Miracle-dz/UniFAR-FTUs huggingface dataset:
Variabilidade genética molecular em uma coleção de germoplasma de Hymenaea stigonocarpa (Fabaceae)

O estudo da variabilidade genetica em populacoes de plantas e fundamental para a compreensao da magnitude da biodiversidade nos ecossistemas. Entender como essa variabilidade esta estruturada fornece evidencias de como a evolucao esta atuando nessas populacoes, trazendo subsidios para tracar estrategias de conservacao pertinentes aquela especie. Uma das especies nativas do Cerrado que vem sendo alvo de estudos em diversas areas e Hymenaea stigonocarpa (Fabaceae). Seu potencial economico e significativo, principalmente gracas a seu valor nutricional. O uso extrativista de plantas nativas serve de alerta, pois os recursos geneticos dessas plantas podem se esgotar, sem ao menos descobrirmos seu total potencial. Diante disto, as colecoes de germoplasma, que conservam os recursos geneticos de organismos ex situ e in vivo, sao uma alternativa para ter acesso as informacoes sobre a especie, ao mesmo tempo em que auxilia na conservacao de sua variabilidade genetica. Este trabalho teve o objetivo de caracterizar, do ponto de vista molecular, os individuos que compoem a colecao de germoplasma de H. stigonocarpa, localizada na Escola de Agronomia da Universidade Federal de Goias. Para tanto, foram utilizados dez marcadores microssatelites com metodos de deteccao por eletroforese capilar. Os individuos da colecao sao provenientes de coletas de frutos ocorridas em 24 localidades de abrangencia no Cerrado. No total, foram avaliados 353 individuos distribuidos em 119 progenies. As subpopulacoes apresentaram nivel moderado de diversidade genetica, para os locos avaliados, com heterozigosidade media esperada de 0,59. Foi detectada estruturacao genetica significativa nas subpopulacoes (𝜃𝑃 = 0,14), com coeficiente de endogamia intrapopulacional (𝑓) de 0,12 e endogamia total (𝐹) de 0,25. Estes resultados sugerem que essas subpopulacoes nao estao se comportando como uma populacao panmitica. As analises de divergencia genetica, por meio da distância genetica de Nei (1972), apresentaram dois grupos distintos subdivididos. A correlacao entre as distâncias genetica e geografica, demonstra que nao ha uma relacao muito forte entre essas matrizes (𝑟 = 0,27), sugerindo que a distância fisica entre as subpopulacoes nao e suficiente para diferencia-las geneticamente. A colecao de germoplasma detem um tamanho efetivo de 60, valor considerado suficiente para a conducao de programas de melhoramento (minimo 50), alem de apresentar uma representatividade alelica de 78,84% com relacao a 32 subpopulacoes naturais. Assim, pode-se concluir que a colecao de germoplasma representa satisfatoriamente a variabilidade genetica de H. stigonocarpa, preservando sua diversidade, o que e fundamental para subsidiar futuros trabalhos de conservacao e melhoramento da especie.
Bottom Trawling Threatens Future Climate Refugia of Rhodoliths Globally

