Miracle-dz/UniFAR-FTUs huggingface dataset:
Paragraph-level Simplification of Medical Texts

We consider the problem of learning to simplify medical texts. This is important because most reliable, up-to-date information in biomedicine is dense with jargon and thus practically inaccessible to the lay audience. Furthermore, manual simplification does not scale to the rapidly growing body of biomedical literature, motivating the need for automated approaches. Unfortunately, there are no large-scale resources available for this task. In this work we introduce a new corpus of parallel texts in English comprising technical and lay summaries of all published evidence pertaining to different clinical topics. We then propose a new metric based on likelihood scores from a masked language model pretrained on scientific texts. We show that this automated measure better differentiates between technical and lay summaries than existing heuristics. We introduce and evaluate baseline encoder-decoder Transformer models for simplification and propose a novel augmentation to these in which we explicitly penalize the decoder for producing “jargon” terms; we find that this yields improvements over baselines in terms of readability.
On the validity of kernel approximations for orthogonally-initialized neural networks

In this note we extend kernel function approximation results for neural networks with Gaussian-distributed weights to single-layer networks initialized using Haar-distributed random orthogonal matrices (with possible rescaling). This is accomplished using recent results from random matrix theory. 1 Initial definitions and statement of problem The main object of our analysis will be a fully-connected “combined layer” f , which is defined by f(z) = φ(Wz), where W ∈ Rm×k is matrix of weights, z ∈ R is a input vector, and φ is a scalar activation function which is applied element-wise for vector inputs. We call this a “combined layer”, as it combines both a fully-connected layer and a nonlinear layer. We are interested in characterizing the high probability initialization-time behavior of f ’s kernel function κf (z, z ′) ≡ 1 m f(z)⊤f(z′) given certain initialization schemes for W . In particular, we wish to show that κf (z, z ′), evaluated with f ’s initial weights, converges in probability as m→ ∞ to f ’s approximate kernel function, which is defined by κ̃f (Σz,z′) = Eu∼N(0,Σz,z′)[φ(u1)φ(u2)], where Σz,z′ ≡ 1 k [ z⊤z z⊤z′ z′⊤z z′⊤z′ ] ∈ R2×2.
ExplainaBoard: An Explainable Leaderboard for NLP

With the rapid development of NLP research, leaderboards have emerged as one tool to track the performance of various systems on various NLP tasks. They are effective in this goal to some extent, but generally present a rather simplistic one-dimensional view of the submitted systems, communicated only through holistic accuracy numbers. In this paper, we present a new conceptualization and implementation of NLP evaluation: the ExplainaBoard, which in addition to inheriting the functionality of the standard leaderboard, also allows researchers to (i) diagnose strengths and weaknesses of a single system (e.g. what is the best-performing system bad at? ) (ii) interpret relationships between multiple systems. (e.g. where does system A outperform system B? What if we combine systems A, B and C? ) and (iii) examine prediction results closely (e.g. what are common errors made by multiple systems or in what contexts do particular errors occur? ). So far, ExplainaBoard covers more than 400 systems, 50 datasets, 40 languages, and 12 tasks. We not only released an online platform at the website but also make our evaluation tool an API with MIT Licence at Github and PyPi that allows users to conveniently assess their models offline. We additionally release all output files from systems that we have run or collected to motivate “output-driven” research in the future.
Assessment of the cosmic distance duality relation using Gaussian process

Two types of distance measurement are important in cosmological observations, the angular diameter distance $d_A$ and the luminosity distance $d_L$. In the present work, we carried out an assessment of the theoretical relation between these two distance measurements, namely the cosmic distance duality relation, from type Ia supernovae (SN-Ia) data, the Cosmic Chronometer (CC) Hubble parameter data, and baryon acoustic oscillation (BAO) data using Gaussian Process. The luminosity distance curve and the angular diameter distance curve are extracted from the SN-Ia data and the combination of BAO and CC data respectively using the Gaussian Process. The distance duality relation is checked by a non-parametric reconstruction using the reconstructed $H$, $d_L$, and the volume-averaged distance $D_v$. We compare the results obtained for different choices of the covariance function employed in the Gaussian Process. It is observed that the theoretical distance duality relation is in well agreement with the present analysis in 2$\sigma$ for the overlapping redshift domain $0 \leq z \leq 2$ of the reconstruction.
New infrared spectra of CO2–Xe: modelling Xe isotope effects, intermolecular bend and stretch, and symmetry breaking of the CO2 bend

