Miracle-dz/UniFAR-FTUs huggingface dataset:
Denoising Method of Nuclear Signal Based on Sparse Representation

Nuclear signals are sensitive to noise which may affect final monitoring results significantly. In order to suppress the nuclear signal noise, a sparse representation method, which is based on the sparse representation of signals and a matching pursuit algorithm, has been proposed for denoising. Time–frequency matching “atoms” have been selected for building an over-complete library by training atoms matching with the characteristics of nuclear signals regardless of the noise. The best time–frequency matching atoms have been extracted by sparsely representing the noisy signals with an Orthogonal Matching Pursuit (OMP) algorithm and the library. The residual ratio threshold has been chosen as a stopping criterion in the OMP algorithm for avoiding the influence of improper selection of iterations on denoising results. At the end, the pulse matching the atom extracted by each iteration has been optimized by performing effective sparse representation on the original noiseless nuclear signal component in noisy nuclear signals. The proposed method has been used to denoise the simulated and measured signals and has been compared with the nuclear denoising result using traditional wavelet theory. The results show that the proposed method can accurately suppress the noise interference of nuclear signals, and the denoising effect is better than that of the traditional wavelet method.
Genotyping-by-Sequencing Based Molecular Genetic Diversity of Pakistani Bread Wheat (Triticum aestivum L.) Accessions

Spring wheat (Triticum aestivum L.) is one of the most imperative staple food crops, with an annual production of 765 million tons globally to feed ∼40% world population. Genetic diversity in available germplasm is crucial for sustainable wheat improvement to ensure global food security. A diversity panel of 184 Pakistani wheat accessions was genotyped using 123,596 high-quality single nucleotide polymorphism (SNP) markers generated by genotyping-by-sequencing with 42% of the SNPs mapped on B, 36% on A, and 22% on D sub-genomes of wheat. Chromosome 2B contains the most SNPs (9,126), whereas 4D has the least (2,660) markers. The mean polymorphic information content, genetic diversity, and major allele frequency of the population were 0.157, 0.1844, and 0.87, respectively. Analysis of molecular variance revealed a higher genetic diversity (80%) within the sub-population than among the sub-populations (20%). The genome-wide linkage disequilibrium was 0.34 Mbp for the whole wheat genome. Among the three subgenomes, A has the highest LD decay value (0.29 Mbp), followed by B (0.2 Mbp) and D (0.07 Mbp) genomes, respectively. The results of population structure, principal coordinate analysis, phylogenetic tree, and kinship analysis also divided the whole population into three clusters comprising 31, 33, and 120 accessions in group 1, group 2, and group 3, respectively. All groups were dominated by the local wheat accessions. Estimation of genetic diversity will be a baseline for the selection of breeding parents for mutations and the genome-wide association and marker-assisted selection studies.
Evaluation of Platelet and Leukocyte Counts in Canine Platelet-Rich Plasma Obtained After Successive Blood Collections From the Same Patient and the Effects of Freezing on the Concentration of Growth Factors Present in It

Objective The purpose of this study was 2-fold: to evaluate whether the timing of collection influences the platelet counts and leukocyte counts of PRP samples, and to evaluate growth factor concentrations in canine PRP after freezing and storage without a preservation agent for 6 months of time. Materials and Methods Whole blood collection was performed three times over the course of 4 weeks. All PRP samples were evaluated with a CBC analysis. The PRP samples were frozen and stored without a preservation agent for the duration of the 4-week study. Results The platelet and leukocyte counts were not statistically significant between the timing of blood draws over the course of 4 weeks. All three growth factors were present in measurable quantities after freezing and storage for 6 months without a preservation agent. Clinical Relevance PDGF, TGF-β1, and VEGF were all present in measurable quantities. Furthermore, PDGF and TGF-β1 were correlated with platelet count of the final PRP. VEGF concentrations were able to be quantified. We correctly hypothesized growth factor concentrations would be present and measurable in canine PRP frozen and stored without a preservation agent for 6 months.
Treatment of a Double Cancer Patient With Primary Inferior Vena Cava Sarcoma and Lung Adenocarcinoma: A Case Report and Literature Review

