Miracle-dz/UniFAR-FTUs huggingface dataset:
Enzyme-Crosslinked Gelatin Hydrogel with Adipose-Derived Stem Cell Spheroid Facilitating Wound Repair in the Murine Burn Model

Engineered skin that can facilitate tissue repair has been a great advance in the field of wound healing. A well-designed dressing material together with active biological cues such as cells or growth factors can overcome the limitation of using auto-grafts from patients. Recently, many studies showed that human adipose-derived stem cells (hASCs) can be used to promote wound healing and skin tissue engineering. hASCs have already been widely applied for clinical trials. hASCs can be harvested abundantly because they can be easily isolated from fat tissue known as the stromal vascular fraction (SVF). On the other hand, increasing studies have proven that cells from spheroids can better simulate the biological microenvironment and can enhance the expression of stemness markers. However, a three-dimensional (3D) scaffold that can harbor implanted cells and can serve as a skin-repaired substitute still suffers from deficiency. In this study, we applied a gelatin/microbial transglutaminase (mTG) hydrogel to encapsulate hASC spheroids to evaluate the performance of 3D cells on skin wound healing. The results showed that the hydrogel is not toxic to the wound and that cell spheroids have significantly improved wound healing compared to cell suspension encapsulated in the hydrogel. Additionally, a hydrogel with cell spheroids was much more effective than other groups in angiogenesis since the cell spheroid has the possibility of cell–cell signaling to promote vascular generation.
Knowledge-Infused Abstractive Summarization of Clinical Diagnostic Interviews: Framework Development Study

Background In clinical diagnostic interviews, mental health professionals (MHPs) implement a care practice that involves asking open questions (eg, “What do you want from your life? ” “What have you tried before to bring change in your life? ”) while listening empathetically to patients. During these interviews, MHPs attempted to build a trusting human-centered relationship while collecting data necessary for professional medical and psychiatric care. Often, because of the social stigma of mental health disorders, patient discomfort in discussing their presenting problem may add additional complexities and nuances to the language they use, that is, hidden signals among noisy content. Therefore, a focused, well-formed, and elaborative summary of clinical interviews is critical to MHPs in making informed decisions by enabling a more profound exploration of a patient’s behavior, especially when it endangers life. Objective The aim of this study is to propose an unsupervised, knowledge-infused abstractive summarization (KiAS) approach that generates summaries to enable MHPs to perform a well-informed follow-up with patients to improve the existing summarization methods built on frequency heuristics by creating more informative summaries. Methods Our approach incorporated domain knowledge from the Patient Health Questionnaire-9 lexicon into an integer linear programming framework that optimizes linguistic quality and informativeness. We used 3 baseline approaches: extractive summarization using the SumBasic algorithm, abstractive summarization using integer linear programming without the infusion of knowledge, and abstraction over extractive summarization to evaluate the performance of KiAS. The capability of KiAS on the Distress Analysis Interview Corpus-Wizard of Oz data set was demonstrated through interpretable qualitative and quantitative evaluations. Results KiAS generates summaries (7 sentences on average) that capture informative questions and responses exchanged during long (58 sentences on average), ambiguous, and sparse clinical diagnostic interviews. The summaries generated using KiAS improved upon the 3 baselines by 23.3%, 4.4%, 2.5%, and 2.2% for thematic overlap, Flesch Reading Ease, contextual similarity, and Jensen Shannon divergence, respectively. On the Recall-Oriented Understudy for Gisting Evaluation-2 and Recall-Oriented Understudy for Gisting Evaluation-L metrics, KiAS showed an improvement of 61% and 49%, respectively. We validated the quality of the generated summaries through visual inspection and substantial interrater agreement from MHPs. Conclusions Our collaborator MHPs observed the potential utility and significant impact of KiAS in leveraging valuable but voluminous communications that take place outside of normally scheduled clinical appointments. This study shows promise in generating semantically relevant summaries that will help MHPs make informed decisions about patient status.
Improvement of Sickle Cell Disease Morbimortality in Children: Experience in a Remote Area of an African Country

