Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance Evaluation of an Electromechanical Linear Actuator with Optimal Trajectories

Emission reduction targets in both highway trucks and off-road vehicles have turned the electrification focus into vehicle mountable equipment such as loader cranes and tail lifts. Nowadays, these devices are powered by hydraulic linear actuators, which provide cost-efficient and robust solutions that also fulfill lifting device safety standards. However, lifting device electrification has the potential to offer several advances, such as efficient utilization of on-board battery with fewer components and energy conversions. In order to contribute to the electrification trend, different aspects of substituting those conventional actuators with the almost new electromechanical linear actuators (EMLA) technology need to be investigated. This paper studies the different energy conversion processes in the EMLA for a heavy-duty mechanism and estimates the loss in each component. The investigation of energy conversion processes in EMLA paves the way for obtaining the efficiency map of the system and its power flow with the purpose of performance analysis. To this end, all the mentioned energy conversions are categorized into desirable and power loss categories. Subsequently, the dynamic model of one degree of freedom (DOF) parallel–serial mechanism is exerted in a trajectory optimization framework. Objectives such as minimum effort, energy expenditure, and delivered power are selected to generate optimal trajectories that feed the efficiency algorithms and examine the EMLA performance. The results show the efficacy of the above-mentioned trajectories concerning the criteria costs, total time, and efficiency of the whole system.
Regarding Topology and Variant Frame Rates for Differentiable WFST-based End-to-End ASR

End-to-end (E2E) Automatic Speech Recognition (ASR) has gained popularity in recent years, with most research focusing on designing novel neural network architectures, speech representations, and loss functions. However, the importance of topology in E2E ASR has been largely neglected. There are many aspects of topology to consider; in this paper, we focus on the relationship between topologies’ minimum traversal time and output frame rate, the number of distinct states for each output unit, and the flexibility of alignments admitted. We examine several different topologies on two datasets: WSJ and Librispeech. Our experiments reveal that different frame rates have varying optimal topologies and that the commonly used Connectionist Temporal Classification (CTC) topology is not always optimal. Our findings suggest that the choice of topology is an important consideration in the design of E2E ASR systems.
Controlling formant frequencies with neural text-to-speech for the manipulation of perceived speaker age

In this paper, we present a framework for formant-controllable neural text-to-speech. We train a model that predicts formant frequencies which then condition melspectrogram generation. We apply this to manipulate perceived speaker age in an indirect fashion, by modifying the predicted formants in a manner that affects perceived vocal tract length. Our ultimate goal is to allow for the control of perceived ageing in children’s text-to-speech voices, since ageing in natural child speech is strongly linked to the growth of a child’s vocal tract. However, our experiments indicate that our method shows strong age control capabilities for adult speech as well.
Novel Machine Learning Linearization Scheme for 6G A-RoF Systems

This article presents the design and implementation of a digital pre-distortion (DPD) scheme based on machine learning (ML) algorithm, which has been envisioned for the sixth generation of mobile communications (6G) analog radio over fiber (A-RoF) system. The DPD scheme employs an augmented real-valued time delay neural network (ARVTDNN) to compensate for the memory and memory-less non-linear effects introduced by the A-RoF communication chain. The ARVTDNN receives the base-band samples of the transmit waveform and stores them in a time-delay line (TDL), which means that the information necessary to compensate for time-correlated effects is available for the ML algorithm. Therefore, the ARVTDNN can compensate for both memory and memory-less effects, including the chromatic dispersion introduced by the optical fiber. The novel DPD can bring a significant positive impact in enhanced remote areas (eRAC) applications since the proposed DPD allows A-RoF to be used to connect the base-band unit with a low-cost radio frequency (RF) radio head installed in remote areas. In this case, the severe non-linearities introduced by the Mach-Zehnder optical modulator (MZM) and power amplifier (PA) and the time dispersive distortions introduced by the fiber can be mitigated, improving the quality of the signal. Furthermore, the novel ML-based linearization scheme can be flexibly tailored to cover variations in the operating scenario by adjusting its hyperparameters and training data. The root mean square error vector magnitude (EVMRMS), normalized mean square error (NMSE) and adjacent channel leakage ratio (ACLR) metrics have been used to evaluate the performance of the proposed DPD. Numerical results demonstrate promising linearization performance, reducing the signal in- and out-band distortions when the fronthaul link is extended to cover remote and rural areas.
Anti-Citrullinated Protein Antibodies as Novel Therapeutic Drugs in Rheumatoid Arthritis

