Miracle-dz/UniFAR-FTUs huggingface dataset:
Structure of the CLC-1 chloride channel from Homo sapiens

CLC channels mediate passive Cl− conduction, while CLC transporters mediate active Cl− transport coupled to H+ transport in the opposite direction. The distinction between CLC-0/1/2 channels and CLC transporters seems undetectable by amino acid sequence. To understand why they are different functionally we determined the structure of the human CLC-1 channel. Its ‘glutamate gate’ residue, known to mediate proton transfer in CLC transporters, adopts a location in the structure that appears to preclude it from its transport function. Furthermore, smaller side chains produce a wider pore near the intracellular surface, potentially reducing a kinetic barrier for Cl− conduction. When the corresponding residues are mutated in a transporter, it is converted to a channel. Finally, Cl− at key sites in the pore appear to interact with reduced affinity compared to transporters. Thus, subtle differences in glutamate gate conformation, internal pore diameter and Cl− affinity distinguish CLC channels and transporters.
Therapeutic Effects of Systemic Administration of Chaperone B-Crystallin Associated with Binding Proinflammatory Plasma Proteins *

Jonathan B. Rothbard, Michael P. Kurnellas, Sara Brownell, Chris M. Adams, Leon Su, Robert C. Axtell, Rong Chen , C. Garrison Fathman, William H. Robinson**, and Lawrence Steinman From the Division of Immunology and Rheumatology, Department of Medicine, Department Neurology and Neurological Sciences, Stanford University Mass Spectrometry, and Division of Systems Medicine, Department of Pediatrics, Stanford University School of Medicine, Stanford, California 94305-5316 and the **Veterans Affairs Palo Alto Health Care System, Palo Alto, California 94304
microrna-769 is downregulated in colorectal cancer and inhibits cancer progression by directly targeting cyclin-dependent kinase 1

Background: In recent years, microRNAs (miRNAs) have been reported to be aberrantly expressed in colorectal cancer (CRC). The deregulation of miRNAs is implicated in the formation and progression of CRC, and participates in the regulation of a wide range of biological behaviors. Considering the crucial role of miRNAs in CRC, miRNAs are thought to have significant promise in the diagnosis and therapy of patients with this malignancy. Material and methods: Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was performed to detect miR-769 expression in CRC tissues and cell lines. MTT assay and flow cytometry analysis were used to determine the effects of miR-769 upregulation in CRC cell proliferation and apoptosis, respectively. The influence of miR-769 overexpression in CRC cell migration and invasion was evaluated through migration and invasion assays. Notably, the possible mechanisms underlying the action of miR-769 in CRC cells were explored. Results: In the present study, miR-769 was frequently found to be poorly expressed in CRC tissues and cell lines. Functional assays showed that recovery of miR-769 expression suppressed CRC cell proliferation, migration, and invasion, increased cell apoptosis in vitro, and inhibited tumor growth in vivo. Cyclin-dependent kinase 1 (CDK1) was the direct target of miR-769 in CRC cells. CDK1 was overexpressed in CRC tissue samples and negatively correlated with miR-769 expression. In addition, CDK1 inhibition imitated the tumor suppressor activity of miR-769 in CRC cells, and restoration of CDK1 expression partially abolished the tumor-suppressing roles of miR-769 in malignant CRC cells. Conclusion: The results of this study demonstrated that miR-769 was downregulated in CRC and directly targeted CDK1 to be implicated in the regulation of CRC cell proliferation, apoptosis, migration and invasion. Thus, the miR-769/CDK1 axis might be an effective thera peutic target for treating patients with CRC.
Using the Borg rating of perceived exertion scale to grade the intensity of a functional training program of the affected upper limb after a stroke: a feasibility study

