hospital of Shandong First Medical University & Shandong Provincial Qianfoshan Hospital, Jinan, Shandong Province, 250014, People’s Republic of China, Email weizhigang321321@163.com; yexintaian2020@163.comPurpose: Microwave ablation (MWA) is a minimally invasive technique for treating lung cancer. It can induce immune response; however, its effect on the immune microenvironment in tumor-draining lymph nodes (TdLN) is not well understood. This study aims to identify changes in the immune microenvironment in TdLN following MWA in a Lewis lung cancer (LLC) mouse model.Methods: LLC mouse model was established and followed by MWA. TdLN were collected at various time points, including pre-MWA and days 1, 2, 4, and 8 post-MWA. Flow cytometry was used to determine the frequencies of CD4+ T cells, CD8+ T cells, regulatory T (Treg) cells, natural killer (NK) cells, dendritic cells (DCs) and other immune cells in the TdLN. Certain cytokines were also detected.Results: Compared with pre-MWA, the frequency of CD4+ T cells significantly increased from day 1 to day 8 post-MWA. The frequency of CD8+ T cells decreased significantly on days 2 and 4, but no significant changes occurred on days 1 and 8. Significant decreases in the fre

Reinforcement learning (RL) based autonomous driving has emerged as a promising alternative to data-driven imitation learning approaches. However, crafting effective reward functions for RL poses challenges due to the complexity of defining and quantifying good driving behaviors across diverse scenarios. Recently, large pretrained models have gained significant attention as zero-shot reward models for tasks specified with desired linguistic goals. However, the desired linguistic goals for autonomous driving such as "drive safely" are ambiguous and incomprehensible by pretrained models. On the other hand, undesired linguistic goals like "collision" are more concrete and tractable. In this work, we introduce LORD, a novel large models based opposite reward design through undesired linguistic goals to enable the efficient use of large pretrained models as zero-shot reward models. Through extensive experiments, our proposed framework shows its efficiency in leveraging the power of large pretrained models for achieving safe and enhanced autonomous driving. Moreover, the proposed approach shows improved generalization capabilities as it outperforms counterpart methods across diverse and challenging driving scenarios.

Surface modification, such as bioinspired nanostructured doubly reentrant surfaces that have presented superhydrophobic wettability even under low-surface-tension liquid, is a very promising technology for controlling droplet dynamics, heat transfer, and evaporation. In this article, we investigate the interfacial effects of nanostructured doubly reentrant surfaces on the flow behaviors and local concentration evolution during the evaporation of an ethanol/water multicomponent droplet. Using particle image velocimetry (PIV) and novel aggregate-induced emission-based (AIE) techniques, the flow patterns and local concentration distributions on both hydrophobic and nanostructured doubly reentrant surfaces were probed and compared. It is found that in addition to the established Marangoni flow-dominated stage, transition stage, and buoyancy-induced flow-dominated stage, a new transition stage and a rolling stage for the nanostructured doubly reentrant surface are detected in the late evaporation period. Differences in the local concentration distribution evolution occur depending on the hydrophobicity of the surface on which the droplet is placed. For the hydrophobic surface, a nonuniform local concentration distribution exists consistently, with a high water fraction in a shell-shaped region near the liquid–air interface and a secondary concentration gradient within this shell-shaped region. The concentration distribution on the nanostructured doubly reentrant surface evolves in a more complex manner, with a strip-shaped region of high water fraction forming in the intermediate stage and then reorganized by rolling flow in the late stage. Finally, theoretical analysis combining PIV and AIE visualization results reveals that the variations in droplet concentration distributions on surfaces with different hydrophobicities exert a significant impact on evaporative behaviors. These behaviors, in turn, affect the evolution of the local concentration distribution.

This research investigates the impact of deuteration on the operational lifetime of blue organic light-emitting diodes (OLEDs). Our study focuses on the kinetic isotope effect (KIE) and how it enhances device lifetime. Specifically, we examine the effects of deuteration on OLEDs with emitters based on common polycyclic aromatic hydrocarbons by deuterating anthracene-based host materials. Our findings show that the deuterated analogues with incremental deuterium substitutions exhibit similar electroluminescence (EL) performances but progressively prolonged device lifetimes as a function of deuteration, indicating a cumulative enhancement effect. We observed a twofold increase in device lifetime with partial deuterium substitutions, while highly deuterated hosts realized up to a fourfold improvement. Furthermore, we found that device degradation is initiated from the interface of the hole transport layer (HTL) and emitting layer (ETL), and deuteration can mitigate the degraded reactions in the devices, thereby improving operational stability. These results provide insight into how deuteration can increase the operational lifetime of OLEDs, which has important implications for developing more efficient and durable OLED materials. A cumulative enhancement effect of deuteration on the blue OLED lifetime was revealed and elucidated using progressively deuterated anthracene-based host materials. The lifetime of deuterated devices is nearly proportional to the deuteration ratio in the host molecule. Up to fourfold lifetime improvement has been achieved with highly deuterated hosts. image

This paper introduces a novel neural audio codec targeting high waveform sampling rates and low bitrates named APCodec, which seamlessly integrates the strengths of parametric codecs and waveform codecs. The APCodec revolutionizes the process of audio encoding and decoding by concurrently handling the amplitude and phase spectra as audio parametric characteristics like parametric codecs. It is composed of an encoder and a decoder with the modified ConvNeXt v2 network as the backbone, connected by a quantizer based on the residual vector quantization (RVQ) mechanism. The encoder compresses the audio amplitude and phase spectra in parallel, amalgamating them into a continuous latent code at a reduced temporal resolution. This code is subsequently quantized by the quantizer. Ultimately, the decoder reconstructs the audio amplitude and phase spectra in parallel, and the decoded waveform is obtained by inverse short-time Fourier transform. To ensure the fidelity of decoded audio like waveform codecs, spectral-level loss, quantization loss, and generative adversarial network (GAN) based loss are collectively employed for training the APCodec. To support low-latency streamable inference, we employ feed-forward layers and causal convolutional layers in APCodec, incorporating a knowledge distillation training strategy to enhance the quality of decoded audio. Experimental results confirm that our proposed APCodec can encode 48 kHz audio at bitrate of just 6 kbps, with no significant degradation in the quality of the decoded audio. At the same bitrate, our proposed APCodec also demonstrates superior decoded audio quality and faster generation speed compared to well-known codecs, such as SoundStream, Encodec, HiFi-Codec and AudioDec., Comment: Submitted to IEEE/ACM Transactions on Audio, Speech, and Language Processing

