Miracle-dz/UniFAR-FTUs huggingface dataset:
Ethical Leadership on the Horizontal Scene-A Case Study on Middle Managers in the Tech Industry

Due to an increased importance for organisations to act according to ethical leadership, we identified the need to extend the perspective of how ethical leadership practices are developed, enhanced and spread among leaders horizontally. Since previous literature has focused on ethical leadership from a top management perspective, it is of interest to examine how ethical leadership is discussed and practised among middle managers as they encounter ethical dilemmas to a greater extent. By conducting a qualitative content analysis of interviews and documents, we declare a case study on middle managers in a tech company. Suggesting social learning as a proper lens to investigate our issue, we found that middle managers learn ethical conduct prominently via identification and knowledge sharing including feedback and reflection. By identifying important elements of ethical leadership, we could further interpret what practices were spread and reinforced among mid-level managers. These were the ability to be transparent, authentic, available and to value diversity, inclusion, and risk-taking for the sake of employees. However, it was evident that the horizontal learning of ethical conduct occurs in a reactive manner, triggered by events in a complex web of social learning. Therefore, in order for ethical leadership to regularly be learnt between the middle managers, a formal structure and the ethical culture can reinforce the way in which ethical leadership practices are spread horizontally.
Chemopreventive Effects of Dietary Folate on Intestinal Polyps in Apc 1 / 2 Msh2 2 / 2 Mice 1

Epidemiological and animal studies (reviewed in Y. I. Kim, J. Nutr. Biochemistry, 10: 66–88, 1999; J. B. and T. Levesque, 10: 1727–1743, 1996) suggest that dietary folate intake is inversely related to the risk of colorectal cancer. However, the optimal timing of folate intervention and mechanisms by which folate modulates colorectal carcinogenesis have not been clearly established. A recently developed murine model of intestinal tumorigenesis, which carries a heterozygous mutation in the Apc gene and a null mutation in the Msh2 gene ( Apc 1 / 2 Msh2 2 / 2 ), was used to determine the effect of dietary folate on intestinal tumorigenesis. Apc 1 / 2 Msh2 2 / 2 mice were randomized to receive either 0 or 8 mg of folate/kg diet starting at either 3 or 6 weeks of age. The 3- and 6-week diet starts represent intervention before and after the establishment of neoplastic foci, respectively. At 11 weeks of age, mice were killed, and the small intestines and colons were analyzed for adenomas and aberrant crypt foci (ACF). Serum folate concentrations were determined by a standard microbiological assay. Genomic DNA methylation was assessed by in vitro [ 3 H]methyl incorporation into hepatic DNA and by a methyl-sensitive restriction digestion method. Microsatellite instability was determined in matched normal and polyp DNA from the small intestine and colon at 5 loci. Serum folate concentrations accurately reflected dietary folate levels ( P < 0.005). Folate supplementation, started before the establishment of neoplastic foci, significantly decreased the number of small intestinal adenomas (by 2.7-fold; P 5 0.004) and colonic ACF (by 2.8-fold; P 5 0.028) and colonic adenomas (by 2.8-fold; P 5 0.1) compared with a moderate degree of folate deficiency. In contrast, a moderately folate-deficient diet, started after the establishment of neoplastic foci, significantly reduced the number of small intestinal adenomas (by 4.2-fold; P 5 0.001) but had no effect on colonic ACF and adenomas compared with folate supplementation. Genomic DNA methylation and microsatellite instability do not seem to play a major role in folate-modulated intestinal and colonic tumorigenesis in this model. In conclusion, in this murine model, dietary folate supplementation significantly protects against small intestinal and colorectal tumorigenesis if it is provided before the establishment of neoplastic foci However, if it is provided after the establishment of neoplastic foci, dietary folate seems to have an opposite effect. These data suggest that the timing of folate intervention is critical in providing an effective and safe chemopreventive effect on intestinal tumorigenesis. Notwithstanding the limitations associated with this model, our data suggest that the optimal timing of folate intervention must be established before folate supplementation can be used as a safe chemopreventive agent against colorectal cancer. groups were assessed by Student’s t test. Statistical anal- yses were performed using SYSTAT 5 for Macintosh (Systat, Evanston, IL).
The health benefits and practical considerations for the adoption of a Mediterranean-style dietary pattern