Background: Sickle cell disease (SCD) is a public health problem in the Democratic Republic of Congo. While reference sickle cell centers have been implemented in capital cities of African countries and have proven to be beneficial for SCD patients, they have never been set up in rural areas for families with very low sources of income. Method: A cohort of 143 children with SCD aged 10 years old (IQR (interquartile range): 6–15 years) (sex ratio male/female = 1.3) were clinically followed for 12 months without any specific intervention aside from the management of acute events, and then for 12 months with a monthly medical visit, biological follow-up, and regular prophylaxis. Results: The median age of patients at the diagnosis of SCD was 2 years (IQR: 1–5). The implementation of standardized and regular follow-ups in a new sickle cell referral center in a remote city showed an increase in the annual mean hemoglobin level from 50 to 70 g/L (p = 0.001), and a decrease in the lymphocyte count and spleen size (p < 0.001). A significant decrease (p < 0.001) in the average annual number of hospitalizations and episodes of vaso-occlusive crises, blood transfusions, infections, and acute chest syndromes were also observed. Conclusions: Creation of a sickle cell referral center and the regular follow-up of children with SCD are possible and applicable in the context of a remote city of an African country and represent simple and accessible measures that can reduce the morbimortality of children with sickle cell disease.
Serial Detection with Neural Network-Based Noise Prediction for Bit-Patterned Media Recording Systems

Ultra-high density data storage has gained high significance given the increasing amounts of data; many technologies have been proposed to achieve a high density. Among them, bit-pattern media recording (BPMR) is a promising technology. In BPMR systems, data are stored on magnetic islands. Therefore, high densities can be achieved by reducing the distance between the magnetic islands. Because of the closeness between the magnetic islands, the readback signal is distorted by two-dimensional (2D) interference, which includes the intersymbol interference according to the down-track direction and the intertrack interference according to the cross-track direction. A simple and effective serial detection algorithm was recently proposed to mitigate the 2D interference. However, serial detection utilizes the hard output in inner detection, and this degrades the serial detection performance. To resolve this problem, a subsequent study used feedback to estimate the noise and used this noise signal to create a soft output for inner detection. Following up, in this paper we propose a model that utilizes a neural network for noise prediction. The proposed neural network-based model and the model with the feedback line were compared in terms of bit error rate (BER). The results show that the proposed model achieves a gain of approximately 1 dB at a BER of 10−6.
Enhancement of Neuroblastoma NK-Cell-Mediated Lysis through NF-kB p65 Subunit-Induced Expression of FAS and PVR, the Loss of Which Is Associated with Poor Patient Outcome

