Miracle-dz/UniFAR-FTUs huggingface dataset:
The effect of maternal iron deficiency on zinc and copper levels and on genes of zinc and copper metabolism during pregnancy in the rat.

Fe deficiency is relatively common in pregnancy and has both short- and long-term consequences. However, little is known about the effect on the metabolism of other micronutrients. A total of fifty-four female rats were fed control (50 mg Fe/kg) or Fe-deficient diets (7·5 mg/kg) before and during pregnancy. Maternal liver, placenta and fetal liver were collected at day 21 of pregnancy for Cu and Zn analysis and to measure expression of the major genes of Cu and Zn metabolism. Cu levels increased in the maternal liver (P=0·002) and placenta (P=0·018) of Fe-deficient rats. Zn increased (P<0·0001) and Cu decreased (P=0·006) in the fetal liver. Hepatic expression of the Cu chaperones antioxidant 1 Cu chaperone (P=0·042) and cytochrome c oxidase Cu chaperone (COX17, P=0·020) decreased in the Fe-deficient dams, while the expression of the genes of Zn metabolism was unaltered. In the placenta, Fe deficiency reduced the expression of the chaperone for superoxide dismutase 1, Cu chaperone for superoxide dismutase (P=0·030), ceruloplasmin (P=0·042) and Zn transport genes, ZRT/IRT-like protein 4 (ZIP4, P=0·047) and Zn transporter 1 (ZnT1, P=0·012). In fetal liver, Fe deficiency increased COX17 (P=0·020), ZRT/IRT-like protein 14 (P=0·036) and ZnT1 (P=0·0003) and decreased ZIP4 (P=0·004). The results demonstrate that Fe deficiency during pregnancy has opposite effects on Cu and Zn levels in the fetal liver. This may, in turn, alter metabolism of these nutrients, with consequences for development in the fetus and the neonate.
Political Attitude and Fertility: Is There a Selection for the Political Extreme?

There is growing evidence that human ideology as well as social and political attitudes also have a genetic basis. In case of some genetic predisposition of political attitude, an association with fertility would be a hint of potential selection on political ideology. We therefore investigated on the basis of men and women that have completed, respectively, almost completed reproduction, of three different data sets (the World Value Survey 1981–2014 covering a wide range of countries and developmental levels, n = 152,380, the Survey of Health, Ageing and Retirement in Europe of 2005, n = 65,912, and the General Social Survey of the United States 1972–2014, n ∼ 6200) whether political attitude is associated with number of children. Overall, in the world wide survey, both extreme political attitudes, albeit more pronounced for right/conservative than for left/liberal attitude, are associated with higher average offspring number compared to intermediate attitudes. If countries are analyzed separately, however, the picture is inconsistent, and in most countries, the association is non-significant. In the European and the US-survey, only the political right is associated with above average number of children. The time series of US data from 1972 to 2014 shows that at least in the US-sample, this pattern emerged during the 1990s: in the 1970s and 1980s, also in the US-sample both political extremes had a reproductive advantage, which vanished for left wing individuals during the 1990s. From an evolutionary perspective, we are not able to draw final conclusions as the association between political attitude and reproduction varies across countries and time. Nonetheless, the overall pattern suggests that in human evolutionary history, both left and right political attitudes may have conveyed fitness benefits so that both attitudes have been kept in the population.
A Teacher Dashboard for Real-Time Intervention

Awareness on the students' progress in a face-to-face classroom is studied. In particular, the type of course being supported is a laboratory for learning basic computer tools such as Excel or Matlab. The specific aspect under consideration is the provision of awareness to the teacher so that she may intervene to help students overcome a difficulty which does not let them continue with the laboratory work. A computer tool for this purpose is presented. The tool is applicable to this scenario. A test was done in which the tool was transparent to the students and gave awareness for teacher's intervention. The tool can be used with other applications or systems as well, provided these systems have an appropriate API.
Unsupervised Pseudo-Labeling for Extractive Summarization on Electronic Health Records