Speech bandwidth extension (BWE) refers to widening the frequency bandwidth range of speech signals, enhancing the speech quality towards brighter and fuller. This paper proposes a generative adversarial network (GAN) based BWE model with parallel prediction of Amplitude and Phase spectra, named AP-BWE, which achieves both high-quality and efficient wideband speech waveform generation. The proposed AP-BWE generator is entirely based on convolutional neural networks (CNNs). It features a dual-stream architecture with mutual interaction, where the amplitude stream and the phase stream communicate with each other and respectively extend the high-frequency components from the input narrowband amplitude and phase spectra. To improve the naturalness of the extended speech signals, we employ a multi-period discriminator at the waveform level and design a pair of multi-resolution amplitude and phase discriminators at the spectral level, respectively. Experimental results demonstrate that our proposed AP-BWE achieves state-of-the-art performance in terms of speech quality for BWE tasks targeting sampling rates of both 16 kHz and 48 kHz. In terms of generation efficiency, due to the all-convolutional architecture and all-frame-level operations, the proposed AP-BWE can generate 48 kHz waveform samples 292.3 times faster than real-time on a single RTX 4090 GPU and 18.1 times faster than real-time on a single CPU. Notably, to our knowledge, AP-BWE is the first to achieve the direct extension of the high-frequency phase spectrum, which is beneficial for improving the effectiveness of existing BWE methods., Comment: Submitted to IEEE/ACM Transactions on Audio, Speech, and Language Processing

This paper proposes a novel bidirectional neural vocoder, named BiVocoder, capable both of feature extraction and reverse waveform generation within the short-time Fourier transform (STFT) domain. For feature extraction, the BiVocoder takes amplitude and phase spectra derived from STFT as inputs, transforms them into long-frame-shift and low-dimensional features through convolutional neural networks. The extracted features are demonstrated suitable for direct prediction by acoustic models, supporting its application in text-to-speech (TTS) task. For waveform generation, the BiVocoder restores amplitude and phase spectra from the features by a symmetric network, followed by inverse STFT to reconstruct the speech waveform. Experimental results show that our proposed BiVocoder achieves better performance compared to some baseline vocoders, by comprehensively considering both synthesized speech quality and inference speed for both analysis-synthesis and TTS tasks., Comment: Accepted by Interspeech 2024

The majority of existing speech bandwidth extension (BWE) methods operate under the constraint of fixed source and target sampling rates, which limits their flexibility in practical applications. In this paper, we propose a multi-stage speech BWE model named MS-BWE, which can handle a set of source and target sampling rate pairs and achieve flexible extensions of frequency bandwidth. The proposed MS-BWE model comprises a cascade of BWE blocks, with each block featuring a dual-stream architecture to realize amplitude and phase extension, progressively painting the speech frequency bands stage by stage. The teacher-forcing strategy is employed to mitigate the discrepancy between training and inference. Experimental results demonstrate that our proposed MS-BWE is comparable to state-of-the-art speech BWE methods in speech quality. Regarding generation efficiency, the one-stage generation of MS-BWE can achieve over one thousand times real-time on GPU and about sixty times on CPU., Comment: Accepted by Interspeech 2024

Our main result is a polynomial time algorithm for deciding realizability for the GXU sublogic of linear temporal logic. This logic is particularly suitable for the specification of embedded control systems, and it is more expressive than GR(1). Reactive control programs for GXU specifications are represented as Mealy machines, which are extended by the monitoring of input events. Now, realizability for GXU specifications is shown to be equivalent to solving a certain subclass of 2QBF satisfiability problems. These logical problems can be solved in cubic time in the size of GXU specifications. For unrealizable GXU specifications, stronger environment assumptions are mined from failed consistency checks based on Padoa's characterization of definability and Craig interpolation.

Recently, Large Language Models (LLMs) are widely adopted in a wide range of tasks, leading to increasing attention towards the research on how scaling LLMs affects their performance. Existing works, termed as Scaling Laws, have discovered that the loss of LLMs scales as power laws with model size, computational budget, and dataset size. However, the performance of LLMs throughout the training process remains untouched. In this paper, we propose the novel concept of Temporal Scaling Law and study the loss of LLMs from the temporal dimension. We first investigate the imbalance of loss on each token positions and develop a reciprocal-law across model scales and training stages. We then derive the temporal scaling law by studying the temporal patterns of the reciprocal-law parameters. Results on both in-distribution (IID) data and out-of-distribution (OOD) data demonstrate that our temporal scaling law accurately predicts the performance of LLMs in future training stages. Moreover, the temporal scaling law reveals that LLMs learn uniformly on different token positions, despite the loss imbalance. Experiments on pre-training LLMs in various scales show that this phenomenon verifies the default training paradigm for generative language models, in which no re-weighting strategies are attached during training. Overall, the temporal scaling law provides deeper insight into LLM pre-training., Comment: Work in progress

In-process penetration monitoring of the pulsed laser welding process remains a great challenge for achieving uniform and reproducible products due to the highly complex nature of the keyhole dynamics within the intense laser-metal interactions. The main purpose of this study is to investigate the feasibility of acoustic emission (AE) measurement for penetration monitoring based on acoustic wave characteristics and deep learning. Firstly, a series of laser welding experiments on aluminum alloys were conducted using high-speed photography and AE techniques. This allowed us to in-situ visualize the complete keyhole dynamics and elucidate the generation mechanism of acoustic waves originating from pressure fluctuations at the keyhole wall. Then, an adaptive time-frequency technique namely VMD (Variational Mode Decomposition) was proposed to characterize the acoustic energy distribution among the nine subsignals with low-frequency and high-frequency components under different welding penetrations. Lastly, a novel hybrid model combing CNN (Convolutional Neural Network) and LSTM (Long Short Term Memory) was designed to deeply mine the spatial and temporal acoustic features from the extracted frequency components. Extensive experiments demonstrate that our proposed approach yields a remarkable classification performance with a test accuracy of 99.8% and a standard deviation of 0.21, which obtains a high recognition rate. This work is a new paradigm in the digitization and intelligence of the laser welding process and contributes to an alternative way of developing an efficient end-to-end penetration monitoring system.

BackgroundThe Tenosynovial giant cell tumor Observational Platform Project (TOPP) registry is an international prospective study that -previously described the impact of diffuse-type tenosynovial giant cell tumour (D-TGCT) on patient-reported outcomes (PROs) from a baseline snapshot. This analysis describes the impact of D-TGCT at 2-year follow-up based on treatment strategies.Material and methodsTOPP was conducted at 12 sites (EU: 10; US: 2). Captured PRO measurements assessed at baseline, 1-year, and 2-year follow-ups were Brief Pain Inventory (BPI), Pain Interference, BPI Pain Severity, Worst Pain, EQ-5D-5L, Worst Stiffness, and -Patient-Reported Outcomes Measurement Information System. Treatment interventions were no current/planned treatment (Off-Treatment) and systemic treatment/surgery (On-Treatment).ResultsA total of 176 patients (mean age: 43.5 years) were included in the full analysis set. For patients without active treatment strategy -(Off-Treatment) at baseline (n = 79), BPI Pain Interference (1.00 vs. 2.86) and BPI Pain Severity scores (1.50 vs. 3.00) were numerically favorable in patients remaining Off-Treatment compared with those who switched to an active treatment strategy at year 1. From 1-year to 2-year -follow-ups, patients who remained Off-Treatment had better BPI Pain Interference (0.57 vs. 2.57) and Worst Pain (2.0 vs. 4.5) scores compared with patients who switched to an alternative treatment strategy. In addition, EQ-5D VAS scores (80.0 vs. 65.0) were higher in patients who remained -Off-Treatment between 1-year and 2-year follow-ups compared with patients who changed treatment strategy. For patients receiving systemic treatment at baseline, numerically favorable scores were seen in patients remaining on systemic therapy at 1-year follow-up: BPI Pain Interference (2.79 vs. 5.93), BPI Pain Severity (3.63 vs. 6.38), Worst Pain (4.5 vs. 7.5), and Worst Stiffness (4.0 vs. 7.5). From 1-year to 2-year follow-up, EQ-5D VAS scores (77.5 vs. 65.0) w