The infrared spectrum of the weakly bound CO2–Xe complex is studied in the region of the carbon dioxide ν3 fundamental vibration (≈2350 cm−1), using a tunable OPO laser source to probe a pulsed supersonic slit jet expansion. The Xe isotope dependence of the spectrum is modelled by scaling the vibrational and rotational parameters, with the help of previous microwave data. The scaling model provides a good simulation of the observed broadening and (partial) splitting of transitions in the fundamental band, and it is essential for understanding the intermolecular bending combination band where some transitions are completely split by isotope effects. The combination band is influenced by a significant bend–stretch Coriolis interaction and by the relatively large Xe isotope dependence of the intermolecular stretch frequency. The weak CO2–Xe spectrum corresponding to the (0111) ← (0110) hot band of CO2 is also detected and analysed, providing a measurement of the symmetry breaking of the CO2 bending mode induced by the nearby Xe atom. This in-plane/out-of-plane splitting is determined to be 2.14 cm−1. GRAPHICAL ABSTRACT
A pr 2 02 1 Gravity as a ‘ Third ’ Quantization Gauge Theory with Minkowski Metric , Non-Zero Torsion and Curvature

It is understood that, in general, a global and unique vacuum state cannot be considered in a curved space. Hence, we have introduced a geometry that, in addition of having non-zero local curvature and local torsion, its corresponding metric tensor is the Minkowski metric. Based on this geometry, from the quantum field theory point of view, we have proposed a ‘third’/vacuum quantization model as a consequence from the Unruh effect. Then, we have shown that, in classical limit, the corresponding ‘third’ quantization fields appear as a Riemannian manifold with an emerged metric on which the second quantization fields are located. In this ‘third’ quantization viewpoint, the standard model of field theory reveals to be an effective theory that is valid on specific energy scales. Moreover, we have modeled the proposed ‘third’ quantization fields as a Yang-Mills gauge theory based on U(1)×SU(4) gauge symmetry. Accordingly, we have shown that an analytical solution of the proposed model, for the trajectory of a particle of ‘third’ quantum field (such as a star), corresponds to the trajectory of a test particle in the Mannheim-Kazanas space. Furthermore, by using solutions of non-perturbative methods in the lattice gauge theory, we have rendered a solution for the potential of this model that can explain the galaxy rotation curves and the gravitational lensing. Finally, from the perspective of this model, we have discussed the phenomenon of cosmic microwave background (CMB) and expansion of the universe.
Very Lightweight Photo Retouching Network With Conditional Sequential Modulation

Photo retouching aims at improving the aesthetic visual quality of images that suffer from photographic defects, especially for poor contrast, over/under exposure, and inharmonious saturation. In practice, photo retouching can be accomplished by a series of image processing operations. As most commonly-used retouching operations are pixel-independent, i.e., the manipulation on one pixel is uncorrelated with its neighboring pixels, we can take advantage of this property and design a specialized algorithm for efficient global photo retouching. We analyze these global operations and find that they can be mathematically formulated by a Multi-Layer Perceptron (MLP). Based on this observation, we propose an extremely lightweight framework – Conditional Sequential Retouching Network (CSRNet). Benefiting from the utilization of $1\times 1$ convolution, CSRNet only contains less than 37 K trainable parameters, which are orders of magnitude smaller than existing learning-based methods. Experiments show that our method achieves state-of-the-art performance on the benchmark MIT-Adobe FiveK dataset quantitively and qualitatively. In addition to achieve global photo retouching, the proposed framework can be easily extended to learn local enhancement effects. The extended model, namely CSRNet-L, also achieves competitive results in various local enhancement tasks.
Thresholded Graphical Lasso Adjusts for Latent Variables: Application to Functional Neural Connectivity

