Miracle-dz/UniFAR-FTUs huggingface dataset:
Blockade of endoplasmic reticulum stress‐induced cell death by Ureaplasma parvum vacuolating factor

Previously, we found that Ureaplasma parvum internalised into HeLa cells and cytosolic accumulation of galectin‐3. U. parvum induced the host cellular membrane damage and survived there. Here, we conducted vesicular trafficking inhibitory screening in yeast to identify U. parvum vacuolating factor (UpVF). U. parvum triggered endoplasmic reticulum (ER) stress and upregulated the unfolded protein response‐related factors, including BiP, P‐eIF2 and IRE1 in the host cells, but it blocked the induction of the downstream apoptotic factors. MicroRNA library screening of U. parvum‐infected cells and UpVF‐transfected cells identified miR‐211 and miR‐214 as the negative regulators of the apoptotic cascade under ER stress. Transient expression of UpVF induced HeLa cell death with intracellular vacuolization; however, some stable UpVF transformant survived. U. parvum‐infected cervical cell lines showed resistance to actinomycin D, and UpVF stable transformant cell lines exhibited resistance to X‐ray irradiation, as well as cisplatin and paclitaxel. UpVF expressing cervical cancer xenografts in nude mice also acquired resistance to cisplatin and paclitaxel. A mycoplasma expression vector based on Mycoplasma mycoides, Syn‐MBA (multiple banded antigen)‐UpVF, reduced HeLa cell survival compared with that of Syn‐MBA after 72 hr of infection. These findings together suggest novel mechanisms for Ureaplasma infection and the possible implications for cervical cancer malignancy.
Paraphrase Generation as Unsupervised Machine Translation

In this paper, we propose a new paradigm for paraphrase generation by treating the task as unsupervised machine translation (UMT) based on the assumption that there must be pairs of sentences expressing the same meaning in a large-scale unlabeled monolingual corpus. The proposed paradigm first splits a large unlabeled corpus into multiple clusters, and trains multiple UMT models using pairs of these clusters. Then based on the paraphrase pairs produced by these UMT models, a unified surrogate model can be trained to serve as the final model to generate paraphrases, which can be directly used for test in the unsupervised setup, or be finetuned on labeled datasets in the supervised setup. The proposed method offers merits over machine-translation-based paraphrase generation methods, as it avoids reliance on bilingual sentence pairs. It also allows human intervene with the model so that more diverse paraphrases can be generated using different filtering criteria. Extensive experiments on existing paraphrase dataset for both the supervised and unsupervised setups demonstrate the effectiveness the proposed paradigm.
The Anticipatory and Task-Driven Nature of Visual Perception

Abstract Humans have a remarkable capacity to arrange and rearrange perceptual input according to different categorizations. This begs the question whether the categorization is exclusively a higher visual or amodal process, or whether categorization processes influence early visual areas as well. To investigate this we scanned healthy participants in a magnetic resonance imaging scanner during a conceptual decision task in which participants had to answer questions about upcoming images of animals. Early visual cortices (V1 and V2) contained information about the current visual input, about the granularity of the forthcoming categorical decision, as well as perceptual expectations about the upcoming visual stimulus. The middle temporal gyrus, the anterior temporal lobe, and the inferior frontal gyrus were also involved in the categorization process, constituting an attention and control network that modulates perceptual processing. These findings provide further evidence that early visual processes are driven by conceptual expectations and task demands.
Model for technological innovation integration and new product development in firms under innovation discontinuities

