Miracle-dz/UniFAR-FTUs huggingface dataset:
A Network Biology Approach for Assessing the Role of Pathologic Adipose Tissues in Insulin Resistance Using Meta-analysis of Microarray Datasets

Background: The role of adipose tissue in Insulin resistance (IR) and Type 2 Diabetes (T2D) has well been received in the biomedical community; being a precursor of T2D, identification of the molecular basis of IR is therefore, vital to elucidate T2D- pathogenesis and meta-analysis of previously conducted microarray studies provides an inexpensive approach to achieve this end. Methods: In this study, we have carried out a statistical meta-analysis of 157 microarray datasets from five independent studies and identified a meta-signature of 1,511 genes; their functional meaning was elucidated by integrated pathways-analysis. Further, a protein-protein interaction network was constructed and key genes along with their high confidence transcriptional- and epigenetic-mediators were identified using a network biology approach. Results: Various inflammation- and immune system-related pathways such as TGF-β signaling, IL7 signaling, Neutrophil degranulation, and Chemokine signaling etc. were enriched in sick adipose tissues; identified transcription factors, and microRNAs were also found to regulate processes relevant to IR/T2D pathophysiology. Conclusion: This study endorses the development of effective bioinformatics workflow and further grants an indication for the acceptance of adiposopathy as the root mechanistic pathology that poses risk for development of type 2 diabetes; concept of adipospathy in place of metabolic syndrome will open the possibility to design drugs, those will ameliorate adipose functions and hence proved to be more effective against Type 2 Diabetes.
Effect of Usnic Acid on Osteoclastogenic Activity

Osteoclasts are the only cells that can resorb bone and they are produced from monocytes/macrophages in the presence of M-CSF and RANKL and are activated in vivo by an immune response. Usnic acid is a secondary metabolite of lichen and has a unique dibenzofuran skeleton. It has been used for years in cosmetics, fragrances, and traditional medicines. It has a wide range of bioactivities, including anti-inflammatory, anti-bacterial, anti-cancer, anti-viral, and so on. However, the anti-osteoclastogenic activity of usnic acid has not been reported yet. In this study, we investigated whether usnic acid could affect RANKL-mediated osteoclastogenesis. Usnic acid significantly inhibited RANKL-mediated osteoclast formation and function by reducing the transcriptional and translational expression of NFATc1, a master regulator of osteoclastogenesis. In addition, it prevented lipopolysaccharides (LPS)-induced bone erosion in mice. Taken together, our results suggest that usnic acid might be a potential candidate for the treatment of osteoporosis.
Anomaly Detection for imbalanced datasets with Deep Generative Models

Many important data analysis applications present with severely imbalanced datasets with respect to the target variable. A typical example is medical image analysis, where positive samples are scarce, while performance is commonly estimated against the correct detection of these positive examples. We approach this challenge by formulating the problem as anomaly detection with generative models. We train a generative model without supervision on the `negative' (common) datapoints and use this model to estimate the likelihood of unseen data. A successful model allows us to detect the `positive' case as low likelihood datapoints. In this position paper, we present the use of state-of-the-art deep generative models (GAN and VAE) for the estimation of a likelihood of the data. Our results show that on the one hand both GANs and VAEs are able to separate the `positive' and `negative' samples in the MNIST case. On the other hand, for the NLST case, neither GANs nor VAEs were able to capture the complexity of the data and discriminate anomalies at the level that this task requires. These results show that even though there are a number of successes presented in the literature for using generative models in similar applications, there remain further challenges for broad successful implementation.
Lecture Notes on Optimal Power Flow (OPF)

These lecture notes cover the DC Optimal Power and AC Optimal Power Flow formulations, as well as the Economic Dispatch for Power Systems. Their aim is to supplement the study material for the course "31765: Optimization in modern power systems" at the Technical University of Denmark (DTU). The first edition of the present lecture notes was prepared for the academic year 2018-2019. Note that the material presented in these notes is a constant work in progress. Future editions will include OPF formulations based on semidefinite programming, detailed derivation of Locational Marginal Prices, and other topics. For any comments, errors, or omissions, you are welcome to contact me at "spchatz_at_elektro. dtu.dk". Special thanks to the students of the 31765 course for their remarks and suggestions to improve these lecture notes.
Conceptualizing a Sustainable Food System in an Automated World: Toward a “Eudaimonian” Future

