Miracle-dz/UniFAR-FTUs huggingface dataset:
3D Adipose Tissue Culture Links the Organotypic Microenvironment to Improved Adipogenesis

Obesity and type 2 diabetes are strongly associated with adipose tissue dysfunction and impaired adipogenesis. Understanding the molecular underpinnings that control adipogenesis is thus of fundamental importance for the development of novel therapeutics against metabolic disorders. However, translational approaches are hampered as current models do not accurately recapitulate adipogenesis. Here, a scaffold‐free versatile 3D adipocyte culture platform with chemically defined conditions is presented in which primary human preadipocytes accurately recapitulate adipogenesis. Following differentiation, multi‐omics profiling and functional tests demonstrate that 3D adipocyte cultures feature mature molecular and cellular phenotypes similar to freshly isolated mature adipocytes. Spheroids exhibit physiologically relevant gene expression signatures with 4704 differentially expressed genes compared to conventional 2D cultures (false discovery rate < 0.05), including the concerted expression of factors shaping the adipogenic niche. Furthermore, lipid profiles of >1000 lipid species closely resemble patterns of the corresponding isogenic mature adipocytes in vivo (R2 = 0.97). Integration of multi‐omics signatures with analyses of the activity profiles of 503 transcription factors using global promoter motif inference reveals a complex signaling network, involving YAP, Hedgehog, and TGFβ signaling, that links the organotypic microenvironment in 3D culture to the activation and reinforcement of PPARγ and CEBP activity resulting in improved adipogenesis.
Improving Prehospital Stroke Diagnosis Using Natural Language Processing of Paramedic Reports

Supplemental Digital Content is available in the text. Background and Purpose: Accurate prehospital diagnosis of stroke by emergency medical services (EMS) can increase treatments rates, mitigate disability, and reduce stroke deaths. We aimed to develop a model that utilizes natural language processing of EMS reports and machine learning to improve prehospital stroke identification. Methods: We conducted a retrospective study of patients transported by the Chicago EMS to 17 regional primary and comprehensive stroke centers. Patients who were suspected of stroke by the EMS or had hospital-diagnosed stroke were included in our cohort. Text within EMS reports were converted to unigram features, which were given as input to a support-vector machine classifier that was trained on 70% of the cohort and tested on the remaining 30%. Outcomes included final diagnosis of stroke versus nonstroke, large vessel occlusion, severe stroke (National Institutes of Health Stroke Scale score >5), and comprehensive stroke center-eligible stroke (large vessel occlusion or hemorrhagic stroke). Results: Of 965 patients, 580 (60%) had confirmed acute stroke. In a test set of 289 patients, the text-based model predicted stroke nominally better than models based on the Cincinnati Prehospital Stroke Scale (c-statistic: 0.73 versus 0.67, P=0.165) and was superior to the 3-Item Stroke Scale (c-statistic: 0.73 versus 0.53, P<0.001) scores. Improvements in discrimination were also observed for the other outcomes. Conclusions: We derived a model that utilizes clinical text from paramedic reports to identify stroke. Our results require validation but have the potential of improving prehospital routing protocols.
Efficient Gradient Support Pursuit With Less Hard Thresholding for Cardinality-Constrained Learning

Recently, stochastic hard thresholding (HT) optimization methods [e.g., stochastic variance reduced gradient hard thresholding (SVRGHT)] are becoming more attractive for solving large-scale sparsity/rank-constrained problems. However, they have much higher HT oracle complexities, especially for high-dimensional data or large-scale matrices. To address this issue and inspired by the well-known Gradient Support Pursuit (GraSP) method, this article proposes a new Relaxed Gradient Support Pursuit (RGraSP) framework. Unlike GraSP, RGraSP only requires to yield an approximation solution at each iteration. Based on the property of RGraSP, we also present an efficient stochastic variance reduction-gradient support pursuit algorithm and its fast version (called stochastic variance reduced gradient support pursuit (SVRGSP+). We prove that the gradient oracle complexity of both our algorithms is two times less than that of SVRGHT. In particular, their HT complexity is about $\kappa _{\widehat {s}}$ times less than that of SVRGHT, where $\kappa _{\widehat {s}}$ is the restricted condition number. Moreover, we prove that our algorithms enjoy fast linear convergence to an approximately global optimum, and also present an asynchronous parallel variant to deal with very high-dimensional and sparse data. Experimental results on both synthetic and real-world datasets show that our algorithms yield superior results than the state-of-the-art gradient HT methods.
Detection of Serum Cross-Reactive Antibodies and Memory Response to SARS-CoV-2 in Prepandemic and Post–COVID-19 Convalescent Samples