This article aims to contribute to a new planning policy in the development of innovative products. To do so, it presents a new modelling proposal to integrate technological innovation and new product development in a company of the traditional pewter sector in Portugal, under innovation discontinuities, carried out according to the following stages: Phase 1: Modelling of the information needs in product development process; Phase 2: Determining of technology integration dimensions to the product; and Phase 3: Evaluating the performance of technology integration dimensions to the product. A case study was conducted in a company of traditional tin sector in Portugal. The investigation was helped by the intervention of specialists. In order to reduce the subjectivity in the obtained results, the methods of Categorical Judgment Law, Artificial Neural Networks, the multi-criteria Electre III methods, compromised programming and Promethee II, multivariate analysis and the neuro fuzzy technology were used. The results were satisfactory, validating the present proposal.
Structural Effect of TiO2 on the Performance of MAPbBr(3) Solar Cells

TiO2 is frequently used as electron transport layer in perovskite solar cells, and its structure can directly affect the performance of MAPbBr3 solar cells. It is necessary to investigate the structural effect of TiO2 to further understand the working mechanism of such kind solar cells. TiO2 thin films with different morphology were prepared by spin coating, and MAPbBr3 (MA = CH3NH3) thin films were further deposited on it through anti-solvent assisted crystallization approach. Then, energy band alignment between TiO2 and MAPbBr3 were characterized by X-ray photoelectron spectroscopy (XPS). According to the experimental results, TiO2 with different morphology possessed different electronic structures and yield different band alignment after contacting with MAPbBr3 perovskite layer. The difference of conduction band value between TiO2 and MAPbBr3 can directly affect the transport and collection feature of electrons, thereby governing the performance of the photovoltaic device.
Factor structure of the Modified Mini Mental State (3MS) exam among participants in inpatient neurorehabilitation

ABSTRACT Objective: To evaluate the factor structure of the Modified Mini Mental State (3MS) Exam and its suitability as a cognitive screening tool among individuals admitted to an inpatient rehabilitation unit for new-onset neurological injury/illness. Method: A retrospective chart review was conducted. Of the 187 individuals meeting the inclusion criteria, 116 had a diffuse pattern of neurological injury/illness; 71 had a focal injury/illness. Confirmatory and exploratory factor analyses (CFA; EFA) were conducted for the whole sample and separately by group. Results: The CFA suggested poor fit indices. The EFA of the total sample suggested a three-factor solution (Orientation/Awareness; Learning/Recall and Executive Functioning; Psychomotor Ability). The EFA of the diffuse subsample suggested a three-factor solution (Attention and Learning/Recall; Psychomotor Ability; Expressive Language). The Orientation/Awareness, Learning/Recall, Executive Functioning, Psychomotor Ability, and Expressive Language four-factor solution observed among the focal subsample was considered invalid. Conclusion: The 3MS provides information about the pattern of cognitive performance among individuals in neurorehabilitation; clinicians are advised to interpret total scores with caution. Among individuals with focal injuries/illnesses, clinicians might use the 3MS to compare the pattern of cognitive ability to expectations for performance and to support strengths-based approaches to participation in rehabilitation therapies.
Amyloid aggregation and secondary structure changes of liver cystatin: Acidic denaturation and TFE induced studies

Abstract A cysteine proteinase inhibitor has been purified by affinity chromatography from the liver of buffalo. Liver cystatin is subjected to incubation at low pH with co-solvent TFE, where we have studied the effect on the conformation, activity and tendency to form aggregates or fibrils. ANS fluorescence was used to study conformational changes. The fibril formation and aggregation was studied using ThT assay, CD, FTIR and fluorescence spectroscopy. At pH 3.0 there was no fibril formation though aggregates were formed but in presence of TFE fibrils appeared. At pH 2.0 and 1.0, TFE induced rapid fibril formation compared to only acid induced state as assessed by Thioflavin T (ThT) fluorescence.TFE stabilized each of the three acid induced intermediates at predenaturational concentrations (20%) and accelerated fibril formation. Solvent conditions had a profound effect on the tendency of liver cystatin to produce fibrils and aggregation. Communicated by Ramaswamy H. Sarma
Hybrid Variation-Aware Network for Angle-Closure Assessment in AS-OCT

