Miracle-dz/UniFAR-FTUs huggingface dataset:
Month of Birth and Cognitive Effort: A Laboratory Study of the Relative Age Effect among Fifth Graders

All around the world, school-entry cohorts are organized on an annual calendar so that the age of students in the same cohort differs by up to one year. It is a well-established finding that this age gap entails a consequential (dis)advantage for academic performance referred to as the relative age effect (RAE). This study contributes to a recent strand of research that has turned to investigate the RAE on non-academic outcomes such as personality traits. An experimental setup is used to estimate the causal effect of monthly age on cognitive effort in a sample of 798 fifth-grade students enrolled in the Spanish educational system, characterized by strict enrolment rules. Participants performed three different real-effort tasks under three different incentive conditions: no rewards; material rewards; and material and status rewards. We observe that older students outwork their youngest peers by two-fifths of a standard deviation, but only when material rewards for performance are in place. Despite the previously reported higher taste for competition among the older students within a school-entry cohort, we do not find that the RAE on cognitive effort increases after inducing competition for peer recognition. Finally, the study also provides suggestive evidence of a larger RAE among boys and students from lower social strata. Implications for sociological research on educational inequality are discussed. To conclude, we outline policy recommendations such as implementing evaluation tools that nudge teachers toward being mindful of relative age differences.
Application of Layered Architecture in Whistleblowing Information System for Supporting Good University Governance in Indonesia University of Education

Whistleblowing system is an information system for facilitating Indonesia University of Education Internal Audit Unit Committee on asset and money controlling from the various shape of fraud, corruption, conflict of interest and gratification guess. This research aims to build a whistleblowing system in the Indonesia University of Education which has status as Legal Entity of Public Universities which has authority for managing finances by itself that confirmed on 53rd Paragraph 3 about Indonesia University of Education’s status in 15th Government Regulation of the Republic of Indonesia at 2014. Whistleblowing system can be a media for receiving any report to finding and identify events, condition, and situation that indicated as a violation which occurs in Indonesia University of Education environment done by stakeholder for supporting the application of Good University Governance. Development of a whistleblowing system will be created in a web application using layered architecture as software architecture that focuses on related function grouping into different stacked layer consisting of a presentation layer, business layer, and data layer. The development process of whistleblowing system using software development approach of prototyping which is rapid development and testing of prototypes of new application through interaction and repetitive process.
Fingerprint Liveness Detection Using Directional Ridge Frequency with Machine Learning Classifiers

As security plays very important role in information era, classical security methods based on password, PIN, key, cards etc. can easily be lost, forgotten, stolen and cannot differentiate between lawful/genuine user and unlawful user. These problems can be resolved with biometric authentication. But, Biometric authentication has a threat of spoofing which, makes biometric Liveness detection very essential. It is the ability of biometric system to distinguish between spoof and real biometric sample of person. Liveness detection is one of the countermeasures needed to be taken to avoid spoof attacks to maintain integrity of biometric system and enhance biometric security. Biometric security can easily be overtaken by providing fake physical biometric to sensors. Fingerprint can easily be spoofs from some materials like silicone, glue and gelatin. To detect such spoofing different algorithms are used which broadly are classified into two approaches like hardware centric and software centric. Software centric techniques are relatively better in cost-effective ness and users have lesser interfaces (extremely vital features needed in liveness detection The paper proposes use of directional ridge frequency as features for livness detection of fingerprint with machine learning classifiers. The ridge frequencies are considered in horizontal, vertical, forward diagonal and backward diagonal directions for feature extraction. Here support vector machine(SVM), Random forest(RM), multilayer perceptron (MLP), J48 and Navies Bayes are used for classification. Experimental result have shown that random forest has given better liveness detection closely followed by SVM.
Light Field Image Sparse Coding via CNN-Based EPI Super-Resolution