We have developed a novel antibody-based treatment for rheumatoid arthritis. In a collagen antibody-induced arthritis mouse model, anti-citrullinated protein antibodies (ACPAs) prevent the onset and/or exacerbation of inflammation, and prevent or strongly reduce joint damage. For the development of this novel therapy, we focussed on rheumatoid arthritis-specific citrullinated peptide epitopes. A growing number of studies indicate that modifications of arginines into citrulline residues are responsible for the initial triggering of autoimmunity and the breaking of tolerance. We identified a subset of human recombinant ACPAs that prevent the onset of inflammation in both collagen-induced arthritis and collagen antibody-induced arthritis mouse models for rheumatoid arthritis. Therapeutic administration of these antibodies resulted in the arrest of the inflammation and prevented a further increase of the inflammatory response. Prophylactic administration significantly prevented the onset of inflammation. Histological analysis of inflamed joints from ACPA treated mice revealed a significant decrease in joint damage, as compared to control animals. To identify the differentiating therapeutic epitope recognized by the therapeutic ACPA (tACPA), we performed comparative immunoprecipitations with therapeutic and non-therapeutic ACPAs using human PAD4- deiminated HEK-293 cell lysates, followed by mass spectrometry analysis. The differentiating peptide epitope that is recognized by tACPAs only, is a citrullinated domain of Histone-2A. A second generation of antibodies was selected against this domain by means of phage display and by hybridoma generation. The second generation of tACPAs was comprised of even more potent inhibitors of the inflammatory response. Here, we describe the identification of a series of antibodies directed against a citrullinated epitope present in murine and human histone-2A. In mouse models for rheumatoid arthritis we demonstrate that these tACPAs exhibit a strong anti-inflammatory activity and prevent the occurrence of swelling and joint damage. We propose anti-histone-2A tACPA as a novel therapeutic treatment for rheumatoid arthritis patients.
Transcriptome Analyses Reveal the Aroma Terpeniods Biosynthesis Pathways of Primula forbesii Franch. and the Functional Characterization of the PfDXS2 Gene

Primula forbesii Franch. is a unique biennial herb with a strong floral fragrance, making it an excellent material for studying the aroma characteristics of the genus Primula. The floral scent is an important ornamental trait that facilitates fertilization. However, the molecular mechanism regulating the floral scent in Primula is unknown. In order to better understand the biological mechanisms of floral scents in this species, this study used RNA sequencing analysis to discuss the first transcriptome sequence of four flowering stages of P. forbesii, which generated 12 P. forbesii cDNA libraries with 79.64 Gb of clean data that formed 51,849 unigenes. Moreover, 53.26% of the unigenes were annotated using public databases. P. forbesii contained 44 candidate genes covering all known enzymatic steps for the biosynthesis of volatile terpenes, the major contributor to the flower’s scent. Finally, 1-deoxy-d-xylulose 5-phosphate synthase gene of P. forbesii (PfDXS2, MK370094), the first key enzyme gene in the 2-c-methyl-d-erythritol 4-phosphate (MEP) pathway of terpenoids, was cloned and functionally verified using virus-induced gene silencing (VIGs). The results showed that PfDXS2-silencing significantly reduced the relative concentrations of main volatile terpenes. This report is the first to present molecular data related to aroma metabolites biosynthesis pathways and the functional characterization of any P. forbesii gene. The data on RNA sequencing provide comprehensive information for further analysis of other plants of the genus Primula.
MPT-Transformer based post equalizer utilized in underwater visible light communication system

