Miracle-dz/UniFAR-FTUs huggingface dataset:
Self-Empathy as a Necessary Element for Regulation of Emotions

The need to care about each other, identifying ourselves with what we have in common, as living beings, human beings on this planet, is currently becoming a focus in studies and reflections among researchers in neuroscience, behaviour, emotions and social relationships, to name a few. In the area of health promotion and psychotherapy, it is no different. Particularly in the last two decades, the expansion of knowledge about the brain and nervous system in neuroscience research has provided information to relate these areas to understanding of the individual and their relationships, contributing to reflections, understanding and proposals for action and possible release from human suffering. Thus, the purpose of this qualitative article is to reflect, based on a Narrative Review of recent literature, on the possibility of understanding empathy as originating from primary or natural emotion. In conclusion, the importance of regulating emotions becomes evident, considering self-empathy so that its function of regulating our instincts with the purpose of survival, well-being and evolution, can happen, in the individual and social fields. This paper appeared originally in Portuguese as. Autoempatia Como Elemento Necessário Para Regulação Das Emoções. Revista Brasileira de Análise Transacional, 2021 and is reproduced here by kind permission of UNAT-BRASIL - União Nacional de Analistas Transacionais – Brasil (https://unat.org.br/portal/rebat-2021.php)
Fungal diversity in shade-coffee plantations in Soconusco, Mexico

Background As forested natural habitats disappear in the world, traditional, shade-coffee plantations offer an opportunity to conserve biodiversity and ecosystem services. Traditional coffee plantations maintain a diversity of tree species that provide shade for coffee bushes and, at the same time, are important repositories for plants and animals that inhabited the original cloud forest. However, there is still little information about shade-coffee plantation’s fungal diversity despite their relevance for ecosystem functioning as decomposers, symbionts and pathogens. Specifically, it is unknown if and what mycorrhizae-forming fungi can be found on the branches and trunks of coffee bushes and trees, which hold a diversity of epiphytes. Here, we evaluate fungal communities on specific plant microsites on both coffee bushes and shade trees. We investigate the ecological roles played by this diversity, with a special focus on mycorrhizae-forming fungi that may enable the establishment and development of epiphytic plants. Methods We collected 48 bark samples from coffee bushes and shade trees (coffee; tree), from four plant microsites (upper and lower trunks, branches and twigs), in two shade-coffee plantations in the Soconusco region in southern Mexico, at different altitudes. We obtained ITS amplicon sequences that served to estimate alpha and beta diversity, to assign taxonomy and to infer the potential ecological role played by the detected taxa. Results The bark of shade trees and coffee bushes supported high fungal diversity (3,783 amplicon sequence variants). There were no strong associations between community species richness and collection site, plant type or microsite. However, we detected differences in beta diversity between collection sites. All trophic modes defined by FUNGuild database were represented in both plant types. However, when looking into guilds that involve mycorrhizae formation, the CLAM test suggests that coffee bushes are more likely to host taxa that may function as mycorrhizae. Discussion We detected high fungal diversity in shade-coffee plantations in Soconusco, Chiapas, possibly remnants of the original cloud forest ecosystem. Several mycorrhiza forming fungi guilds occur on the bark of coffee bushes and shade trees in this agroecosystem, with the potential of supporting epiphyte establishment and development. Thus, traditional coffee cultivation could be part of an integrated strategy for restoration and conservation of epiphytic populations. This is particularly relevant for conservation of threatened species of Orchidaceae that are highly dependent on mycorrhizae formation.
Study and Design of Reconfigurable Wireless and Radio- Frequency Components Based on RF MEMS for Low-Power Applications