In neuroscience, researchers seek to uncover the connectivity of neurons from largescale neural recordings or imaging; often people employ graphical model selection and estimation techniques for this purpose. But, existing technologies can only record from a small subset of neurons leading to a challenging problem of graph selection in the presence of extensive latent variables. Chandrasekaran et al. (2012) proposed a convex program to address this problem that poses challenges from both a computational and statistical perspective. To solve this problem, we propose an incredibly simple solution: apply a hard thresholding operator to existing graph selection methods. Conceptually simple and computationally attractive, we demonstrate that thresholding the graphical Lasso, neighborhood selection, or CLIME estimators have superior theoretical properties in terms of graph selection consistency as well as stronger empirical results than existing approaches for the latent variable graphical model problem. We also demonstrate the applicability of our approach through a neuroscience case study on calcium-imaging data to estimate functional neural connections.
병풀 잎에서 triterpene glycosides의 시기별 함량변화

5월부터 10월까지 병풀의 지상부에 triterpene glycosides인 madecassoside와 asiaticoside의 시기별 함량변화를 조사하였다. 잎에서는 9월에 약 108.1 mg/g으로 가장 높은 수준의 함량을 보였다. 엽병에서는 19.02 mg/g으로 잎에서 보이는 수준보다는 낮은 수준을 나타냈다. 병풀의 triterpene glycosides는 잎에 주로 축적되며, 가장 적절한 병풀의 수확시기는 9월로 잠적 확인되었다. 【The seasonal variation of two triterpene glycosides, madecassoside and asisaticoside, in the aerial parts of Centella asiatica from May through October was investigated. In leaves, the highest level of triterpene glycosides content was about 108.1 mg/g in September. In petiols, the content was as low as 19.02 mg/g. According to the experimental results obtained, we found that Centella asiatica leaf can accumulate the triterpene glycosides and September is the optimal season for the collection. 】
Novel deep learning-based survival prediction for oral cancer by analyzing tumor-infiltrating lymphocyte profiles through CIBERSORT

ABSTRACT The tumor microenvironment (TME) within mucosal neoplastic tissue in oral cancer (ORCA) is greatly influenced by tumor-infiltrating lymphocytes (TILs). Here, a clustering method was performed using CIBERSORT profiles of ORCA data that were filtered from the publicly accessible data of patients with head and neck cancer in The Cancer Genome Atlas (TCGA) using hierarchical clustering where patients were regrouped into binary risk groups based on the clustering-measuring scores and survival patterns associated with individual groups. Based on this analysis, clinically reasonable differences were identified in 16 out of 22 TIL fractions between groups. A deep neural network classifier was trained using the TIL fraction patterns. This internally validated classifier was used on another individual ORCA dataset from the International Cancer Genome Consortium data portal, and patient survival patterns were precisely predicted. Seven common differentially expressed genes between the two risk groups were obtained. This new approach confirms the importance of TILs in the TME and provides a direction for the use of a novel deep-learning approach for cancer prognosis.
Advances on Graphene-Based Nanomaterials and Mesenchymal Stem Cell-Derived Exosomes Applied in Cutaneous Wound Healing

Abstract Graphene is a new type of carbon nanomaterial discovered after fullerene and carbon nanotube. Due to the excellent biological properties such as biocompatibility, cell proliferation stimulating, and antibacterial properties, graphene and its derivatives have become emerging candidates for the development of novel cutaneous wound dressings and composite scaffolds. On the other hand, pre-clinical research on exosomes derived from mesenchymal stem cells (MSC-Exos) has been intensified for cell-free treatment in wound healing and cutaneous regeneration, via ameliorating the damaged microenvironment of the wound site. Here, we provide a comprehensive understanding of the latest studies and observations on the various effects of graphene-based nanomaterials (GBNs) and MSC-Exos during the cutaneous wound repair process, as well as the putative mechanisms thereof. In addition, we propose the possible forward directions of GBNs and MSC-Exos applications, expecting to promote the clinical transformation.
A Novel Immunotype-based Risk Stratification Model Predicts Postoperative Prognosis and Adjuvant TACE Benefit in Chinese Patients with Hepatocellular Carcinoma

