Miracle-dz/UniFAR-FTUs huggingface dataset:
Clorofitas, euglenofitas y cianobacterias de humedales de altura de tres provincias argentinas

El objetivo fue contribuir al conocimiento de la composición y estructura de la taxocenosis conformada por cianobacterias, clorofitas y euglenofitas de estos ambientes extremos, en relación a variables abióticas (conductividad eléctrica, salinidad y pH del agua, altura sobre el nivel del mar y superficie lacustre). En 2005, se efectuó un muestreo interdisciplinario de veintiséis puntos de colecta en tres provincias del noroeste argentino (NOA). Los datos abióticos y bióticos obtenidos en campo y en laboratorio se hicieron según protocolos convencionales. De acuerdo a los resultados, la mayoría de los ejemplares en Catamarca (coincidente con Jujuy y Salta) fueron cosmopolitas y afines con la salinidad, destacándose, en general, las cianobacterias y Chroococcus turgidus dentro de este grupo; en segundo lugar, estuvieron las algas verdes y, en el caso de las euglenofitas, no fueron registradas en dicha provincia. La taxocenosis estuvo constituida tanto por especies propias del fitoplancton como de otros hábitats. Los valores bajos a intermedios de diversidad (0,01-2,85) estarían vinculados al predominio de especies raras y de unas pocas dominantes. La biomasa mostró sensibilidad a las condiciones ambientales, en general disminuyó con el aumento de la salinidad, varió entre 24.007-60.396. 324 mg/ml de C. La aplicación del Análisis de Componentes Principales, tanto para variables abióticas como bióticas, fueron de utilidad porque aportaron datos complementarios sobre la distribución de los sitios. Por último, la cuantificación de cianobacterias capaces de ocasionar floraciones y potencial­mente tóxicas, como Chroococcus turgidus, Microcystis flos-aquae y Nodularia spumigena conducen a sugerir un control periódico de estos cuerpos de agua.
Use of mobile app to enhance functional outcomes and adherence of home-based rehabilitation program for elderly with hip fracture: A randomized controlled trial

Background: Mobile app has been used to improve exercise adherence and outcomes in populations with different health conditions. However, the effectiveness of mobile app in delivering home-based rehabilitation program to elderly patients with hip fracture is unclear. Objective: The aim of this study was to test the effectiveness of mobile app in delivering home-based rehabilitation program for improving functional outcomes and reducing caregiver stress with enhancing adherence among the elderly patients with hip fracture. Methods: A randomized controlled trial with an intervention period of two months was performed. Eligible participants were randomized into either experimental group with home-based rehabilitation program using a mobile app or control group with home-based rehabilitation program using an exercise pamphlet. Primary outcomes were Modified Functional Ambulatory Category (MFAC), Elderly Mobility Scale (EMS) and Lower Extremity Functional Scale (LEFS). Secondary outcomes were exercise adherence and Modified Caregiver Strain Index (M-CSI). The outcomes were collected at pre-discharge training session, one month and two months after hospital discharge. Results: A total of 50 participants were enrolled, with 19 participants in the experimental group and 20 participants in the control group. Eleven participants had withdrawn from the study. The experimental group showed higher exercise adherence than the control group in first month (p=0.03). There were no between-group differences in MFAC, EMS, LEFS and M-CSI at the first month and second month. Conclusion: Use of the mobile app improved exercise adherence, yet it did not improve physical performance, self-efficacy and reduce caregiver stress when compared to a standard home rehabilitation program for elderly patients with hip fracture. Further studies to investigate the benefits of mobile apps are required. (ClinicalTrials.gov ID: NCT04053348.)
The Role of m6A RNA Methylation in Cancer: Implication for Nature Products Anti-Cancer Research