Extractive summarization is very useful for physicians to better manage and digest Electronic Health Records (EHRs). However, the training of a supervised model requires disease-specific medical background and is thus very expensive. We studied how to utilize the intrinsic correlation between multiple EHRs to generate pseudo-labels and train a supervised model with no external annotation. Experiments on real-patient data validate that our model is effective in summarizing crucial disease-specific information for patients.
A cysteine protease–like domain enhances the cytotoxic effects of the Photorhabdus asymbiotica toxin PaTox

The nematode mutualistic bacterium Photorhabdus asymbiotica produces a large virulence-associated multifunctional protein toxin named PaTox. A glycosyltransferase domain and a deamidase domain of this large toxin function as effectors that specifically target host Rho GTPases and heterotrimeric G proteins, respectively. Modification of these intracellular regulators results in toxicity toward insects and mammalian cells. In this study, we identified a cysteine protease–like domain spanning PaTox residues 1844–2114 (PaToxP), upstream of these two effector domains and characterized by three conserved amino acid residues (Cys-1865, His-1955, and Asp-1975). We determined the crystal structure of the PaToxP C1865A variant by native single-wavelength anomalous diffraction of sulfur atoms (sulfur-SAD). At 2.0 Å resolution, this structure revealed a catalytic site typical for papain-like cysteine proteases, comprising a catalytic triad, oxyanion hole, and typical secondary structural elements. The PaToxP structure had highest similarity to that of the AvrPphB protease from Pseudomonas syringae classified as a C58-protease. Furthermore, we observed that PaToxP shares structural homology also with non–C58-cysteine proteases, deubiquitinases, and deamidases. Upon delivery into insect larvae, PaToxP alone without full-length PaTox had no toxic effects. Yet, PaToxP expression in mammalian cells was toxic and enhanced the apoptotic phenotype induced by PaTox in HeLa cells. We propose that PaToxP is a C58-like cysteine protease module that is essential for full PaTox activity.
Therapeutic Targets for Treatment of Heart Failure: Focus on GRKs and β-Arrestins Affecting βAR Signaling

Heart failure (HF) is a heart disease that is classified into two main types: HF with reduced ejection fraction (HFrEF) and HF with preserved ejection fraction (HFpEF). Both types of HF lead to significant risk of mortality and morbidity. Pharmacological treatment with β-adrenergic receptor (βAR) antagonists (also called β-blockers) has been shown to reduce the overall hospitalization and mortality rates and improve the clinical outcomes in HF patients with HFrEF but not HFpEF. Although, the survival rate of patients suffering from HF continues to drop, the management of HF still faces several limitations and discrepancies highlighting the need to develop new treatment strategies. Overstimulation of the sympathetic nervous system is an adaptive neurohormonal response to acute myocardial injury and heart damage, whereas prolonged exposure to catecholamines causes defects in βAR regulation, including a reduction in the amount of βARs and an increase in βAR desensitization due to the upregulation of G protein-coupled receptor kinases (GRKs) in the heart, contributing in turn to the progression of HF. Several studies show that myocardial GRK2 activity and expression are raised in the failing heart. Furthermore, β-arrestins play a pivotal role in βAR desensitization and, interestingly, can mediate their own signal transduction without any G protein-dependent pathway involved. In this review, we provide new insight into the role of GRKs and β-arrestins on how they affect βAR signaling regarding the molecular and cellular pathophysiology of HF. Additionally, we discuss the therapeutic potential of targeting GRKs and β-arrestins for the treatment of HF.
DynamicGEM: A Library for Dynamic Graph Embedding Methods

DynamicGEM is an open-source Python library for learning node representations of dynamic graphs. It consists of state-of-the-art algorithms for defining embeddings of nodes whose connections evolve over time. The library also contains the evaluation framework for four downstream tasks on the network: graph reconstruction, static and temporal link prediction, node classification, and temporal visualization. We have implemented various metrics to evaluate the state-of-the-art methods, and examples of evolving networks from various domains. We have easy-to-use functions to call and evaluate the methods and have extensive usage documentation. Furthermore, DynamicGEM provides a template to add new algorithms with ease to facilitate further research on the topic.
Short Symmetric-End Antimicrobial Peptides Centered on β-Turn Amino Acids Unit Improve Selectivity and Stability