Background and Aims: The tumor microenvironment can be divided into inflamed, immune-excluded and immune-desert phenotypes according to CD8+ T cell categories with differential programmed cell death protein 1 (PD-L1) expression. The study aims to construct a novel immunotype-based risk stratification model to predict postsurgical survival and adjuvant trans-arterial chemoembolization (TACE) response in patients with hepatocellular carcinoma (HCC). Methods: A total of 220 eligible HCC patients participated in this study. CD8+ T cell infiltration and PD-L1 expression mode were estimated by immunohistochemical staining. A risk stratification model was developed and virtualized by a nomogram that integrated these independent prognostic factors. The postoperative prognosis and adjuvant TACE benefits were evaluated with a novel immunotype-based risk stratification model. Results: A total of 220 patients were finally identified. Immune-desert, immune-excluded, and inflamed immunotypes represented 45%, 24%, and 31% of HCC, respectively. Univariate and multivariate analyses identified immunotype and PD-L1 expression mode as independent prognostic factors for overall survival time (OS) and recurrence-free survival time (RFS). The nomogram was constructed by integrating immunotype, PD-L1 expression, Barcelona Clinic Liver Cancer (BCLC) stage and tumor grade. The C-index was 0.794 in the training cohort and 0.813 in the validation cohort. A risk stratification system was constructed based on the nomogram classifying HCC patients into 3 risk groups. The average OS times in the low-risk, intermediate-risk and high-risk groups in all cohorts were 77.1 months (95% CI 71.4-82.9), 53.7 months (95% CI 48.2-59.2), and 25.6 months (95% CI 21.4-29.7), respectively. Further analysis showed that OS was significantly improved by adjuvant TACE in the low- and intermediate-risk groups (P=0.041 and P=0.010, respectively) but not in the high-risk group (P=0.398). Conclusion: A novel immunotype-based risk stratification model was built to predict postoperative prognosis and adjuvant TACE benefit in HCC patients. These tools can assist in building a more customized method of HCC treatment.
When ecological marginality is not geographically peripheral: exploring genetic predictions of the centre-periphery hypothesis in the endemic plant Lilium pomponium

Background Quantifying variation of genetic traits over the geographical range of species is crucial for understanding the factors driving their range dynamics. The center-periphery hypothesis postulates, and many studies support, the idea that genetic diversity decreases and genetic differentiation increases toward the geographical periphery due to population isolation. The effects of environmental marginality on genetic variation has however received much less attention. Methods We tested the concordance between geographical and environmental gradients and the genetic predictions of center-periphery hypothesis for endemic Lilium pomponium in the southern Alps. Results We found little evidence for concordance between genetic variation and both geographical and environmental gradients. Although the prediction of increased differentiation at range limits is met, genetic diversity does not decrease towards the geographical periphery. Increased differentiation among peripheral populations, that are not ecologically marginal, may be explained by a decrease in habitat availability that reduces population connectivity. In contrast, a decrease of genetic diversity along environmental but not geographical gradients may be due to the presence of low quality habitats in the different parts of the range of a species that reduce effective population size or increase environmental constraints. As a result, environmental factors may affect population dynamics irrespective of distance from the geographical center of the range. In such situations of discordance between geographical and environmental gradients, the predictions of decreasing genetic diversity and increasing differentiation toward the geographical periphery may not be respected.
Molecular and Electrophysiological Characterization of Dorsal Horn Neurons in a GlyT2-iCre-tdTomato Mouse Line