Automatic angle-closure assessment in Anterior Segment OCT (AS-OCT) images is an important task for the screening and diagnosis of glaucoma, and the most recent computer-aided models focus on a binary classification of anterior chamber angles (ACA) in AS-OCT, i.e., open-angle and angle-closure. In order to assist clinicians who seek better to understand the development of the spectrum of glaucoma types, a more discriminating three-class classification scheme was suggested, i.e., the classification of ACA was expended to include open-, appositional- and synechial angles. However, appositional and synechial angles display similar appearances in an AS-OCT image, which makes classification models struggle to differentiate angle-closure subtypes based on static AS-OCT images. In order to tackle this issue, we propose a 2D-3D Hybrid Variation-aware Network (HV-Net) for open-appositional-synechial ACA classification from AS-OCT imagery. Specifically, taking into account clinical priors, we first reconstruct the 3D iris surface from an AS-OCT sequence, and obtain the geometrical characteristics necessary to provide global shape information. 2D AS-OCT slices and 3D iris representations are then fed into our HV-Net to extract cross-sectional appearance features and iris morphological features, respectively. To achieve similar results to those of dynamic gonioscopy examination, which is the current gold standard for diagnostic angle assessment, the paired AS-OCT images acquired in dark and light illumination conditions are used to obtain an accurate characterization of configurational changes in ACAs and iris shapes, using a Variation-aware Block. In addition, an annealing loss function was introduced to optimize our model, so as to encourage the sub-networks to map the inputs into the more conducive spaces to extract dark-to-light variation representations, while retaining the discriminative power of the learned features. The proposed model is evaluated across 1584 paired AS-OCT samples, and it has demonstrated its superiority in classifying open-, appositional- and synechial angles.
Rare Words Degenerate All Words

Despite advances in neural network language model, the representation degeneration problem of embeddings is still challenging. Recent studies have found that the learned output embeddings are degenerated into a narrow-cone distribution which makes the similarity between each embeddings positive. They analyzed the cause of the degeneration problem has been demonstrated as common to most embeddings. However, we found that the degeneration problem is especially originated from the training of embeddings of rare words. In this study, we analyze the intrinsic mechanism of the degeneration of rare word embeddings with respect of their gradient about the negative log-likelihood loss function. Furthermore, we theoretically and empirically demonstrate that the degeneration of rare word embeddings causes the degeneration of non-rare word embeddings, and that the overall degeneration problem can be alleviated by preventing the degeneration of rare word embeddings. Based on our analyses, we propose a novel method, Adaptive Gradient Partial Scaling(AGPS), to address the degeneration problem. Experimental results demonstrate the effectiveness of the proposed method qualitatively and quantitatively.
Lightweight, Multi-Stage, Compiler-Assisted Application Specialization

Program debloating aims to enhance the performance and reduce the attack surface of bloated applications. Several techniques have been recently proposed to specialize programs. These approaches are either based on unsound strategies or demanding techniques, leading to unsafe results or a high-overhead debloating process. In this paper, we address these limitations by applying partial-evaluation principles to generate specialized applications. Our approach relies on a simple observation that an application typically consists of configuration logic, followed by the main logic of the program. The configuration logic specifies what functionality in the main logic should be executed. LMCAS performs partial interpretation to capture a precise program state of the configuration logic based on the supplied inputs. LMCAS then applies partial-evaluation optimizations to generate a specialized program by propagating the constants in the captured partial state, eliminating unwanted code, and preserving the desired functionalities. Our evaluation of LMCAS-on commonly used benchmarks and real-world applications-shows that it successfully removes unwanted features while preserving the functionality and robustness of the debloated programs, runs faster than prior tools, and reduces the attack surface of specialized programs. LMCAS runs 1500x, 4.6x, and 1.2x faster than the state-of-the-art debloating tools CHISEL, RAZOR, and OCCAM, respectively; achieves 25% reduction in the binary size; demonstrates favorable gadgets elimination trade-off; and eliminates 87.5% of the known CVE vulnerabilities in our test corpus.
The large-scale environment of thermonuclear and core-collapse supernovae