Climate driven range shifts are driving the redistribution of marine species and threatening the functioning and stability of marine ecosystems. For species that are the structural basis of marine ecosystems, such effects can be magnified into drastic loss of ecosystem functioning and resilience. Rhodoliths are unattached calcareous red algae that provide key complex three-dimensional habitats for highly diverse biological communities. These globally distributed biodiversity hotspots are increasingly threatened by ongoing environmental changes, mainly ocean acidification and warming, with wide negative impacts anticipated in the years to come. These are superimposed upon major local stressors caused by direct destructive impacts, such as bottom trawling, which act synergistically in the deterioration of the rhodolith ecosystem health and function. Anticipating the potential impacts of future environmental changes on the rhodolith biome may inform timely mitigation strategies integrating local effects of bottom trawling over vulnerable areas at global scales. This study aimed to identify future climate refugia, as regions where persistence is predicted under contrasting climate scenarios, and to analyze their trawling threat levels. This was approached by developing species distribution models with ecologically relevant environmental predictors, combined with the development of a global bottom trawling intensity index to identify heavily fished regions overlaying rhodoliths. Our results revealed the importance of light, thermal stress and pH driving the global distribution of rhodoliths. Future projections showed poleward expansions and contractions of suitable habitats at lower latitudes, structuring cryptic depth refugia, particularly evident under the more severe warming scenario RCP 8.5. Our results suggest that if management and conservation measures are not taken, bottom trawling may directly threaten the persistence of key rhodolith refugia. Since rhodoliths have slow growth rates, high sensitivity and ecological importance, understanding how their current and future distribution might be susceptible to bottom trawling pressure, may contribute to determine the fate of both the species and their associated communities.
A Pervasive Community Detection Method Through Modular Decomposition of Markov Chain for Social Media Analysis

In a network structure, a subset with a dense link is called a community. Many networks in the real world are composed of loosely linked multiple communities. Individual communities are compatible with a functional unit constituting a complex system represented by the network. Therefore, if the community structure of the network is clarified, it can be seen how the entire complex system is composed of individual functional units. Clarifying the community structure is a fundamental and essential task for understanding complex systems expressed in networks. Many researchers have been studying to detect communities from networks effectively and efficiently. Meanwhile, the connection density of water pipes affects the failure rate. Therefore, the connection of water pipes is regarded as a network, it is possible to estimate the trouble spot through an application of community detection. Clarifying a community of members is to demonstrate a link between members. However, autonomously formed communities in the real world have a ubiquitous (pervasive) structure. If a community has a ubiquitous structure, it implies pervasive overlapping between communities. Though studies on the community detection method (modularity maximization and infomap method divide communities into the partition that do not overlap with each other. It follows that each member belongs to only one community. Under these circumstances, in this research, we propose a new community detection method through the modular decomposition of Markov chain method for social network activity.
Fine Tuning Muscarinic Acetylcholine Receptor Signaling Through Allostery and Bias

The M1 and M4 muscarinic acetylcholine receptors (mAChRs) are highly pursued drug targets for neurological diseases, in particular for Alzheimer’s disease and schizophrenia. Due to high sequence homology, selective targeting of any of the M1-M5 mAChRs through the endogenous ligand binding site has been notoriously difficult to achieve. With the discovery of highly subtype selective mAChR positive allosteric modulators in the new millennium, selectivity through targeting an allosteric binding site has opened new avenues for drug discovery programs. However, some hurdles remain to be overcome for these promising new drug candidates to progress into the clinic. One challenge is the potential for on-target side effects, such as for the M1 mAChR where over-activation of the receptor by orthosteric or allosteric ligands can be detrimental. Therefore, in addition to receptor subtype selectivity, a drug candidate may need to exhibit a biased signaling profile to avoid such on-target adverse effects. Indeed, recent studies in mice suggest that allosteric modulators for the M1 mAChR that bias signaling toward specific pathways may be therapeutically important. This review brings together details on the signaling pathways activated by the M1 and M4 mAChRs, evidence of biased agonism at these receptors, and highlights pathways that may be important for developing new subtype selective allosteric ligands to achieve therapeutic benefit.
Data Collection Method of Space-based Internet of Things Based on Layered Fountain Code

With the rapid development of the space-based information network, the network coverage has been further expanded. Many areas where ground infrastructure is difficult to deploy can be accessed via satellite. The application scope of the Internet of things has been greatly expanded. Ground sensors collect data, UAV as an air sink node, data is transmitted to the Internet through satellite, this type of Internet of things application is called space-based Internet of things. In space-based Internet of things applications, the communication resources are more valuable and the data transmission link is more unstable, data transmission is the major challenge in the application of space-based Internet of things. With the capability of acquiring reliable communications, Data coding plays an important role in satellite communications. However, the usage of data coding in space-based Internet of Things are still an open problem. In this paper, the space-based Internet of things scenario is modeled, and a layered fountain code are proposed in this scenario. Sensed data is coded and collected through a distributed and a layered approach, which is suitable to the space-based Internet of Thing applications. Simulation results show that the proposed approach can guarantee high reliability and throughput in data collection.
BBAAS: Blockchain-Based Anonymous Authentication Scheme for Providing Secure Communication in VANETs