Simple Summary Neuroblastoma (NB) cells adopt several molecular strategies to evade the Natural Killer (NK)-mediated response. Herein, we found that the overexpression of the NF-kB p65 subunit in NB cell lines upregulates the expression of both the death receptor FAS and the activating ligand PVR, thus rendering NB cells more susceptible to NK-cell-mediated apoptosis, recognition, and killing. These data could provide a clue for a novel NK-cell-based immunotherapy of NB. In addition, array CGH analysis performed in our cohort of NB patients showed that loss of both the FAS and PVR genes correlated with low survival, thus revealing a novel biomarker predicting the outcome of NB patients. Abstract High-risk neuroblastoma (NB) is a rare childhood cancer whose aggressiveness is due to a variety of chromosomal genetic aberrations, including those conferring immune evasion. Indeed, NB cells adopt several molecular strategies to evade recognition by the immune system, including the downregulation of ligands for NK-cell-activating receptors. To date, while molecular strategies aimed at enhancing the expression of ligands for NKG2D- and DNAM-1-activating receptors have been explored, no evidence has been reported on the immunomodulatory mechanisms acting on the expression of death receptors such as Fas in NB cells. Here, we demonstrated that transient overexpression of the NF-kB p65 subunit upregulates the surface expression of Fas and PVR, the ligand of DNAM-1, thus making NB cell lines significantly more susceptible to NK-cell-mediated apoptosis, recognition, and killing. In contrast, IFNγ and TNFα treatment, although it induced the upregulation of FAS in NB cells and consequently enhanced NK-cell-mediated apoptosis, triggered immune evasion processes, including the strong upregulation of MHC class I and IDO1, both of which are involved in mechanisms leading to the impairment of a proper NK-cell-mediated killing of NB. In addition, high-resolution array CGH analysis performed in our cohort of NB patients revealed that the loss of FAS and/or PVR genes correlated with low survival independently of the disease stage. Our data identify the status of the FAS and PVR genes as prognostic biomarkers of NB that may predict the efficacy of NK-cell-based immunotherapy of NB. Overall, restoration of surface expression of Fas and PVR, through transient upregulation of NF-kB, may be a clue to a novel NK-cell-based immunotherapy of NB.
MDLdroid: A ChainSGD-Reduce Approach to Mobile Deep Learning for Personal Mobile Sensing

Personal mobile sensing is fast permeating our daily lives to enable activity monitoring, healthcare and rehabilitation. Combined with deep learning, these applications have achieved significant success in recent years. Different from conventional cloud-based paradigms, running deep learning on devices offers several advantages including data privacy preservation and low-latency response for both model inference and update. Since data collection is costly in reality, Google’s Federated Learning offers not only complete data privacy but also better model robustness based on data from multiple users. However, personal mobile sensing applications are mostly user-specific and highly affected by environment. As a result, continuous local changes may seriously affect the performance of a global model generated by Federated Learning. In addition, deploying Federated Learning on a local server, e.g., edge server, may quickly reach the bottleneck due to resource limitation. Towards pushing deep learning on devices, we present MDLdroid, a novel decentralized mobile deep learning framework to enable resource-aware on-device collaborative learning for personal mobile sensing applications. To address resource limitation, we propose a ChainSGD-reduce approach which includes a novel chain-directed Synchronous Stochastic Gradient Descent algorithm to effectively reduce overhead among multiple devices. We also design an agent-based multi-goal reinforcement learning mechanism to balance resources in a fair and efficient manner. Our evaluations show that our model training on off-the-shelf mobile devices achieves 2x to 3.5x faster than single-device training, and 1.5x faster on average than the existing master-slave approach.
Disruptions, Decisions and Discourses: Mothering in the Context of the COVID-19 Pandemic

Since the COVID-19 pandemic hit North America in 2020 life has yet to return to “normal. ” New realities included remote learning, physical distancing, lockdown measures, and mandatory masking. The pandemic has increased social isolation, stress and anxiety, employment loss, and financial instability. Even more, the domestic workload that mothers are usually responsible for in addition to their paid work, what Arlie Hochschild (2012) refers to as 'the second shift,' has been compounded and expanded, creating a 'third' and 'fourth' shift that involves homeschooling, increased carework, and 'worrywork' that burdens mothers during a crisis (O'Reilly & Green, 2021, p. 21). Mothers are the unrecognized 'front-line workers' of the pandemic – caring for sick family members, trying to balance working from home with childcare and homeschooling that has pushed mothers to their breaking points. This has left many mothers overworked, overstressed, overwhelmed, taking a substantial toll on their well-being. The purpose of this research is to examine the pressures, changes, and challenges around paid work, care, and family during the pandemic that mothers face– and the strategies they use to navigate these difficult situations. This study involves 11 qualitative interviews with Canadian mothers. The aim was to discover how women define and understand their own experiences of pandemic parenting, and how their experiences and choices were shaped by their constraining circumstances and contexts. This study examines the norms surrounding ‘who cares? ’ and how disparities in carework underpin many of the gender inequalities women experience that blur the boundaries between their private and public lives.
Tourist Reviews Sentiment Classification using Deep Learning Techniques: A Case Study in Saudi Arabia