This paper proposes a novel light field (LF) image compression scheme by super resolving the epipolar plane image (EPI) via convolutional neural network (CNN). In the scheme, we first decompose the LF image into sub-aperture images (SAIs), and only one quarter of them are compressed on the encoding side to reduce the bitrate. On the decoding side, we use these selected SAIs to reconstruct the entire LF by taking advantage of the special structure of EPI. The low-resolution EPIs generated from the sparse SAIs are super resolved by using deep residual network and the output high-resolution EPIs are used to rebuild the dense SAIs. Experimental results show the superior performance of our scheme, which achieve 1.46 dB quality improvement and 35.85 percent bit rate reduction on average compared with the typical pseudo-sequence-based coding method.
A Dance Movements Recognition System Based on Movement Kinematics

Dance movements recognition is a challenging research issue, due to the complexity of movement, complex attitude transition, and the movement interpretation diversity between human subjects. Moreover, for a static camera, dancer’s behavior is often occluded by a human’s body parts or other objects. A real-time prototype is proposed to verify that the complicated dancing behaviors can be recognized.
MicroRNAs in Neuroinflammation: Implications in Disease Pathogenesis, Biomarker Discovery and Therapeutic Applications

The central nervous system can respond to threat via the induction of an inflammatory response. Under normal circumstances this response is tightly controlled, however uncontrolled neuroinflammation is a hallmark of many neurological disorders. MicroRNAs are small non-coding RNA molecules that are important for regulating many cellular processes. The ability of microRNAs to modulate inflammatory signaling is an area of ongoing research, which has gained much attention in recent years. MicroRNAs may either promote or restrict inflammatory signaling, and either exacerbate or ameliorate the pathological consequences of excessive neuroinflammation. The aim of this review is to summarize the mode of regulation for several important and well-studied microRNAs in the context of neuroinflammation, including miR-155, miR-146a, miR-124, miR-21 and let-7. Furthermore, the pathological consequences of miRNA deregulation during disorders that feature neuroinflammation are discussed, including Multiple Sclerosis, Alzheimer’s disease, Parkinson’s disease, Prion diseases, Japanese encephalitis, Herpes encephalitis, ischemic stroke and traumatic brain injury. There has also been considerable interest in the use of altered microRNA signatures as biomarkers for these disorders. The ability to modulate microRNA expression may even serve as the basis for future therapeutic strategies to help treat pathological neuroinflammation.
Strategic Planning for Tuberculosis Control in the Republic of Fiji

The tuberculosis (TB) health burden in Fiji has been declining in recent years, although challenges remain in improving control of the diabetes co-epidemic and achieving adequate case detection across the widely dispersed archipelago. We applied a mathematical model of TB transmission to the TB epidemic in Fiji that captured the historical reality over several decades, including age stratification, diabetes, varying disease manifestations, and incorrect diagnoses. Next, we simulated six intervention scenarios that are under consideration by the Fiji National Tuberculosis Program. Our findings show that the interventions were able to achieve only modest improvements in disease burden, with awareness raising being the most effective intervention to reduce TB incidence, and treatment support yielding the highest impact on mortality. These improvements would fall far short of the ambitious targets that have been set by the country, and could easily be derailed by moderate increases in the diabetes burden. Furthermore, the effectiveness of the interventions was limited by the extensive pool of latent TB infection, because the programs were directed at only active cases, and thus were unlikely to achieve the desired reductions in burden. Therefore, it is essential to address the co-epidemic of diabetes and treat people with latent TB infection.
Effective elimination of Staphylococcal contamination from hospital surfaces by a bacteriophage–probiotic sanitation strategy: a monocentric study