Background Leiomyosarcoma of the inferior vena cava (IVC) is a rare malignancy. Here, we present the case of a 38-year-old woman with a primary IVC leiomyosarcoma and lung adenocarcinoma. Case Report The patient, a 38-year-old Chinese female, presented to the general surgical outpatients clinic with a 18-month history of intermittent right upper abdominal pain. Contrast-enhanced computed tomography (CT) showed a tumor of IVC (3.4*2.7 cm) extending to the renal veins. In addition, chest CT revealed a mass lesion in the upper left lung lobe. Then, the patient underwent resection of the IVC tumor and wedge resection of the upper lobe of the left lung. The patient then received gefitinib (250 mg/day) as a maintenance therapy until the tumor recurrence or metastasis in the follow-up period. Pulmonary metastasis of the sarcoma were first diagnosed 20 month after the resection of the IVC leiomyosarcoma. So the patient again received thoracoscopic wedge pneumonectomy, and it was confirmed to be metastasis of IVC leiomyosarcoma. The patient received oral anlotinib treatment (12 mg once daily) after the last operation. During on-going regular follow-up visits no evidence of recurrence or metastasis was observed from December 2020 to October 2021. Conclusions The patient with a primary IVC leiomyosarcoma and lung adenocarcinoma is extremely rare. Surgery is still an effective treatment for patients with a primary IVC leiomyosarcoma and lung adenocarcinoma at present.
Elevated Serum Lactate Dehydrogenase Predicts Unfavorable Outcomes After rt-PA Thrombolysis in Ischemic Stroke Patients

Background and Purpose Currently, acute ischemic stroke (AIS) is one of the most common and serious diseases in the world and is associated with very high mortality and morbidity even after thrombolysis therapy. This study aims to research the relationship between lactic dehydrogenase (LDH) and prognosis in AIS patients treated with intravenous rtPA. Method This study (a Multicenter Clinical Trial of Revascularization Treatment for Acute Ischemic Stroke, TRAIS) included 527 AIS patients in 5 cooperative medical institutions in China from January 2018 to February 2021. The primary outcome was major disability and death within 3 months (mRS score of 3–6), and the secondary outcomes were early neurological improvement (ENI), early neurological deterioration (END), moderate-severe cerebral edema (CE), and symptomatic intracranial hemorrhage (sICH). Results The mean age of the 527 patients was 65.6 ± 11.7 years, and the median baseline NIHSS score was 4 (interquartile range, 2–7). The median serum LDH level was 184 U/L (interquartile range, 163–212 U/L). In total, 287 (54.5%) patients acquired ENI, 68 (13.0%) patients suffered END, 53 (12.1%) patients were observed with moderate-severe CE, and 28 (6.2%) patients showed sICH. Within 3 months, 127 (25.15%) patients experienced the primary outcome and 42 (8.3%) patients died. Serum LDH levels before thrombolysis showed an independent association with the risk of primary outcome [adjusted odds ratio, 3.787; (95% CI, 1.525–9.404); P = 0.014]. When log-transformed LDH increased each standard deviation, the risk of primary outcome was raised by 80.1% (95% CI, 28.9–251.7%). A positive linear dependence between the risk of primary outcome and serum LDH levels (P of linearity = 0.0248, P of non-linearity = 0.8284) was shown in multivariable-adjusted spline regression models. Pre-thrombolysis LDH quartile also provided a conventional risk model and significant improvement of the prediction for clinical outcomes, with a net reclassification improvement index (NRI) = 41.86% (P < 0.001) and integrated discrimination improvement (IDI) = 4.68% (P < 0.001). Conclusions Elevated serum LDH levels predicted unfavorable clinical outcomes after intravenous thrombolysis in AIS patients.
WT1: The Hinge Between Anemia Correction and Cancer Development in Chronic Kidney Disease

