Miracle-dz/UniFAR-FTUs huggingface dataset:
Sexual dimorphism in the methane seep-dwelling Costa Rican yeti crab Kiwa puravida (Decapoda: Anomura: Kiwaidae)

Deep-sea chemosynthesis-based ecosystems support unique biological communities, but human impacts are an increasing threat. Understanding the life-history traits of species from deep-sea chemosynthesis-based ecosystems can help to develop adequate management strategies, as these can have impacts on ecological responses to changes in the environment. Here we examined the occurrence of sexual dimorphism in the yeti crab Kiwa puravida, an endemic species from the Costa Rican Pacific margin that aggregates at active methane seeps and depends on chemosynthetic bacteria for nutrition. The two morphological features examined included the claws, suspected to be under sexual selection and used for defense, and the carpus of the second pereopod not suspected to be under sexual selection. A total of 258 specimens, 161 males, 81 females, 16 juveniles, were collected from Mound 12 at 1,000-1,040 m depth in 2017 and 2018 and analyzed. We found that males have larger and wider claws than females, while there were no differences in carpus length. These results suggest that claw weaponry is under sexual selection in K. puravida, which is probably related to the mating system of this deep-sea species. This is the first attempt to study the reproductive biology of K. puravida, and additional observations will be necessary to shed more light on this matter.
A Novel method for IDC Prediction in Breast Cancer Histopathology images using Deep Residual Neural Networks

Invasive ductal carcinoma (IDC), which is also sometimes known as the infiltrating ductal carcinoma, is the most regular form of breast cancer. It accounts to about 80% of all breast cancers. According to American Cancer Society [1], more than 180, 000 women in the United States are diagnosed with invasive breast cancer each year. The survival rate associated with this form of cancer is about 77% to 93% depending on the stage at which they are being diagnosed. The invasiveness and the frequency of the occurrence of these disease makes it one of the difficult cancers to be diagnosed. Our proposed methodology involves diagnosing the invasive ductal carcinoma with a deep residual convolution network to classify the IDC affected histopathological images from the normal images. The dataset for the purpose used is a benchmark dataset known as the Breast Histopathology Images [2]. The microscopic RGB images are converted into a seven channel image matrix, which are then fed to the network. The proposed model produces a 99.29% accurate approach towards prediction of IDC in the histopathology images with an AUROC score of 0.9996. Classification ability of the model is tested using standard performance metrics. The following methodology has been described in the next sections. Index Terms–Residual learning, CIELAB color space, Grad-CAM, Contrast adaptive histogram equalization (CLAHE), Gaussian filtering
Evaluation of Simplified Methodology for Obtaining mmWave MIMO Channels from Ray-Tracing Simulations

The use of higher frequencies and MIMO is important in many 5G use cases. However, the available channel models for millimeter waves (mmWaves) currently demand investigation and the number of measurements is still limited. Using simulators is a current practice in mmWave MIMO research and ray tracing is considered one of the most accurate techniques. Due to the relatively long time of ray tracing simulations, it is common practice to adopt a simplified simulation methodology in which omnidirectional antennas are simulated and, later, the results are used together with a geometrical model to consider that antenna arrays were used. This allows flexibility and decreases the overall time spent with simulations. This paper investigates the corresponding assumptions and how accurate are the results of the simplified methodology when compared to effectively using antenna arrays in the ray tracing simulation. The preliminary results indicate that the distance between transmitter and receiver needs to be sufficiently large.
On the geometry of asymptotically flat manifolds

In this paper, we investigate the geometry of asymptotically flat manifolds with controlled holonomy. We show that any end of such manifold admits a refined torus fibration over an ALE manifold. In addition, we prove a Hitchin-Thorpe inequality for oriented Ricci-flat $4$-manifolds with curvature decay and controlled holonomy. As an application, we show that any complete asymptotically flat Ricci-flat metric on a $4$-manifold which is homeomorphic to $\mathbb R^4$ must be isometric to the Euclidean or the Taub-NUT metric, provided that the tangent cone at infinity is not $\mathbb R \times \mathbb R_+$.
Trend Analysis of the Central Anatolia Region Precipitation Data

Recently, trend analysis of hyrological variables has came into prominence by reason of the global climate change. The trend of hydrological time series can be determined by using parametric and nonparametric tests.This research objective is to determine whether the statistical trend of monthly areal precipitation data (1960-2013) which were obtained from 7 precipitation station in Central Anatolia Region of Turkey by using Mann-Kendall rank correlation. According to the results of Mann-Kendall Rank Correlation test, % 42.9 (36 count) tendency to decrease and also %57.2 ( 47 count) increase is determined. Whereas tendency to decrease isn’t found statistically significant in the Central Anatolia Region for the significance level of 0.05 , tendency to increase %2.28(2 count) for the same significance level is observed. The Gauged precipitation changes are analysed at the 7 precipitation stations in Central Anatolia Region and especially it is determined tendecy to decrease at the station of Ankara (8 count),Nevşehir (6 count), Kırşehir(7count). The tendecy to increase is generally observed at the Sivas station and also significant increases have occured in August and October at Niğde station.
A virtual reality social conditioned place preference paradigm for humans: Does trait social anxiety affect approach and avoidance of virtual agents?

