Miracle-dz/UniFAR-FTUs huggingface dataset:
Genetic Characterization of Philippine Saba Germplasm Collection Using Microsatellite Markers

The genetic diversity among 75 Philippine Saba gene bank collections and four non-Saba accessions were analyzed using microsatellite markers or simple sequence repeats (SSRs). Eleven primer pairs were found to be polymorphic across all genotypes and amplified a total of 28 alleles. The discriminatory power of the primers was moderately high with mean polymorphism information content (PIC) value of 0.38. Genetic diversity was measured in terms of heterozygosity, fixation index, and genetic distance. Average gene diversity or expected heterozygosities ranged from 0.09–0.58, with an average of 0.48. Observed heterozygosities ranged from 0.09–0.95, with a mean of 0.80. Fixation indices (mean = –0.61) were negative, which suggests an excess of heterozygotes and shows that diversity has been maintained within the gene bank collection. Cluster analysis revealed that the greatest genetic distance (GD = 0.69) was observed between Lakatan (AAA) and Cardaba 6 (ABB/BBB) bananas. Therefore, the SSR markers used were able to separate the Saba cultivars from the non-Saba cultivars. Furthermore, the markers were able to separate the seedy diploid cultivar Pacol from the rest of the triploid Saba cultivars. Future studies can survey other potential SSRs for better discrimination among Saba samples.
Adaptive Learning in Macroeconomics

While rational expectations (RE) remains the benchmark paradigm in macro-economic modeling, bounded rationality, especially in the form of adaptive learning, has become a mainstream alternative. Under the adaptive learning (AL) approach, economic agents in dynamic, stochastic environments are modeled as adaptive learners forming expectations and making decisions based on forecasting rules that are updated in real time as new data become available. Their decisions are then coordinated each period via the economy’s markets and other relevant institutional architecture, resulting in a time-path of economic aggregates. In this way, the AL approach introduces additional dynamics into the model—dynamics that can be used to address myriad macroeconomic issues and concerns, including, for example, empirical fit and the plausibility of specific rational expectations equilibria. AL can be implemented as reduced-form learning, that is, the implementation of learning at the aggregate level, or alternatively, as discussed in a companion contribution to this Encyclopedia, Evans and McGough, as agent-level learning, which includes pre-aggregation analysis of boundedly rational decision making. Typically learning agents are assumed to use estimated linear forecast models, and a central formulation of AL is least-squares learning in which agents recursively update their estimated model as new data become available. Key questions include whether AL will converge over time to a specified RE equilibrium (REE), in which cases we say the REE is stable under AL; in this case, it is also of interest to examine what type of learning dynamics are observed en route. When multiple REE exist, stability under AL can act as a selection criterion, and global dynamics can involve switching between local basins of attraction. In models with indeterminacy, AL can be used to assess whether agents can learn to coordinate their expectations on sunspots. The key analytical concepts and tools are the E-stability principle together with the E-stability differential equations, and the theory of stochastic recursive algorithms (SRA). While, in general, analysis of SRAs is quite technical, application of the E-stability principle is often straightforward. In addition to equilibrium analysis in macroeconomic models, AL has many applications. In particular, AL has strong implications for the conduct of monetary and fiscal policy, has been used to explain asset price dynamics, has been shown to improve the fit of estimated dynamic stochastic general equilibrium (DSGE) models, and has been proven useful in explaining experimental outcomes.
An Ontology-based Conversational Governmental Portal

Public service provision is a complex process influenced by various factors. Public services have a complex structure and usually span across different public agencies. Hence, citizens are usually not aware of the way services have been organized within the administrative system. Moreover, the legislation that governs a public service is usually hard to understand. Governments worldwide have developed portals providing eGovernment services in order to simplify and facilitate public service provision. But most of these portals do not manage to hide from users the complexity of public services and they become themselves complex and difficult to use. At the same time they fail to address the specific needs of a particular user as the analysis of the services they usually make available is only enough for obtaining general information about the service. This work presents an ontology-based conversational governmental portal that helps citizens find the public services that address their individual needs. The portal uses a user-friendly, self-explanatory interface that supports the citizens and offers them the necessary guidance. Additionally, it consults on the eligibility of a citizen for receiving a particular public service and provides a rich, structured and personalized public service description. Finally, the citizen can use the portal in order to invoke the identified service, if available on-line.
LncRNA DSCAM-AS1 interacts with YBX1 to promote cancer progression by forming a positive feedback loop that activates FOXA1 transcription network