This chapter intends to deal with the challenging field of communication systems known as reconfigurable radio-frequency systems. Mainly, it will present and analyze the design of different reconfigurable components based on radio-frequency microelectromechanical systems (RF MEMS) for different applications. This chapter will start with the description of the attractive properties that RF MEMS structures offer, giving flexibility in the RF systems design, and how these properties may be used for the design of reconfigurable RF MEMS-based devices. Then, the chapter will discuss the design, modeling, and simu- lation of reconfigurable components based on both theoretical modeling and well-known electromagnetic computing tools such as ADS, CST-MWS, and HFSS to evaluate the performance of such devices. Finally, the chapter will deal with the design and performance assessment of RF MEMS-based devices. Non-radiating devices, such as phase shifter and resonators, which are very important components in the hardware RF boards, will be addressed. Also, three types of frequency reconfigurable antennas, for the three different applications (radar, satellite, and wireless communication), will be proposed and evaluated. From this study, based on theoretical design and electromagnetic computing evaluation, it has been shown that RF MEMS-based devices can be an enabling solution in the design of the multiband reconfigurable radio-frequency devices.
Let’s verify and rectify! Examining the nuanced influence of risk appraisal and norms in combatting misinformation

Mounting concerns about COVID-19 misinformation and its insidious fallout drive the search for viable solutions. Both scholarly and practical efforts have turned toward raising risk appraisal of misinformation and motivating verification and debunking behaviors. However, individuals remain reluctant to verify and correct misinformation, suggesting a need to develop persuasion strategies to motivate such behaviors. Therefore, with an experiment of 256 participants recruited from Amazon MTurk, this study examines how effectively norm-based messages improve positive behavioral intentions during the COVID-19 pandemic. Findings suggest that among individuals with high perceived severity of misinformation, exposure to both descriptive and injunctive norms about verification reduced their intention to rectify misinformation. However, both descriptive and injunctive norms about debunking misinformation increased intentions to engage in preventive behaviors. By probing the “self–other” discrepancy and the “trade-off effect” of risk appraisal, the study further reveals that the perceived severity of misinformation merits in-depth exploration in future research.
The role of cohorts in the understanding of the changes in fertility in Czechia since 1990

This article presents a detailed analysis of the fertility changes in Czechia since 1990 using the cohort approach and contributes to the overall understanding of the fertility postponement process. Because the timing of childbearing since 1990 has changed significantly, particular attention is devoted to the differences in the timing of fertility between cohorts. Data from the Human Fertility Database was analyzed via both standard (based on age-specific fertility rates) and advanced methods (postponement and recuperation indicators, parity progression ratio). Four groups of cohorts with specific fertility patterns were identified: 1965–1970, 1971–1976, 1977–1982, and 1983–1990. These groups were impacted by the political, economic and social transformation of the 1990s, the financial crisis of 2008–2012 and other socio-economic changes during the study period in different ways. While the 1965–1970 cohort was associated with the rapid occurrence of postponement, it still reflected the early fertility pattern. The 1971–1976 cohort was associated with the most intensive degree of postponement, the 1977–1982 cohort can be linked to the onset of the deceleration of the postponement process, and the 1983–1990 cohort appears to be the first to stabilize their fertility at later ages.
Agglomerations around natural resources in the hospitality industry: Balancing growth with the Sustainable Development Goals

Many tourism agglomerations are situated near natural resources, which implies a need to balance business growth with environmental preservation. Our analysis of the location decisions of 295 luxury beach hotels in Spain between 1960 and 2015 reveals two main findings. First, we confirm the positive relationship between the existence of demand-related urbanization services around natural resources and the attractiveness of agglomerations to new entrants. Second, we find that an agglomeration’s attractiveness negatively affects the density of firms in the agglomeration if that attractiveness hinders firms’ access to the same natural resources. Our results contribute to the strategy literature on agglomerations and provide a better understanding of how the tourism industry can work toward achieving the sustainable development goals (SDGs). JEL CLASSIFICATION: L83, M13, M14, Q50, Q26, R30
Spontaneous Rupture and Hemorrhage of WON: A Case Report