Approach and avoidance of positive and negative social cues are fundamental to prevent isolation and ensure survival. High trait social anxiety is characterized by an avoidance of social situations and extensive avoidance is a risk factor for the development of social anxiety disorder (SAD). Therefore, experimental methods to assess social avoidance behavior in humans are essential. The social conditioned place preference (SCPP) paradigm is a well-established experimental paradigm in animal research that is used to objectively investigate social approach–avoidance mechanisms. We retranslated this paradigm for human research using virtual reality. To this end, 58 healthy adults were exposed to either a happy- or angry-looking virtual agent in a specific room, and the effects of this encounter on dwell time as well as evaluation of this room in a later test without an agent were examined. We did not observe a general SCPP effect on dwell time or ratings but discovered a moderation by trait social anxiety, in which participants with higher trait social anxiety spent less time in the room in which the angry agent was present before, suggesting that higher levels of trait social anxiety foster conditioned social avoidance. However, further studies are needed to verify this observation and substantiate an association with social anxiety disorder. We discussed the strengths, limitations, and technical implications of our paradigm for future investigations to more comprehensively understand the mechanisms involved in social anxiety and facilitate the development of new personalized treatment approaches by using virtual reality.
Application Report Coating of fibers Application report

Fibers affect all of us in one way or another during our daily lives. They may be used in the form of a composite material, a woven or non-woven technical textile or even in reinforced concrete structures. The surface properties of these fibers play a key role in delivering the performance required by a particular process or final product. To impart the fiber with the appropriate surface properties, surface treatments or coatings are applied and most often serve to protect, functionalize, lubricate, color code and sometimes decorate the surface. This article focuses upon two important aspects of the surface chemistry related to fibers: the influence of surfactant concentration in coating formulations upon the coating thickness and the optimization of finished coating performance through the exploitation of fiber-coating adhesion energy and interfacial tension values calculated from contact angle and surface tension measurements. Surface tension, CMC and coating thickness on fibers Generally, coating is achieved by drawing a fiber at a given speed through a bath containing the coating liquid. As the fiber traverses the liquid air interface, a thin layer of coating is deposited onto it. Early work on film coating problems was carried out by Landeau and Levich [1] and Derjaguin [2] who determined the relationship between substrate geometry (fiber) and speed (U) in the low capillary number limit,
Maternal diabetes and the role of neonatal reticulocyte hemoglobin content as a biomarker of iron status in the perinatal period

Aims We aimed to evaluate the effects of maternal diabetes on neonatal iron status, measuring erythrocyte indices including hemoglobin, hematocrit, reticulocytes, mean corpuscular volume (MCV), percent (%) hypochromia, ferritin, and additionally mean reticulocyte hemoglobin content (MCHr) as an early marker of iron deficiency, and examine the association between neonatal MCHr, red cell indices, and ferritin. Materials and Methods We conducted a hospital-based prospective cohort study in a tertiary neonatal unit of a University Hospital from 2018 to 2020. We enrolled 126 maternal-infant pairs of mothers whose pregnancy was associated with diabetes and 74 maternal-infant pairs from uncomplicated pregnancies. Erythrocyte indices were analyzed within the first twelve hours after birth. Erythrocyte parameters were compared between infants of the diabetes and the non-diabetic group. We examined the correlation of the neonatal MCHr with perinatal characteristics, including gestation, birth weight, maternal body mass index, the erythrocytic indices, maternal diabetes, maternal obesity, prematurity, small-for-gestational-age status, maternal preeclampsia, and maternal anemia. Finally, we evaluated the discordance between neonatal MCHr and neonatal ferritin. Results Infants of the diabetes group had a significantly lower MCHr (32.6 pg vs. 34.2 pg, p=0.003) compared with infants of uncomplicated pregnancies. Neonatal MCHr was significantly correlated with maternal hypochromia (r=-0.237, p=0.004) and neonatal MCV (r=0.674, p<0.001). Neonatal MCHr was significantly associated with maternal diabetes [standardized coefficients 0.21, 95% confidence interval (CI) 0.05-0.58, p=0.003) and maternal preeclampsia (standardized coefficients 0.17, 95% CI 0.02-0.92, p=0.019), after adjusting for maternal anemia, maternal obesity, prematurity, and small-for-gestational-age status. Those results were consistent also when analyzing maternal-infant pairs with pre-existing diabetes, and maternal-infant pairs with gestational diabetes. There was significant discordance between neonatal MCHr and neonatal ferritin (p=0.001). Conclusions MCHr was significantly lower in infants of mothers whose pregnancy was associated with diabetes compared with infants of non-diabetic mothers and correlated with neonatal and maternal red cell indices of iron deficiency. Since there was significant discordance between neonatal MCHr and ferritin during the first postnatal day, it is possible that MCHr could be used as a screening test for iron deficiency, especially in infants.
Optimal income taxation in the presence of networks of altruism∗