Purpose: Intensity of a training program is a critical variable in treatment gains poststroke, but there are no guidelines to adequately dose the intensity of functional training (FT); the recommended type of training to promote poststroke recovery. Such guidelines are made available for strength training (ST) using the 1 repetition maximum (1RM), which has been linked to individu-als’ self-rated level of exertion using the Borg rating of perceived exertion (BRPE) scale. The BRPE could be a valuable tool for clinicians to dose FT intensity after a stroke, but this remains to be tested. The main objective of the study was to evaluate the feasibility of the BRPE at grading FT intensity of the affected upper limb in older adults with a chronic stroke and secondarily to explore the clinical changes between FT and ST when the intensity is regulated with BRPE. Patients and methods: Twelve participants were randomized into a FT or ST group and trained their affected upper limb (3 times/week for 4 weeks) with the intensity standardized with BRPE. Feasibility was assessed by adherence, occurrence of adverse events, and comparison of BRPE ratings between groups. Clinical changes were defined as improvements on the Fugl-Meyer motor assessment (FMA) and Wolf motor function test (WMFT). Results: All participants adhered to FT/ST without adverse effects, and comparable BRPE ratings were noted between groups throughout the training ( P $ 0.42). Both groups showed significant gains at the FMA (ST: 5 ± 4 points/FT: 6 ± 4 points; P = 0.04) and WMFT (ST: 0.4 ± 0.3 points/FT: 0.6 ± 0.4 points; P = 0.05), which were comparable between groups ( P $ 0.47). Conclusion: The results suggest that it is feasible to use the BRPE scale to adjust FT intensity. Gains in motor function in both groups suggest that undergoing therapy, regardless of its type, might be a sufficient stimulus to produce gains when intensity is adequately adjusted. Further studies are needed to validate the current observations.
Pseudoxanthoma Elasticum-like Papillary Dermal Elastolysis

Elastolysis refers to a loss of elastic fibers. It can affect the papillary dermis, the reticular dermis, or both. Pseudoxanthoma elasticum-like papillary dermal elastolysis (PXE-PDE) is a clinicopathological entity first described by Rongioletti and Rebora in 1992 (1). It is an acquired elastolytic disorder that mainly affects the papillary dermis. Clinically it resembles pseudoxanthoma elasticum (PXE), but it differs histologically and there is no systemic involvement, in contrast to PXE.
SERIES-HYBRID RETROFIT OF AN XV-15 TILTROTOR AND EMERGENCY PROCEDURE ENERGETIC ANALYSIS

This paper presents the main outcomes of a research activity aimed at defining a series-hybrid version of the tiltrotor XV-15 through a retrofit process of its power train. This research is motivated by the interest in electrification of aircraft propulsion and the concurrent envisaged rising of short-range air mobility services, which makes tiltrotors an appealing solution due to its capability of vertical take-off and the possibility of alleviating several of its shortcomings through electrification. Indeed, in a Internal Combustion Engine tiltrotor, both engines must be capable to generate the power required to hover in case of one engine failure. This causes the need to mount engines heavier than needed. Moreover, the need to have null overall yawing moment imposes the presence of a shaft along the entire wing to transmit power. This shaft conditions the wing shape and structure, causing small efficiency and heavy weight. The proposed propulsive retrofit is based on a turboshaft/electric generator group powering the two electric motors, with a battery pack acting as a peaker for high-power phases like hovering and climb. During the low-power descent and loiter phases, the battery pack is recharged by the turboshaft. In order to optimize the battery weight, the pack is split into two: one is formed by Li Ion cells, which have high specific energy and the other is formed by Li-Po cells, which have higher specific power and lower specific energy. With the technology available today, the maximum take-off weight of the XV-15 cannot be either reduced or maintained with a simple propulsive retrofit, mainly due to battery weight. Considering a technological improvement similar to that experienced by batteries during last two decades, it is likely to reach a break even point in few years (<20). The presence of a battery pack onboard allows to have only one internal combustion engine with fewer safety issues in case of an engine failure. One of the remaining safety issues is the possible insurgence of engine failure when the batteries are at the lowest state of charge, e.g. after the initial climb in a typical mission profile. In the proposed emergency procedure, the aircraft rotors extract power from the flow during the gliding flight recharging the batteries, becoming able to conduct a fully-powered landing in helicopter mode, with an hovering time varying from 30 to 110 seconds, depending on the descent strategy.
LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding

Although large language models (LLMs) demonstrate impressive performance for many language tasks, most of them can only handle texts a few thousand tokens long, limiting their applications on longer sequence inputs, such as books, reports, and codebases. Recent works have proposed methods to improve LLMs' long context capabilities by extending context windows and more sophisticated memory mechanisms. However, comprehensive benchmarks tailored for evaluating long context understanding are lacking. In this paper, we introduce LongBench, the first bilingual, multi-task benchmark for long context understanding, enabling a more rigorous evaluation of long context understanding. LongBench comprises 21 datasets across 6 task categories in both English and Chinese, with an average length of 6,711 words (English) and 13,386 characters (Chinese). These tasks cover key long-text application areas including single-doc QA, multi-doc QA, summarization, few-shot learning, synthetic tasks, and code completion. All datasets in LongBench are standardized into a unified format, allowing for effortless automatic evaluation of LLMs. Upon comprehensive evaluation of 8 LLMs on LongBench, we find that: (1) Commercial model (GPT-3.5-Turbo-16k) outperforms other open-sourced models, but still struggles on longer contexts. (2) Scaled position embedding and fine-tuning on longer sequences lead to substantial improvement on long context understanding. (3) Context compression technique such as retrieval brings improvement for model with weak ability on long contexts, but the performance still lags behind models that have strong long context understanding capability. The code and datasets are available at https://github.com/THUDM/LongBench.
Many-valued coalgebraic logic over semi-primal varieties

We study many-valued coalgebraic logics with semi-primal algebras of truth-degrees. We provide a systematic way to lift endofunctors defined on the variety of Boolean algebras to endofunctors on the variety generated by a semi-primal algebra. We show that this can be extended to a technique to lift classical coalgebraic logics to many-valued ones, and that (one-step) completeness and expressivity are preserved under this lifting. For specific classes of endofunctors, we also describe how to obtain an axiomatization of the lifted many-valued logic directly from an axiomatization of the original classical one. In particular, we apply all of these techniques to classical modal logic.
Categorical data analysis using discretization of continuous variables to investigate associations in marine ecosystems

Understanding and predicting interactions between predators and prey and their environment are fundamental for understanding food web structure, dynamics, and ecosystem function in both terrestrial and marine ecosystems.Thus, estimating the conditional associations between species and their environments is important for exploring connections or cooperative links in the ecosystem, which in turn can help to clarify such causal relationships. For this purpose, a relevant and practical statistical method is required to link presence/absence observations with biomass, abundance, and physical quantities obtained as continuous real values.These data are sometimes sparse in oceanic space and too short as time series data. To meet this challenge, we provide an approach based on applying categorical data analysis to present/absent observations and real-number data.This approach consists of a two-step procedure for categorical data analysis:1) finding the appropriate threshold to discretize the real-number data for applying an independent test;and 2) identifying the best conditional probability model to investigate the possible associations among the data based on a statistical information criterion.We conduct a simulation study to validate our proposed approach. Furthermore, the approach is applied to two datasets: 1) one collected during an international synoptic krill survey in the Scotia Sea west of the Antarctic Peninsula to investigate associations among krill, fin whale (Balaenoptera physalus),surface temperature, depth, slope in depth, and temperature gradient; 2) the other collected by ecosystem surveys conducted during August-September in 2014 - 2017 to investigate associations among common minke whales, the predatory fish Atlantic cod, and their main prey groups in Arctic Ocean waters to the west and north of Svalbard, Norway.
Learning variational autoencoders via MCMC speed measures