N6-methyladenosine (m6A) RNA methylation is identified as the most common, abundant and reversible RNA epigenetic modification in messenger RNA (mRNA) and non-coding RNA, especially within eukaryotic messenger RNAs (mRNAs), which post-transcriptionally directs many important processes of RNA. It has also been demonstrated that m6A modification plays a pivotal role in the occurrence and development of tumors by regulating RNA splicing, localization, translation, stabilization and decay. Growing number of studies have indicated that natural products have outstanding anti-cancer effects of their unique advantages of high efficiency and minimal side effects. However, at present, there are very few research articles to study and explore the relationship between natural products and m6A RNA modification in tumorigenesis. m6A is dynamically deposited, removed, and recognized by m6A methyltransferases (METTL3/14, METTL16, WTAP, RBM15/15B, VIRMA, CBLL1, and ZC3H13, called as “writers”), demethylases (FTO and ALKBH5, called as “erasers”), and m6A-specific binding proteins (YTHDF1/2/3, YTHDC1/2, IGH2BP1/2/3, hnRNPs, eIF3, and FMR1, called as “readers”), respectively. In this review, we summarize the biological function of m6A modification, the role of m6A and the related signaling pathway in cancer, such as AKT, NF-kB, MAPK, ERK, Wnt/β-catenin, STAT, p53, Notch signaling pathway, and so on. Furthermore, we reviewed the current research on nature products in anti-tumor, and further to get a better understanding of the anti-tumor mechanism, thus provide an implication for nature products with anti-cancer research by regulating m6A modification in the future.
Combined Therapy with a CCR2/CCR5 Antagonist and FGF21 Analogue Synergizes in Ameliorating Steatohepatitis and Fibrosis

(1) Background: With new potential drug targets emerging, combination therapies appear attractive to treat non-alcoholic steatohepatitis (NASH) and fibrosis. Chemokine receptor CCR2/5 antagonists can improve fibrosis by reducing monocyte infiltration and altering hepatic macrophage subsets. Fibroblast growth factor 21 (FGF21) may improve NASH by modulating lipid and glucose metabolism. We compared effects of single drug to combination treatment as therapeutic strategies against NASH. (2) Methods: We analyzed serum samples and liver biopsies from 85 nonalcoholic fatty liver disease (NAFLD) patients. A CCR2/5 inhibitor (BMS-687681-02-020) and a pegylated FGF21 agonist (BMS-986171) were tested in male C57BL/6J mice subjected to dietary models of NASH and fibrosis (choline-deficient, L-amino acid-defined, high-fat diet (CDAHFD) up to 12 weeks; short- (2w) or long-term (6w) treatment). (3) Results: In NAFLD patients, chemokine CCL2 and FGF21 serum levels correlated with inflammatory serum markers, only CCL2 was significantly associated with advanced liver fibrosis. In rodent NASH, CCR2/5 inhibition significantly reduced circulating Ly6C+ monocytes and hepatic monocyte-derived macrophages, alongside reduced hepatic inflammation and fibrosis. FGF21 agonism decreased body weight, liver triglycerides and histological NASH activity. Combination treatment reflected aspects of both compounds upon short- and long-term application, thereby amplifying beneficial effects on all aspects of steatohepatitis and fibrosis. (4) Conclusions: CCR2/5 inhibition blocks hepatic infiltration of inflammatory monocytes, FGF21 agonism improves obesity-related metabolic disorders. Combined therapy ameliorates steatohepatitis and fibrosis more potently than single drug treatment in rodent NASH, corroborating the therapeutic potential of combining these two approaches in NASH patients.
GENERALIST OR SPECIALIST? A RESEARCH ON MANAGERIAL SKILLS OF UPPER ECHELONS

Çalışmanın amacı, Üst Kademe Teorisi (Upper Echelons Theory) kapsamında, firmaların en üst kademe yöneticisi konumundaki CEO (Chief Executive Officer) veya genel müdürlerin kariyerleri boyunca kazandıkları deneyimlere dayanarak, yönetsel becerileri açısından genelci mi yoksa uzman mı olduğunu ve sektörlere göre bu özelliklerin nasıl dağılım gösterdiğini ortaya koymaktır. Araştırma kapsamında, BIST 100 Endeksi’nde işlem gören firmaların yöneticilerinin yönetsel becerileri, Custodio ve diğerleri (2013) tarafından geliştirilen Genel Beceri Endeksi ile değerlendirilmiştir. Araştırma bulguları, BIST 100 kapsamında eşit sayıda genelci (45) ve uzman (45) CEO bulunduğunu ortaya koymuştur. CEO’ların sektörlere dağılımı incelendiğinde, imalat sektörünün uzman, mali kuruluşlar sektörünün ise genelci CEO’ların hakimiyetinde olduğu görülmektedir.
Analysis of the Relationship between Transformational Leadership and Educational Management in Higher Education Based on Deep Learning