—Now-a-days, social media sites and travel blogs have become one of the most vital expression sources. Tourists express everything related to their experiences, reviews, and opinions about the place they visited. Moreover, the sentiment classification of tourist reviews on social media sites plays an increasingly important role in tourism growth and development. Accordingly, these reviews are valuable for both new tourists and officials to understand their needs and improve their services based on the assessment of tourists. The tourism industry anywhere also relies heavily on the opinions of former tourists. However, most tourists write their reviews in their local dialect, making sentiment classification more difficult because there are no specific rules to control the writing system. Moreover, there is a gap between Modern Standard Arabic (MSA) and local dialects. one of the most prominent issues in sentiment analysis is that the local dialect lexicon has not seen significant development. Although a few lexicons are available to the public, they are sparse and small. Thus, this paper aims to build a model capable of accurate sentiment classification in the Saudi dialect for Arabic in tourist place reviews using deep learning techniques. Machine learning techniques help classifying these reviews into (positive, negative, and neutral). In this paper, three machine learning algorithms were used, Support -Vector Machine (SVM), Long short-term memory (LSTM), and Recurrent Neural Network (RNN). These algorithms are classified using Google Map data set for tourist places in Saudi Arabia. Performance classification of these algorithms is done using various performance measures such as accuracy, precision, recall and F-score. The results show that the SVM algorithm outperforms the deep learning techniques. The result of SVM was 98%, outperforming the LSTM, and RNN had the same performance of 96%.
Classification of Rank 2 Cluster Varieties

We classify rank $2$ cluster varieties (those for which the span of the rows of the exchange matrix is $2$-dimensional) according to the deformation type of a generic fiber $U$ of their ${\mathcal X}$-spaces, as defined by Fock and Goncharov [Ann. Sci. Ec. Norm. Super. (4) 42 (2009), 865-930]. Our approach is based on the work of Gross, Hacking, and Keel for cluster varieties and log Calabi-Yau surfaces. Call $U$ positive if $\dim[\Gamma(U,{\mathcal O}_U)] = \dim(U)$ (which equals 2 in these rank 2 cases). This is the condition for the Gross-Hacking-Keel construction [Publ. Math. Inst. Hautes Etudes Sci. 122 (2015), 65-168] to produce an additive basis of theta functions on $\Gamma(U,{\mathcal O}_U)$. We find that $U$ is positive and either finite-type or non-acyclic (in the usual cluster sense) if and only if the inverse monodromy of the tropicalization $U^{\rm trop}$ of $U$ is one of Kodaira's monodromies. In these cases we prove uniqueness results about the log Calabi-Yau surfaces whose tropicalization is $U^{\rm trop}$. We also describe the action of the cluster modular group on $U^{\rm trop}$ in the positive cases.
Two probabilistic models of competition

Author(s): Antunovic, Tonci | Advisor(s): Chatterjee, Sourav; Peres, Yuval | Abstract: In this thesis we introduce and study two probabilistic models of competition and their applications. The first model is a particular contact process, and is intended to simulate propagation dynamics in real social networks. The second one stems from game theory and is of more theoretical value, as it is used to prove the existence of solutions to a certain non-linear partial differential equation.The first model consists of two competing first passage percolation processes started from uniformly chosen subsets of a random regular graph on N vertices. The processes are allowed to spread with different rates, start from vertex subsets of different sizes or at different times. We obtain tight results regarding the sizes of the vertex sets occupied by each process, showing that in the generic situation one process will occupy Theta(1)N^α vertices, for some 0 l α l 1. The value of α is calculated in terms of the relative rates of the processes, as well as the sizes of the initial vertex sets and the possible time advantage of one process.The second model is a version of the stochastic "Tug-of-War" game, played on graphs and smooth domains, with the empty set of terminal states. We prove that, when the running payoff function is shifted by an appropriate constant, the values of the game after n steps converge in the continuous case and the case of finite graphs with loops. Using this we prove the existence of solutions to the infinity Laplace equation with vanishing Neumann boundary condition.
Supernova 2008J: early time observations of a heavily reddened SN 2002ic-like transient