ENVISAGE-TAVI AF (Edoxaban versus Standard of Care and Their Effects on Clinical Outcomes in Patients Having Undergone Transcatheter Aortic Valve Implantation–Atrial Fibrillation; NCT02943785) was a prospective, randomized, open-label trial comparing non–vitamin K oral anticoagulant (NOAC) edoxaban with vitamin K antagonists (VKAs) in patients with atrial fibrillation after successful transcatheter aortic valve replacement (TAVR). The effect of edoxaban- or VKA-based therapy on patient-reported outcomes remains unknown, as most studies focus on efficacy and safety. Pre-TAVR patient-reported expectations and post-TAVR Treatment Satisfaction and Convenience with edoxaban or VKA treatment (at months 3 and 12) were analyzed using the Perception of Anticoagulation Treatment Questionnaire (PACT-Q). This analysis included randomized and dosed patients with an evaluable PACT-Q1 assessment at baseline and ≥1 postbaseline assessment (PACT-Q2). Subanalyses included patients stratified by pre-TAVR anticoagulant (NOAC, VKA, no NOAC/VKA). Edoxaban- (n = 585) and VKA-treated (n = 522) patients had similar baseline characteristics and treatment expectations. Pre-TAVR anticoagulant use did not affect treatment expectations. After TAVR, edoxaban-treated patients had significantly higher Treatment Satisfaction and Convenience scores compared with VKA-treated patients at all time points (p <0.001 for all). Among edoxaban-treated patients, those who received VKAs pre-TAVR were significantly more satisfied with treatment than those who received NOACs (p <0.001) or no NOACs/VKAs (p = 0.003); however, there was no significant difference in the perception of convenience (p = 0.927 and p = 0.092, respectively). Conversely, among VKA-treated patients, the type of anticoagulant used pre-TAVR did not affect Treatment Satisfaction or Convenience scores post-TAVR. In conclusion, patients with atrial fibrillation who received edoxaban post-TAVR reported significantly higher Treatment Sati

The cumulative effects of deuteration on the device lifetime of blue OLEDs with emitters based on common polycyclic aromatic hydrocarbons were studied using two anthracene-based host materials. Their deuterated analogues with incremental deuterium substitutions exhibited similar EL efficiencies but progressively prolonged device lifetimes as a function of deuteration.

Integrating a wettability-patterned surface into a multiscale micro/nanostructured evaporator has proven to enhance the thermal performance of an ultrathin vapor chamber (UTVC), but the mechanism of the enhancement is unknown. To reveal the effect of the wettability-patterned multiscale evaporator on the thermal performance, the hydrophobic area fraction of the wettability-patterned surface in this study varies from 6.25% to 66.9%. The inner surface of the evaporator substrate is chemically patterned to form the wettability-patterned evaporator. The evaporator and a condenser with a supporting micropillar array comprise the UTVC with a less than 0.25 mm thickness. The thermal performance of UTVCs with various hydrophobic area fractions is measured under the optimum charge ratio. Specifically, the optimum hydrophobic area fraction is found at around 34%. Besides, a visualization experiment is conducted on the evaporation of a wettability-patterned glass substrate covered with a wire mesh to help figure out the mechanism of the enhancement. It is found that the wettability-patterned surface elongates the triple contact line, enhancing the evaporation rate. Based on its effect on the evaporation and the thermal resistance network of a vapor chamber, an evaporation model of the wettability-patterned evaporator in the UTVC is proposed. The model predicts the optimum hydrophobic area fraction is 29.9%, close to the experimental results. Moreover, it also well estimates the thermal performance of UTVCs with wettability-patterned evaporators at various hydrophobic area fractions. © 2023 Begell House Inc.. All rights reserved.

To tackle challenging combinatorial optimization problems, analog computing machines based on the nature-inspired Ising model are attracting increasing attentions in order to disruptively overcome the impending limitations on conventional electronic computers. Photonic spatial Ising machine has become an unique and primitive solution with all-to-all connections to solve large-scale Max-cut problems. However, spin configuration and flipping requires two independent sets of spatial light modulators (SLMs) for amplitude and phase modulation, which will lead to tremendous engineering difficulty of optical alignment and coupling. We report a novel quadrature photonic spatial-Euler Ising machine to realize large-scale and flexible spin-interaction configuration and spin-flip in a single spatial light modulator, and develop a noise enhancement approach by adding digital white noise onto detected optical signals. We experimentally show that such proposal accelerates solving (un)weighted, (non)fully connected, 20736-node Max-cut problems, which offers obvious advantages over simulation and heuristic algorithm results in digital computers., Comment: 8 pages,3 figures

The combination of nitrogen (N) fertilization and straw incorporation has complex influences on soil N transformations and derived nitrous oxide (N2O) emissions. This study aimed to reveal the coupled effects of straw returning forms and N fertilizer management on soil N2O emissions. Here, carbon (C) sources with varied availabilities were supplied by different straw returning forms, including straw, charred straw, and combined application of straw and charred straw. The time gaps between additions of exogenous N and C sources were carried out by managing the timing of N fertilization, including N application simultaneous with or delayed after straw return. Soil N2O emissions under different straw returning forms and their response relationships to soil C, N, microbial biomass, as well as soil pH and oxygen (O2) were explored. Co-application of straw and N fertilizer provided sufficient C and N sources, enhanced the microbial biomass, and consequently increased N2O emissions. Delayed N fertilization could decouple the response of N2O emissions to straw addition, because of constrained N availability and limited pH decline, which decreased the cumulative N2O emissions significantly. Possibly due to straw induced N immobilization coupled with charred straw induced pH elevation, the combined application of straw and charred straw constrained soil N2O emissions compared with straw only application, regardless of N fertilizer management. By shaping the microbial biomass and soil C, N, and O2 dynamics, delayed application of N fertilizer could further enhance the inhibition effect of the charred straw and straw combination on soil N2O emissions. Therefore, the varied straw returning forms and the timing of N fertilization could affect the supply of available C and N, influencing the hot moments of N2O emissions, and the joint addition of straw and ch