Rationale: The forkhead box A1 (FOXA1) is a crucial transcription factor in initiation and development of breast, lung and prostate cancer. Previous studies about the FOXA1 transcriptional network were mainly focused on protein-coding genes. Its regulatory network of long non-coding RNAs (lncRNAs) and their role in FOXA1 oncogenic activity remains unknown. Methods: The Cancer Genome Atlas (TCGA) data, RNA-seq and ChIP-seq data were used to analyze FOXA1 regulated lncRNAs. RT-qPCR was used to detect the expression of DSCAM-AS1, RT-qPCR and Western blotting were used to determine the expression of FOXA1, estrogen receptor α (ERα) and Y box binding protein 1 (YBX1). RNA pull-down and RIP-qPCR were employed to investigate the interaction between DSCAM-AS1 and YBX1. The effect of DSCAM-AS1 on malignant phenotypes was examined through in vitro and in vivo assays. Results: In this study, we conducted a global analysis of FOXA1 regulated lncRNAs. For detailed analysis, we chose lncRNA DSCAM-AS1, which is specifically expressed in lung adenocarcinoma, breast and prostate cancer. The expression level of DSCAM-AS1 is regulated by two super-enhancers (SEs) driven by FOXA1. High expression levels of DSCAM-AS1 was associated with poor prognosis. Knockout experiments showed DSCAM-AS1 was essential for the growth of xenograft tumors. Moreover, we demonstrated DSCAM-AS1 can regulate the expression of the master transcriptional factor FOXA1. In breast cancer, DSCAM-AS1 was also found to regulate ERα. Mechanistically, DSCAM-AS1 interacts with YBX1 and influences the recruitment of YBX1 in the promoter regions of FOXA1 and ERα. Conclusion: Our study demonstrated that lncRNA DSCAM-AS1 was transcriptionally activated by super-enhancers driven by FOXA1 and exhibited lineage-specific expression pattern. DSCAM-AS1 can promote cancer progression by interacting with YBX1 and regulating expression of FOXA1 and ERα.
AI BASED RADAR WAVEFORM CLASSIFICATION SYSTEM

- Data flowing from radar and electronic warfare (EW) systems to the analyst's screen will determine the course of action in any given mission. Bearing in mind that decisions need to be made, at times in seconds, it's critical for radar and EW systems to quickly sift through that data and turn it into actionable intelligence. To achieve this goal, the industry is using artificial intelligence (AI), machine learning (ML)
A Proposed Clustered knowledge Management Development Framework (CKMD)

— Knowledge Management (KM) is a paradigm that seeks to improve the organization performance by maintaining and leveraging the present and future value of knowledge assets. Knowledge management is not a single paradigm but it is an integration of fields of study. This paper provides a framework for enhancing knowledge management performance using clustered knowledge base. This clustered knowledge management framework (CKMD) may help in the development of KM as we divided the framework into five main phases, each phase consists of sub modules explaining the entire processes in the module. This partitioning is useful for increasing the specificity of the framework as each module will have its separate tasks and algorithms. In addition this framework has the ability to cluster the knowledge extracted, which will help in knowledge utilization process to make decisions.
Hidden in plain sight? The human resource management practitioner's role in dealing with workplace conflict as a source of organisational–professional power

This article examines the perceived importance of human resource (HR) practitioners' role as ‘organisational professionals’ at national and organisational levels. Informed by institutionalist theory and drawing upon interviews at national and organisational levels, a dissonance is identified in the degree to where HR's non‐substitutable expertise lies. It is concluded that HR's role in dealing with workplace conflict is under‐recognised at national level as it does not fit with the proactive ‘strategic’ narrative seen to be what HR needs to achieve to be influential. At organisational level, however, conflict management is a source of power because, unlike many other HR roles—valued as they may be—conflict management is the role that can least be substituted by non‐specialist HR practitioners because of its unpredictability.
Chapter 4 Phosphate-dependent root system architecture responses to salt stress *

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T63 induces apoptosis in nasopharyngeal carcinoma cells through mitochondrial dysfunction and inhibition of PI3K/Akt signaling pathway

Background Nasopharyngeal carcinoma (NPC) is a common malignant tumor in china. T63 is one of novel curcumin analogues, which reduces the tumorigenic potential effectively. Methods We investigated the mechanism of anti-cancer activity on NPC cells treatment with T63 for the first time. Cell viability was monitored using the method of MTT and colony formation assay. Morphological changes observed by fluorescence microscope with Hoechst33342 staining. The cell cycle, the rates of cell apoptotic and mitochondrial membrane potential were identified by flow cytometry, and apoptotic proteins were identified by western blot analysis. Results In our study, we found a significant decrease in the number of viable cells and in colony formation abilities of NPC cells (CNE2 and CNE2R) after 24, 48 and 72 h treatment with T63. T63 promoted morphologic changes of apoptosis and increased apoptotic rate. T63 promoted also cell cycle arrest of G2/M phases followed by decreased cell viability in CNE2 and CNE2R after 48 h. Then the loss of the mitochondrial membrane potential and release of cytochrome-c were demonstrated. Western blotting analysis found a decrease in Bcl-2/Bax ratio and activation of caspase-8, -9, -3 and PARP. And the changes of PTEN and p-Aktp473 suggested PTEN/PI3K/Akt might be an important signaling pathway in the anticancer role of T63. Conclusions T63 definitely induced apoptosis in radiosensitive and radioresistant NPC cell lines. But the apoptotic effect was stronger on radioresistant cells than that on radiosensitive cells through activation of mitochondria-mediated apoptotic pathway.
Evolution of Chest CT findings in patients with COVID-19 pneumonia: A retrospective study of the new Pandora’s box from Morocco