Abstract Background A notable feature of coronavirus disease 2019 (COVID-19) is that children are less susceptible to severe disease. Children are known to experience more infections with endemic human coronaviruses (HCoVs) compared to adults. Little is known whether HCoV infections lead to cross-reactive anti-severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) antibodies. Methods We investigated the presence of cross-reactive anti–SARS-CoV-2 IgG antibodies to spike 1 (S1), S1-receptor-binding domain (S1-RBD), and nucleocapsid protein (NP) by enzyme-linked immunosorbent assays, and neutralizing activity by a SARS-CoV-2 pseudotyped virus neutralization assay, in prepandemic sera collected from children (n = 50) and adults (n = 45), and compared with serum samples from convalescent COVID-19 patients (n = 16). Results A significant proportion of children (up to 40%) had detectable cross-reactive antibodies to SARS-CoV-2 S1, S1-RBD, and NP antigens, and the anti-S1 and anti–S1-RBD antibody levels correlated with anti–HCoV-HKU1 and anti–HCoV-OC43 S1 antibody titers in prepandemic samples (P < .001). There were marked increases of anti–HCoV-HKU1 and - OC43 S1 (but not anti-NL63 and -229E S1-RBD) antibody titers in serum samples from convalescent COVID-19 patients (P < .001), indicating an activation of cross-reactive immunological memory to β-coronavirus spike. Conclusions We demonstrated cross-reactive anti–SARS-CoV-2 antibodies in prepandemic serum samples from children and young adults. Promoting this cross-reactive immunity and memory response derived from common HCoV may be an effective strategy against SARS-COV-2 and future novel coronaviruses.
Identification of the Ingredients and Mechanisms of Curcumae Radix for Depression Based on Network Pharmacology and Molecular Docking

Background Curcumae Radix (CR), derived from the dry roots of Curcuma longa L. , family Zingiberaceae, is widely used to treat depression. However, the ingredients and mechanisms of CR are still unclear. The purpose of this study was to solve this problem using network pharmacology and molecular docking. Methods The active ingredients of CR were screened through TCMSP, and the depression-related genes were obtained through the Genetic Association, GeneCards, and OMIM databases. Then, DisGeNET score was performed to evaluate the correlation between co-genes and depression. Topological analysis was conducted to screen hub genes and proteins, molecular docking was performed to evaluate the binding ability of the hub protein with active ingredients, and gene ontology (Go) function analysis, gene tissue localization, and KEGG pathway analysis were conducted to explore the function and location of genes, as well as the mechanism of CR for treating depression. Results Eight ingredients of CR were screened based on pharmacokinetic properties, five of which are closely related to depression, including (E)−5-hydroxy-7-(4-hydroxyphenyl)−1-phenyl-1-heptene, (E)−1,7-diphenyl-3-hydroxy-1-hepten-5-one, oxycurcumenol, β-sitosterol, and sitosterol. They interacted with 45 co-genes and co-proteins with a DisGeNET score ≥0.3. AR, NOS2, PTGS2, and TYK2 were pivot genes. EGFR, PTGS2, HSP90AA1, MAPK8, and ESR1 were hub proteins. PTGS2 was found to have good binding potential with oxycurcumenol, (E)−1,7-diphenyl-3-hydroxy-1-hepten-5-one and (E)−5-hydroxy-7-(4-hydroxyphenyl)−1-phenyl-1-heptene. Go functional analysis indicated that co-genes involved complex biological processes, cellular components and molecular functions. PER2, P2RX7, GRM1, TACR1, MAPK8, HCRTR1, EGFR, and TYK2 were highly expressed in the prefrontal cortex. The potential pathways for CR to exert antidepressant effects were calcium, estrogen, PI3K-Akt and ErbB signaling pathways. Conclusions This study revealed the ingredients, effective targets and mechanisms of CR in the treatment of depression, which provides a new perspective for the development of new antidepressants.
Impacts of Number of Poles and Slots on Armature Reaction and Performance of Ironless Permanent Magnet Motors