This paper proposes a source-filter-based generative adversarial neural vocoder named SF-GAN, which achieves high-fidelity waveform generation from input acoustic features by introducing F0-based source excitation signals to a neural filter framework. The SF-GAN vocoder is composed of a source module and a resolution-wise conditional filter module and is trained based on generative adversarial strategies. The source module produces an excitation signal from the F0 information, then the resolution-wise convolutional filter module combines the excitation signal with processed acoustic features at various temporal resolutions and finally reconstructs the raw waveform. The experimental results show that our proposed SF-GAN vocoder outperforms the state-of-the-art HiFi-GAN and Fre-GAN in both analysis-synthesis (AS) and text-to-speech (TTS) tasks, and the synthesized speech quality of SF-GAN is comparable to the ground-truth audio., Comment: Accepted by NCMMSC 2022

We investigated the local concentration evolution in pre-mixed and in-situ mixing ethanol/water multicomponent sessile droplets during the evaporation process on a hydrophobic substrate. Local concentration tracking experiments were conducted through a technique based on the aggregate-induced emission (AIE) effect, to investigate the characteristics of the concentration distribution and their formation mechanism. A consistent pattern of concentration distribution was observed in a pre-mixed multicomponent droplet: A high water fraction was detected in a shell-shaped region near the liquid-air interface, and a secondary concentration gradient existed within this shell-shaped region. After the preliminary mixed state, where the low-density ethanol predominantly accumulated at the upper position and the high-density water at the lower position, the evolution of concentration distribution can be divided into three distinct stages according to their different distribution patterns. In the early stage, a low-high-low ethanol concentration distribution appeared along the liquid-air interface from the apex region to the triple line region. In the intermediate stage, a region with high ethanol fraction was formed near the triple line, while the central region showed a high water fraction. Finally, a stable non-uniform concentration distribution emerged in the late evaporation stage, which was similar to that observed in a pre-mixed droplet. Additionally, a sudden increase of local water fraction took place at the triple line in this late evaporation stage, resulting in a change of surface tension and the subsequent depinning of the triple line. © 2023, Avestia Publishing. All rights reserved.

In kidney exchange programmes (KEP) patients may swap their incompatible donors leading to cycles of kidney transplants. Nowadays, countries try to merge their national patient-donor pools leading to international KEPs (IKEPs). As shown in the literature, long-term stability of an IKEP can be achieved through a credit-based system. In each round, every country is prescribed a "fair" initial allocation of kidney transplants. The initial allocation, which we obtain by using solution concepts from cooperative game theory, is adjusted by incorporating credits from the previous round, yielding the target allocation. The goal is to find, in each round, an optimal solution that closely approximates this target allocation. There is a known polynomial-time algorithm for finding an optimal solution that lexicographically minimizes the country deviations from the target allocation if only $2$-cycles (matchings) are permitted. In practice, kidney swaps along longer cycles may be performed. However, the problem of computing optimal solutions for maximum cycle length $\ell$ is NP-hard for every $\ell\geq 3$. This situation changes back to polynomial time once we allow unbounded cycle length. However, in contrast to the case where $\ell=2$, we show that for $\ell=\infty$, lexicographical minimization is only polynomial-time solvable under additional conditions (assuming P $\neq$ NP). Nevertheless, the fact that the optimal solutions themselves can be computed in polynomial time if $\ell=\infty$ still enables us to perform a large scale experimental study for showing how stability and total social welfare are affected when we set $\ell=\infty$ instead of $\ell=2$.

Motion prediction has been an essential component of autonomous driving systems since it handles highly uncertain and complex scenarios involving moving agents of different types. In this paper, we propose a Multi-Granular TRansformer (MGTR) framework, an encoder-decoder network that exploits context features in different granularities for different kinds of traffic agents. To further enhance MGTR's capabilities, we leverage LiDAR point cloud data by incorporating LiDAR semantic features from an off-the-shelf LiDAR feature extractor. We evaluate MGTR on Waymo Open Dataset motion prediction benchmark and show that the proposed method achieved state-of-the-art performance, ranking 1st on its leaderboard (https://waymo.com/open/challenges/2023/motion-prediction/)., Comment: Accepted to ICRA 2024

Retinal disease is one of the primary causes of visual impairment, and early diagnosis is essential for preventing further deterioration. Nowadays, many works have explored Transformers for diagnosing diseases due to their strong visual representation capabilities. However, retinal diseases exhibit milder forms and often present with overlapping signs, which pose great difficulties for accurate multi-class classification. Therefore, we propose a new framework named Multi-Scale Patch Message Passing Swin Transformer for multi-class retinal disease classification. Specifically, we design a Patch Message Passing (PMP) module based on the Message Passing mechanism to establish global interaction for pathological semantic features and to exploit the subtle differences further between different diseases. Moreover, considering the various scale of pathological features we integrate multiple PMP modules for different patch sizes. For evaluation, we have constructed a new dataset, named OPTOS dataset, consisting of 1,033 high-resolution fundus images photographed by Optos camera and conducted comprehensive experiments to validate the efficacy of our proposed method. And the results on both the public dataset and our dataset demonstrate that our method achieves remarkable performance compared to state-of-the-art methods., Comment: 9 pages, 7 figures

The spatial and quantitative parameters of macular holes are vital for diagnosis, surgical choices, and post-op monitoring. Macular hole diagnosis and treatment rely heavily on spatial and quantitative data, yet the scarcity of such data has impeded the progress of deep learning techniques for effective segmentation and real-time 3D reconstruction. To address this challenge, we assembled the world's largest macular hole dataset, Retinal OCTfor Macular Hole Enhancement (ROME-3914), and a Comprehensive Archive for Retinal Segmentation (CARS-30k), both expertly annotated. In addition, we developed an innovative 3D segmentation network, the Dual-Encoder FuGH Network (DEFN), which integrates three innovative modules: Fourier Group Harmonics (FuGH), Simplified 3D Spatial Attention (S3DSA) and Harmonic Squeeze-and-Excitation Module (HSE). These three modules synergistically filter noise, reduce computational complexity, emphasize detailed features, and enhance the network's representation ability. We also proposed a novel data augmentation method, Stochastic Retinal Defect Injection (SRDI), and a network optimization strategy DynamicWeightCompose (DWC), to further improve the performance of DEFN. Compared with 13 baselines, our DEFN shows the best performance. We also offer precise 3D retinal reconstruction and quantitative metrics, bringing revolutionary diagnostic and therapeutic decision-making tools for ophthalmologists, and is expected to completely reshape the diagnosis and treatment patterns of difficult-to-treat macular degeneration. The source code is publicly available at: https://github.com/IIPL-HangzhouDianUniversity/DEFN-Pytorch., Comment: 25pages,15figures,7tables