Persistent contamination of hospital surfaces and antimicrobial resistance (AMR) is recognized as major causes of healthcare‐associated infections (HAI). We recently showed that probiotic‐based sanitation (PCHS) can stably decrease surface pathogens and reduce AMR and HAIs. However, PCHS action is slow and non‐specific. By contrast, bacteriophages have been proposed as a decontamination method as they can rapidly attack specific targets, but their routine application has never been tested. Here, we analysed the feasibility and effectiveness of phage addition to PCHS sanitation, aiming to obtain a rapid and stable abatement of specific pathogens in the hospital environment. Staphylococcal contamination in the bathrooms of General Medicine wards was analysed, being those areas the most contaminated and Staphylococci the most prevalent bacteria in such settings. Results showed that a daily phage application by nebulization induced a rapid and significant decrease in Staphylococcus spp. load on treated surfaces, up to 97% more than PCHS alone (P < 0.001), suggesting that such a system might be considered as a part of prevention and control strategies, to counteract outbreaks of specific pathogens and prevent associated infections.
Kraft (Nano)Lignin as Reactive Additive in Epoxy Polymer Bio-Composites

The demand for high-performance bio-based materials towards achieving more sustainable manufacturing and circular economy models is growing significantly. Kraft lignin (KL) is an abundant and highly functional aromatic/phenolic biopolymer, being the main side product of the pulp and paper industry, as well as of the more recent 2nd generation biorefineries. In this study, KL was incorporated into a glassy epoxy system based on the diglycidyl ether of bisphenol A (DGEBA) and an amine curing agent (Jeffamine D-230), being utilized as partial replacement of the curing agent and the DGEBA prepolymer or as a reactive additive. A D-230 replacement by pristine (unmodified) KL of up to 14 wt. % was achieved while KL–epoxy composites with up to 30 wt. % KL exhibited similar thermo-mechanical properties and substantially enhanced antioxidant properties compared to the neat epoxy polymer. Additionally, the effect of the KL particle size was investigated. Ball-milled kraft lignin (BMKL, 10 μm) and nano-lignin (NLH, 220 nm) were, respectively, obtained after ball milling and ultrasonication and were studied as additives in the same epoxy system. Significantly improved dispersion and thermo-mechanical properties were obtained, mainly with nano-lignin, which exhibited fully transparent lignin–epoxy composites with higher tensile strength, storage modulus and glass transition temperature, even at 30 wt. % loadings. Lastly, KL lignin was glycidylized (GKL) and utilized as a bio-based epoxy prepolymer, achieving up to 38 wt. % replacement of fossil-based DGEBA. The GKL composites exhibited improved thermo-mechanical properties and transparency. All lignins were extensively characterized using NMR, TGA, GPC, and DLS techniques to correlate and justify the epoxy polymer characterization results.
Adolescent Parenting: Global Perspective, Local Action

In recent decades, a significant decrease in the rates of teen pregnancy has been observed worldwide. Though the decline is significant, teen pregnancy continues to account for more than 10% of births globally [1]. Furthermore, the vast majority of these births to adolescent parents occur in nations of low or middle income [2]. Sub-Saharan Africa and Mexico are found to have the highest rates of adolescent births. Amongst wealthier nations, the United States has the highest rates of live births by teen mothers [1, 3]. Teen pregnancy rates are influenced by a trifecta of social, cultural, and economic circumstances and rates may vary widely across socio-economic groups within countries. Regardless of cultural expectations there are many implications of becoming a teen parent. This paper seeks to review adolescent pregnancy and parenting from a global perspective and highlight the importance of an interdisciplinary approach when addressing teen parenting and its manifestations and predictors.
Enhanced Diagnosis of Pneumothorax with an Improved Real-Time Augmentation for Imbalanced Chest X-rays Data Based on DCNN

Pneumothorax is a common pulmonary disease that can lead to dyspnea and can be life-threatening. X-ray examination is the main means to diagnose this disease. Computer-aided diagnosis of pneumothorax on chest X-ray, as a prerequisite for a timely cure, has been widely studied, but it is still not satisfactory to achieve highly accurate results. In this paper, an image classification algorithm based on the deep convolutional neural network (DCNN) is proposed for high-resolution medical image analysis of pneumothorax X-rays, which features a Network In Network (NIN) for cleaning the data, random histogram equalization data augmentation processing, and a DCNN. The experimental results indicate that the proposed method can effectively increase the correct diagnosis rate of pneumothorax, and the Area under Curve (AUC) of the test verified in the experiment is 0.9844 on ZJU-2 test data and 0.9906 on the ChestX-ray14, respectively. In addition, a large number of atmospheric pleura samples are visualized and analyzed based on the experimental results and in-depth learning characteristics of the algorithm. The analysis results verify the validity of feature extraction for the network. Combined with the results of these two aspects, the proposed X-ray image processing algorithm can effectively improve the classification accuracy of pneumothorax photographs.
Controlled attenuation parameter for the assessment of liver steatosis in comparison with liver histology: a single-centre real life experience.