In this work, we proposed a mixed precision training-based transformer (MPT-Transformer) model to process signal post-equalization in underwater visible light communication channel (UVLC). The application of MPT-transformer model in 1.2m underwater experimental platform using 64 quadrature amplitude modulation-carrierless amplitude (64QAM-CAP) modulation achieves 3.08Gbps while the bit error rate (BER) below the 7% forward error correction (FEC) limit of $3.8\times 10^{-3}$, which is 120Mbps faster than traditional least mean squires (LMS) method.
RESIDENCE LOCATIONS AND MOBILITY TOOL OWNERSHIP DURING THE LIFE COURSE: RESULTS FROM A RETROSPECTIVE SURVEY IN SWITZERLAND

The paper will briefly describe the survey conducted and explain the weighting scheme implemented. It will concentrate on the analyses of the long-term mobility decisions during the life course using the collected retrospective data. The main focus will lie on the dynamics of mobility tool ownership over the last 20 years, while at the same time looking at their contemporaneous relationships with residential choices as well as with education and employment locations. For the covering abstract see ITRD E135582.
HLEGF: An Effective Hypernetwork Community Detection Algorithm Based on Local Expansion and Global Fusion

Community structure is crucial for understanding network characteristics, and the local expansion method has performed well in detecting community structures. However, there are two problems with this method. Firstly, it can only add nodes or edges on the basis of existing clusters, and secondly, it can produce a large number of small communities. In this paper, we extend the local expansion method based on ordinary graph to hypergraph, and propose an effective hypernetwork community detection algorithm based on local expansion (LE) and global fusion (GF), which is referred to as HLEGF. The LE process obtains multiple small sub-hypergraphs by deleting and adding hyperedges, while the GF process optimizes the sub-hypergraphs generated by the local expansion process. To solve the first problem, the HLEGF algorithm introduces the concepts of community neighborhood and community boundary to delete some nodes and hyperedges in hypergraphs. To solve the second problem, the HLEGF algorithm establishes correlations between adjacent sub-hypergraphs through global fusion. We evaluated the performance of the HLEGF algorithm in the real hypernetwork and six synthetic random hypernetworks with different probabilities. Because the HLEGF algorithm introduces the concepts of community boundary and neighborhood, and the concept of a series of similarities, the algorithm has superiority. In the real hypernetwork, the HLEGF algorithm is consistent with the classical Spectral algorithm, while in the random hypernetwork, when the probability is not less than 0.95, the NMI value of the HLEGF algorithm is always greater than 0.92, and the RI value is always greater than 0.97. When the probability is 0.95, the HLEGF algorithm achieves a 2.3% improvement in the NMI value, compared to the Spectral algorithm. Finally, we applied the HLEGF algorithm to the drug–target hypernetwork to partition drugs with similar functions into communities.
Comprehensive Analysis of Lung Cancer Classification Using Gaussian Process Classifier: Unveiling Exceptional Performance and Clinical Implications

In this analysis, the performance of the Gaussian Process Classifier (GPC) was evaluated for classifying instances of lung cancer using various metrics. The GPC model achieved impressive results, with accuracy ranging from approximately 83.87% to 96.67%. Precision values ranged from 75.86% to 96.79%, recall values ranged from 83.87% to 96.67%, and F1-score values ranged from 81.09% to 96.36%. These metrics highlight the GPC model's exceptional performance in accurately classifying lung cancer cases. The findings from this analysis have significant implications for improving lung cancer diagnosis, treatment planning, and ultimately enhancing patient outcomes.
DEPOT: graph learning delineates the roles of cancers in the progression trajectories of chronic kidney disease using electronic medical records