Leadership behavior has been emphasized as one of the most important influencing factors in the innovation process. Leaders can encourage subordinates to innovate by creating the right environment, promoting knowledge integration, and setting specific goals. However, different leadership styles make different decisions and behaviors in the innovation process, and the final innovation effect is also different. Today, in the context of China, most business leaders still adopt “paternalistic” or “authoritarian” leadership behaviors, but more and more entrepreneurs and scholars are aware of the importance of this leadership style in enhancing employee creativity. Authoritarian leaders are more likely to exercise more control and supervision over team members, limit the autonomy of team members, and reduce work initiative and creativity. Although the positive effect of transformational leadership on employee creativity has been recognized by some scholars, in the real work environment, this leadership style rarely appears, especially in the context of China. This study first constructs a theoretical model of how transformational leaders in colleges and universities affect educational management innovation through the atmosphere of school organizational innovation, based on the deep learning theory and other related research results, and then puts forward research hypotheses on this basis. Secondly, a measurement scale was designed according to the existing research results, and the scale was revised through the pretest to form the formal questionnaire of this study. This research uses cluster sampling and random sampling to conduct a questionnaire survey on 1022 college teachers and uses the SPSS20.0 and AMOS21.0 to conduct an empirical analysis on the survey data. Each measurement scale was tested by exploratory factor analysis and confirmatory factor analysis. The experimental results show that the transformational leadership style of college principals has a positive impact on teachers' teaching innovation. There is a positive correlation between the influence of charisma and teachers' teaching management, and there is a positive correlation between intellectual stimulation and teachers' teaching management.
Carbon 5–60 Years After Fire: Planting Trees Does Not Compensate for Losses in Dead Wood Stores

Wildfire is a natural disturbance in many forested biomes, with the loss of carbon to the atmosphere and mortality of trees actively sequestering carbon of global concern as a contribution to climate change. Natural regeneration is often successful at reestablishing a forest in ecosystems adapted to fire, but there is increasing concern that the changing size, frequency and severity of wildfire is causing regeneration failures or inadequate densities of trees that sequester and store carbon following these disturbances. It remains unclear whether the action of planting trees accelerates carbon storage following fire compared to forests established through natural regeneration. The central interior of British Columbia recently experienced multiple years of record-breaking fire activity. Rehabilitation planting focused on reestablishing trees in the managed forest but was also prescribed in previously unmanaged forests to initiate carbon sequestration. Planting is often accompanied by other stand treatments such as salvage harvesting or snag removal and debris clearing to ensure planter safety. Here, we determine carbon recovery and stores in 21 wildfires across a chronosequence from the early 1960s to 2015. We measured above and belowground carbon pools to determine the effect of time since fire and planting treatments on carbon. Tree planting did not increase total ecosystem carbon over time, but rather decreased carbon through the loss of dead wood from site preparation. All carbon pools were affected by time since fire except the mineral soil pool, which was best predicted by soil clay content and coarse fragments positive effects. Live tree carbon increased over time, with more stored in planted stands over 60 years compared to stands that were not planted. Projecting growth to 100 years since fire suggests we may see increasing divergence in carbon stores in planted stands over a full fire-return interval, but these differences remain relatively small [mean (sd): 140.8 (19.6) Mg⋅ha–1 in planted compared to 136.9 (27.5) Mg⋅ha–1 in not-planted stands], with 1.4 Mg⋅ha–1 year–1 sequestered in not-planted compared to 1.5 Mg⋅ha–1 year–1 in planted stands. To meet carbon objectives, replanting trees on average sites in burned forests of BC’s central interior would require preserving the carbon legacy of fire, including dead wood.
A Hybrid Model and Data-Driven Vision-Based Framework for the Detection, Tracking and Surveillance of Dynamic Coastlines Using a Multirotor UAV