Variational autoencoders (VAEs) are popular likelihood-based generative models which can be efficiently trained by maximizing an Evidence Lower Bound (ELBO). There has been much progress in improving the expressiveness of the variational distribution to obtain tighter variational bounds and increased generative performance. Whilst previous work has leveraged Markov chain Monte Carlo (MCMC) methods for the construction of variational densities, gradient-based methods for adapting the proposal distributions for deep latent variable models have received less attention. This work suggests an entropy-based adaptation for a short-run Metropolis-adjusted Langevin (MALA) or Hamiltonian Monte Carlo (HMC) chain while optimising a tighter variational bound to the log-evidence. Experiments show that this approach yields higher held-out log-likelihoods as well as improved generative metrics. Our implicit variational density can adapt to complicated posterior geometries of latent hierarchical representations arising in hierarchical VAEs.
High-risk Factor Prediction in Lung Cancer Using Thin CT Scans: An Attention-Enhanced Graph Convolutional Network Approach

Lung cancer, particularly in its advanced stages, remains a leading cause of death globally. Though early detection via low-dose computed tomography (CT) is promising, the identification of high-risk factors crucial for surgical mode selection remains a challenge. Addressing this, our study introduces an Attention-Enhanced Graph Convolutional Network (AE-GCN) model to classify whether there are high-risk factors in stage I lung cancer based on the preoperative CT images. This will aid surgeons in determining the optimal surgical method before the operation. Unlike previous studies that relied on 3D patch techniques to represent nodule spatial features, our method employs a GCN model to capture the spatial characteristics of pulmonary nodules. Specifically, we regard each slice of the nodule as a graph vertex, and the inherent spatial relationships between slices form the edges. Then, to enhance the expression of nodule features, we integrated both channel and spatial attention mechanisms with a pre-trained VGG model for adaptive feature extraction from pulmonary nodules. Lastly, the effectiveness of the proposed method is demonstrated using real-world data collected from the hospitals, thereby emphasizing its potential utility in the clinical practice.
Lava planets interior dynamics govern the long-term evolution of their magma oceans

Lava planets are rocky exoplanets that orbit so close to their host star that their day-side is hot enough to melt silicate rock. Their short orbital periods ensure that lava planets are tidally locked into synchronous rotation, with permanent day and night hemispheres. Such asymmetric magma oceans have no analogs in the Solar System and will exhibit novel fluid dynamics. Here we report numerical simulations of lava planet interiors showing that solid-liquid fractionation in the planetary interior has a major impact on the compositional structure and evolution of the planet. We explored two styles of dynamics that depend primarily on the interior thermal state : 1) a hot fully molten interior, and 2) a mostly solid interior with a shallow day-side magma ocean. In the hot interior scenario, the atmosphere reflects the planet's bulk silicate composition and the night-side crust is gravitationally unstable and constantly replenished. In the cool interior scenario, the distilled atmosphere will lack Na, K and FeO, and the night-side mantle is entirely solid, with a cold surface. These two end-member cases can be distinguished with observations from the James Webb Space Telescope, offering an avenue to probe the diversity of terrestrial exoplanet evolutions.
Aetiological role of common respiratory viruses in acute lower respiratory infections in children under five years: A systematic review and meta–analysis

Background Acute lower respiratory infection (ALRI) remains a major cause of childhood hospitalization and mortality in young children and the causal attribution of respiratory viruses in the aetiology of ALRI is unclear. We aimed to quantify the absolute effects of these viral exposures. Methods We conducted a systematic literature review (across 7 databases) of case–control studies published from 1990 to 2014 which investigated the viral profile of 18592 children under 5 years with and without ALRI. We then computed a pooled odds ratio and virus–specific attributable fraction among the exposed of 8 common viruses – respiratory syncytial virus (RSV), influenza (IFV), parainfluenza (PIV), human metapneumovirus (MPV), adenovirus (AdV), rhinovirus (RV), bocavirus (BoV), and coronavirus (CoV). Findings From the 23 studies included, there was strong evidence for causal attribution of RSV (OR 9.79; AFE 90%), IFV (OR 5.10; AFE 80%), PIV (OR 3.37; AFE 70%) and MPV (OR 3.76; AFE 73%), and less strong evidence for RV (OR 1.43; AFE 30%) in young children presenting with ALRI compared to those without respiratory symptoms (asymptomatic) or healthy children. However, there was no significant difference in the detection of AdV, BoV, or CoV in cases and controls. Conclusions This review supports RSV, IFV, PIV, MPV and RV as important causes of ALRI in young children, and provides quantitative estimates of the absolute proportion of virus–associated ALRI cases to which a viral cause can be attributed.
Bayesian deep operator learning for homogenized to fine-scale maps for multiscale PDE