, , The new generation of wide-ﬁeld time-domain surv e ys has made it feasible to study the clustering of supernova (SN) host galaxies in the large-scale structure (LSS) for the ﬁrst time. We investigate the LSS environment of SN populations, using 106 dark matter density realisations with a resolution of ∼ 3.8 Mpc, constrained by the 2M + + galaxy surv e y. We limit our analysis to redshift z < 0.036, using samples of 498 thermonuclear and 782 core-collapse SNe from the Zwicky Transient Facility’s Bright Transient Survey and Census of the Local Universe catalogues. We detect clustering of SNe with high signiﬁcance; the observed clustering of the two SNe populations is consistent with each other. Further, the clustering of SN hosts is consistent with that of the Sloan Digital Sky Survey (SDSS) Baryon Oscillation Spectroscopic Survey DR12 spectroscopic galaxy sample in the same redshift range. Using a tidal shear classiﬁer, we classify the LSS into voids, sheets, ﬁlaments, and knots. We ﬁnd that both SNe and SDSS galaxies are predominantly found in sheets and ﬁlaments. SNe are signiﬁcantly under-represented in voids and o v er-represented in knots compared to the volume fraction in these structures. This work opens the potential for using forthcoming wide-ﬁeld deep SN surv e ys as a complementary LSS probe.
Analysis of MRI Biomarkers for Brain Cancer Survival Prediction

Prediction of Overall Survival (OS) of brain cancer patients from multi-modal MRI is a challenging field of research. Most of the existing literature on survival prediction is based on Radiomic features, which does not consider either non-biological factors or the functional neurological status of the patient(s). Besides, the selection of an appropriate cut-off for survival and the presence of censored data create further problems. Application of deep learning models for OS prediction is also limited due to the lack of large annotated publicly available datasets. In this scenario we analyse the potential of two novel neuroimaging feature families, extracted from brain parcellation atlases and spatial habitats, along with classical radiomic and geometric features; to study their combined predictive power for analysing overall survival. A cross validation strategy with grid search is proposed to simultaneously select and evaluate the most predictive feature subset based on its predictive power. A Cox Proportional Hazard (CoxPH) model is employed for univariate feature selection, followed by the prediction of patient-specific survival functions by three multivariate parsimonious models viz. Coxnet, Random survival forests (RSF) and Survival SVM (SSVM). The brain cancer MRI data used for this research was taken from two open-access collections TCGA-GBM and TCGA-LGG available from The Cancer Imaging Archive (TCIA). Corresponding survival data for each patient was downloaded from The Cancer Genome Atlas (TCGA). A high cross validation $C-index$ score of $0.82\pm.10$ was achieved using RSF with the best $24$ selected features. Age was found to be the most important biological predictor. There were $9$, $6$, $6$ and $2$ features selected from the parcellation, habitat, radiomic and region-based feature groups respectively.
Predicting Mood Disorder Symptoms with Remotely Collected Videos Using an Interpretable Multimodal Dynamic Attention Fusion Network

We developed a novel, interpretable multimodal classification method to identify symptoms of mood disorders viz. depression, anxiety and anhedonia using audio, video and text collected from a smartphone application. We used CNN-based unimodal encoders to learn dynamic embeddings for each modality and then combined these through a transformer encoder. We applied these methods to a novel dataset - collected by a smartphone application - on 3002 participants across up to three recording sessions. Our method demonstrated better multimodal classification performance compared to existing methods that employed static embeddings. Lastly, we used SHapley Additive exPlanations (SHAP) to prioritize important features in our model that could serve as potential digital markers.
On the Convergence of Decentralized Adaptive Gradient Methods