The industrialized world has entered a new era of widespread automation, and although this may create long-term gains in economic productivity and wealth accumulation, many professions are expected to disappear during the ensuing shift, leading to potentially significant disruptions in labor markets and associated socioeconomic difficulties. Food production, like many other industrial sectors, has also undergone a century of mechanization, having moved toward increasingly large-scale monoculture production—especially in developed economies—with higher yields but detrimental environmental impacts on a global scale. Certain characteristics of the food sector and its products cast doubts on whether future automation will influence it in the same ways as in other sectors. We conceptualize a model of future food production within the socioeconomic conditions created by widespread automation. We ideate that despite immediate shocks to the economy, in the long run higher productivity can free up human activity to be channeled toward more interactive, skill-intensive food production systems, where communal efforts can reduce industrial reliance, diversify farming, and reconnect people to the biosphere—a realization of human well-being that resembles the classical philosophical ideal of Eudaimonia. We explore food production concepts, such as communal gardens and polyculture, and the economic conditions and institutions needed to underwrite them [e.g., a universal basic income (UBI)]. However, arguments can be raised as to why social-ecological systems would benefit from more labor-intensive food production. In this paper we: (1) discuss the current state of the food system and the need to reform it in light of its environmental and social impacts; (2) present automation as a lever that could move society toward more sustainable food production; (3) highlight the beneficial attributes of a Eudaimonian model; and (4) discuss the potential challenges to its implementation. Our purpose is to highlight a possible outcome that future research will need to refine and expand based on evidence and successful case studies. The ultimate aim is to promote a food system that can provide food security while staying within the safe operating space of planetary boundaries, produce more nutritious diets, enhance social capital, and reconnect communities with the biosphere.
A Multi-Robotic System for Environmental Cleaning

There is a lot of waste in an industrial environment that could cause harmful effects to both the products and the workers resulting in product defects, itchy eyes or chronic obstructive pulmonary disease, etc. While automative cleaning robots could be used, the environment is often too big for one robot to clean alone in addition to the fact that it does not have adequate stored dirt capacity. We present a multi-robotic dirt cleaning system algorithm for multiple automatic iRobot Creates teaming to efficiently clean an environment. Moreover, since some spaces in the environment are clean while others are dirty, our multi-robotic system possesses a path planning algorithm to allow the robot team to clean efficiently by spending more time on the area with higher dirt level. Overall, our multi-robotic system outperforms the single robot system in time efficiency while having almost the same total battery usage and cleaning efficiency result.
Investigation of Future Reference Expressions in Trend Information

When we categorize textual data according to time categories the three main types of information that come up are the past, the present, and the future. In this paper we present our study in predicting the future. In particular, we aim at detecting expressions which refer to future events (temporal expressions, etc.) and apply them to support the prediction of probable future outcomes. In order to realize the future prediction support, we ﬁrstly need to be able to ﬁnd out whether a sentence refers to the future or not in general. We propose a method of bi-polar text classiﬁcation for sentences into either future-related or non-future-related (other). To do this we use a machine learning based sentence pattern extraction system SPEC and report on the accuracy of extracted patterns. We train the classiﬁer using semantic representations of sentences and show that it is possible to extract fully automatically frequent patterns from sentences referring to the future.
Improvement of neutrophil gelatinase-associated lipocalin sensitivity and specificity by two plasma measurements in predicting acute kidney injury after cardiac surgery

