Miracle-dz/UniFAR-FTUs huggingface dataset:
Social Connection Induces Cultural Contraction: Evidence from Hyperbolic Embeddings of Social and Semantic Networks

Research has repeatedly demonstrated the influence of social connection and communication on convergence in cultural tastes, opinions and ideas. Here we review recent studies and consider the implications of social connection on cultural, epistemological and ideological contraction, then formalize these intuitions within the language of information theory. To systematically examine connectivity and cultural diversity, we introduce new methods of manifold learning to map both social networks and topic combinations into comparable, two-dimensional hyperbolic spaces or Poincare disks, which represent both hierarchy and diversity within a system. On a Poincare disk, radius from center traces the position of an actor in a social hierarchy or an idea in a cultural hierarchy. The angle of the disk required to inscribe connected actors or ideas captures their diversity. Using this method in the epistemic culture of 21st Century physics, we empirically demonstrate that denser university collaborations systematically contract the space of topics discussed and ideas investigated more than shared topics drive collaboration, despite the extreme commitments academic physicists make to research programs over the course of their careers. Dense connections unleash flows of communication that contract otherwise fragmented semantic spaces into convergent hubs or polarized clusters. We theorize the dynamic interplay between structural expansion and cultural contraction and explore how this introduces an essential tension between the enjoyment and protection of difference.
Shift towards larger diatoms in a natural phytoplankton assemblage under combined high-CO2 and warming conditions

Abstract An indoor mesocosm experiment was carried out to investigate the combined effects of ocean acidification and warming on the species composition and biogeochemical element cycling during a winter/spring bloom with a natural phytoplankton assemblage from the Kiel fjord, Germany. The experimental setup consisted of a “Control” (ambient temperature of ~4.8 °C and ~535 ± 25 μatm pCO2), a “High-CO2” treatment (ambient temperature and initially 1020 ± 45 μatm pCO2) and a “Greenhouse” treatment (~8.5 °C and initially 990 ± 60 μatm pCO2). Nutrient replete conditions prevailed at the beginning of the experiment and light was provided at in situ levels upon reaching pCO2 target levels. A diatom-dominated bloom developed in all treatments with Skeletonema costatum as the dominant species but with an increased abundance and biomass contribution of larger diatom species in the Greenhouse treatment. Conditions in the Greenhouse treatment accelerated bloom development with faster utilization of inorganic nutrients and an earlier peak in phytoplankton biomass compared to the Control and High CO2 but no difference in maximum concentration of particulate organic matter (POM) between treatments. Loss of POM in the Greenhouse treatment, however, was twice as high as in the Control and High CO2 treatment at the end of the experiment, most likely due to an increased proportion of larger diatom species in that treatment. We hypothesize that the combination of warming and acidification can induce shifts in diatom species composition with potential feedbacks on biogeochemical element cycling.
Overexpression of Mechano-Growth Factor Modulates Inflammatory Cytokine Expression and Macrophage Resolution in Skeletal Muscle Injury

In muscle regeneration, infiltrating myeloid cells, such as macrophages mediate muscle inflammation by releasing key soluble factors. One such factor, insulin-like growth factor 1 (IGF-1), suppresses inflammatory cytokine expression and mediates macrophage polarization to anti-inflammatory phenotype during muscle injury. Previously the IGF-1Ea isoform was shown to be anti-inflammatory. Another isoform of IGF-1, mechano-growth factor (MGF), is structurally and functionally distinct from IGF-1Ea, but its role in muscle inflammation has not yet been characterized. In this study, we hypothesized that MGF expression in muscle injury modulates muscle inflammation. We first investigated changes of transcription and expression of MGF in response to skeletal muscle injury induced by cardiotoxin (CTX) in vivo. At 1–2 days post-injury, Mgf expression was significantly upregulated and positively correlated with that of inflammatory cytokines. Immunostaining revealed that infiltration of neutrophils and macrophages coincided with Mgf upregulation. Furthermore, infiltrating neutrophils and macrophages expressed Mgf, suggesting their contribution to MGF upregulation in muscle injury. Macrophages seem to be the predominant source of MGF in muscle injury, whereas neutrophil depletion did not affect muscle Mgf expression. Given the association of MGF and macrophages, we then studied whether MGF could affect macrophage infiltration and polarization. To test this, we overexpressed MGF in CTX-injured muscles and evaluated inflammatory marker expression, macrophage populations, and muscle regeneration outcomes. MGF overexpression delayed the resolution of macrophages, particularly the pro-inflammatory phenotype. This coincided with upregulation of inflammatory markers. Annexin V-based flow cytometry revealed that MGF overexpression likely delays macrophage resolution by limiting macrophage apoptosis. Although MGF overexpression did not obviously affect muscle regeneration outcomes, the findings are novel and provide insights on the physiological roles of MGF in muscle regeneration.
"Bilingual Expert" Can Find Translation Errors