Smart driving has become conceivable due to the rapid growth of vehicular ad hoc networks. VANETs are considered as the main platform for providing safety road information and instant vehicle communication. Nevertheless, due to the open wireless nature of communication channels, VANET is susceptible to security attacks by malicious users. For this reason, secure anonymous authentication schemes are essential in VANETs. However, when vehicles reach a new roadside unit (RSU) coverage area, the vehicles need to perform reauthentication with the current RSU, which significantly diminishes the efficiency of the entire VANET. Therefore, the introduction of blockchain technology has created opportunities for VANETs to resolve the aforementioned challenges. Due to the decentralized nature of blockchain technology, rapid reauthentication of vehicles is achieved in this paper through secure authentication code transfer between the consecutive RSUs. The security strength of the proposed blockchain-based anonymous authentication scheme against various harmful security attacks is proven in the security analysis section to ensure that it provides better security. In addition, blockchain, as presented in the performance analysis section, is used to substantially diminish the computational cost compared to conventional authentication schemes.
Evaluation of Process Communication Model training for surgeons and other healthcare professionals: a survey

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D-penicillamine-induced lupus erythematosus as an adverse reaction of treatment of Wilson's Disease.

Wilson’s Disease (WD) is an autosomal recessive disorder of copper metabolism characterised by pathological copper accumulation, particularly in the liver and brain [1]. There is a wide spectrum of clinical symptoms, but most often the hepatic and/or neuropsychiatric phenotypic presentation of WD predominates [1]. Of note, WD can be successfully treated with pharmacological agents, resulting in a negative copper balance. Currently, two groups of drugs are used: 1) chelators [d-penicillamine (DPA) or trientine (TN)], which increase urinary copper excretion; and 2) zinc salts, which decrease copper absorption from the digestive tract [1]. Long-term studies have documented positive outcomes in almost 85% of correctly treated patients. For treatment success, lifelong treatment is necessary, requiring ongoing monitoring for efficacy and adverse events [1]. Failure to comply with lifelong therapy can lead to recurrence and liver failure. In addition, therapeutic success relies on early diagnosis, which may be challenging [1]. Diagnosis must be confirmed in experienced WD centres by clinical, laboratory and genetic studies. There are several pitfalls en route to a correct WD diagnosis, and physicians must be aware that even the presence of two confirmed pathogenic mutations may not establish such a diagnosis, because both mutations may be localised on one allele (uniparental isodisomy). In situations where results diverge, a Cu radioactive copper incorporation test can be performed [2]. DPA, developed in 1956 by John Walshe, is still the most frequently used treatment, with probably the best, and longest, experience [1]. However, DPA can be responsible for a number of potentially important adverse drug-related events (ADRs) (Tab. 1) [3]. Because of this, the safety of DPA treatment should be closely monitored, not only at initiation, but also throughout the duration of treatment. We present the case of a 32-year-old man who had initially been diagnosed with an anxiety syndrome aged 23 and treated with several medications (sertraline, paroxetine, and ultimately a combination of escitalopram and chlorproxiten) with minimal benefit. After eight years, he reported abdominal pain, and after diagnostic tests [liver function test (LFT): ALT (31 U/L, normal: 10-40 U/L), AST (27 U/L, normal: 10-40 U/L), GGTP (156 U/L, normal: 7-64 U/L)], oesophageal varices in gastroscopy, and picture of liver cirrhosis in abdominal ultrasound), compensated liver cirrhosis was diagnosed (Child-Pugh class A). The triggers of cirrhosis, infectious (viral) and autoimmune disorders were excluded. Based on the hepatic and psychiatric symptoms, abnormal results of copper metabolism (low serum ceruloplasmin (17.4 mg/dL, normal: 25-45 mg/dL); increased urinary copper excretion (145 μg/24 hours, normal: 0-50 μg/24 hours), the presence of a Kayser-Fleischer ring (Fig. 1), and genetic
Why and How Audits Must Change: Practical Guidance to Improve Your Audits