Antimicrobial peptides (AMPs) are excellent candidates to combat the increasing number of multi- or pan-resistant pathogens worldwide based on their mechanism of action, which is different from that of antibiotics. In this study, we designed short peptides by fusing an α-helix and β-turn sequence-motif in a symmetric-end template to promote the higher cell selectivity, antibacterial activity and salt-resistance of these structures. The results showed that the designed peptides PQ and PP tended to form an α-helical structure upon interacting with a membrane-mimicking environment. They displayed high cell selectivity toward bacterial cells over eukaryotic cells. Their activities were mostly maintained in the presence of different conditions (salts, serum, heat, and pH), which indicated their stability in vivo. Fluorescence spectroscopy and electron microscopy analyses indicated that PP and PQ killed bacterial cells through membrane pore formation, thereby damaging membrane integrity. This study revealed the potential application of these designed peptides as new candidate antimicrobial agents.
Camera recognition with deep learning

Abstract In this paper, camera recognition with the use of deep learning technique is introduced. To identify the various cameras, their characteristic photo-response non-uniformity (PRNU) noise pattern was extracted. In forensic science, it is important, especially for child pornography cases, to link a photo or a set of photos to a specific camera. Deep learning is a sub-field of machine learning which trains the computer as a human brain to recognize similarities and differences by scanning it, in order to identify an object. The innovation of this research is the use of PRNU noise patterns and a deep learning technique in order to achieve camera identification. In this paper, AlexNet was modified producing an improved training procedure with high maximum accuracy of 80%–90%. DIGITS showed to have identified correctly six cameras out of 10 with a success rate higher than 75% in the database. However, many of the cameras were falsely identified indicating a fault occurring during the procedure. A possible explanation for this is that the PRNU signal is based on the quality of the sensor and the artefacts introduced during the production process of the camera. Some manufacturers may use the same or similar imaging sensors, which could result in similar PRNU noise patterns. In an attempt to form a database which contained different cameras of the same model as different categories, the accuracy rate was low. This provided further proof of the limitations of this technique, since PRNU is stochastic in nature and should be able to distinguish between different cameras from the same brand. Therefore, this study showed that current convolutional neural networks (CNNs) cannot achieve individualization with PRNU patterns. Nevertheless, the paper provided material for further research.
Proof of Delivery of Digital Assets Using Blockchain and Smart Contracts

There is an immense need of a proof of delivery (PoD) of today’s digital media and content, especially those that are subject to payment. Current PoD systems are mostly centralized and heavily dependent on a trusted third party (TTP) especially for payment. Such existing PoD systems often lack security, transparency, and visibility, and are not highly credible, as the TTP can be subject to failure, manipulation, corruption, compromise, and hacking. In this paper, we propose a decentralized PoD solution for PoD of digital assets. Our solution leverages key features of blockchain and Ethereum smart contracts to provide immutable and tamper-proof logs, accountability, and traceability. Ethereum smart contracts are used to orchestrate and govern all interactions and transactions including automatic payments in Ether cryptocurrency between customers, digital-content provider, and the file server hosting the digital content. All entities are incentivized to act honestly, and our solution has a mechanism to handle dispute if arisen among participants. The solution has an off-chain secure download phase involving the file server and customers. Moreover, our solution leverages the benefits of interplanetary file system to store the agreed upon terms and conditions between the smart contract actors. A security analysis of our proposed system has been provided. The full code of the smart contract has been publicly made available on Github.
Effectiveness of Supervisory Boards in Coordination With Audit Committees in China