Abstract Armature reaction is considered as one of the challenging problems in on-load permanent magnet (PM) motors. Reducing the undesirable impacts of the armature reaction is necessary to improve the PM motors performance. The ironless stator PM motor is known as an appropriate choice for low armature reaction. Although in these motors, disturbing high-order harmonics of armature reaction are removed, improper winding distribution still causes asynchronous field harmonics. Choosing the number of poles and slots leads to various structures with different armature windings for ironless stator PM motors. Impacts of armature reaction upon the performance of the motors with different structures are addressed in this article. This is done using precisely and quickly by a combined analytical model. This model can model the PMs magnetic fields, the air gap magnets field, and the armature reaction field. The finite element modeling (FEM) results confirm the accuracy of the proposed analytical model. Then, the analytical method and FEM are used to predict the performance of the ironless stator PM motors with the different numbers of poles and slots. This makes it possible to suggest an appropriate number of poles and slots for lower armature reaction, lower disturbed voltage harmonics, lower torque ripple, higher output power, and higher winding distribution factor.
Unconscious loss processing in diabetes: associations with medication adherence and quality of care

Recent psychodynamic research hypothesises a connection between diabetes adaptation and the feeling of loss and damage enacted by such a chronic disease. This study aims at exploring the associations between damaged ego-related strategies in persons with diabetes and: 1) age, gender, type and duration of diabetes, and related treatment, 2) depressive symptoms, 3) indicators of adherence and quality of care. A convenience sample of 109 persons (56% female; n = 61 with type 2 diabetes) with a mean age of 56.12 years (SD = 15.30) was enrolled. Participants completed measures of damaged ego-related strategies (empathic identification, mania, frustration, destructiveness) (PET), depression (BDI-II), self-care (SDSCA), medication adherence (Morisky Scale) and quality of care (Score-Q index). Linear multiple stepwise regressions and Pearson’s correlations were performed. Age was negatively associated with empathic identification (β = .317, p < .01) and positively with destructiveness (β = .323, p < .01). Depressive symptoms negatively correlated with empathic identification (r = −.34, p < .01). Moreover, medication adherence was associated with higher empathic identification (r = .22, p < .05) and lower mania (r = −.30, p < .01), while quality of care was negatively associated with destructiveness (r = −.25, p < .05). Overall, a successful loss processing seems promising in lowering depression and non-adherence in persons with diabetes. Specifically, the need for psychological support for older persons is advocated.
Application of polyethylene glycol-coated iron oxide nanoparticles for magnetic solid phase extraction of copper from seawater samples and its determination by graphite furnace atomic absorption spectrometry using response surface methodology for optimization of extraction

Abstract In this research, a novel solid phase extraction method combined with graphite furnace atomic absorption spectrometry has been employed for the extraction and trace determination of copper in tap and seawater samples. The technique was based on poly ethylene glycol-coated iron oxide nanoparticle as adsorbent for solid phase extraction of copper ions. Optimal conditions including pH, extraction and desorption time, concentration and volume of eluent, temperature, volume of sample, amount of PEG and sorbent were investigated and optimized using both the one-variable-at-a-time method and response surface methodology. Under the optimal experimental conditions, the calibration graph was linear in range of 0.5–125.0 µg/L with detection limit of 6 ng/L. Good enrichment factor of 121 folds for the analyte was obtained. The repeatability of the method was satisfactory (RSD ≤ 4.8%). Finally a few seawater samples of Chabahar Bay (Southeast Iran) were analyzed successfully with proposed procedure.
Optimal Voltage Regulation Scheme for PV-Rich Distribution Systems Interconnected with D-STATCOM