Objectives To describe the imaging features of hospitalized patients with confirmed COVID-19 infection. To analyze the evolvement patterns of chest CT images. Methods: A retrospective study of initial chest CT imaging findings of 34 confirmed patients admitted to our university hospital, between 23 March and 23 April 2020. A follow-up chest CT was performed to analyze the evolvement manifestations of initial CT imaging findings. Results: Initial and follow-up chest CT images were collected and analyzed. M/F ratio 24/10. The mean age was 55,11±12.8 years. The predominant imaging feature was ground-glass opacification in 30(88%) patients, crazy paving pattern in 18(53%) patchy consolidation in 18(53%) patients, fibrous stripes 16(47%) patients. The follow-up chest CT examination was performed in 11 from 34 patients (average of 5-8 days). The progression of the lesions was found in 7(64%) patients, including the increasing and the consolidation of ground-glass opacification besides the appearance of crazy paving patterns, and the fibrous stripes. 2(6%) patients presented absorbed lesions. Initial normal chest CT remains normal in one patient, and ground glass nodule appeared in the RUL in one patient. The second follow-up CT-scan, performed in 3(38%) patients, showed also a progression of the previous lesions in 2 patients, and absorption of the RUL’s ground-glass nodule in one patient. Conclusion: Chest CT has a key role to play in both diagnosis and follow-up of patients with COVID-19 pneumonia. The imaging features are multiple and can change quickly allowing the evaluation of the severity of this infection. Highlights:  Chest High-resolution CT (HRCT) plays a key role in both the diagnosis and follow-up of patients with COVID-19 pneumonia.  The imaging features are multiple and can change quickly allowing the evaluation of the severity of this infection.
NURUL IZYAN KAMARUDDINA et al.: DIFFERENTIAL EVOLUTION BASED MAXIMUM POWER POINT TRACKER FOR PHOTOVOLTAIC ARRAY UNDER NON-UNIFORM ILLUMINATION CONDITION DOI: 10.21917/ijsc.2020.0295

Photovoltaic system (PV) is an important technological asset for renewable energy production. It works by converting solar cell energy from the sun into electrical direct current. In reality, the photovoltaic module usually receives non-uniform solar irradiance at different light intensity due to non-atmospheric hindrance. Under such conditions, the PV system exhibits multiple peaks on the energy characteristic curve, generally known as the partial shading condition (PSC). Therefore, in order to maximize the energy harvested by the photovoltaic system (PV), maximum power point tracking (MPPT) method is suggested to extract all possible maxima that have been produced by the PV system under various circumstances through the non-uniform irradiance of the sunlight. Based on previous researches, it is found that conventional method such as perturb and observed (P&O) method failed to track the maximum power and was trapped at the local maximum power (LMPP). This paper focuses on exploring a metaheuristic method which is the differential evolution (DE) algorithm in optimizing the energy harvested by the PV system. The platform chosen for modelling in this paper is a 33 PV array. The PV array is tested with different conditions of partial shading where random irradiance values are set. Comparing the performance of PV between P&O and DE based MPPT controller, the DE based MPPT controller is inferred to have a higher success rate to escape from being trapped in LMPP and thus produce more total energy.
LIMIT THEOREM FOR HIGH LEVEL A-UPCROSSINGS BY χ-PROCESS

Properties of high level intersection sets by trajectories of Gaussian random processes on infinitely increasing time horizon are well elaborated, see [2], [3] and references therein. Many important results in this direction have been obtained for Gaussian fields, [3]. In contrast, there are only few results about limit behavior of the number of large excursions of Gaussian vector processes. First Poisson limit theorem for a-exit points over level u, where Gaussian vector process of arbitrary dimension was investigated, was established in [4]. The present paper deals with A-upcrossing (A-exit) points over high level u. We use similar technic as in [4]. We consider the stationary random process
Sporadic Form of Recurrent Atrial Myxoma: The Blob Strikes Back