Adaptive gradient methods including Adam, AdaGrad, and their variants have been very successful for training deep learning models, such as neural networks. Meanwhile, given the need for distributed computing, distributed optimization algorithms are rapidly becoming a focal point. With the growth of computing power and the need for using machine learning models on mobile devices, the communication cost of distributed training algorithms needs careful consideration. In this paper, we introduce novel convergent decentralized adaptive gradient methods and rigorously incorporate adaptive gradient methods into decentralized training procedures. Specifically, we propose a general algorithmic framework that can convert existing adaptive gradient methods to their decentralized counterparts. In addition, we thoroughly analyze the convergence behavior of the proposed algorithmic framework and show that if a given adaptive gradient method converges, under some specific conditions, then its decentralized counterpart is also convergent. We illustrate the benefit of our generic decentralized framework on a prototype method, i.e., AMSGrad, both theoretically and numerically.
PEEK: A Large Dataset of Learner Engagement with Educational Videos

Educational recommenders have received much less attention in comparison to e-commerce and entertainment-related recommenders, even though efficient intelligent tutors have great potential to improve learning gains. One of the main challenges in advancing this research direction is the scarcity of large, publicly available datasets. In this work, we release a large, novel dataset of learners engaging with educational videos in-the-wild. The dataset, named Personalised Educational Engagement with Knowledge Topics PEEK, is the first publicly available dataset of this nature. The video lectures have been associated with Wikipedia concepts related to the material of the lecture, thus providing a humanly intuitive taxonomy. We believe that granular learner engagement signals in unison with rich content representations will pave the way to building powerful personalization algorithms that will revolutionise educational and informational recommendation systems. Towards this goal, we 1) construct a novel dataset from a popular video lecture repository, 2) identify a set of benchmark algorithms to model engagement, and 3) run extensive experimentation on the PEEK dataset to demonstrate its value. Our experiments with the dataset show promise in building powerful informational recommender systems. The dataset and the support code is available publicly.
Robust Discriminant Subspace Clustering With Adaptive Local Structure Embedding

Unsupervised dimension reduction and clustering are frequently used as two separate steps to conduct clustering tasks in subspace. However, the two-step clustering methods may not necessarily reflect the cluster structure in the subspace. In addition, the existing subspace clustering methods do not consider the relationship between the low-dimensional representation and local structure in the input space. To address the above issues, we propose a robust discriminant subspace (RDS) clustering model with adaptive local structure embedding. Specifically, unlike the existing methods which incorporate dimension reduction and clustering via regularizer, thereby introducing extra parameters, RDS first integrates them into a unified matrix factorization (MF) model through theoretical proof. Furthermore, a similarity graph is constructed to learn the local structure. A constraint is imposed on the graph to guarantee that it has the same connected components with low-dimensional representation. In this spirit, the similarity graph serves as a tradeoff that adaptively balances the learning process between the low-dimensional space and the original space. Finally, RDS adopts the $\ell _{2,1}$ -norm to measure the residual error, which enhances the robustness to noise. Using the property of the $\ell _{2,1}$ -norm, RDS can be optimized efficiently without introducing more penalty terms. Experimental results on real-world benchmark datasets show that RDS can provide more interpretable clustering results and also outperform other state-of-the-art alternatives.
Persistence in randomly switched Lotka-Volterra food chains

We consider a dynamical system obtained by the random switching between $N$Lotka-Volterra food chains. Our key assumption will be that at least two vector fields only differ on the resources allocated to the growth rate of the first species. We will show that the existence of a positive equilibrium of the average vector field is equivalent to the persistence of all species. Under this condition, the semi-group converges exponentially quickly to a unique invariant probability measure on the positive orthant. If this condition fails to hold, we have two possibilities. The first possibility is the extinction case, in which a group of species becomes extinct exponentially quicklywhile the distribution of the remaining species converges weakly to another invariant probability measure. The second possibility is the critical case, in which there is a weaker form of persistence of some species, whilst some of the remaining become extinct exponentially quickly. We will also analyse the sensitivity of this model to the parameters.