Phase information has a significant impact on speech perceptual quality and intelligibility. However, existing speech enhancement methods encounter limitations in explicit phase estimation due to the non-structural nature and wrapping characteristics of the phase, leading to a bottleneck in enhanced speech quality. To overcome the above issue, in this paper, we proposed MP-SENet, a novel Speech Enhancement Network that explicitly enhances Magnitude and Phase spectra in parallel. The proposed MP-SENet comprises a Transformer-embedded encoder-decoder architecture. The encoder aims to encode the input distorted magnitude and phase spectra into time-frequency representations, which are further fed into time-frequency Transformers for alternatively capturing time and frequency dependencies. The decoder comprises a magnitude mask decoder and a phase decoder, directly enhancing magnitude and wrapped phase spectra by incorporating a magnitude masking architecture and a phase parallel estimation architecture, respectively. Multi-level loss functions explicitly defined on the magnitude spectra, wrapped phase spectra, and short-time complex spectra are adopted to jointly train the MP-SENet model. A metric discriminator is further employed to compensate for the incomplete correlation between these losses and human auditory perception. Experimental results demonstrate that our proposed MP-SENet achieves state-of-the-art performance across multiple speech enhancement tasks, including speech denoising, dereverberation, and bandwidth extension. Compared to existing phase-aware speech enhancement methods, it further mitigates the compensation effect between the magnitude and phase by explicit phase estimation, elevating the perceptual quality of enhanced speech., Comment: Submmited to IEEE Transactions on Audio, Speech and Language Processing

Speech phase prediction, which is a significant research focus in the field of signal processing, aims to recover speech phase spectra from amplitude-related features. However, existing speech phase prediction methods are constrained to recovering phase spectra with short frame shifts, which are considerably smaller than the theoretical upper bound required for exact waveform reconstruction of short-time Fourier transform (STFT). To tackle this issue, we present a novel long-frame-shift neural speech phase prediction (LFS-NSPP) method which enables precise prediction of long-frame-shift phase spectra from long-frame-shift log amplitude spectra. The proposed method consists of three stages: interpolation, prediction and decimation. The short-frame-shift log amplitude spectra are first constructed from long-frame-shift ones through frequency-by-frequency interpolation to enhance the spectral continuity, and then employed to predict short-frame-shift phase spectra using an NSPP model, thereby compensating for interpolation errors. Ultimately, the long-frame-shift phase spectra are obtained from short-frame-shift ones through frame-by-frame decimation. Experimental results show that the proposed LFS-NSPP method can yield superior quality in predicting long-frame-shift phase spectra than the original NSPP model and other signal-processing-based phase estimation algorithms., Comment: Published at IEEE Signal Processing Letters

The spatial photonic Ising machine has achieved remarkable advancements in solving combinatorial optimization problems. However, it still remains a huge challenge to flexibly mapping an arbitrary problem to Ising model. In this paper, we propose a general spatial photonic Ising machine based on interaction matrix eigendecomposition method. Arbitrary interaction matrix can be configured in the two-dimensional Fourier transformation based spatial photonic Ising model by using values generated by matrix eigendecomposition. The error in the structural representation of the Hamiltonian decreases substantially with the growing number of eigenvalues utilized to form the Ising machine. In combination with the optimization algorithm, as low as 65% of the eigenvalues is required by intensity modulation to guarantee the best probability of optimal solution for a 20-vertex graph Max-cut problem, and this probability decreases to below 20% for zero best chance. Our work provides a viable approach for spatial photonic Ising machines to solve arbitrary combinatorial optimization problems with the help of multi-dimensional optical property.

Perturbed maternal diet and prenatal exposure to air pollution (AP) affect the fetal brain, predisposing to postnatal neurobehavioral disorders. Glucose transporters (GLUTs) are key in fueling neurotransmission; deficiency of the neuronal isoform GLUT3 culminates in autism spectrum disorders. Along with the different neurotransmitters, serotonin (5-HT) and oxytocin (OXT) are critical for the development of neural connectivity. Serotonin transporter (SERT) modulates synaptic 5-HT levels, while the OXT receptor (OXTR) mediates OXT action. We hypothesized that perturbed brain GLUT1/GLUT3 regulated 5-HT-SERT imbalance, which serves as a contributing factor to postnatal neuropsychiatric phenotypes, with OXT/OXTR providing a counterbalance. Employing maternal diet restriction (intrauterine growth restriction [IUGR]), high-fat (HF) dietary modifications, and prenatal exposure to simulated AP, fetal (E19) murine brain 5-HT was assessed by ELISA with SERT and OXTR being localized by immunohistochemistry and measured by quantitative Western blot analysis. IUGR with lower head weights led to a 48% reduction in male and female fetal brain GLUT3 with no change in GLUT1, when compared to age- and sex-matched controls, with no significant change in OXTR. In addition, a ∼50% (p = 0.005) decrease in 5-HT and SERT concentrations was displayed in fetal IUGR brains. In contrast, despite emergence of microcephaly, exposure to a maternal HF diet or AP caused no significant changes. We conclude that in the IUGR during fetal brain development, reduced GLUT3 is associated with an imbalanced 5-HT-SERT axis. We speculate that these early changes may set the stage for altering the 5HT-SERT neural axis with postnatal emergence of associated neurodevelopmental disorders.

A solid phase adsorption method for selective isolation of hyaluronan (HA) from biological samples is presented. Following enzymatic degradation of protein, HA can be separated from sulfated glycosaminoglycans, other unsulfated glycosaminoglycans, nucleic acids, and proteolytic fragments by adsorption to amorphous silica at specific salt concentrations. The adsorbed HA can be released from silica using neutral and basic aqueous solutions. HA ranging in size from ∼9 kDa to MDa polymers has been purified by this method from human serum and conditioned medium of cultured cells.

Recent clinical development of KRAS inhibitors has heightened interest in the genomic landscape of KRAS-altered cancers. We performed a pan-cancer analysis of KRAS-altered samples from 426,706 adult patients with solid or hematologic malignancies using comprehensive genomic profiling; additional analyses included 62,369 liquid biopsy and 7241 pediatric samples. 23% of adult pan-cancer samples had KRAS alterations; 88% were mutations, most commonly G12D/G12V/G12C/G13D/G12R, and prevalence was similar in liquid biopsies. Co-alteration landscapes were largely similar across KRAS mutations but distinct from KRAS wild-type, though differences were observed in some tumor types for tumor mutational burden, PD-L1 expression, microsatellite instability, and other mutational signatures. Prognosis of KRAS-mutant versus other genomic cohorts of lung, pancreatic, and colorectal cancer were assessed using a real-world clinicogenomic database. As specific KRAS inhibitors and combination therapeutic strategies are being developed, genomic profiling to understand co-alterations and other biomarkers that may modulate response to targeted or immunotherapies will be imperative.