Hypoxia-inducible factor-prolyl hydroxylase inhibitors (HIF-PHIs) emerge as promising agents to treat anemia in chronic kidney disease (CKD) but the major concern is their correlated risk of cancer development and progression. The Wilms’ tumor gene, WT1, is transcriptionally regulated by HIF and is known to play a crucial role in tumorigenesis and invasiveness of certain types of cancers. From the mechanism of action of HIF–PHIs, to cancer hypoxia and the biological significance of WT1, this review will discuss the link between HIF, WT1, anemia correction, and cancer. We aimed to reveal the research gaps and offer a focused strategy to monitor the development and progression of specific types of cancer when using HIF–PHIs to treat anemia in CKD patients. In addition, to facilitate the long-term use of HIF–PHIs in anemic CKD patients, we will discuss the strategy of WT1 inhibition to reduce the development and progression of cancer.
Why Handwriting is Good for Your Brain

As laptops and tablets become more popular, handwriting is slowly fading away. While new technology has many benefits such as helping us stay connected with each other and allowing us to quickly search for information, activities such as printing our letters by hand may help the brain learn. There are many potential reasons for this. When people write letters by hand, they: (1) actively see and feel the letter being written; (2) see several different versions of that letter; and (3) pay more attention to what they are doing. In this article, you will learn about how handwriting helps us learn symbols and aids in remembering information. You will also learn how your brain responds when you write by hand compared with when you type. Handwriting is still important, even if most of how we communicate these days is through a keyboard or touchscreen.
Parecoxib Reduced Postsurgical Pain and Facilitated Movement More Than Patient Controlled Analgesia

Background Postoperative pain management is an imperative issue for patients undergoing lumbar spinal fusion surgery. Delayed pain relief is associated with poor clinical outcomes. This study compared the effects of intravenously administered patient-controlled analgesia (PCA) with intravenous parecoxib, both commonly used methods for analgesic pain control after surgery. Methods A non-randomized study was used to recruit 68 patients who were scheduled to receive lumbar spinal fusion surgery at a hospital in Taiwan from April through December of 2020. The group treated with parecoxib received an initial perioperative dose of parecoxib 40 mg during a 30-min period and then postoperative intravenous parecoxib at 40 mg per 12-h period, for 72 h. Those with PCA received morphine (0.4 mg/ml), droperidol (0.02 mg/ml), diphenhydramine (0.48 mg/ml), midazolam (0.02 mg/ml) and saline solution during the 3-day study course. Major outcomes, including visual scale pain score and Barthel index of activities of daily living, were collected via review of medical records at 4 times: 12, 24, 48 and 72 h after surgery. Comparative effects between two groups were assessed by the generalized estimating equations. Results After adjusting for potential confounders, the administration of parecoxib was associated with a significant decrease in pain scores and an increase in the Barthel Index, when compared with the PCA group (all p < 0.05). Notably, both effects would maintain for 72 h after surgery. Discussion This is the first trial of which the authors are aware, that supports intravenous parecoxib as significantly enhancing patient mobility, in addition to having pain control efficacy, when compared with PCA. This study could be used as a reference when instituting interventions to improve the adaptation process and clinical prognoses after lumbar spinal fusion surgery.
Accreditation: A Standards-Based Model for Aligning Learning Outcomes in a Master's Degree Educational Leadership Preparation Program.