Following the tradition of Mirrlees (1971), static optimal income taxation problems are typically concerned with public redistribution by the State between independent individuals or couples. This paper is the first to depart from this independence assumption and to account for the interaction between public redistribution and private transfers occurring between individuals within their familial and social networks. Here, individuals value the private utilities of their group’s members and differ in their productivity levels. As a result, they have two dimensions of choice: their productive effort and the transfers they make to other members of their network. I characterize theoretically the optimal linear and non-linear income tax schemes in this context and provide sufficient statistics formulas for both, which highlight how tax design is affected by the existence of altruistic private transfers. I show that public and private redistribution are substitutes but at the optimum, private transfers are only partially crowded-out: the government may rely on these transfers to achieve its equity objective. Depending on the structure of groups, taking into account private redistribution may induce large efficiency gains – a finding I illustrate using numerical simulations. These novel insights can then be applied to a variety of settings, such as those of family taxation and of taxation in developing countries, for which I document the structure and nature of informal transfers using the World Bank’s Living Standard Measurement Surveys.
Development of a multiplex RT-PCR method for the detection of four porcine enteric coronaviruses

Porcine enteric coronaviruses are pathogens that cause viral diarrhea in pigs and are widely prevalent worldwide. Moreover, studies have shown that some porcine enteric coronaviruses can infect humans and poultry. In order to effectively monitor these viruses, it is necessary to establish a multiple detection method to understand their prevalence and conduct in-depth research. Common porcine enteric coronaviruses include Porcine epidemic diarrhea virus (PEDV), Porcine transmissible gastroenteritis virus (TGEV), Porcine delta coronavirus (PDCoV), and Swine acute diarrhea syndrome coronavirus (SADS-CoV). Pigs infected with these viruses have the common clinical symptoms that are difficult to distinguish. A quadruplex RT-PCR (reverse transcription-polymerase chain reaction) method for the simultaneous detection of PEDV, PDCoV, TGEV and SADS-CoV was developed. Four pairs of specific primers were designed for the PEDV M gene, PDCoV N gene, TGEV S gene and SADS-CoV RdRp gene. Multiplex RT-PCR results showed that the target fragments of PDCoV, SADS-CoV, PEDV and TGEV could be amplified by this method. and the specific fragments with sizes of 250 bp, 368 bp, 616 bp and 801 bp were amplified, respectively. This method cannot amplify any fragment of nucleic acids of Seneca Valley virus (SVV), Porcine Reproductive and Respiratory Syndrome Virus (PRRSV) and Atypical Porcine Pestivirus (APPV), and has good specificity. The lowest detection limits of PDCoV, PEDV, TGEV and SADS-CoV were 5.66 × 105 copies/μL, 6.48 × 105 copies/μL, 8.54 × 105 copies/μL and 7.79 × 106 copies/μL, respectively. A total of 94 samples were collected from pig farms were analyzed using this method. There were 15 positive samples for PEDV, 3 positive samples for mixed infection of PEDV and PDCoV, 2 positive samples for mixed infection of PEDV and TGEV, and 1 positive sample for mixed infection of PEDV, TGEV, and PDCoV. Multiplex RT-PCR method could detect four intestinal coronaviruses (PEDV, PDCoV, TGEV, and SADS-CoV) in pigs efficiently, cheaply and accurately, which can be used for clinical large-scale epidemiological investigation and diagnosis.
Talking about values : a dialogic approach to citizenship education as an integral part of history classes

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How college experiences impact student learning outcomes: Insights from Chinese undergraduate students