Background and objectivesDiffuse-tenosynovial giant cell tumor (D-TGCT) is a rare, locally aggressive, typically benign neoplasm affecting mainly large joints, representing a wide clinical spectrum. We provide a picture of the treatment journey of D-TGCT patients as a 2-year observational follow-up.MethodsThe TGCT Observational Platform Project registry was a multinational, multicenter, prospective observational study at tertiary sarcoma centers spanning seven European countries and two US sites. Histologically confirmed D-TGCT patients were categorized as either those who remained on initial treatment strategy (determined at baseline visit) or those who changed treatment strategy with specific changes documented (e.g., systemic treatment to surgery) at the 1-year and/or 2-year follow-up visits.ResultsA total of 176 patients were assessed, mean diagnosis age was 38.4 (SD ± 14.6) years; most patients had a knee tumor (120/176, 68.2%). For the 2-year observation period, most patients (75.5%) remained on the baseline treatment strategy throughout, 54/79 patients (68.4%) remained no treatment, 30/45 patients (66.7%) remained systemic treatment, 39/39 patients (100%) remained surgery. Those who changed treatment strategy utilized multimodal treatment options.ConclusionsThis is the first prospectively collected analysis to describe D-TGCT patient treatments over an extended follow-up and demonstrates the need for multidisciplinary teams to determine an optimal treatment strategy.

PurposeTo assess the response to pexidartinib treatment in six cohorts of adult patients with advanced, incurable solid tumors associated with colony-stimulating factor 1 receptor (CSF1R) and/or KIT proto-oncogene receptor tyrosine kinase activity.Patients and methodsFrom this two-part phase I, multicenter study, pexidartinib, a small-molecule tyrosine kinase inhibitor that targets CSF1R, KIT, and FMS-like tyrosine kinase 3 (FLT3), was evaluated in six adult patient cohorts (part 2, extension) with advanced solid tumors associated with dysregulated CSF1R. Adverse events, pharmacokinetics, and tumor responses were assessed for all patients; patients with tenosynovial giant cell tumor (TGCT) were also evaluated for tumor volume score (TVS) and patient-reported outcomes (PRO). CSF1 transcripts and gene expression were explored in TGCT biopsies.ResultsNinety-one patients were treated: TGCT patients (n = 39) had a median treatment duration of 511 days, while other solid tumor patients (n = 52) had a median treatment duration of 56 days. TGCT patients had response rates of 62% (RECIST 1.1) and 56% (TVS) for the full analysis set. PRO assessments for pain showed improvement in patient symptoms, and 76% (19/25) of TGCT tissue biopsy specimens showed evidence of abnormal CSF1 transcripts. Pexidartinib treatment of TGCT resulted in tumor regression and symptomatic benefit in most patients. Pexidartinib toxicity was manageable over the entire study.ConclusionsThese results offer insight into outcome patterns in cancers whose biology suggests use of a CSF1R inhibitor. Pexidartinib results in tumor regression in TGCT patients, providing prolonged control with an acceptable safety profile.

Matching games naturally generalize assignment games, a well-known class of cooperative games. Interest in matching games has grown recently due to some breakthrough results and new applications. This state-of-the-art survey provides an overview of matching games and extensions, such as $b$-matching games and partitioned matching games; the latter originating from the emerging area of international kidney exchange. In this survey we focus on computational complexity aspects of various game-theoretical solution concepts, such as the core, nucleolus and Shapley value, when the input is restricted to a matching game or one if its variants.

Integrating wettability-patterned surfaces into a multiscale micro/nanostructured evaporator has demonstrated promising thermal performance enhancement of an ultrathin vapor chamber (UTVC). However, the mechanism behind it still remains unknown. To investigate the synergetic effect of wettability-patterned micro/nanostructures on the thermal performance of the UTVC, the hydrophobic area fraction of the wettability-patterned surface in this study varies from 6.25% to 66.9%. The wettability-patterned surface is fabricated on the substrate of the evaporator, and a micro/nanostructured mesh functions as a multiscale wick structure intimately pressed on the wettability-patterned surface to form the evaporator. The total thickness of UTVCs under the investigation is less than 250 µm including the multiscale evaporator, the condenser, and the supporting micropillar array. The thermal performance of UTVCs with different hydrophobic area fractions is evaluated under the optimum charge ratio. To understand the mechanism of the enhancement of wettability-patterned surfaces, a visualization experiment was conducted utilizing a wettability-patterned glass substrate. It is found that the wettability-patterned surface can increase the length of the triple contact line, which enhances the evaporation process. Specifically, the optimum hydrophobic area fraction was found at around 34%. The UTVC with the wettability-patterned surface achieves a maximum 13,820 W/(m·K) in-plane effective thermal conductivity, which is a 162% enhancement compared to the one with a hydrophilic surface. Furthermore, it also provides a 29.1% maximum reduction of vertical thermal resistance of UTVCs. An evaporation model of the wettability-patterned surface integrated with the wire mesh is proposed based on its elongation effect on the triple contact line. The optimum hydrophobic area fraction predicted by the model is 29.9%, which is very close to the experimental results. © 2022

BackgroundTenosynovial giant cell tumor (TGCT) is a rare, locally aggressive neoplasm arising from the synovium of joints, bursae, and tendon sheaths affecting small and large joints. It represents a wide spectrum ranging from minimally symptomatic to massively debilitating. Most findings to date are mainly from small, retrospective case series, and thus the morbidity and actual impact of this rare disease remain to be elucidated. This study prospectively explores the management of TGCT in tertiary sarcoma centers.MethodsThe TGCT Observational Platform Project registry was a multinational, multicenter, prospective observational study involving 12 tertiary sarcoma centers in 7 European countries, and 2 US sites. This study enrolled for 2 years all consecutive ≥ 18 years old patients, with histologically diagnosed primary or recurrent cases of diffuse-type TGCT. Patient demographic and clinical characteristics were collected at baseline and every 6 months for 24 months. Quality of life questionnaires (PROMIS-PF and EQ-5D) were also administered at the same time-points. Here we report baseline patient characteristics.Results166 patients were enrolled between November 2016 and March 2019. Baseline characteristics were: mean age 44 years (mean age at disease onset: 39 years), 139/166 (83.7%) had prior treatment, 71/166 patients (42.8%) had ≥ 1 recurrence after treatment of their primary tumor, 76/136 (55.9%) visited a medical specialist ≥ 5 times, 66/116 (56.9%) missed work in the 24 months prior to baseline, and 17/166 (11.6%) changed employment status or retired prematurely due to disease burden. Prior treatment consisted of surgery (i.e., arthroscopic, open synovectomy) (128/166; 77.1%) and systemic treatments (52/166; 31.3%) with imatinib (19/52; 36.5%) or pexidartinib (27/52; 51.9%). Treatment strategies at baseline visits consisted mainly of watchful waiting (81/166; 48.8%), surgery (41/166; 24.7%), or targeted systemic therapy (3