BACKGROUND AND AIMS Accurate diagnosis and staging of non-alcoholic fatty liver disease are essential for the management of this disorder. Controlled attenuation parameter (CAP) has been suggested as a new noninvasive measurement made during transient elastography to assess liver steatosis. The aim of this study was to evaluate CAP as a diagnostic tool for identifying the presence and degree of hepatic steatosis in consecutive patients in an outpatient liver unit of a tertiary centre. METHODS Between March 2015 and August 2016, all patients who underwent liver biopsy underwent liver stiffness measurement with simultaneous CAP determination using the FibroScan® M or XL probe. Steatosis, inflammatory activity and fibrosis were assessed using the histological SAF scoring system. In addition, fibrosis was scored according to the METAVIR system, and body mass index (BMI) and the underlying liver disease were also recorded. RESULTS 224 patients were included in the analysis; 146 (65.2%) were male. Steatosis grades were distributed as follows: S0 n = 85 (37.9%), S1 n = 82 (36.6%), S2 n = 33 (14.7%), S3 n = 24 (10.7%). Mean BMI was 26.8 kg/m2, for the S0 group 24.9 kg/m2, S1 26.5 kg/m2, S2 27.3 kg/m2 and S3 32.5 kg/m2. The CAP differed significantly between steatosis groups S0 to S3. The area under receiver operating characteristics curve for S0 vs S1–S3 was 0.78, for S0/1 vs S2/3 0.83 and for S0–2 vs S3 0.82. Calculated cut-off values were 258.5 dB/m for S0 vs S1–3, 282.5 dB/m for S0/1 vs S2/3 and 307.5 dB/m for S0–2 vs S3. CONCLUSIONS CAP values are strongly associated with the degree of steatosis irrespective of the underlying liver disease. Integrating CAP measurements in the standard work-up may identify patients with NAFLD.
TPR-containing proteins control protein organization and homeostasis for the endoplasmic reticulum

Abstract The endoplasmic reticulum (ER) is a complex, multifunctional organelle comprised of a continuous membrane and lumen that is organized into a number of functional regions. It plays various roles including protein translocation, folding, quality control, secretion, calcium signaling, and lipid biogenesis. Cellular protein homeostasis is maintained by a complicated chaperone network, and the largest functional family within this network consists of proteins containing tetratricopeptide repeats (TPRs). TPRs are well-studied structural motifs that mediate intermolecular protein–protein interactions, supporting interactions with a wide range of ligands or substrates. Seven TPR-containing proteins have thus far been shown to localize to the ER and control protein organization and homeostasis within this multifunctional organelle. Here, we discuss the roles of these proteins in controlling ER processes and organization. The crucial roles that TPR-containing proteins play in the ER are highlighted by diseases or defects associated with their mutation or disruption.
Improved Secret Information Hiding Using SHA-256 and Invisible ASCII Character Replacement Technology

This is the information era. Lots amount of information is get transmitted from one end to another. The text document exchange is most widely used technique over internet for communication. This may contain some valuable information or some general data. If these text documents contain valuable information and if it gets hacked by some unauthorized user then they may change it or make misuse of this information. So that we need some technique to hide our important and valuable text information while communicating over internet. Still there are many hiding technique present also proposed by many authors but these having some limitation and drawbacks like poor robustness, lower embedding rate and semantic clutter. Due to all these weakness in existing system, interceptors may hacked or extracted the information. To overcome all these drawbacks of existing system, we proposed a new technique based on the invisible ASCII character replacement method the hash function. In this approach we first find the binary formatted information, after that the space character in every carrier is replace by SOH with the help of some replacement algorithm. In third stage a hash value is generated. At last the hash value is compare with the encoded secrete information
Assessment of Outcomes of Inpatient or Clinic-Based vs Home-Based Rehabilitation After Total Knee Arthroplasty