Cardiac myxoma is a benign neoplasm composed of stellate to plump, cytologically bland mesenchymal cells set in a myxoid stroma. Although benign, as they can lead to severe complications, they are often removed surgically. A 39-year-old female presented with a chief complaint of generalized fatigue. Patient had a history of a large 7cm x 2.5cm left atrial myxoma resected at the age of 32 years after she presented with symptoms of dyspnea on exertion. The dyspnea was due to prolapse of the mass through the mitral valve during diastole, leading to functional severe mitral stenosis. The mass was resected with clear margins confirmed on biopsy. On physical examination, heart rate was regular with no murmurs. No signs of congestive heart failure were noted. A 2D echo revealed a mobile structure in the left atrium along with mild mitral regurgitation. Cardiac MRI showed a 21mm x 9mm well defined, pedunculated, mobile mass in the left atrium arising from inter-atrial septum. The mass was hyperintense on T2 weighted images with patchy delayed hyper-enhancement consistent with recurrence of a myxoma. The patient underwent a repeat median sternotomy with the removal of left atrial mass and repair of atrial septum with hemashield patch. The mass was sent for pathological evaluation confirming the diagnosis of recurrent myxoma. On genetic testing, patient tested negative for mutations in PRKAR1A gene (mutated in up to 60%-80% cases with Carney complex), MEN1, RET and sarcoma (TP53) genes. Cardiac myxomas are rare primary benign tumors of the heart with a small recurrence rate. Follow-up studies have rarely reported recurrences after complete resection. However, in our case not only did the patient have the sporadic form of myxoma with recurrence, but it also occurred within three years of the previous resection despite complete removal with clear margins.
Wolbachia infected anthropophilic mosquitoes in The Gambia are not infected with filarial nematode

Wuchereria bancrofti has been reported to cause 90% of all cases of lymphatic filariasis in Africa. wolbachia endosymbiot infect a wide range of insects and nematodes. This study was conducted in 48 settlements (8 from each division). A total of 2003 mosquitoes was pooled into 120 clusters from the 48 settlements in The Gambia. Polymerase chain reaction (PCR) was used to detect the presence of Wolbachia and filarial nematode and further differentiate Wolbachia into super groups among the positive samples. The level of association between Wolbachia and the genera of anthropophilic mosquitoes in The Gambia was also determined. Microscopic results showed 64.9% Anopheles, 32.0% Culex, 3.0% Aedes. PCR showed that, 34.17% of Wolbachia in the mosquito obtained varied among the three mosquito genera, with highest being among Anopheles. Only Wolbachia super group B was identified in Culex and Aedes. The purpose of this study carried out in The Gambia was to describe the most common mosquito species and to identify, by means of PCR, their association with the Wolbachia bacteria and the presence of nematodes responsible for filiariasis, in order to understand the role that this bacterium plays in the chain of filarial transmission. Key words: Wuchereria bancrofti, Filariasis, Anopheles gambiae, Culex quinquefasciatus, elephantiasis.
Taking the Financing Decision Considering Various Funding Possibilities for Entrepreneurs

Entrepreneurs in emerging countries have various and numerous innovative ideas that imply solutions for developing new or existing companies. Technological enterprises have the most to benefit from these ideas, but unfortunately they have few possibilities for implementing them. In the current paper we propose a method that can be a highly objective and helpful tool in determining the best financing solution for an enterprise with no or insufficient funds. The proposed model and analysis determines the total profit obtained from developing a project, by taking into consideration two possible cases. The first case determines the profit obtained after financing the project with borrowed capital from a bank, a private person or company which demands an interest. The second case that the model takes into consideration is obtaining a public fund, governmental, European or other public funds. Also taken into consideration is equity financing, a particular case of loan-based financing. The model proposed in this paper is a fast decision instrument for entrepreneurs, for identifying the most profitable solution for financing their innovative ideas, applicable both for a large enterprise but also for a start-up. Key-Words: Virtual Enterprise, Funding sources, Borrowed capital, Regional Development, Business Models, Profitability, Comparison, Forecast
Anxiety and depression levels in patients with chronic orofacial pain

Introduction: Orofacial pain can be highly distressing, affecting 10% of the population and is associated with psychological symptoms. This study aims to assess the frequency of such psychological symptoms in orofacial pain patients compared to a control group and how these symptoms change with treatment. The effect of social deprivation will also be explored. Method: We conducted a prospective study comparing 342 consecutive referrals to the pain clinic over a nine-year period with 100 controls. Exclusion criteria involved patients with dental/dentoalveolar pain or trigeminal neuralgia. The chronic faical pain patient’s data was collected from validated questionnaires completed at every consultation. The somatic diagnoses were based on a interview process with one consultant conducting the pain clinic.