BackgroundTenosynovial giant cell tumor (TGCT) is a rare, locally aggressive neoplasm arising from the synovium of joints, bursae, and tendon sheaths affecting small and large joints. It represents a wide spectrum ranging from minimally symptomatic to massively debilitating. Most findings to date are mainly from small, retrospective case series, and thus the morbidity and actual impact of this rare disease remain to be elucidated. This study prospectively explores the management of TGCT in tertiary sarcoma centers.MethodsThe TGCT Observational Platform Project registry was a multinational, multicenter, prospective observational study involving 12 tertiary sarcoma centers in 7 European countries, and 2 US sites. This study enrolled for 2 years all consecutive ≥ 18 years old patients, with histologically diagnosed primary or recurrent cases of diffuse-type TGCT. Patient demographic and clinical characteristics were collected at baseline and every 6 months for 24 months. Quality of life questionnaires (PROMIS-PF and EQ-5D) were also administered at the same time-points. Here we report baseline patient characteristics.Results166 patients were enrolled between November 2016 and March 2019. Baseline characteristics were: mean age 44 years (mean age at disease onset: 39 years), 139/166 (83.7%) had prior treatment, 71/166 patients (42.8%) had ≥ 1 recurrence after treatment of their primary tumor, 76/136 (55.9%) visited a medical specialist ≥ 5 times, 66/116 (56.9%) missed work in the 24 months prior to baseline, and 17/166 (11.6%) changed employment status or retired prematurely due to disease burden. Prior treatment consisted of surgery (i.e., arthroscopic, open synovectomy) (128/166; 77.1%) and systemic treatments (52/166; 31.3%) with imatinib (19/52; 36.5%) or pexidartinib (27/52; 51.9%). Treatment strategies at baseline visits consisted mainly of watchful waiting (81/166; 48.8%), surgery (41/166; 24.7%), or targeted systemic therapy (3

With the development of the aerospace and automotive industries, high heat exchange efficiency is a challenge facing the development of various industries. Pure copper has excellent mechanical and physical properties, especially high thermal conductivity and electrical conductivity. These excellent properties make pure copper the material of choice for the manufacture of heat exchangers and other electrical components. However, the traditional processing method is difficult to achieve the production of pure copper complex parts, so the production of pure copper parts through additive manufacturing has become a problem that must be overcome in industrial development. In this article, we not only reviewed the current status of research on the structural design and preparation of complex pure copper parts by researchers using selective laser melting (SLM), selective electron beam melting (SEBM) and binder jetting (BJ) in recent years, but also reviewed the forming, physical properties and mechanical aspects of pure copper parts prepared by different additive manufacturing methods. Finally, the development trend of additive manufacturing of pure copper parts is also prospected.

With the development of the aerospace and automotive industries, high heat exchange efficiency is a challenge facing the development of various industries. Pure copper has excellent mechanical and physical properties, especially high thermal conductivity and electrical conductivity. These excellent properties make pure copper the material of choice for the manufacture of heat exchangers and other electrical components. However, the traditional processing method is difficult to achieve the production of pure copper complex parts, so the production of pure copper parts through additive manufacturing has become a problem that must be overcome in industrial development. In this article, we not only reviewed the current status of research on the structural design and preparation of complex pure copper parts by researchers using selective laser melting (SLM), selective electron beam melting (SEBM) and binder jetting (BJ) in recent years, but also reviewed the forming, physical properties and mechanical aspects of pure copper parts prepared by different additive manufacturing methods. Finally, the development trend of additive manufacturing of pure copper parts is also prospected.

We investigated the thermal performance of a novel multiscale evaporator of a 0.23 mm thickness ultrathin vapor chamber which combines a microstructured wettability patterned surface with a nanostructured woven mesh wick. The microstructured wettability patterned surface underneath the nanostructured woven mesh wick can pin the three-phase contact lines on the hydrophilic/hydrophobic boundaries, enlarging the area of thin-film evaporation. Besides, the nanostructured mesh not just provides a large amount of backflow water for maintaining the evaporation on the three-phase contact lines, but also gives more thin-film evaporation area owing to its needle-like nanostructure. Microstructured wettability pattern is fabricated on the inner surface of the evaporator and the wick is pressed onto the wettability patterned surface by a micropillar array to make them close contact with each other. The micropillar array fabricated by electroplating on the inner surface of the condenser is also used to support a vapor core as a vapor flow path. The horizontal and vertical thermal resistances are measured to evaluate the thermal performance of the ultrathin vapor chamber for different charge amounts of water when the input heat flux ranges from 8.59 W/cm2 to 23.91 W/cm2 and the heat sink is 22 ℃ forced air cooling with 6.875 cfm flow rate. The measurement results show that the novel evaporator not just greatly reduce the horizontal thermal resistance, but also lower the vertical thermal resistance compared with the evaporator without wettability pattern. The highest in-plane effectivity of the ultrathin vapor chamber with the novel evaporator can reach 11499.5 W/(m·K) at 23.91 W/cm2, giving a 212.7% improvement compared with the ultrathin vapor chamber without wettability pattern on its evaporator. © 2021, Avestia Publishing. All rights reserved.

[Background] Tenosynovial giant cell tumor (TGCT) is a rare, locally aggressive neoplasm arising from the synovium of joints, bursae, and tendon sheaths affecting small and large joints. It represents a wide spectrum ranging from minimally symptomatic to massively debilitating. Most findings to date are mainly from small, retrospective case series, and thus the morbidity and actual impact of this rare disease remain to be elucidated. This study prospectively explores the management of TGCT in tertiary sarcoma centers., [Methods] The TGCT Observational Platform Project registry was a multinational, multicenter, prospective observational study involving 12 tertiary sarcoma centers in 7 European countries, and 2 US sites. This study enrolled for 2 years all consecutive ≥ 18 years old patients, with histologically diagnosed primary or recurrent cases of diffuse-type TGCT. Patient demographic and clinical characteristics were collected at baseline and every 6 months for 24 months. Quality of life questionnaires (PROMIS-PF and EQ-5D) were also administered at the same time-points. Here we report baseline patient characteristics., [Results] 166 patients were enrolled between November 2016 and March 2019. Baseline characteristics were: mean age 44 years (mean age at disease onset: 39 years), 139/166 (83.7%) had prior treatment, 71/166 patients (42.8%) had ≥ 1 recurrence after treatment of their primary tumor, 76/136 (55.9%) visited a medical specialist ≥ 5 times, 66/116 (56.9%) missed work in the 24 months prior to baseline, and 17/166 (11.6%) changed employment status or retired prematurely due to disease burden. Prior treatment consisted of surgery (i.e., arthroscopic, open synovectomy) (128/166; 77.1%) and systemic treatments (52/166; 31.3%) with imatinib (19/52; 36.5%) or pexidartinib (27/52; 51.9%). Treatment strategies at baseline visits consisted mainly of watchful waiting (81/166; 48.8%), surgery (41/166; 24.7%), or targeted systemic therapy (37/166; 22.3%). Patients indicated for treatment reported more impairment compared to patients indicated for watchful waiting: worst stiffness NRS 5.16/3.44, worst pain NRS 6.13/5.03, PROMIS-PF 39.48/43.85, and EQ-5D VAS 66.54/71.85., [Conclusion] This study confirms that diffuse-type TGCT can highly impact quality of life. A prospective observational registry in rare disease is feasible and can be a tool to collect curated-population reflective data in orphan diseases., [Name of registry] Tenosynovial Giant Cell Tumors (TGCT) Observational Platform Project (TOPP)., [Trial registration number] NCT02948088., [Date of registration] 10 October 2016., [URL of Trial registry record] https://clinicaltrials.gov/ct2/show/NCT02948088?term=NCT02948088&draw=2.