A hybrid model-based and data-driven framework is proposed in this paper for autonomous coastline surveillance using an unmanned aerial vehicle. The proposed approach comprises three individual neural network-assisted modules that work together to estimate the state of the target (i.e., shoreline) to contribute to its identification and tracking. The shoreline is first detected through image segmentation using a Convolutional Neural Network. The part of the segmented image that includes the detected shoreline is then fed into a CNN real-time optical flow estimator. The position of pixels belonging to the detected shoreline, as well as the initial approximation of the shoreline motion, are incorporated into a neural network-aided Extended Kalman Filter that learns from data and can provide on-line motion estimation of the shoreline (i.e., position and velocity in the presence of waves) using the system and measurement models with partial knowledge. Finally, the estimated feedback is provided to a Partitioned Visual Servo tracking controller for autonomous multirotor navigation along the coast, ensuring that the latter will always remain inside the onboard camera field of view. A series of outdoor comparative studies using an octocopter flying along the shoreline in various weather and beach settings demonstrate the effectiveness of the suggested architecture.
The Relationship between Enterprise Risk Management and Cost of Capital

This paper investigates the effect of enterprise risk management (ERM) implementation on the cost of capital (cost of debt [Cd], cost of equity [Ce], and weighted average cost of capital [WACC]) for the oil and gas industry. The research is conducted using panel data analysis from 2008? 2017 for 41 oil and gas companies publicly listed on the Bursa Malaysia. ERM implementation data is collected from company annual reports, while the cost of capital data is obtained from Thomson Reuters DataStream. The results indicate that an increase in the level of ERM implementation reduces the cost of capital, which we argue is one mechanism through which ERM increases firm value. Future research can use our investigation to delve deeper into ERM and value creation topics.
Functional Magnetic Resonance Urography in Ureteropelvic Junction Obstruction: Proposal for a Pediatric Quantitative Score

Background: Ureteropelvic junction obstruction (UPJO) is one of the most frequent causes of congenital hydronephrosis. It is essential to distinguish UPJO which needs surgical treatment. fMRU combines high quality morphological details of the kidney and excretory pathways with functional data. Objective This study aims to introduce a new radiological score based on fMRU findings to be able to differentiate surgical from non-surgical kidneys. Materials and Methods We retrospectively selected patients with hydronephrosis due to UPJO who underwent fMRU (January 2009–June 2018). A multidisciplinary team identified a list of fMRU morpho-functional predictive variables to be included in the analysis. To evaluate the role of different independent variables in predicting the outcome, a multivariable logistic regression model has been performed; the outcome variable was the surgical intervention. For each predictive variable, Odds Ratio and 95% Confidence Intervals were calculated. The likelihood ratio test was used to assess the significance of the variables. Using the regression model, we assigned a numerical value to each predictive variable, rounding up the beta-coefficients. The cut-off value of the total score was obtained from the ROC curve analysis. Results A total of 192 patients were enrolled, corresponding to 200 pathological kidneys. All of them underwent fMRU; 135 were surgically treated, while 65 underwent ultrasound or MRU follow-up. Predictive variables significantly associated with surgery resulted to be the urographic phase, the presence of abnormal vessels, and a baseline anterior-posterior pelvic diameter >23 mm. Beta coefficients of the logistic regression model were then converted in scores. The ROC curve of the score showed high sensitivity (84.3%) and specificity (81.3%) with a cut-off > 2.5. Conclusions We propose a new fMRU score able to identify surgical vs. non-surgical kidneys with UPJO.
Classification of Forest Vertical Structure in South Korea from Aerial Orthophoto and Lidar Data Using an Artificial Neural Network

Every vegetation colony has its own vertical structure. Forest vertical structure is considered as an important indicator of a forest’s diversity and vitality. The vertical structure of a forest has typically been investigated by field survey, which is the traditional method of forest inventory. However, this method is very time- and cost-consuming due to poor accessibility. Remote sensing data such as satellite imagery, aerial photography, and lidar data can be a viable alternative to the traditional field-based forestry survey. In this study, we classified forest vertical structures from red-green-blue (RGB) aerial orthophotos and lidar data using an artificial neural network (ANN), which is a powerful machine learning technique. The test site was Gongju province in South Korea, which contains single-, double-, and triple-layered forest structures. The performance of the proposed method was evaluated by comparing the results with field survey data. The overall accuracy achieved was about 70%. It means that the proposed approach can classify the forest vertical structures from the aerial orthophotos and lidar data.
Association of Airway Eosinophilia with Small Airway Dysfunction in Patients with Mild and at Risk for COPD