Introduction Acute kidney injury (AKI) remains among the most severe complication after cardiac surgery. The aim of this study was to evaluate the neutrophil gelatinase-associated lipocalin (NGAL) as possible biomarker for the prediction of AKI in an adult cardiac population. Materials and methods Sixty-nine consecutive patients who underwent cardiac surgeries in our hospital were prospectively evaluated. In the intensive care unit (ICU) NGAL was measured as a new biomarker of AKI besides serum creatinine (sCrea). Patients with at least two factors of AKI risk were selected and samples collected before the intervention and soon after the patient’s arrival in ICU. As reference standard, sCrea measurements and urine outputs were evaluated to define the clinical AKI. A Triage Meter for plasma NGAL fluorescence immunoassay was used. Results Acute kidney injury occurred in 24 of the 69 patients (35%). Analysis of post-operative NGAL values demonstrated an AUC of 0.71, 95% CI (0.60 - 0.82) with a cut-off = 154 ng/mL (sensitivity = 76%, specificity = 59%). Moreover, NGAL after surgery had a good correlation with the AKI stage severity (P ≤ 0.001). Better diagnostic results were obtained with two consecutive tests: sensitivity 86% with a negative predictive value (NPV) of 87%. At 10-18 h after surgery sCrea measurement, as confirmatory test, allowed to reach a more sensitivity and specificity with a NPV of 96%. Conclusions The assay results showed an improvement of NGAL diagnostic accuracy evaluating two tests. Consequently, NGAL may be useful for a timely treatment or for the AKI rule out in ICU patients.
How Can the Blind Men See the Elephant?

There is no denying the fact that AI’s original aim of reproducing human-level intelligence has taken a back seat in favor of the development of practical and efﬁ-cient systems for important but narrow domains under numerous umbrellas. While the importance of perception for intelligence is now well-understood, a major hurdle is to discover the appropriate representation and processes that can seamlessly support low-level perception and high-level cognition in a computational architecture. There is no shortage of cognitive architectures (see Samsonovich 2010 for a catalog), however, principled design is scarce. In this paper, we explicate our position and report on our ongoing investigations on representation and processes for developing an intelligent agent from ﬁrst principles.
Informing the Adaptation and Implementation of a Lifestyle Modification Program in Hispanics: A Qualitative Study Among Low-Income Hispanic Adults

Low-income Hispanics are a hard to reach population that face unique challenges in achieving behavior change. Understanding such challenges can inform the adaptation of lifestyle behavioral interventions and increase participant engagement. This descriptive qualitative study aimed to (1) inform lifestyle modification programs focused on improving the physical activity and healthy eating of low-income Hispanic adults and (2) explore potential gender differences necessary for consideration. Five gender-specific focus groups were conducted with Hispanic adults from a free community clinic (n = 45, 64% women, 53.1 ± 10.1 years). Sessions were audio-recorded, transcribed, and coded using a general inductive approach. The qualitative software program Dedoose was used to perform a content analysis. Emergent themes included topics of interest, methods of increasing engagement in healthy behaviors, and the delivery of lifestyle modification programs. Men expressed greater interest in physical activity, while women desired to learn about healthy eating. Men described the importance of physician communication, while women focused on the role of social support as facilitators for participating in such programs. Both genders described education as key to behavior change and viewed text messaging as a valuable complement. Results from this study can inform the adaptation of lifestyle modification programs and increase the engagement of low-income Hispanic populations.
Examining How the MAFB Transcription Factor Affects Islet β-Cell Function Postnatally

The sustained expression of the MAFB transcription factor in human islet β-cells represents a distinct difference in mice. Moreover, mRNA expression of closely related and islet β-cell–enriched MAFA does not peak in humans until after 9 years of age. We show that the MAFA protein also is weakly produced within the juvenile human islet β-cell population and that MafB expression is postnatally restricted in mouse β-cells by de novo DNA methylation. To gain insight into how MAFB affects human β-cells, we developed a mouse model to ectopically express MafB in adult mouse β-cells using MafA transcriptional control sequences. Coexpression of MafB with MafA had no overt impact on mouse β-cells, suggesting that the human adult β-cell MAFA/MAFB heterodimer is functionally equivalent to the mouse MafA homodimer. However, MafB alone was unable to rescue the islet β-cell defects in a mouse mutant lacking MafA in β-cells. Of note, transgenic production of MafB in β-cells elevated tryptophan hydroxylase 1 mRNA production during pregnancy, which drives the serotonin biosynthesis critical for adaptive maternal β-cell responses. Together, these studies provide novel insight into the role of MAFB in human islet β-cells.
Native Language Identification using i-vector