We present a new framework for computing fine-scale solutions of multiscale Partial Differential Equations (PDEs) using operator learning tools. Obtaining fine-scale solutions of multiscale PDEs can be challenging, but there are many inexpensive computational methods for obtaining coarse-scale solutions. Additionally, in many real-world applications, fine-scale solutions can only be observed at a limited number of locations. In order to obtain approximations or predictions of fine-scale solutions over general regions of interest, we propose to learn the operator mapping from coarse-scale solutions to fine-scale solutions using a limited number (and possibly noisy) observations of the fine-scale solutions. The approach is to train multi-fidelity homogenization maps using mathematically motivated neural operators. The operator learning framework can efficiently obtain the solution of multiscale PDEs at any arbitrary point, making our proposed framework a mesh-free solver. We verify our results on multiple numerical examples showing that our approach is an efficient mesh-free solver for multiscale PDEs.
Towards Standardized Disturbance Rejection Testing of Legged Robot Locomotion with Linear Impactor: A Preliminary Study, Observations, and Implications

Dynamic locomotion in legged robots is close to industrial collaboration, but a lack of standardized testing obstructs commercialization. The issues are not merely political, theoretical, or algorithmic but also physical, indicating limited studies and comprehension regarding standard testing infrastructure and equipment. For decades, the approaches we have been testing legged robots were rarely standardizable with hand-pushing, foot-kicking, rope-dragging, stick-poking, and ball-swinging. This paper aims to bridge the gap by proposing the use of the linear impactor, a well-established tool in other standardized testing disciplines, to serve as an adaptive, repeatable, and fair disturbance rejection testing equipment for legged robots. A pneumatic linear impactor is also adopted for the case study involving the humanoid robot Digit. Three locomotion controllers are examined, including a commercial one, using a walking-in-place task against frontal impacts. The statistically best controller was able to withstand the impact momentum (26.376 kg$\cdot$m/s) on par with a reported average effective momentum from straight punches by Olympic boxers (26.506 kg$\cdot$m/s). Moreover, the case study highlights other anti-intuitive observations, demonstrations, and implications that, to the best of the authors' knowledge, are first-of-its-kind revealed in real-world testing of legged robots.
DiffI2I: Efficient Diffusion Model for Image-to-Image Translation

The Diffusion Model (DM) has emerged as the SOTA approach for image synthesis. However, the existing DM cannot perform well on some image-to-image translation (I2I) tasks. Different from image synthesis, some I2I tasks, such as super-resolution, require generating results in accordance with GT images. Traditional DMs for image synthesis require extensive iterations and large denoising models to estimate entire images, which gives their strong generative ability but also leads to artifacts and inefficiency for I2I. To tackle this challenge, we propose a simple, efficient, and powerful DM framework for I2I, called DiffI2I. Specifically, DiffI2I comprises three key components: a compact I2I prior extraction network (CPEN), a dynamic I2I transformer (DI2Iformer), and a denoising network. We train DiffI2I in two stages: pretraining and DM training. For pretraining, GT and input images are fed into CPEN$_{S1}$ to capture a compact I2I prior representation (IPR) guiding DI2Iformer. In the second stage, the DM is trained to only use the input images to estimate the same IRP as CPEN$_{S1}$. Compared to traditional DMs, the compact IPR enables DiffI2I to obtain more accurate outcomes and employ a lighter denoising network and fewer iterations. Through extensive experiments on various I2I tasks, we demonstrate that DiffI2I achieves SOTA performance while significantly reducing computational burdens.