China’s supervisory mechanism is peculiar in that it integrated the supervisory board (SB) of the German corporate governance (CG) model in 1993, and has simultaneously adopted the audit committee (AC) of the Anglo-American model since 2002. This paper aims to reveal the effectiveness of SBs in coordination with ACs in China. The regulations for Anglo-American and Chinese systems are compared and further evidence is provided through interviewing two governors of all listed companies in the SSE and of 79 investment corporations in Hangzhou. Both statements indicate that China still faces difficulties and problems in meeting the two indexes of independence and expertise of supervision to give effective supervisory functions. Both opinions differed in terms of the co-ordination of SBs and ACs in the future. The evidence presented forms a basis for proposals of how key characteristics of effectiveness and co-ordination can be improved to make supervisory functions in China more effective.
Comparison of impact of target delineation of computed tomography- and magnetic resonance imaging-guided brachytherapy on dose distribution in cervical cancer

Purpose The dose distributions obtained from three imaging approaches for target delineation in cervical cancer using high-dose-rate (HDR) brachytherapy were investigated. Material and methods Ten cervical cancer patients receiving four fractions of HDR brachytherapy were enrolled. Based on different imaging approaches, three brachytherapy plans were developed for each patient: with the high-risk clinical target volume (HRCTV) delineated on magnetic resonance (MRI) images for every fraction (approach A; MRI-only); on MRI for the first fraction and computed tomography (CT) images for the subsequent fractions (approach B; MRI1st/CT); and on CT images for all fractions (approach C; CT-only). The volume, height, width at point A, width at maximum level, and dosimetric parameters (D100, D98, D95, and D90 of the HRCTV; and D0.1cc, D1cc, and D2cc of all organs at risk, or organ at risk – OAR: bladder, rectum, sigmoid colon, and bowel) provided by each approach were compared. Results The mean HRCTV volume, width, and height obtained from approach C (CT-only) were overestimated compared to those from approaches A (MRI-only) and B (MRI1st/CT). The doses to the HRCTV for approaches A and B were similar. However, the HRCTV doses for approach C were significantly lower than those for approaches A and B for all parameters (D95-D100). As to the OAR, the three approaches showed no differences. Conclusions A combination of MRI and CT is a safe alternative approach for cervical cancer HDR brachytherapy. The technique provides comparable dosimetric outcomes to MRI-based planning, while being more cost-effective.
Cyclic Loading of Underground Cables Including the Variations of Backfill Soil Thermal Resistivity and Specific Heat With Temperature Variation

The aim of this paper is to calculate the temperature distribution of a cable conductor and the soil surrounding the cable under cyclic loading conditions, taking into account the formation of a drying zone in the backfill material. The thermal model of an underground cable is considered by using the IEC 60853-2 method, including the effects of the variations in temperature on the thermal resistivity and specific heat of the soil. The results are compared with IEC 60853-2 and thermoelectric equivalent method calculations. This study is done on eight sandy soil types as backfill materials, and calculations are carried out by using a 33-kVx cable. The results indicate that the modifications done on the IEC 60853-2 method are required to improve its capability to simulate the field conditions during load cycling of power cables.
Hierarchical and Dependency-Aware Task Mapping for NoC-based Systems

To support more efficient parallel processing, Network-on-Chip (NoC) is being increasingly adopted as a new alternative interconnection architecture. With the limited number of processing elements (PEs) on an NoC, to perform application tasks efficiently on the PEs has become a key issue. In this work, we propose a hierarchical and dependency-aware (HDA) task mapping that covers the concepts of spatial mapping and inter-task dependency (temporal) to enhance the elasticity of task mapping. Experiments also show that, compared to the existing task mapping methods, including the first-fit method and the region-based method, the HDA method can reduce the total processing time by up to 40.79% and 25.5%, respectively, and reduce the total energy consumption by up to 33.16% and 12.02%, respectively.
Bayesian Belief Network Model Quantification Using Distribution-Based Node Probability and Experienced Data Updates for Software Reliability Assessment