The performances of machine translation (MT) systems are usually evaluated by the metric BLEU when the golden references are provided. However, in the case of model inference or production deployment, golden references are usually expensively available, such as human annotation with bilingual expertise. In order to address the issue of translation quality estimation (QE) without reference, we propose a general framework for automatic evaluation of the translation output for the QE task in the Conference on Statistical Machine Translation (WMT). We first build a conditional target language model with a novel bidirectional transformer, named neural bilingual expert model, which is pre-trained on large parallel corpora for feature extraction. For QE inference, the bilingual expert model can simultaneously produce the joint latent representation between the source and the translation, and real-valued measurements of possible erroneous tokens based on the prior knowledge learned from parallel data. Subsequently, the features will further be fed into a simple Bi-LSTM predictive model for quality estimation. The experimental results show that our approach achieves the state-of-the-art performance in most public available datasets of WMT 2017/2018 QE task.
Loosely-Coupled Semi-Direct Monocular SLAM

We propose a novel semi-direct approach for monocular simultaneous localization and mapping (SLAM) that combines the complementary strengths of direct and feature-based methods. The proposed pipeline loosely couples direct odometry and feature-based SLAM to perform three levels of parallel optimizations: 1) photometric bundle adjustment (BA) that jointly optimizes the local structure and motion, 2) geometric BA that refines keyframe poses and associated feature map points, and 3) pose graph optimization to achieve global map consistency in the presence of loop closures. This is achieved in real-time by limiting the feature-based operations to marginalized keyframes from the direct odometry module. Exhaustive evaluation on two benchmark datasets demonstrates that our system outperforms the state-of-the-art monocular odometry and SLAM systems in terms of overall accuracy and robustness.
Hydrogen Sulfide Biochemistry and Interplay with Other Gaseous Mediators in Mammalian Physiology

Hydrogen sulfide (H2S) has emerged as a relevant signaling molecule in physiology, taking its seat as a bona fide gasotransmitter akin to nitric oxide (NO) and carbon monoxide (CO). After being merely regarded as a toxic poisonous molecule, it is now recognized that mammalian cells are equipped with sophisticated enzymatic systems for H2S production and breakdown. The signaling role of H2S is mainly related to its ability to modify different protein targets, particularly by promoting persulfidation of protein cysteine residues and by interacting with metal centers, mostly hemes. H2S has been shown to regulate a myriad of cellular processes with multiple physiological consequences. As such, dysfunctional H2S metabolism is increasingly implicated in different pathologies, from cardiovascular and neurodegenerative diseases to cancer. As a highly diffusible reactive species, the intra- and extracellular levels of H2S have to be kept under tight control and, accordingly, regulation of H2S metabolism occurs at different levels. Interestingly, even though H2S, NO, and CO have similar modes of action and parallel regulatory targets or precisely because of that, there is increasing evidence of a crosstalk between the three gasotransmitters. Herein are reviewed the biochemistry, metabolism, and signaling function of hydrogen sulfide, as well as its interplay with the other gasotransmitters, NO and CO.
Joint Beamforming and Resource Allocation for Multi-User Full-Duplex Wireless Powered Communication Networks