1LungenClinic Grosshansdorf, Airway Research Center North (ARCN), German Center for Lung Research (DZL), Grosshansdorf, Germany; 2Pulmonary Research Institute at LungenClinic Grosshansdorf, Airway Research Center North (ARCN), German Center for Lung Research (DZL), Grosshansdorf, Germany; 3Pneumology and Critical Care Medicine, Thoraxklinik at University Hospital Heidelberg, Translational Lung Research Center Heidelberg (TLRC), Member of German Center for Lung Research (DZL), Heidelberg, Germany; 4Department of Biomedical Informatics (DBMI) at the Center for Preventive Medicine and Digital Health Baden-Württemberg (CPD-BW), University Medical Center Mannheim, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
Short- and Long-Term Effectiveness of Low-Level Laser Therapy Combined with Strength Training in Knee Osteoarthritis: A Randomized Placebo-Controlled Trial

Background: Both physical activity and low-level laser therapy (LLLT) can reduce knee osteoarthritis (KOA) inflammation. We conducted a randomized clinical trial to investigate the short- and long-term effectiveness of LLLT combined with strength training in persons with KOA. Methods: Fifty participants were randomly divided in two groups, one with LLLT plus strength training (n = 26) and one with placebo LLLT plus strength training (n = 24). LLLT and strength training were performed triweekly for 3 and 8 weeks, respectively. In the laser group, 3 joules 904 nm wavelength laser was applied to fifteen points (45 joules) per knee per session. Patient-reported outcomes, physical tests, and ultrasonography assessments were performed at baseline and 3, 8, 26, and 52 weeks after initial LLLT or placebo therapy. The primary outcomes were pain on movement, at rest, at night (Visual Analogue Scale), and globally (Knee injury and Osteoarthritis Outcome Score (KOOS) subscale). Parametric data were assessed with analysis of variance using Šidák’s correction. Results: There were no significant between-group differences in the primary outcomes. However, in the laser group there was a significantly reduced number of participants using analgesic and non-steroidal anti-inflammatory drugs and increased performance in the sit-to-stand test versus placebo-control at week 52. The joint line pain pressure threshold (PPT) improved more in the placebo group than in the laser group, but only significantly at week 8. No other significant treatment effects were present. However, pain on movement and joint line PPT were worse in the placebo group at baseline, and therefore, it had more room for improvement. The short-term percentage of improvement in the placebo group was much higher than in similar trials. Conclusions: Pain was reduced substantially in both groups. LLLT seemed to provide a positive add-on effect in the follow-up period in terms of reduced pain medication usage and increased performance in the sit-to-stand test.
Vermi-Composting using AI in IoT

Vermicomposting is the process of converting agricultural waste into vermi composts by combining the actions of worms and microorganisms. Not only it is highly controlled, less expensive, it saves energy, but it also acquires a more efficient biological and recycling process. Vermicompost is a nutritious blend of organic matter and soil quality that is determined by a number of elements, including the number of earth's compounds present, the conditions under which it is produced, as well as the waste that is generated. Designing an automated system for the effective generation of vermicompost using Internet of Things (IoT) technology that maintains the proper environment by managing humidity and temperature. As a result, the information collected will effectively assist in maintaining good pit conditions and improving composting.
The Impact of Segment Length on EEG Based Biometric System

The biometric system is one of the best systems that can be used to replace the password system. Several modalities, such as fingerprints, face recognition, palm prints, and voice recognition, are reported to perform well as biometric modalities. Therefore, the current biometric system is still facing some issues. Artificial biometric data is the main issue in recent biometric systems. The electroencephalogram (EEG) signal is the potential modality to resolve the issue. Many studies have found that the EEG biometric system is able to achieve high accuracy. Most of them require a signal segmentation process to enrich the features and achieve high accuracy. In spite of that, there are only a few studies that focus on the impact of signal segmentation. In this study, 60, 30, 15, 10, 7.5, 6, 5 second lengths of segment were investigated. The power spectral density (PSD) feature from the delta (0.5 – 4 Hz), tetha (4 – 8 Hz), alpha (8 – 14 Hz), beta (14 – 30 Hz), and gamma (30 – 50 Hz) bands were extracted from each segment. It found that there was no linear correction between segment length and accuracy.