In recent years, student development and learning outcomes have become a central focus of quality higher education research. Identifying factors associated with student learning outcomes is the key to continuous quality improvement. The purpose of this study was to explore the impact of college experiences on student learning outcomes. A sample of 17,609 undergraduates from 23 Chinese colleges/universities were selected for the study and asked to complete the Chinese version of the College Student Experiences Questionnaire. Structural equation modeling analyses suggested academic and social activity involvement was an important factor influencing student learning outcomes. Campus facilities and the college environment also predicted student learning outcomes. Family background, on the other hand, exerted minimal influence on student learning outcomes. Students’ involvement in academic and social activities served as strong mediators between campus facilities/environment and student learning outcomes. The results highlighted the significance of providing high-quality college experiences. They also informed policymakers and practitioners of possible pathways to support student learning outcomes by enhancing students’ involvement in social and academic activities.
A participatory tool for assessing land footprint in city-region food systems—A case study from Metropolitan Copenhagen

The ongoing COVID-19 pandemic has exposed the fragility of current food systems to feed populations around the world. Particularly in urban centers, consumers have been confronted with this vulnerability, highlighting reliance on just-in-time logistics, imports and distant primary production. Urban food demand, regional food supply, land use change, and transport strategies are considered key factors for reestablishing resilient landscapes as part of a sustainable food system. Improving the sustainability of food systems in such circumstances entails working on the interrelations between food supply and demand, rural and urban food commodity production sites, and groups of involved actors and consumers. Of special significance is the agricultural land in close proximity to urban centers. Calling for more holistic approaches in the sense of inclusiveness, food security, citizen involvement and ecological principles, this article describes the use of a new decision support tool, the Metropolitan Foodscape Planner (MFP). The MFP features up-to-date European datasets to assess the potential of current agricultural land use to provide food resources (with special attention to both plant- and animal-based products) and meet the demand of city dwellers, and help to empower citizens, innovators, companies, public authorities and other stakeholders of regional food systems to build a more regionalized food supply network. The tool was tested in the context of the food system of the Copenhagen City Region in two collaborative workshops, namely one workshop with stakeholders of the Copenhagen City Region representing food consultancies, local planning authorities and researchers, and one in-person workshop masterclass with MSc students from the University of Copenhagen. Workshop participants used the tool to learn about the impacts of the current food system at the regional and international level with regard to the demand-supply paradigm of city-regions. The ultimate goal was to develop a participatory mapping exercise and test three food system scenarios for a more regionalized and sustainable food system and, therefore, with increased resilience to crises. Results from this implementation also demonstrated the potential of the tool to identify food production sites at local level that are potentially able to feed the city region in a more sustainable, nutritious and way.
Meminimalisasi Nilai Error Peramalan dengan Algoritma Extreme Learning Machine

This study uses a machine learning algorithm which is the extreme model one of the new learning method of neural networks. Determining the value of forecasting based on actual value. Output generated more quickly in this process, because the learning done in a fast speed and better accuracy rate than conventional forecasting methods. Epoch using the parameter changes and changes in the range of accuracy of test results demonstrate the value of error is quite good during the system testing. Keywords : neural networks, epoch, value of error.
Distinct characteristics of microglia from neurogenic and non-neurogenic regions of the human brain in patients with Mesial Temporal Lobe Epilepsy

The study of microglia isolated from adult human brain tissue provides unique insight into the physiology of these brain immune cells and their role in adult human brain disorders. Reports of microglia in post-mortem adult human brain tissue show regional differences in microglial populations, however, these differences have not been fully explored in living microglia. In this study biopsy tissue was obtained from epileptic patients undergoing surgery and consisted of both cortical areas and neurogenic ventricular and hippocampal (Hp) areas. Microglia were concurrently isolated from both regions and compared by immunochemistry. Our initial observation was that a greater number of microglia resulted from isolation and culture of ventricular/Hp tissue than cortical tissue. This was found to be due to a greater proliferative capacity of microglia from ventricular/Hp regions compared to the cortex. Additionally, ventricular/Hp microglia had a greater proliferative response to the microglial mitogen Macrophage Colony-Stimulating Factor (M-CSF). This enhanced response was found to be associated with higher M-CSF receptor expression and higher expression of proteins involved in M-CSF signalling DAP12 and C/EBPβ. Microglia from the ventricular/Hp region also displayed higher expression of the receptor for Insulin-like Growth Factor-1, a molecule with some functional similarity to M-CSF. Compared to microglia isolated from the cortex, ventricular/Hp microglia showed increased HLA-DP, DQ, DR antigen presentation protein expression and a rounded morphology. These findings show that microglia from adult human brain neurogenic regions are more proliferative than cortical microglia and have a distinct protein expression profile. The data present a case for differential microglial phenotype and function in different regions of the adult human brain and suggest that microglia in adult neurogenic regions are “primed” to an activated state by their unique tissue environment.