In this paper, we investigate a wireless powered communication network (WPCN) protocol in which mobile stations (MS) with energy harvesting (EH) communicate with a full-duplex access point (FD- AP). The FD-AP is equipped with multiple antennas, while MSs have a single antenna and operate in half-duplex (HD) mode. The MSs are assigned two groups according to the time slot and their access channel state for either uplink or downlink communication with the FD-AP. Channel assignment, time resource allocation, and power allocation is jointly optimized for maximizing the sum-rate. An iterative algorithm based on the relationship between rate and minimum mean squared error (MMSE) is proposed to maximize the sum-rate by examining the Lagrangian problem. The proposed algorithm for FD-WPCN is compared to its HD counterpart. It is shown from simulation results that the proposed FD-WPCN scheme outperforms the HD mode when low transmit power is used although the FD-WPCN performance is degraded at high transmit power regime due to the presence of residual self- interference (RSI) at the FD-AP node.
Vitamin D and its metabolites: from now and beyond

Total 25-hydroxyvitamin D is currently considered as the most representative metabolite of vitamin D status. There are a multitude of challenges that still deserve to be addressed and despite recent technological advances its determination remains complicated. This current review gives an abbreviated overview of the phases of development that vitamin D metabolite determination has gone through and discusses the difficulties that still require resolving. Furthermore, given the different platforms and methodologies available, the critical issue of standardization and all efforts made as far towards its realization have been discussed. And last but not least, the concepts of ‘free’ and ‘bioavailable’ vitamin D along with the ‘Vitamin D Metabolism Ratio’ have been discussed.
Meta-learning autoencoders for few-shot prediction

Compared to humans, machine learning models generally require significantly more training examples and fail to extrapolate from experience to solve previously unseen challenges. To help close this performance gap, we augment single-task neural networks with a meta-recognition model which learns a succinct model code via its autoencoder structure, using just a few informative examples. The model code is then employed by a meta-generative model to construct parameters for the task-specific model. We demonstrate that for previously unseen tasks, without additional training, this Meta-Learning Autoencoder (MeLA) framework can build models that closely match the true underlying models, with loss significantly lower than given by fine-tuned baseline networks, and performance that compares favorably with state-of-the-art meta-learning algorithms. MeLA also adds the ability to identify influential training examples and predict which additional data will be most valuable to acquire to improve model prediction.
CAR T Cells in Solid Tumors: Blueprints for Building Effective Therapies

Genetic redirection of T lymphocytes with chimeric antigen receptors (CARs) has soared from treating cancers preclinically to FDA approval for hematologic malignancies and commercial-grade production scale in under 30 years. To date, solid tumors are less susceptible to CAR therapies and instead have been treated more successfully with immune checkpoint blockade or tumor-infiltrating lymphocyte therapy. Here, we discuss the current challenges in treating solid tumors with CAR T cells, and the obstacles within the host and tumor microenvironment hindering their efficacy. We present a novel three-pronged approach for enhancing the efficacy of CAR T cells whereby a single infusion product can synergize the power of an optimal CAR construct, a highly potent T cell subset, and rejuvenate the endogenous immune response to conquer therapeutically-resistant solid tumors.
A Novel Geometry-Based Model for Localization Based on Received Signal Strength

A novel geometry-based model (GBM) is proposed to locate the transmitter based on received signal strength (RSS), where there is no anchor point. Previous studies were mostly based on maximum likelihood (ML) estimator. The novel GBM estimator gives a new approach for localization based on the geometrical significance, which is to find a point with least sum of distance to the edge of circles. The corresponding Cramer-Rao Lower Bound (CRLB) of unbiased estimator and a time-saving Monte Carlo approach to the CRLB of GBM are derived. In addition, the confidence interval of Root-Mean-Square Error (RMSE) is also derived based on Central Limit Theorem in order to make sure the results of simulation are statistical significant. The simulation results demonstrate that GBM estimator can achieve much higher accuracy compared to ML. The proposed GBM algorithm is verified with experimental data set from commercial cellular networks, and shows better performances.
Enhanced Uplink Transmission Performance Based on WFRFT for Future Communication Systems