This document examines speci c elements found in a curriculum guide for learning outcomes in a university master's degree educational leadership preparation program. Each element is presented as a sample for formatting of a matrix that aligns speci c curriculum components. The matrix design is a framework for connecting the program's logic and identi ed elements to national standards, national accreditation, and state program approval. This is especially important for faculty in educational leadership preparation programs who are interested in Teacher Education Accreditation Council (TEAC) as a means to obtain national accreditation. The opportunity is provided to observe samples generated from an existing program and how they might aligned to meet TEAC quality standards and principles. The guide can be used for improvement in principal preparation program design and accountability for 21st
Modeling of E ﬀ ective Heat Di ﬀ usion in a Building Atrium via Koopman Mode Decomposition

—We develop a method for modeling of heat transfer dynamics in a building atrium based on observa-tional data on temperature. This paper introduces a two-dimensional heat di ﬀ usion equation with an e ﬀ ective dif-fusion coe ﬃ cient in order to represent the heat transfer mainly due to the air movement inside an atrium, where the air slowly moves on a length-scale smaller than the distance between rooms. Then we propose a method for identifying the coe ﬃ cient based on a spatio-temporal oscillatory pattern extracted from the data via Koopman mode decomposition. The calculated coe ﬃ cient is veriﬁed with the characteristic numbers of the air ﬂow in the atrium and its architectural geometry.
Noteworthy occurrences among six marine species documented with community engagement in the Canadian Arctic

Abstract Arctic marine ecosystems are changing, one aspect of which appears to be distributional expansions of sub-arctic species. For Arctic marine systems, there is limited occurrence information for many species, especially those found in restricted habitats (e.g., ice-covered, far north, or deep-water). Increasing observations through on-going Fisheries and Oceans Canada (DFO) community-based monitoring programs (e.g., Arctic Coast, Cambridge Bay Arctic Char stock assessment, Arctic Salmon, and Kugluktuk coastal surveys), community observation networks, and local media have augmented opportunities to document new occurrences of marine fishes. Combined data from historical records and contemporary observations at the local scale can then delineate these among three types of occurrences: gradual distributional expansion, episodic vagrants, and rare endemics. Here we document nine occurrences of unusual sightings across six fish species (Pink Salmon Oncorhynchus gorbuscha, Bering Wolffish Anarhichas orientalis, Greenland Shark Somniosus microcephalus, Broad Whitefish Coregonus nasus, Banded Gunnel Pholis fasciata and Salmon Shark Lamna ditropis) from six northern Canadian communities and classify the nature of each observation as rare, vagrant, or expanding distributions. Uniting scientific and local observations represents a novel approach to monitor distributional changes suitable for a geographically large but sparsely populated area such as the Canadian Arctic. The new occurrences are important for discerning the potential effects of the presence of these species in Arctic ecosystems. These observations more broadly will build on our understanding of northern biodiversity change associated with warming Arctic environments.
Taylor Expansion Finitely Simulates Infinitary β-Reduction

— Originating in Girard’s Linear logic, Ehrhard and Regnier’s Taylor expansion of λ-terms has been broadly used as a tool to approximate the terms of several variants of the λ-calculus. Many results arise from a Commutation theorem relating the normal form of the Taylor expansion of a term to its Böhm tree. This led us to consider extending this formalism to the infinitary λ-calculus, since the Λ 001∞ version of this calculus has Böhm trees as normal forms and seems to be the ideal framework to reformulate the Commutation theorem. We give a (co-)inductive presentation of Λ 001∞ . We define a Taylor expansion on this calculus, and state that the infinitary β-reduction can be simulated through this Taylor expansion. The target language is the usual resource calculus, and in particular the resource reduction remains finite, confluent and terminating. Finally, we state the generalised Commutation theorem and use our results to provide simple proofs of some normalisation and confluence properties in the infinitary λ-calculus.
Optical Proximity Sensing for Pose Estimation During In-Hand Manipulation

During in-hand manipulation, robots must be able to continuously estimate the pose of the object in order to generate appropriate control actions. The performance of algorithms for pose estimation hinges on the robot's sensors being able to detect discriminative geometric object features, but previous sensing modalities are unable to make such measurements robustly. The robot's fingers can occlude the view of environment- or robot-mounted image sensors, and tactile sensors can only measure at the local areas of contact. Motivated by fingertip-embedded proximity sensors' robustness to occlusion and ability to measure beyond the local areas of contact, we present the first evaluation of proximity sensor based pose estimation for in-hand manipulation. We develop a novel two-fingered hand with fingertip-embedded optical time-of-flight proximity sensors as a testbed for pose estimation during planar in-hand manipulation. Here, the in-hand manipulation task consists of the robot moving a cylindrical object from one end of its workspace to the other. We demonstrate, with statistical significance, that proximity-sensor based pose estimation via particle filtering during in-hand manipulation: a) exhibits 50% lower average pose error than a tactile-sensor based baseline; b) empowers a model predictive controller to achieve 30% lower final positioning error compared to when using tactile-sensor based pose estimates.
Audio-Visual Multi-Channel Speech Separation, Dereverberation and Recognition