Preface. 1. A Wake-Up Call for All Auditors. 2. Risk-Based Auditing: Savior or Villain? 3. The Risk of "Cooking the Books". 4. Other Risks and Considerations. 5. The Many Benefits of Analytical Procedures. 6. Common Pitfalls of Analytical Procedures. 7. An Easy Approach to Implementing Analytical Procedures. 8. Internal Control: The Misunderstood Child. 9. To Test or Not to Test. 10. Talkin' to the Client. 11. Data Extraction Software. 12. Action Steps. Index.
Assessment, Measurement, and Prediction for Personnel Decisions

Part 1. Foundations for Personnel Decisions. 1. Membership Decisions in Organizations. 2. Analysis of Selection Problems. 3. Developing the Predictive Hypothesis. 4. The Legal Context for Personnel Decisions. Part 2. Psychometric and Statistical Foundations 5. Basic Concepts in Measurement. 6. Further Concepts in Measurement. 7. Bivariate Prediction of Performance. 8. The Use of Multivariate Statistics. 9. Bias, Group Differences, and Adverse Impact. Part 3. Assessment Methods: Practice and Research 10. Challenges to Traditional Ways. 11. Assessments by Tests and Inventories. 12. Judgement as Assessment and Prediction. 13. Assessment by Rating. 14. Assessment by Interviews, Resumes, and "Assembled Tests. " 15. Multiple Assessment Methods.
Evaluation of AIoT performance in Cloud and Edge computational models for mask detection

COVID-19 has caused serious health damage, infecting millions of people and unfortunately causing the several deaths around the world. The vaccination programs of each government have reduced those rates. Nevertheless, new coronavirus mutations have emerged in different countries, which are highly contagious, causing concern with vaccination effectiveness. So far, wearing facemasks in public continues being the most effective protocol to avoid and prevent COVID-19 spread. In this context, there is a demand of automatic facemask detection services to remind people the importance of wearing them appropriately. In this work, a performance evaluation of an AIoT system to detect correct, inappropriate, and nonfacemask wearing, based on two computational models: Cloud and Edge, was conducted. Having as objective to determine which model better suits a real environment (indoor and outdoor), based on: reliability of the detector algorithm, use of computational resources, and response time. Experimental results show that Edge-implementation got better performance in comparison to Cloud-implementation. La COVID-19 ha provocado graves daños a la salud: centenas de millones de personas infectadas y varios millones de fallecidos en el mundo. Los programas de vacunación de cada Gobierno han influido en el decaimiento de estos índices, pero con la aparición de nuevas mutaciones del coronavirus más contagiosas, la preocupación sobre la efectividad de las vacunas se hace presente. Frente a esta situación el uso de mascarillas sigue siendo eficaz para prevenir la transmisión y contagio de la COVID-19. Lo que ha generado una creciente demanda de servicios de detección automática de mascarillas, que permita recordar a las personas la importancia del empleo de estas. En este trabajo se plantea un análisis del rendimiento de un sistema AIoT para la detección del uso correcto, incorrecto y sin mascarilla basado en dos modelos computacionales de Cloud y Edge, con la finalidad de determinar qué modelo se adecua mejor en un entorno real (interior y exterior) sobre la base de la confiabilidad del algoritmo, uso de recursos computacionales y tiempo de respuesta. Los resultados experimentales demuestran que el modelo computacional Edge presentó un mejor desempeño en comparación con el Cloud.
SOC Estimation Using Deep Bidirectional Gated Recurrent Units With Tree Parzen Estimator Hyperparameter Optimization