Purpose Spinal glycinergic neurons function as critical elements of a spinal gate for pain and itch. We have recently documented that spinal PKCγ+ neurons receive the feedforward inhibitory input driven by Aβ primary afferent. The glycinergic neurons control the excitability of PKCγ+ neurons and therefore gate mechanical allodynia. However, a dynamic or electrophysiological analysis of the synaptic drive on spinal glycinergic interneurons from primary afferent fibers is largely absent. The present study was aimed to analyze the synaptic dynamics between spinal glycinergic interneurons and primary afferents using a genetic labeled animal model. Materials and Methods The GlyT2-P2A-iCre mice were constructed by the CRISPR/Cas9 technology. The GlyT2-iCre-tdTomato mice were then generated by crossing the GlyT2-P2A-iCre mice with fluorescent reporter mice. Patch-clamp whole-cell recordings were used to analyze the dynamic synaptic inputs to glycinergic neurons in GlyT2-iCre-tdTomato mice. The distribution of GlyT2-tdTomato neurons in the spinal dorsal horn was examined by the immunohistochemistry method. The firing pattern and morphological features of GlyT2-tdTomato neurons were also examined by electrophysiological recordings and intracellular injection of biocitin. Results The GlyT2-P2A-iCre and GlyT2-tdTomato mice were successfully constructed. GlyT2-tdTomato fluorescence was colocalized extensively with immunoreactivity of glycine, GlyT2 and Pax2 in somata, confirming the selective expression of the transgene in glycinergic neurons. GlyT2-tdTomato neurons were mainly distributed in spinal lamina IIi through IV. The firing pattern and morphological properties of GlyT2-tdTomato neurons met the features of tonic central or islet type of spinal inhibitory interneurons. The majority (72.1%) of the recorded GlyT2-tdTomato neurons received primary inputs from Aβ fibers. Conclusion The present study indicated that spinal GlyT2-positive glycinergic neurons mainly received primary afferent Aβ fiber inputs; the GlyT2-P2A-iCre and GlyT2-tdTomato mice provided a useful animal model to further investigate the function of the GlyT2+-PKCγ+ feedforward inhibitory circuit in both physiological and pathological conditions.
Predictive factors of stereopsis outcomes following strabismus surgery

Purpose: To determine the predictive factors of post-operative stereopsis in patients with strabismus. Method: In this retrospective study, records of patients who received surgical treatment for strabismus were reviewed. All types of strabismus were included. Pre- and post-treatment stereoacuity were measured using the Titmus Stereo Fly test, and predictive factors of stereopsis were evaluated. Results: A total of 194 patients (132 females and 62 males) with a mean age of 14.8 ± 8.4 years were included. There was a statistically significant improvement in stereopsis following surgery (p value < 0.001). Patients with a higher amount of deviation at baseline had poorer stereopsis on the final examination (p value < 0.001). Stereopsis improvement was more prominent in the pure horizontal strabismus group, compared to combined horizontal and vertical deviations. Baseline and the final stereopsis were higher in the “exotropia” group as compared to the “esotropia” group (p value = 0.003 and 0.0155, respectively); however, the within group change of stereopsis was not significantly different between these two groups (p value = 0.144). Post surgical residual deviation was associated with a poorer stereopsis (p value = 0.002, r = 0.251). A longer duration of strabismus before surgery was associated with poorer final stereopsis levels (p value = 0.026). The presence of amblyopia before surgery was associated with poorer stereopsis on last examination (p value < 0.001 for both correlations). Conclusion: Based on the result of this study, final stereopsis after strabismus surgery could be affected by the type, duration, and the amount of deviation before surgery, amblyopia, and post operative ocular deviation.
Changes in postoperative long-term nutritional status and quality of life after total pancreatectomy

Purpose Quality of life (QoL) is widely known to be poor after total pancreatectomy (TP) due to the loss of pancreatic function and poor nutritional status, but prospective studies on changes in QoL over time are lacking. The aim of this study was to prospectively evaluate the short- and long-term consequences of pancreatic exocrine insufficiency, changes in nutritional status, and their associated effects on QoL after TP. Methods Prospective data were collected from patients who underwent TP between 2008 and 2018. Validated questionnaires (European Organisation for Research and Treatment of Cancer Quality of Life Questionnaire [EORTC QLQ] Core 30, EORTC QLQ-pancreatic cancer module, and the Mini Nutritional Assessment), measured frequency of bowel movement, relative body weight (RBW), triceps skinfold thickness (TSFT), and serum levels of protein, albumin, transferrin, and hemoglobin A1c were collected serially for 1 year. Results Thirty patients who underwent TP were eligible for the study. Bowel movement frequency increased over time, and the RBW and TSFT were lowest by 1 year. The global health status score showed no significant difference over time. At 3 months, physical and role function scores as well as symptoms of fatigue, constipation, and digestive difficulties worsened significantly. Most indices recovered after 1 year, but poorer physical function scores, digestive difficulties, and altered bowel habits persisted. Conclusion Because some symptoms do not recover over time, careful follow-up and supportive postoperative management are needed for TP patients, including nutritional support with pancreatic enzyme replacement and education about medication adherence and diet.