Supernovae (SNe) are the final stage in the life of massive stars. Their explosion unbinds the progenitor star revealing its inner layers. The SN ejecta interact with the circumstellar material (CSM), providing further information on the progenitor star.In this work we present the study of rare SN subtypes, aiming to investigate their observational and physical properties and those of their progenitor stars.These studies include the analysis of SN samples as well as that of single objects.Two main SN classes are discussed: radioactively-powered events and SNe interacting with their CSM.Within the first group, we investigated the rare (~1% of core-collapse SNe) family of SN 1987A-like events. These SNe are found to be the explosion of compact, hydrogen-rich blue supergiant (BSG) stars, and to occur mainly in moderately low metallicity environs. We also studied a sample of 20 stripped-envelope (SE) SNe, which are also powered by the decay of radioactive 56Ni. These SNe are the result of the core-collapse of massive, hydrogen or even helium-poor stars stripped of their outer envelopes by line-driven winds and/or by the accretion onto companion stars.We investigated the differences among the early-time light curves of the subtypes forming the SE SN group (IIb, Ib, Ic, Ic-BL) and found that in all of them the 56Ni is strongly mixed out in the ejecta. This result suggests that the difference between helium-poor and helium-rich SNe is due to an actual lack of helium in SNe Ic and Ic-BL rather than to a different degree of 56Ni mixing.Our work on CSM-interacting SNe include the study of a sample of SNe IIn, i.e. core-collapse SNe interacting with hydrogen-rich CSM, and the analysis of SN 2008J, a particularly rare event which we interpreted as the interaction of a thermonuclear SN Ia with a thick hydrogen-rich CSM. Spectral analysis of the SN IIn sample suggests that these SNe are likely to be the explosion of luminous blue variable stars (LBVs), although other channels are not excluded.
Projection Operators in the Theory of Two-Time Green Functions

New projection operators are introduced in the theory of two-time Green functions. The self-energy part of the Green function is expressed in terms of the random force whose time development differes from usual one by a factor (1- P ), P being the projection operator. In the case of the Bose system, we obtained the time-relaxation expression for the width function \(\varGamma\)( k , ω) of the dynamical structure factor, which satisfies the sum rule by Puff.
Adaptively Clustering Neighbor Elements for Image Captioning

We design a novel global-local Transformer named \textbf{Ada-ClustFormer} (\textbf{ACF}) to generate captions. We use this name since each layer of ACF can adaptively cluster input elements to carry self-attention (Self-ATT) for learning local context. Compared with other global-local Transformers which carry Self-ATT in fixed-size windows, ACF can capture varying graininess, \eg, an object may cover different numbers of grids or a phrase may contain diverse numbers of words. To build ACF, we insert a probabilistic matrix C into the Self-ATT layer. For an input sequence {{s}_1,. ,{s}_N , C_{i,j} softly determines whether the sub-sequence {s_i,. ,s_j} should be clustered for carrying Self-ATT. For implementation, {C}_{i,j} is calculated from the contexts of {{s}_i,. ,{s}_j}, thus ACF can exploit the input itself to decide which local contexts should be learned. By using ACF to build the vision encoder and language decoder, the captioning model can automatically discover the hidden structures in both vision and language, which encourages the model to learn a unified structural space for transferring more structural commonalities. The experiment results demonstrate the effectiveness of ACF that we achieve CIDEr of 137.8, which outperforms most SOTA captioning models and achieve comparable scores compared with some BERT-based models. The code will be available in the supplementary material.