Abstract This paper proposes an optimal voltage regulation scheme (OVRS) for distribution systems with rich photovoltaic (PV). Various regulation devices are optimally controlled in a coordinated manner: PV inverter, D-STATCOM, step voltage regulator (SVR), and on-load-tap-changer (OLTC). A data structure algorithm is proposed to split the distribution system into layered zones considering the radial structure of the system. The solution process of the proposed scheme is accomplished by a meta-heuristic optimizer. OVRS addresses the voltage violations while yielding a coordinated operation of the various control devices. The proposed OVRS involves three control levels to completely prevent voltage violations. In the first control level, the PV inverter and D-STATCOM mitigate rapidly the local voltage deviation through injecting/absorbing optimized reactive power. The second control level is a decentralized-based control scheme that utilizes the voltage control devices in each zone to handle the voltage violations if any. For each zone, the control devices in the upper-stream zones (parent zones) are managed by the third control level to ensure cooperative control actions. The simulation results on the 119-bus distribution system, with clear, low fluctuation, and high fluctuation of solar radiation profiles, demonstrate the effectiveness of the proposed OVRS.
Long non-coding RNA TUG1 aggravates cerebral ischemia and reperfusion injury by sponging miR-493-3p/miR-410-3p

Abstract Background Cerebral ischemia and reperfusion injury (CIRI) affects bodily function by causing irreversible damage to brain cells. The diverse pathophysiological course factors hinder the research work to go deeper. Long noncoding RNA taurine-upregulated gene 1 (TUG1) has been reported to be related to CIRI. This study explored the undefined regulatory pathway of TUG1 in CIRI. Methods Quantitative real-time polymerase chain reaction was applied to test the expression of TUG1, microRNA (miR)-493-3p and miR-410-3p. The viability and apoptosis of oxygen and glucose deprivation/reoxygen (OGD/R) model cells were evaluated by cell counting kit-8 and flow cytometry assay, respectively. The determination of inflammatory factors of interleukin-6, interleukin-1β and tumor necrosis factor-α was presented by enzyme-linked immunosorbent assay. The oxidative stress was performed by measuring the generation of malondialdehyde, reactive oxygen species and the activity of superoxide dismutase. Cytotoxicity was presented by measuring the generation of lactate dehydrogenase. Western blot assay was devoted to assessing the level of apoptosis-related factors (cleaved-caspase-3 and cleaved-caspase-9) and the protein level of c-Jun N-terminal kinase (JNK) and p38 mitogen-activated protein kinase (p38 MAPK) pathway-related factors in neuro-2a cells treated by OGD/R. Besides, online database starBase was applied to predict the potential binding sites of TUG1 to miR-493-3p and miR-410-3p, which was further confirmed by the dual-luciferase reporter system. Results The expression of TUG1 was upregulated, while miR-493-3p or miR-410-3p was downregulated in the serum of CIRI and OGD/R model cells. Meanwhile, knockdown of TUG1 eliminated the suppression in proliferation, the promotion in apoptosis, inflammation and oxidative stress, as well as the cytotoxicity in OGD/R model cells. Interestingly, the inhibition of miR-493-3p or miR-410-3p allayed the above effects. In addition, TUG1 harbored miR-493-3p or miR-410-3p and negatively regulated their expression. Finally, the TUG1 activated JNK and p38 MAPK pathways by sponging miR-493-3p/miR-410-3p. Conclusion TUG1 motivated the development of CIRI by sponging miR-493-3p/miR-410-3p to activate JNK and p38 pathways. The novel role of TUG1 in CIRI may contribute to the advancement of CIRI treatment.
lncRNA MALAT1 regulated ATAD2 to facilitate retinoblastoma progression via miR-655-3p

Abstract Long noncoding RNA (lncRNA) metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) was reported as an oncogene in many tumors including retinoblastoma (RB). This research mainly focused on the functions and mechanism of MALAT1 in RB. MALAT1 was upregulated in RB tissues and cells, and it served as a competing endogenous RNA (ceRNA) and inhibited miRNA-655-3p (miR-655-3p) expression, which eventually regulated the expression of miR-655-3p downstream target ATPase Family AAA Domain Containing 2 (ATAD2). The level of ATAD2 significantly increased, while that of miR-655-3p remarkably decreased in RB tissues and cells. MALAT1 depletion inhibited cell proliferation, metastasis, and epithelial–mesenchymal transition (EMT), but promoted apoptosis in vitro and blocked xenograft tumor growth in vivo. MALAT1 exerted its oncogenic functions in RB by regulating miR-655-3p/ATAD2 axis.
How Can Digital Education Contribute To A Pedagogy For Environmental Care?