Chronic kidney disease (CKD) is a common, complex, and heterogeneous disease impacting aging populations. Determining the landscape of disease progression trajectories from midlife to senior age in a real-world context allows us to better understand the progression of CKD, the heterogeneity of progression patterns among the risk population, and the interactions with other clinical conditions like cancers. In this study, we use electronic health records (EHRs) to outline the CKD progression trajectory roadmap for the Wake Forest Baptist Medical Center (WFBMC) patient population. We establish an EHR cohort (n = 79,434) with patients' health status identified by 18 Essential Clinical Indices across 508,732 clinical encounters. We develop the DisEase PrOgression Trajectory (DEPOT) approach to model CKD progression trajectories and individualize clinical decision support. The DEPOT is an evidence-driven, graph-based clinical informatics approach that addresses the unique challenges in longitudinal EHR data by systematically using the graph artificial intelligence (graph-AI) model for representation learning and reverse graph embedding for trajectory reconstruction. Moreover, DEPOT includes a prediction model to assign new patients along the progression trajectory. We successfully establish the EHR-based CKD progression trajectories with DEPOT in the WFUBMC cohort. We annotate the trajectories with clinical features, including kidney function, age, and other indices, including cancer. This CKD progression trajectory roadmap reveals diverse kidney failure pathways associated with different clinical conditions. Specifically, we have identified one high-risk trajectory and two low-risk trajectories. Switching pathways from low-risk trajectories to high-risk ones is associated with accelerated decline in kidney function. On this roadmap, high-risk patients are enriched in the skin and GU cancers, which differs from low-risk patients, suggesting fundamentally different disease progression mechanisms. Overall, the CKD progression trajectory roadmap reveals novel diverse renal failure pathways in type 2 diabetes mellitus and highlights disease progression patterns associated with cancer phenotypes.
Association between cannabis use and brain imaging phenotypes in UK Biobank: an observational and Mendelian randomization study

Cannabis use during adolescence and young adulthood has been associated with brain structure and functional connectivity. However, despite a rapid increase in cannabis use among older adults in the past decade, the impact on brain structure and function in this population remains understudied. We examined 3,641 self-reported lifetime cannabis users (mean age = 61.00 years, standard deviation (SD) = 7.16) and 12,255 controls (mean age = 64.49 years, SD = 7.58) from the UK Biobank. Brain imaging-derived phenotypes (IDPs) were used as measures of structural and functional connectivity. Associations with cannabis use were assessed using multiple linear regression while controlling for potential confounders. After correcting for false discovery rate (FDR) for multiple testing, in exploratory analyses significant associations were observed with diffusion metrics in the genu of the corpus callosum and with resting-state functional connectivity (rsFC) between the default mode, central executive, and salience networks. Hypothesis-driven analyses did not show any significant association between cannabis use and hippocampal or amygdala volumes. Furthermore, mendelian randomization (MR) analyses did not support a causal relationship between cannabis use and brain structure or function. Our findings indicate that associations between lifetime cannabis use and later life brain structure and function are not likely causal in nature.
The social phenomenon of climate change: contextual vulnerability, risk perception and adaptation in the ski industry of Queenstown, New Zealand

s from conference presentations of this research 5a. Managing Alpine Futures (2011) 5b. IAG (2012) 5c. Tourism, Climate Change & Sustainability (2012) 5d. CAUTHE (2013) 5e. AAG (2013) 5f. New Zealand Climate Change Conference (2013)
How ligands regulate the binding of PARP1 with DNA: Deciphering the mechanism at the molecular level

The catalytic (CAT) domain is a key region of poly (ADP-ribose) polymerase 1 (PARP1), which has crucial interactions with inhibitors, DNA, and other domains of PARP1. To facilitate the development of potential inhibitors of PARP1, it is of great significance to clarify the differences in structural dynamics and key residues between CAT/inhibitors and DNA/PARP1/inhibitors through structure-based computational design. In this paper, conformational changes in PAPR1 and differences in key residue interactions induced by inhibitors were revealed at the molecular level by comparative molecular dynamics (MD) simulations and energy decomposition. On one hand, PARP1 inhibitors indirectly change some residues of the CAT domain which interact with DNA and other domains. Furthermore, the interaction between ligands and catalytic binding sites can be transferred to the DNA recognition domain of PARP1 by a strong negative correlation movement among multi-domains of PARP1. On the other hand, it is not reliable to use the binding energy of CAT/ligand as a measure of ligand activity, because it may in some cases differs greatly from the that of PARP1/DNA/ligand. For PARP1/DNA/ligand, the stronger the binding stability between the ligand and PARP1, the stronger the binding stability between PARP1 and DNA. The findings of this work can guide further novel inhibitor design and the structural modification of PARP1 through structure-based computational design.
An elevational shift facilitated the Mesoamerican diversification of Azure‐hooded Jays (Cyanolyca cucullata) during the Great American Biotic Interchange