The task of determining a speaker's native language based only on his speeches in a second language is known as Native Language Identification or NLI. Due to its increasing applications in various domains of speech signal processing, this has emerged as an important research area in recent times. In this paper we have proposed an i-vector based approach to develop an automatic NLI system using MFCC and GFCC features. For evaluation of our approach, we have tested our framework on the 2016 ComParE Native language sub-challenge dataset which has English language speakers from 11 different native language backgrounds. Our proposed method outperforms the baseline system with an improvement in accuracy by 21.95% for the MFCC feature based i-vector framework and 22.81% for the GFCC feature based i-vector framework.
Ultra‐high‐frequency deep brain stimulation at 10,000 Hz improves motor function

Stimulation frequency has been considered a crucial determinant of efficacy in deep brain stimulation (DBS). DBS at frequencies over 250 Hz is not currently employed and consensus in the field suggests that higher frequencies are not clinically effective. With the recent demonstration of clinically effective ultrahigh frequency (UHF) spinal cord stimulation at 10 kHz we tested whether UHF stimulation could also be clinically useful in movement disorder patients with DBS. We evaluated the clinical effects and safety of UHF DBS in patients with subthalamic nucleus (STN) or ventral intermediate thalamic nucleus (VIM) DBS. We studied the effects of conventional (130 Hz) and UHF stimulation in 5 patients with Parkinson's disease (PD) with STN DBS and in one patient with essential tremor (ET) with VIM DBS. We compared the clinical benefit and adverse effects of stimulation at various amplitudes either intraoperatively or postoperatively with the electrodes externalized. Motor performance improved in all 6 patients with UHF DBS. About 10 kHz stimulation at amplitudes = 3.0 mA appeared to be as effective as 130 Hz in improving motor symptoms (46.2% vs 53.5% motor score reduction, P = .110, N = 90 trials). Interestingly, 10 kHz stimulation resulted in fewer stimulation-induced paresthesiae and speech adverse effects than 130 Hz stimulation. Our results indicate that DBS at 10 kHz produces clinical benefits in patients with movement disorders. Like 10 kHz spinal cord stimulation, 10 kHz DBS has the potential to produce clinical benefits while possibly reducing stimulation-induced adverse effects. Further studies will be required to optimize UHF DBS stimulation parameters and to determine its clinical utility.
Global map matching using BLE beacons for indoor route and stay estimation

Recently, location service using Bluetooth Low-Energy (BLE) beacon is gaining popularity. There also exist researches that estimate the route of the user from the location estimation results, visualize, and analyze it. In the conventional route estimation method based on the BLE beacon, after estimating the all locations from the radio field strength of the BLE, the route is estimated from the sequence of the estimated locations. Therefore, one of the causes of deterioration in accuracy could be the fact that the estimation was repeated twice. Therefore, in this paper, we propose a novel estimation method of the user's route and stay using BLE beacons. Specifically, we apply the idea of global map matching, used in the field of GPS, to route estimation based on BLE beacons. Thus, the global optimum route in beacon network can be estimated from the radio field intensities of the BLE beacon directly. We associate a beacon network with path network to estimate the route and stay of users. We installed approximately 1,600 BLE beacon transmitters in all the classrooms and corridors of Nagoya Institute of Technology, and confirmed the effectiveness of the proposed method by experiments.
Loss of Human Leukocyte Antigen Class I Expression Is Associated with Poor Prognosis in Patients with Advanced Breast Cancer