State-of-charge (SOC) is a crucial battery quantity that needs constant monitoring to ensure cell longevity and safe operation. However, SOC is not an observable quantity and cannot be practically measured outside of laboratory environments. Hence, machine learning (ML) has been employed to map correlated observable signals such as voltage, current, and temperature to SOC values. In recent studies, deep learning (DL) has been a prominent ML approach outperforming many existing methods for SOC estimation. However, yielding optimal performance from DL models relies heavily on an appropriate selection of hyperparameters. At present, researchers relied on established heuristics to select hyperparameters through manual tuning or exhaustive search methods such as grid search and random search. This results in lengthy development time in addition to less accurate and inefficient models. This study proposes a systematic and automated approach to hyperparameter selection with a Bayesian optimization strategy known as tree Parzen estimator (TPE) in combination with the Hyperband pruning algorithm. The TPE optimization is run on various DL models such as BGRU, GRU, LSTM, CNN, FCN, and DNN to optimize for the best hyperparameter combination for each architecture. The Hyperband pruning algorithm is used to prune unpromising trials during the TPE run which results in time and computational resource savings. Experiment results show that the best performing model, BGRU-TPE incurs a low computation cost with no compromise in model accuracy, maintaining the best balance of both. The proposed model BGRU-TPE achieves the lowest RMSE and MAE error at 0.8% and 0.56%, respectively on various electric vehicle drive cycles at varying ambient temperatures and maintains a low computational cost at 15 600 FLOPS with the model size of only 193.3 KB.
Managing evolution risk of cross-organizational services

The world's resource whose production, transport, refinement and consumption have increased incredibly more than all other ones is information. Organizations produce, exchange and consume more information every second. They need to share data with an increasing number of partners to manage their operations. This means that an organization must often adapt its information systems such that it can work with a new partner or because its current partners require it. These necessary adaptations must be made while the organization continues to operate and without causing any damage or disruptions. Thus, each organization must allocate an amount of resources to adapt its applications when required by its changing external environment. However, it may not be easy for an organization to know how many resources it needs for this. This is because of the multiplicity of dependencies and because of the uncoordinated nature of changes among all organizations. In this thesis, we propose a model to determine how many resources an organization needs to adapt to its evolving world. We also show how an organization can price an extension of the contractual adaptation time of its used external services.
Some studies on the effect of fly ash and lime on physical and mechanical properties of expansive clay

Fly ash is one of the most plentiful and versatile of the industrial by-products. At present, nearly 150 million tonnes of fly ash is being generated annually in India posing dual problem of environmental pollution and difficulty in disposal. This calls for establishing strategies to use the same effectively and efficiently. However, it is only in geotechnical engineering applications such as the construction of embankments/dykes, as back fill material, as a sub-base material etc., its large-scale utilization is possible either alone or with soil. Soil stabilization can be achieved by various means such as compaction, soil replacement, chemical improvement, earth reinforcement etc. Usually, in the case of clay soils, chemical improvement is commonly most effective since it can strengthen the soil, to remove its sensitivity both to water and its subsequent stress history. Among chemical means or additives, fly ash/lime provides an economic and powerful means of improvement, as demonstrated by the significant transformation that is evident on mixing with heavy clay. In the present investigation, different percent fly ashes (10%, 20%, 40%, 60% & 80%) were added to a highly expansive soil from India by dry weight of the natural soil, and subjected to various tests. The important properties that are necessary for using fly ash in many geotechnical applications are index properties, compaction characteristics, compressibility characteristics, permeability and strength. Based on test results, it has been found that using fly ash for improvement of soils has a two-fold advantage. First, to avoid the tremendous environmental problems caused by large scale dumping of fly ash and second, to reduce the cost of stabilization of problematic/marginal soils and improving their engineering properties for safe construction of Engineering Structures.