Abstract The Great American Biotic Interchange (GABI) was a key biogeographic event in the history of the Americas. The rising of the Panamanian land bridge ended the isolation of South America and ushered in a period of dispersal, mass extinction, and new community assemblages, which sparked competition, adaptation, and speciation. Diversification across many bird groups, and the elevational zonation of others, ties back to events triggered by the GABI. But the exact timing of these events is still being revealed, with recent studies suggesting a much earlier time window for faunal exchange, perhaps as early as 20 million years ago (Mya). Using a time‐calibrated phylogenetic tree, we show that the jay genus Cyanolyca is emblematic of bird dispersal trends, with an early, pre‐land bridge dispersal from Mesoamerica to South America 6.3–7.3 Mya, followed by a back‐colonization of C. cucullata to Mesoamerica 2.3–4.8 Mya, likely after the land bridge was complete. As Cyanolyca species came into contact in Mesoamerica, they avoided competition due to a prior shift to lower elevation in the ancestor of C. cucullata. This shift allowed C. cucullata to integrate itself into the Mesoamerican highland avifauna, which our time‐calibrated phylogeny suggests was already populated by higher‐elevation, congeneric dwarf‐jays (C. argentigula, C. pumilo, C. mirabilis, and C. nanus). The outcome of these events and fortuitous elevational zonation was that C. cucullata could continue colonizing new highland areas farther north during the Pleistocene. Resultingly, four C. cucullata lineages became isolated in allopatric, highland regions from Panama to Mexico, diverging in genetics, morphology, plumage, and vocalizations. At least two of these lineages are best described as species (C. mitrata and C. cucullata). Continued study will further document the influence of the GABI and help clarify how dispersal and vicariance shaped modern‐day species assemblages in the Americas.
Long term prophylactic anticoagulation for portal vein thrombosis after splenectomy: A systematic review and meta-analysis

Aim The aim of this study was to evaluate the efficacy and safety of the anticoagulants for the prevention of portal vein system thrombosis (PVST) in patients with cirrhosis after splenectomy and explore the optimal time of anticoagulant administration. Methods A systematic literature search was performed using PubMed, Embase and China Biology Medicine disc (CBM)databases, so as to screen out studies comparing the prognoses between cirrhotic post-splenectomy patients treated with and without anticoagulants. The parameters that were analyzed included the incidence of PVST and postoperative bleeding. Results With a total of 592 subjects, we included 8 studies (6 observational and 2 randomized trials) that fulfilled the inclusion criteria. We found that the incidence of PVST was significantly lower in the anticoagulation group during the first 6 months of anticoagulant administration. And the largest difference in the incidence of PVST between the anticoagulation and control groups was observed at 3 months (odds ratio 0.17(0.11~0.27); P = 0.767; I2 = 0.0%) and 6 months (OR = 0.21(0.11~0.40); P = 0.714; I2 = 0.0%) postoperatively. The incidence of bleeding was not significantly higher in the anticoagulation group (odds ratio 0.71 (0.30~1.71); P = 0.580; I2 = 0.0%). Conclusion Low-molecular weight heparin (LMWH) and warfarin can decrease the incidence of PVST in post-splenectomy cirrhotic patients without an increased risk of bleeding. And the optimal use time of warfarin is 6 months after splenectomy.