The long term evolution-advanced (LTE-A) and worldwide interoperability for microwave access (WiMAX) standards are the two main contenders in the 4th generation (4G) wireless systems, which adopt the single carrier-frequency division multiplexing access (SC-FDMA) and orthogonal frequency division multiplexing access (OFDMA) schemes in the uplink, respectively. However, these two schemes have certain advantages and disadvantages. As the weighted fractional Fourier transform (WFRFT)-based system can merge the SC- FDMA scheme with the OFDMA scheme, it inherits these two schemes' characteristics and has the potential to obtain better performance. For enhancing the bit error rate (BER) performance of the future uplink communication, in this paper, we propose a new order selection method for the WFRFT- based system to obtain better BER performance than the existing conventional uplink SC-FDMA or OFDMA scheme concerning the peak-to-average power ratio (PAPR) and the uplink power control (UPC) parameter under frequency selective fading channels.
Basal Endogenous Steroid Hormones, Sex Hormone-Binding Globulin, Physical Fitness, and Health Risk Factors in Young Adult Men

Purpose: Few large-scale population-based studies have adequately examined the relationships between steroid hormones, health status and physical fitness. The purpose of the study was to describe the relationship of serum basal endogenous steroid hormones (testosterone, TES; empirical free testosterone, EFT; cortisol, COR) and sex hormone-binding globulin (SHBG) to body composition, cardiovascular risk factors, and physical fitness in young healthy men. Methods: Male reservists (25 ± 4 years, N = 846) participated in the study. Basal TES, EFT, COR, and SHBG were measured in morning fasted blood. Stepwise regression analyses were used to examine associations between individual hormones to four separate categories: (1) body composition; (2) cardiovascular risk factors; (3) relative, and (4) absolute physical fitness. Results: Higher TES, EFT, and SHBG were associated with lower waist circumference (TES: β = -0.239, p < 0.001; EFT: β = -0.385, p < 0.001), % body fat (TES: β = -0.163, p = 0.003), and body mass index (SHBG: β = -0.435, p < 0.001). Lower cardiovascular risk factors were associated with higher TES, EFT and SHBG concentrations, especially between SHBG and triglycerides (β = -0.277, p < 0.001) and HDL (β = 0.154, p < 0.001). Greater maximal relative aerobic capacity was concurrent with higher TES, EFT, and SHBG (β = 0.171, 0.113, 0.263, p < 0.001, =0.005, <0.001, respectively). Conclusion: Higher basal concentrations of TES, EFT, and SHBG were weakly associated with healthier body composition, fewer cardiovascular risk factors and greater relative aerobic capacity in healthy young men. It would be interesting to investigate whether these relationships are still evident after a few decades, and how different training modes (endurance, strength or their combination) positively affect physical fitness, body composition and their regulatory mechanisms over the decades.
Proteomic analysis of canine oral tumor tissues using MALDI-TOF mass spectrometry and in-gel digestion coupled with mass spectrometry (GeLC MS/MS) approaches

Oral tumors, including highly invasive and metastatic oral melanoma (OM), non-tonsillar oral squamous cell carcinoma (OSCC) and benign tumors (BN), are common neoplasms in dogs. Although these tumors behave differently, limited data of their protein expression profiles have been exhibited, particularly at the proteome level. The present study aimed to i. ) characterize peptide-mass fingerprints (PMFs) and identify potential protein candidates of OM, OSCC, BN and normal control subjects, using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) and liquid chromatography tandem mass spectrometry (LC-MS/MS), ii. ) identify potential protein candidates associated with the diseases, using in-gel digestion coupled with mass spectrometric analysis (GeLC-MS/MS) and iii. ) search for relationships between chemotherapy drugs and disease-perturbed proteins. A distinct cluster of each sample group and unique PMFs with identified protein candidates were revealed. The unique peptide fragment at 2,274 Da of sacsin molecular chaperone (SACS) was observed in early-stage OM whereas the fragment at 1,958 Da of sodium voltage-gated channel alpha subunit 10 (SCN10A) was presented in early- and late-stage OM. The peptide mass at 2,316 Da of Notch1 appeared in early-stage OM and benign oral tumors while the peptide mass at 2,505 Da of glutamate ionotropic receptor N-methyl-D-aspartate type subunit 3A (GRIN3A) was identified in all groups. Markedly expressed proteins from GeLC-MS/MS included Jumonji domain containing 1C (JMJD1C) in benign tumors, inversin (INVS) and rho guanine nucleotide exchange factor 28 (ARHGEF28) in OM, BTB domain-containing 16 (BTBD16) in OSCC, and protein tyrosine phosphatase non-receptor type 1 (PTPN1), BRCA2, DNA repair associated (BRCA2), WW domain binding protein 2 (WBP2), purinergic receptor P2Y1 and proteasome activator subunit 4 (PSME4) in all cancerous groups. The network connections between these proteins and chemotherapy drugs, cisplatin and doxorubicin, were also demonstrated. In conclusion, this study unveiled the unique PMFs and novel candidate protein markers of canine oral tumors.
Establishment of the hematology reference intervals in a healthy population of adults in the Northwest of Morocco (Tangier-Tetouan region)