Background Pancreatic pseudocysts are characterized by the leakage of pancreatic juice caused by various reasons, which leads to pancreatic juice accumulates around the pancreas, and stimulates the greater omentum and other tissues to form an area of fibrotic loculated effusion with an integrated capsule. Approximately, one-third of patients experienced recurrent pancreatic juice leakage, compression symptoms, infection, and bleeding, which requires surgical intervention, but spontaneous rupture cases are extremely rare. Case presentation We here present the case of 40-year-old male who presented with abdominal pain and vomiting for two days and 10 h, respectively. He had a history of chronic pancreatitis and pseudocysts. The symptoms of abdominal pain worsened in the second day. Laboratory tests showed a progressive decrease in hemoglobin. Then, emergency pancreatoduodenectomy was performed. Intraoperative exploration found a small blood clot in the abdominal cavity and a hematoma that had formed in the intestinal cavity and retroperitoneum. Conclusion This case showed that pseudocysts of the pancreas can rupture under certain circumstances, leading to intraperitoneal bleeding and hematoma formation, which can endanger the life of the patient. And surgical treatment can be the first choice for hemorrhagic pseudocysts.
The third spectrum of praseodymium in magnetic Ap stars

The identification of Pr III lines in the red spectrum of magnetic Ap stars is reported. The great strength of these lines in some of the observed stars is in contrast to the relative weakness of the second spectrum of praseodymium in upper main-sequence chemically peculiar stars. The present identification is an important step toward a knowledge of the abundance systematics of the lanthanides in chemically peculiar stars and an understanding of the chemical fractionation processes that they undergo
Soil biological properties as affected by the conjunction of chemical fertilizers, bacterial consortia and bio-enhancers in foxtail millet cultivation

During Kharif 2019, a field experiment was conducted with eight treatments replicated three times in Randomized Block Design at College farm, PJTSAU, Rajendranagar, Hyderabad. The aim of the study is to investigate the combined effect of chemical fertilizers and bio-agents (bacterial consortia and bio-enhancers) on soil biological parameters (bacterial population, urease, dehydrogenase, acid phosphatase and alkaline phosphatase) under foxtail millet cultivation in semi-arid region where soil and climatic constraints prevail in general. The bacterial population in the rhizosphere soil was found to be greater in all the treatments that received bacterial consortia appended with bio-enhancers compared to the remaining treatments as they are rich in microbial population. The soil enzyme activity was found to be higher when bioagents were used in conjunction with fertilizers, similar to bacterial population. The use of bacterial consortia or bio-enhancers alone also improved enzyme activity when compared to the control, while fertilizers alone, were poor in the activity of above enzymes. The percentage increase in the overall biological activity over the initial value was found to be highest when bioagents were used along with the chemical fertilizers at 50% flowering and harvest stages whereas it was found least in the control and lower in the treatments applied with chemical fertilizers alone.
Spatial-Temporal Graph Network for Video Crowd Counting

In recent years, researchers have developed many deep-learning-based methods to count crowd numbers in static images. However, much fewer works focus on video-based crowd counting, in which the critical challenge of temporal correlation has not been well explored. This paper proposes a Spatial-Temporal Graph Network (STGN) to achieve efficient and accurate crowd counting in videos via learning pixel-wise and patch-wise relations in local spatial-temporal domains. Specifically, we design a pyramid graph module to leverage multi-scale features. In each scale, we sequentially construct three graphs: spatial-temporal pixel graph, temporal patch graph, and spatial pixel graph, in which we apply the self-attention mechanism to capture pixel-wise relation, learn structure-aware relation, and aggregate local features, respectively. Furthermore, we propose spatial-aware channel-wise attention to effectively fuse multi-scale features. To demonstrate the effectiveness of the proposed method, we conduct experiments on five crowd counting datasets, including a large-scale video crowd dataset (FDST). Moreover, the proposed model is also applied in the vehicle counting dataset (TRANCOS). The results show that the proposed model outperforms existing spatial-temporal crowd counting models and achieves state-of-the-art. The code is available at https://github.com/wuzhe71/STGN
The Inhibition of DNA Viruses by the Amphibian Antimicrobial Peptide Temporin G: A Virological Study Addressing HSV-1 and JPCyV