Key Points Question Is inpatient or clinic-based rehabilitation associated with superior outcomes after total knee arthroplasty compared with home programs? Findings This systematic review and meta-analysis included 5 unique studies involving 752 unique participants comparing clinic- and home-based rehabilitation and 1 study comparing inpatient rehabilitation with a home-based program. Based on low- to moderate-quality evidence, no associations between settings, no clinically important differences for mobility or patient-reported pain and function at 10 and 52 postoperative weeks, and no significant differences in quality of life or range of motion were found. Meaning For adults who underwent total knee arthroplasty, clinic or inpatient vs home-based rehabilitation appeared to offer no clinically important advantages.
Cognitive Heterogeneity of Math Difficulties: A Bottom-up Classification Approach

Math learning difficulties (MD) correspond to math achievement below the 25th percentile and are cognitively heterogeneous. It is not known precisely how cognitive mechanisms underlie distinct subtypes of MD. A bottom-up, cluster-analytic strategy, based on visuoconstructional, visuospatial and phonological working memory, and non-symbolic and symbolic magnitude processing accuracy, was used to form subgroups of children from 3rd to 5th grades according to their math achievement. All children had nonverbal intelligence above the 20th percentile and presented a broad spectrum of variation in math ability. External validity of subgroups was examined considering intelligence and math achievement. Groups did not differ in age. Two groups with a high incidence of MD were associated, respectively, with low visuospatial/visuoconstructional and low magnitude processing accuracy. One group with average cognitive performance also presented above average intelligence and a small incidence of MD. A fourth group with high cognitive performance presented high math performance and high intelligence. Phonological working memory was associated with high but not with low math achievement. MD may be related to complex patterns of associations and dissociations between intelligence and specific cognitive abilities in distinct subgroups. Consistency and stability of these subgroups must be further characterized. However, a bottom-up classification strategy contributes to reducing the cognitive complexity of MD.
A molecular model for self-assembly of the synaptonemal complex protein SYCE3

The synaptonemal complex (SC) is a supramolecular protein assembly that mediates homologous chromosome synapsis during meiosis. This zipper-like structure assembles in a continuous manner between homologous chromosome axes, enforcing a 100-nm separation along their entire length and providing the necessary three-dimensional framework for cross-over formation. The mammalian SC comprises eight components—synaptonemal complex protein 1–3 (SYCP1–3), synaptonemal complex central element protein 1–3 (SYCE1–3), testis-expressed 12 (TEX12), and six6 opposite strand transcript 1 (SIX6OS1)—arranged in transverse and longitudinal structures. These largely α-helical, coiled-coil proteins undergo heterotypic interactions, coupled with recursive self-assembly of SYCP1, SYCE2–TEX12, and SYCP2–SYCP3, to achieve the vast supramolecular SC structure. Here, we report a novel self-assembly mechanism of the SC central element component SYCE3, identified through multi-angle light scattering and small-angle X-ray scattering (SAXS) experiments. These analyses revealed that SYCE3 adopts a dimeric four-helical bundle structure that acts as the building block for concentration-dependent self-assembly into a series of discrete higher-order oligomers. We observed that this is achieved through staggered lateral interactions between self-assembly surfaces of SYCE3 dimers and through end-on interactions that likely occur through intermolecular domain swapping between dimer folds. These mechanisms are combined to achieve potentially limitless SYCE3 assembly, particularly favoring formation of dodecamers of three laterally associated end-on tetramers. Our findings extend the family of self-assembling proteins within the SC and reveal additional means for structural stabilization of the SC central element.