To overcome incompatibility issues, kidney patients may swap their donors. In international kidney exchange programmes (IKEPs), countries merge their national patient-donor pools. We consider a recently introduced credit system. In each round, countries are given an initial ``fair'' allocation of the total number of kidney transplants. This allocation is adjusted by a credit function yielding a target allocation. The goal is to find a solution that approaches the target allocation as closely as possible, to ensure long-term stability of the international pool. As solutions, we use maximum matchings that lexicographically minimize the country deviations from the target allocation. We perform, for the first time, a computational study for a {\it large} number of countries. For the initial allocations we use two easy-to-compute solution concepts, the benefit value and the contribution value, and four classical but hard-to-compute concepts, the Shapley value, nucleolus, Banzhaf value and tau value. By using state-of-the-art software we show that the latter four concepts are now within reach for IKEPs of up to fifteen countries. Our experiments show that using lexicographically minimal maximum matchings instead of ones that only minimize the largest deviation from the target allocation (as previously done) may make an IKEP up to 54% more balanced.

In contrast to conventional pipeline Spoken Language Understanding (SLU) which consists of automatic speech recognition (ASR) and natural language understanding (NLU), end-to-end SLU infers the semantic meaning directly from speech and overcomes the error propagation caused by ASR. End-to-end slot filling (SF) from speech is an essential component of end-to-end SLU, and is usually regarded as a sequence-to-sequence generation problem, heavily relied on the performance of language model of ASR. However, it is hard to generate a correct slot when the slot is out-of-vovabulary (OOV) in training data, especially when a slot is an anti-linguistic entity without grammatical rule. Inspired by object detection in computer vision that is to detect the object from an image, we consider SF as the task of slot detection from speech. In this paper, we formulate the SF task as a matching task and propose an end-to-end knowledge-based SF model, named Speech-to-Slot (Speech2Slot), to leverage knowledge to detect the boundary of a slot from the speech. We also release a large-scale dataset of Chinese speech for slot filling, containing more than 830,000 samples. The experiments show that our approach is markedly superior to the conventional pipeline SLU approach, and outperforms the state-of-the-art end-to-end SF approach with 12.51% accuracy improvement.

We present a novel two-layer hierarchical reinforcement learning approach equipped with a Goals Relational Graph (GRG) for tackling the partially observable goal-driven task, such as goal-driven visual navigation. Our GRG captures the underlying relations of all goals in the goal space through a Dirichlet-categorical process that facilitates: 1) the high-level network raising a sub-goal towards achieving a designated final goal; 2) the low-level network towards an optimal policy; and 3) the overall system generalizing unseen environments and goals. We evaluate our approach with two settings of partially observable goal-driven tasks -- a grid-world domain and a robotic object search task. Our experimental results show that our approach exhibits superior generalization performance on both unseen environments and new goals., Comment: CVPR2021

PurposeThe purpose of this study was to evaluate the psychometric properties of the PROMIS-Physical Function (PF) and Worst Stiffness Numeric Rating Scale (NRS) among patients with tenosynovial giant cell tumors (TGCT).MethodsMeasurement properties of the customized lower extremity (LE) and upper extremity (UE) PROMIS-PF scales and Worst Stiffness NRS were assessed using data from the Phase 3 ENLIVEN trial (n = 120). Anchor- and distribution-based analyses were utilized to derive a responder threshold for meaningful change over time. The Patient Global Rating of Concept (PGRC)-Physical Functioning and Patient Global Impression of Change (PGIC)-Stiffness served as anchors. Responsiveness and responder threshold analyses were from baseline to week 25.ResultsCronbach's alpha values for internal consistency reliability were 0.93 and 0.91 for the PROMIS-PF LE and UE, respectively. Test-retest reliability intra-class correlation coefficients were > 0.75 for both instruments. Convergent validity for both instruments was supported by moderate to strong correlations (≥0.30) with the Brief Pain Inventory and EQ-5D. Known-groups validity was established between subgroups stratified by pain level (p < 0.05). Responsiveness was supported by evaluating change scores among different levels of change in PGRC-Physical Functioning and PGIC-Stiffness (overall F values < 0.001). Triangulation of responder definition analyses resulted in a threshold of ≥3 for the PROMIS-PF and ≥ 1 for the Worst Stiffness NRS.ConclusionThis study is the first to establish the psychometric properties of patient-reported outcome measures in TGCT. The evidence demonstrates that the PROMIS-PF and Worst Stiffness NRS have good reliability, validity, and responsiveness, and provides guidance for the interpretation of meaningful change.

Objectives: The aim of this study is to validate the Global Leadership Initiative on Malnutrition (GLIM) criteria and determine the number of Nutritional Risk Screening 2002 (NRS2002)-positive patients who do not meet the GLIM, as well as examine whether these patients would benefit from nutritional support therapy.Methods: A reanalysis of a published prospective observational study was performed. The subjects were rediagnosed per the NRS2002 and GLIM criteria. The prevalence of malnutrition was reported, and the differ-ence in rate of infection complications and total complications between the nutritional support therapy and glucose-electrolyte cohorts was calculated.Results: Among 1831 cases in the original database, 827 cases (45.2%) were NRS2002-positive. A total of 391 cases were identified by the GLIM criteria as malnourished (21.4%) and of these, subjects in the nutritional support therapy cohort had fewer infection complications than those in the glucose-electrolyte cohort (13.0% vs. 23.0%; P = 0.010). The remaining 436 patients were NRS2002 positive but GLIM negative (23.8%). The rate of infection was also significantly lower in the support cohort than in the nonsupport cohort (8.0% vs. 15.7%; P = 0.011). Nutritional support was proven o be a protective factor for infection complications in both GLIM-positive (odds ratio: 0.407; 95% confidence interval, 0.232-0.714; P = 0.002) and NRS2002-positive/GLIM-negative patients [odds ratio: 0.314; 95% confidence interval, 0.161-0.612; P = 0.001).Conclusions: The GLIM criteria have been validated, and are useful in identifying malnourished patients who may have fewer infection complications due to nutritional support therapy. However, the criteria neglected half of the patients identified by NRS2002, among whom nutritional support therapy also decreased the rate of infection complications. (c) 2020 Elsevier Inc. All rights reserved.