Herpes simplex virus type-1 (HSV-1) and John Cunningham polyomavirus (JCPyV) are widely distributed DNA viruses causing mainly asymptomatic infection, but also mild to very severe diseases, especially when these viruses reach the brain. Some drugs have been developed to inhibit HSV-1 replication in host cells, but their prolonged use may induce resistance phenomena. In contrast, to date, there is no cure for JCPyV. The search for alternative drugs that can reduce viral infections without undermining the host cell is moving toward antimicrobial peptides (AMPs) of natural occurrence. These include amphibian AMPs belonging to the temporin family. Herein, we focus on temporin G (TG), showing that it strongly affects HSV-1 replication by acting either during the earliest stages of its life cycle or directly on the virion. Computational studies have revealed the ability of TG to interact with HSV-1 glycoprotein B. We also found that TG reduced JCPyV infection, probably affecting both the earliest phases of its life cycle and the viral particle, likely through an interaction with the viral capsid protein VP1. Overall, our results are promising for the development of short naturally occurring peptides as antiviral agents used to counteract diseases related to HSV-1 and JCPyV.
P-AgBot: In-Row & Under-Canopy Agricultural Robot for Monitoring and Physical Sampling

In this work, we present a novel agricultural robot called the Purdue AgBot or P-AgBot that has been designed for in-row and under canopy crop monitoring and physical sampling. We suggest approaches to autonomous navigation, crop monitoring, and crop sampling that can be applied in crop rows and under canopies for different agricultural environments. Each monitoring approach was designed to extract key morphological characteristics of the crops. The proposed approaches of P-AgBot have been experimentally verified not only in simulation but also with real corn and sorghum crops. Crop heights and stalk diameters are able to be estimated in real-time with less than 10% error. Vision-based detection of leaf samples was implemented and physical sampling is accomplished with a more than 80% success rate.
Runtime Energy Savings Based on Machine Learning Models for Multicore Applications

To improve the power consumption of parallel applications at the runtime, modern processors provide frequency scaling and power limiting capabilities. In this work, a runtime strategy is proposed to maximize energy savings under a given performance degradation. Machine learning techniques were utilized to develop performance models which would provide accurate performance prediction with change in operating core-uncore frequency. Experiments, performed on a node (28 cores) of a modern computing platform showed significant energy savings of as much as 26% with performance degradation of as low as 5% under the proposed strategy compared with the execution in the unlimited power case.
Joint Time and Time-Frequency Optimal Detection of K-Complexes in Sleep EEG

Automated detection of waveforms such as delta and K-complex in the EEG is an important component of sleep stage monitoring. The K-complex is a key feature that contributes to sleep stages assessment. However, its automated detection is still difficult due to the stochastic nature of the EEG. In this paper, we propose a detection structure which can be interpreted as joint linear filtering operations in time and time-frequency domains. We also introduce a method of obtaining the optimum detector from training data, and we show that the resulting receiver offers better performances than the one obtained via the Fisher criterion maximization. The efficiency of this approach for K-complexes detector design is explored. It results from this study that the obtained receiver is potentially the best one which can be found in the literature. Finally, it is emphasized that this methodology can be advantageously used to solve many other detection problems. © 1998 Academic Press
Robust Sure Independence Screening for Ultrahigh Dimensional Models