Background Human leukocyte antigen class I (HLA-I) molecules play important roles in regulating immune responses. Loss or reduction of HLA-I expression has been shown to be associated with prognosis in several cancers. Regulatory T-cells (Tregs) also play critical functions in immune response regulation. Evaluation of HLA-I expression status by the EMR8-5 antibody and its clinical impact in breast cancer have not been well studied, and its relationship with Tregs remains unclear. Methods We evaluated HLA-I expression and Treg infiltration by immunohistochemistry in 465 surgically resected breast cancer samples. We examined the correlation between HLA-I expression and Treg infiltration and clinicopathologic characteristics and survival analyses were performed. Results Total loss of HLA-I expression was found in 84 breast cancer samples (18.1%). Univariate survival analysis revealed that loss of HLA-I expression was significantly associated with worse disease-specific survival (DSS) (p = .029). HLA-I was not an independent prognostic factor in the entire patient group, but it was an adverse independent prognostic factor for DSS in patients with advanced disease (stage II–IV) (p = .031). Treg numbers were significantly higher in the intratumoral stroma of HLA-I–positive tumors than in HLA-I–negative tumors (median 6.3 cells/high power field vs 2.1 cells/high power field, p < .001). However, Tregs were not an independent prognostic factor in our cohort. Conclusions Our findings suggest that the loss of HLA-I expression is associated with poor prognosis in breast cancer patients, highlighting the role of HLA-I alterations in immune evasion mechanisms of breast cancer. HLA-I could be a promising marker that enables the application of more effective and precise immunotherapies for patients with advanced breast cancer.
Exploring the structure and function of temporal networks with dynamic graphlets

Motivation: With increasing availability of temporal real-world networks, how to efficiently study these data? One can model a temporal network as a single aggregate static network, or as a series of time-specific snapshots, each being an aggregate static network over the corresponding time window. Then, one can use established methods for static analysis on the resulting aggregate network(s), but losing in the process valuable temporal information either completely, or at the interface between different snapshots, respectively. Here, we develop a novel approach for studying a temporal network more explicitly, by capturing inter-snapshot relationships. Results: We base our methodology on well-established graphlets (subgraphs), which have been proven in numerous contexts in static network research. We develop new theory to allow for graphlet-based analyses of temporal networks. Our new notion of dynamic graphlets is different from existing dynamic network approaches that are based on temporal motifs (statistically significant subgraphs). The latter have limitations: their results depend on the choice of a null network model that is required to evaluate the significance of a subgraph, and choosing a good null model is non-trivial. Our dynamic graphlets overcome the limitations of the temporal motifs. Also, when we aim to characterize the structure and function of an entire temporal network or of individual nodes, our dynamic graphlets outperform the static graphlets. Clearly, accounting for temporal information helps. We apply dynamic graphlets to temporal age-specific molecular network data to deepen our limited knowledge about human aging. Availability and implementation: http://www.nd.edu/∼cone/DG. Contact: tmilenko@nd.edu Supplementary information: Supplementary data are available at Bioinformatics online.
Magnetic and defect probes of the SmB6 surface state

The topological nature of surface states in SmB6 is revealed using samples with magnetic substituents or magnetic tunneling tips. The impact of nonmagnetic and magnetic impurities on topological insulators is a central focus concerning their fundamental physics and possible spintronics and quantum computing applications. Combining scanning tunneling spectroscopy with transport measurements, we investigate, both locally and globally, the effect of nonmagnetic and magnetic substituents in SmB6, a predicted topological Kondo insulator. Around the so-introduced substitutents and in accord with theoretical predictions, the surface states are locally suppressed with different length scales depending on the substituent’s magnetic properties. For sufficiently high substituent concentrations, these states are globally destroyed. Similarly, using a magnetic tip in tunneling spectroscopy also resulted in largely suppressed surface states. Hence, a destruction of the surface states is always observed close to atoms with substantial magnetic moment. This points to the topological nature of the surface states in SmB6 and illustrates how magnetic impurities destroy the surface states from microscopic to macroscopic length scales.