Since digital instrumentation and control systems are expected to play an essential role in safety systems in nuclear power plants (NPPs), the need to incorporate software failures into NPP probabilistic risk assessment has arisen. Based on a Bayesian belief network (BBN) model developed to estimate the number of software faults considering the software development lifecycle, we performed a pilot study of software reliability quantification using the BBN model by aggregating different experts’ opinions. In this paper, we suggest the distribution-based node probability table (D-NPT) development method which can efficiently represent diverse expert elicitation in the form of statistical distributions and provides mathematical quantification scheme. Besides, the handbook data on U.S. software development and V&V and testing results for two nuclear safety software were used for a Bayesian update of the D-NPTs in order to reduce the BBN parameter uncertainty due to experts’ different background or levels of experience. To analyze the effect of diverse expert opinions on the BBN parameter uncertainties, the sensitivity studies were conducted by eliminating the significantly different NPT estimates among expert opinions. The proposed approach demonstrates a framework that can effectively and systematically integrate different kinds of available source information to quantify BBN NPTs for NPP software reliability assessment.
Neural Non-Stationary Spectral Kernel

Standard kernels such as Mat\'ern or RBF kernels only encode simple monotonic dependencies within the input space. Spectral mixture kernels have been proposed as general-purpose, flexible kernels for learning and discovering more complicated patterns in the data. Spectral mixture kernels have recently been generalized into non-stationary kernels by replacing the mixture weights, frequency means and variances by input-dependent functions. These functions have also been modelled as Gaussian processes on their own. In this paper we propose modelling the hyperparameter functions with neural networks, and provide an experimental comparison between the stationary spectral mixture and the two non-stationary spectral mixtures. Scalable Gaussian process inference is implemented within the sparse variational framework for all the kernels considered. We show that the neural variant of the kernel is able to achieve the best performance, among alternatives, on several benchmark datasets.
Silicon‐Enhanced Adipogenesis and Angiogenesis for Vascularized Adipose Tissue Engineering

The enhancement of adipogenic differentiation of bone marrow mesenchymal stem cells (BMSCs) and sufficient vascularization remain great challenges for the successful reconstruction of engineered adipose tissue. Here, the bioactive effects of silicon (Si) ions on adipogenic differentiation of human BMSCs (HBMSCs) and the stimulation of vascularization during adipose tissue regeneration are reported. The results show that Si ions can enhance adipogenic differentiation of HBMSCs through the stimulation of the expression of adipogenic differentiation switches such as peroxisome proliferator‐activated receptor γ and CCAAT/enhancer‐binding protein α. Furthermore, Si ions can enhance both angiogenesis and adipogenesis, and inhibit dedifferentiation of cocultured adipocytes by regulating the interactions between HBMSC‐derived adipocytes and human umbilical vein endothelial cells, in which the promotion of the expression of insulin‐like growth factor 1 and vascular endothelial growth factor plays vital roles. The in vivo studies further demonstrate that the designed composite hydrogel with the ability to release bioactive Si ions clearly stimulates neovascularization and adipose tissue regeneration. The study suggests that Si ions released from biomaterials are important chemical cues for adipogenic differentiation and biomaterials with the ability to release Si ions can be designed for adipose tissue engineering.
Computation of coastal sea water absorption coefficients and retrieval of water quality parameters from EO1 Hyperion data

This work aims to extract optical parameters of coastal sea water by fitting the hyperspectral reflectance spectra acquired from the Hyperion instrument on-board the EO-1 satellite to a coupled sea water reflectance and atmospheric transmission model. This method is able to retrieve the absorption and scattering coefficients of the various components of coastal sea water.
Access Level Privacy Protection for Security

Access control is a fundamental security technique in systems in which multiple users share access to common resources. It is the process of stating and enforcing security A approach in network security for misbehavior detection system present a method for detecting malicious misbehavior activity within networks. Along with the detection, it also blocks the malicious system within the network and adds it to Blacklist. Malicious node defined as a compromised machine within the network that performs the task provided by i.e. it does not forward the legitimate message to another node in the network or sends some other message to a neighbor node. This system is based on Probabilistic threat propagation. This scheme is used in graph analysis for community detection. The proposed system enhances the prior community detection work by propagating threat probabilities across graph nodes. To demonstrate Probabilistic Threat Propagation (PTP) considers the task of detecting malicious node in the network. Proposed System also shows the relationship between PTP and loopy belief propagation.