In this paper we unpack the theory and application of a pedagogy for environmental care and the place of digital technologies and interaction design within this pedagogy. The paper illustrates its application using exemplar projects, primarily drawn from primary education. We draw attention to the role of digital education in a place-based curriculum and discuss how teaching through local environments can contribute to understanding of global environmental issues and active participation locally and globally.
PlushPal: Storytelling with Interactive Plush Toys and Machine Learning

This paper presents PlushPal, a web-based design tool for children to make plush toys interactive with machine learning (ML). With PlushPal, children attach micro:bit hardware to stuffed animals, design custom gestures for their toy, and build gesture-recognition ML models to trigger their own sounds. We describe how, in the context of storytelling, PlushPal introduces core concepts in ML including data sampling and model evaluation. We conducted online workshops and in-person play sessions with 11 children between 8-14 years old building interactive stuffed animals with PlushPal. In these play sessions, we observed how children imagined bringing their toys to life using ML, as well as how children’s data literacy changed as a result of experimenting with sensors, data sampling, and building their own ML models. Our work contributes a novel design space for children to express their ideas using gesture, as well as a description of observed debugging practices, building on efforts to support children using ML to enhance creative play.
Digital Design Literacy in K-9 Education: Experiences from Pioneer Teachers

Based on a comprehensive account of digital design literacy, we report from an interview study with pioneer teachers to disclose their experiences from teaching design in formal K-9 education. Unique for the study is that the interviewees were highly qualified K-9 teachers with research-based in-service training in design and more than three years of teaching experience in the area. Our findings suggest that digital design literacy supports children's agency and empowerment in relation to digital technology. Moreover, digital design literacy provides children with four broad competence areas of effective teamwork, contextual inquiry, imagination and creation, and design process navigation. Our study also demonstrates that digital design literacy does not fit seamlessly within the path of conventional school courses. Several structural barriers in formal education such as summative assessments, teacher-roles, failure-prevention and children's lack of experiences with open-ended assignments are reported and discussed in relation to previous studies in child-computer interaction research.
OutletSpy: cross-outlet application inference via power factor correction signal

Trade secrets such as intellectual properties are the inherent values for firms. Although companies have exploited strict access management policies and isolated their networks from the public Internet, trade secrets are still vulnerable to side-channel attacks. Side-channels can reveal the computing processes of computers in forms of various physical signals such as light, electromagnetism, and even heat. Such side-channels can bypass the isolation mechanism and therefore bring about severe threats. However, existing side-channels can only perform well within a short-distance (e.g., less than 1 meter) due to the high attenuation of signals. In this paper, we seek to utilize the built-in power lines in a building and construct a power side-channel that enables remote, i.e., cross-outlet attack against trade secrets. To this end, we investigate the power factor correction (PFC) module inside the power supply units of commodity computers and find that the PFC signals observed from an outlet can precisely reveal the power consumption information of all the connected devices, even from the outlets in adjacent rooms. Based upon this insight, we design and implement OutletSpy, a power side-channel attack that can infer application launching from a remote outlet and therefore enjoys the stealthiness property. We validate and evaluate OutletSpy with a dataset under different background APPs, time variations and different locations. The experiment results show OutletSpy can infer the application launching with 98.25% accuracy.
Towards Early Detection and Burden Estimation of Atrial Fibrillation in an Ambulatory Free-living Environment

Early detection and accurate burden estimation of atrial fibrillation (AFib) can provide the foundation for effective physician treatment. New approaches to accomplish this have attracted tremendous attention in recent years. In this paper, we develop a novel passive smartwatch-based system to detect AFib episodes and estimate the AFib burden in an ambulatory free-living environment without user engagement. Our system leverages a built-in PPG sensor to collect heart rhythm without user engagement. Then, a data preprocessor module includes time-frequency (TF) analysis to augment features in both the time and frequency domain. Finally, a lightweight multi-view convolutional neural network consisting of 19 layers achieves the AFib detection. To validate our system, we carry out a research study that enrolls 53 participants across three months, where we collect and annotate more than 27,622 hours of data. Our system achieves an average of 91.6% accuracy, 93.0% specificity, and 90.8% sensitivity without dropping any data. Moreover, our system takes 0.51 million parameters and costs 5.18 ms per inference. These results reveal that our proposed system can provide a clinical assessment of AFib in daily living.