Introduction Among the most useful biological examinations in common medical practice, blood count is the most prescribed. The reference intervals of the hematological parameters of this examination are of major importance for clinical orientations and therapeutic decisions. In Morocco, the reference values used by the laboratories of medical biology and used by doctors are ones collected from Caucasian and European individuals. These values could be different in the Moroccan population. Besides, reference intervals of the blood count specific to the various Moroccan regions are missing. We decided to determine the reference intervals from a population of healthy adults of the Tangier-Tetouan region by following the procedures recommended by the IFCC-CLSI guidelines in 2008 and comparing them to those of the literature. Methods Blood samples were taken from 15840 adult volunteers (8402 men from 18 to 55 years old and 7438 women from 18 to 50 years old) from the regional transfusion center of Tangier and Tetouan during a period between November 2014 and May 2016. The complete blood count was measured by the Sysmex KX21N® analyzer. For each sample a systematic blood smear was done to determine the leukocyte differential. The data analysis was made by the software SPSS 20.0 by using percentiles 2.5th and 97.5th. Results A significant difference between both sexes was noted (p<0,001) for all the hematological parameters (red blood cells, hematocrit, hemoglobin, mean corpuscular volume, mean corpuscular hemoglobin, mean corpuscular hemoglobin concentration, leukocytes, neutrophils, basophils, eosinophils, monocytes, platelets and mean platelet volume) except for the numeration of lymphocytes (p = 0.552). The values of this study were compared with those reported in Arabic, Caucasian and African populations. Said comparisons showed the existence of significant differences. Conclusion This study tries to accentuate the necessity of proceeding with the establishment of reference intervals specific to the blood count of the Moroccan population to avoid errors of diagnosis, allow clinicians to interpret with greater specificity the hematological examinations and to improve the quality of medical care distributed to patients.
An Empirical NB-IoT Power Consumption Model for Battery Lifetime Estimation

The NB-IoT radio technology is a key enabler of the wireless Internet of Things. A competitive parameter is device battery lifetime, in addition to wide-area coverage and low device cost. In this paper, we present the first publicly available empirical power consumption measurements on two NB-IoT devices. The target is to provide a power consumption model for Internet of Things battery lifetime estimation. The measurement results show that the two devices consume 710-840 mW when transmitting at 23 dBm, due to a power amplifier efficiency of about 37 %. Receiving control and data channels require 210- 240 mW, while the Discontinuous Reception and Power Save Mode sleep states consume 21-23 mW and 14-35 uW, respectively. In general, the power consumption levels are higher than the 3GPP estimates, and we thus estimate the battery lifetime is 21-85 % shorter, with these first generation devices.
Applying Domain Randomization to Synthetic Data for Object Category Detection

Recent advances in deep learning-based object detection techniques have revolutionized their applicability in several fields. However, since these methods rely on unwieldy and large amounts of data, a common practice is to download models pre-trained on standard datasets and fine-tune them for specific application domains with a small set of domain relevant images. In this work, we show that using synthetic datasets that are not necessarily photo-realistic can be a better alternative to simply fine-tune pre-trained networks. Specifically, our results show an impressive 25% improvement in the mAP metric over a fine-tuning baseline when only about 200 labelled images are available to train. Finally, an ablation study of our results is presented to delineate the individual contribution of different components in the randomization pipeline.