Despite the rapid advance of automatic speech recognition (ASR) technologies, accurate recognition of cocktail party speech characterised by the interference from overlapping speakers, background noise and room reverberation remains a highly challenging task to date. Motivated by the invariance of visual modality to acoustic signal corruption, audio-visual speech enhancement techniques have been developed, although predominantly targeting overlapping speech separation and recognition tasks. In this paper, an audio-visual multi-channel speech separation, dereverberation and recognition approach featuring a full incorporation of visual information into all three stages of the system is proposed. The advantage of the additional visual modality over using audio only is demonstrated on two neural dereverberation approaches based on DNN-WPE and spectral mapping respectively. The learning cost function mismatch between the separation and dereverberation models and their integration with the back-end recognition system is minimised using fine-tuning on the MSE and LF-MMI criteria. Experiments conducted on the LRS2 dataset suggest that the proposed audio-visual multi-channel speech separation, dereverberation and recognition system outperforms the baseline audio-visual multi-channel speech separation and recognition system containing no dereverberation module by a statistically significant word error rate (WER) reduction of 2.06 % absolute (8.77 % relative).
Online No-regret Model-Based Meta RL for Personalized Navigation

The interaction between a vehicle navigation system and the driver of the vehicle can be formulated as a model-based reinforcement learning problem, where the navigation systems (agent) must quickly adapt to the characteristics of the driver (environmental dynamics) to provide the best sequence of turn-by-turn driving instructions. Most modern day navigation systems (e.g, Google maps, Waze, Garmin) are not designed to personalize their low-level interactions for individual users across a wide range of driving styles (e.g., vehicle type, reaction time, level of expertise). Towards the development of personalized navigation systems that adapt to a variety of driving styles, we propose an online no-regret model-based RL method that quickly conforms to the dynamics of the current user. As the user interacts with it, the navigation system quickly builds a user-specific model, from which navigation commands are optimized using model predictive control. By personalizing the policy in this way, our method is able to give well-timed driving instructions that match the user's dynamics. Our theoretical analysis shows that our method is a no-regret algorithm and we provide the convergence rate in the agnostic setting. Our empirical analysis with 60+ hours of real-world user data using a driving simulator shows that our method can reduce the number of collisions by more than 60%.
Single Image Internal Distribution Measurement Using Non-Local Variational Autoencoder

Deep learning-based super-resolution methods have shown great promise, especially for single image super-resolution (SISR) tasks. Despite the performance gain, these methods are limited due to their reliance on copious data for model training. In addition, supervised SISR solutions rely on local neighbourhood information focusing only on the feature learning processes for the reconstruction of low-dimensional images. Moreover, they fail to capitalize on global context due to their constrained receptive field. To combat these challenges, this paper proposes a novel image-specific solution, namely non-local variational autoencoder (\texttt{NLVAE}), to reconstruct a high-resolution (HR) image from a single low-resolution (LR) image without the need for any prior training. To harvest maximum details for various receptive regions and high-quality synthetic images, \texttt{NLVAE} is introduced as a self-supervised strategy that reconstructs high-resolution images using disentangled information from the non-local neighbourhood. Experimental results from seven benchmark datasets demonstrate the effectiveness of the \texttt{NLVAE} model. Moreover, our proposed model outperforms a number of baseline and state-of-the-art methods as confirmed through extensive qualitative and quantitative evaluations.