Independent screening is a variable selection method that uses a ranking criterion to select significant variables particularly for the statistical model with NP-dimensionality or “large p, small n” paradigms when p can even be as large as exponential of the sample size n. However, it requires exponential tails of variables and has not yet been applied to semiparametric models. In this paper, we propose a rank correlation screening (RCS) to deal with ultra-high dimensional data. The new procedure possesses the sure independence screening property without the assumption on exponential tails of variables even when the number of predictor variables grows as fast as exponential of the sample size. Furthermore, the proposed method can be used to deal with semiparametric models such as transformation regression models and single-index models. The estimation efficiency of our method is demonstrated through extensive comparisons with other methods by simulation studies.
Fibronectin: structure, functions, clinical significance (review)

Plasma fibronectin is a high molecular weight adhesive glycoprotein. There are two types of fibronectin: plasma (soluble) and cellular derived (insoluble). Electron microscopy revealed two types of structural organization of fibronectin: compact and expanded. In solution, fibronectin has a compact conformation, and after binding to certain substrates (collagen, fibrin, heparin), it is expanded. Plasma fibronectin is one of the main opsonins of blood plasma in relation to the “targets” of phagocytosis of a predominantly non-bacterial nature, as well as to some types of bacteria. For the treatment of septic processes, as well as respiratory distress syndrome of adults with severe fibronectin deficiency, plasma cryoprecipitate is used – a donor plasma preparation containing a large amount of plasma fibronectin (more than 2 mg/ml). It was proposed to replenish the level of fibronectin in patients with sepsis and other conditions that cause plasma fibronectin deficiency with the help of donor freshly frozen plasma. Transfusion of large volumes of freshly frozen plasma (up to 1000–1500 ml) to patients effectively eliminates the deficiency of plasma fibronectin. The concentration of plasma fibronectin in the blood significantly decreases after the addition of severe infectious processes to hematological diseases, as well as acute DIC syndrome. Extracorporeal methods of blood purification – selective plasmapheresis – have been developed to correct immunocomplex and fibronectin-complex pathology. Two variants of selective plasmapheresis have been proposed: the method of heparinocryoprecipitation of plasma proteins and the method of heparinocryofractionation. In 1987, a plasma heparin precipitate was proposed as a source of fibronectin for the treatment of patients with trophic skin lesions. In 1992, a new method was proposed for obtaining blood preparations with a high concentration of plasma fibronectin from patients themselves (heparin cryofractionation). Autofibronectin preparations obtained by such methods are effective in the local treatment of trophic ulcers in 90–93% of cases. The proposed drugs are safe against infection of patients with infectious diseases transmitted through the blood.
Klasifikasi Tweet Tidak Senonoh Twitter dengan Naïve Bayes Classifier

Popularitas Twitter berkembang pesat, Setiap orang dapat mengungkapkan pendapat mereka melalui Twitter. Pendapat yang sering disampaikan biasanya tentang keluhan-keluhan tentang aktivitas sehari-hari mereka. Aktivitas di basis Twitter disalahgunakan oleh pengguna dengan mengirimkan pertanyaan atau tweet yang mengandung kata-kata kotor, meskipun banyak pengikut Twitter dalam kategori di bawah umur. Proses penyaringan sangat diperlukan untuk memilah mana yang baik dan buruk di media sosial. Ada banyak akun anonim yang mengirimkan tweet dalam bentuk kata-kata tidak senonoh, yang tidak baik untuk dibaca oleh pengguna Twitter di bawah umur. Klasifikasi teks dapat menjadi penting dalam proses evaluasi topik masalah proses. Tujuannya adalah untuk mengetahui polaritas sentimen positif dan negatif. Dataset diperoleh dari Twitter. Tweet yang terkait dengan kata kunci yang dicari dikompilasi menggunakan Alat API Twitter dan bahasa pemrograman Python. Telah dibuat sistem yang dapat mengklasifikasikan suatu tweet dapat dikategorikan cabul atau tidak dengan menggunakan algoritma Naive Bayes dan ekstraksi fitur Tf-Idf. Hasil klasifikasi tweet senonoh adalah 99,5% dengan nilai recall 100% dan presisi 99%.