The term Mediterranean diet first appeared in the late 1940s(1). It is described by UNESCO as ‘a set of skills, knowledge, practices and traditions ranging from the landscape to the table, including the crops, harvesting, fishing, conservation, processing, preparation and, particularly, consumption of food’. A Mediterranean dietary pattern (MDP) varies in composition among the twentyone countries which make up the Mediterranean region but is typically characterised by high intakes of minimally processed plant-based foods such as fruits, vegetables, nuts, seeds, legumes and whole grains. Extra virgin olive oil is the main culinary fat, with a moderate intake of dairy products and a variety of herbs and spices used as condiments, rather than salt. Fish/seafood is typically consumed two to three times perweek. Red and processed meat and discretionary foods including sugar or honeysweetened food and drinks are consumed in low amounts. Wine, and in particular red wine, is consumed in moderation and with meals. Although in many ways analogous to other global healthy plant-based dietary patterns, it is the high intakes of olive oil, nuts and redwine, whichmake theMDP unique. The MDP has been used as a benchmark for comparison with other dietary patterns(2) and has influenced the dietary guidelines for non-Mediterranean countries like the USA, Canada and Australia. As highlighted by Hutchins-Wiese et al.(3), a large number of scoring systems exist to quantify adherence to a MDP, which vary in the number of components quantified, grouping of foods and approach to scoring (above or below the population median, continuous and binary scales). Such variability, although may result in a scoring system better adopted to the local context makes the integration of results from different observational studies and randomised controlled trials (RCT), and the establishment of size effects, public health messaging and clinical application, difficult.
Finite sample complexity of sequential Monte Carlo estimators on multimodal target distributions

We prove finite sample complexities for sequential Monte Carlo (SMC) algorithms which require only local mixing times of the associated Markov kernels. Our bounds are particularly useful when the target distribution is multimodal and global mixing of the Markov kernel is slow; in such cases our approach establishes the benefits of SMC over the corresponding Markov chain Monte Carlo (MCMC) estimator. The lack of global mixing is addressed by sequentially controlling the bias introduced by SMC resampling procedures. We apply these results to obtain complexity bounds for approximating expectations under mixtures of log-concave distributions and show that SMC provides a fully polynomial time randomized approximation scheme for some difficult multimodal problems where the corresponding Markov chain sampler is exponentially slow. Finally, we compare the bounds obtained by our approach to existing bounds for tempered Markov chains on the same problems.
Macromolecular crowding converts the human recombinant PrP C to the soluble neurotoxic (cid:1) -oligomers

Prion diseases are fatal neurodegenerative disorders and are linked with the conversion of the cellular isoform of the prion protein (PrP C ) into the abnormal (cid:1) -sheet-rich isoform. It is widely accepted that the soluble oligomers of (cid:1) -PrP are neurotoxic and that they are more pathologically signiﬁcant. To unravel the molecular mechanism under the conversion process, it is critical to identify the factors that can promote the conversion from PrP C to the (cid:1) -oligomers. By recording circular dichroism spectra and perform-ing a size-exclusion HPLC assay, we found that the conformation of the recombinant human prion protein (rPrP C ) was converted from an (cid:2) -helical conformation into (cid:1) -sheet oligomers under a macromolecular crowding condition. The soluble (cid:1) -oligomers of rPrP were resistant to proteinase K digestion and could bind to the dyes thioﬂavin T and 8-anilino-1-naphthalene sulfonate. Furthermore, by the 3-(4, 5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2 H tetrazolium assay, we showed that the soluble (cid:1) -oligomers were neurotoxic. These results suggest that macromolecular crowding, which has not been considered before, is a key intracellular factor in the formation of soluble neurotoxic (cid:1) -oligomers in prion diseases. — crowding converts the human recombinant PrP C to the soluble neurotoxic (cid:1) -oligomers. FASEB J. 24, 3536–3543
Quality of Life in Patients with Atrial Fibrillation after Percutaneous Left Atrial Appendage Closure

Introduction: Atrial Fibrillation (AF) and it is possibly complications have a significant impact on the Quality of Life (QoL). Percutaneous Left Atrial Appendage Closure (LAAC) is a procedure dedicated to patients with AF and contraindication to anticoagulation or failure of this therapy in prevention of thromboembolic events. Aim: To determine QoL in patients with AF after trans catheter LAAC. Methods: 25 adult patients (10 F, 15 M) a mean age of 68, 16 ± 8, 23 (49-83) who underwent LAAC were enrolled into the study. QoL was measured using the SF36 questionnaire (SF36q). Scores were transformed to a scale of 0-100, where higher scores represent higher functioning. The items were assigned to 8 domains: Physical Functioning (PF), Role-Functioning (RP), Bodily Pain (BP), General Health (GH), Vitality (VT), Social Functioning (SF), Role-Emotional (RE) and Mental Health (MH). SF36q, clinical examination and echocardiography were performed before the procedure, as well as after the following 45 days, 3 months and 12 months. Results: The LAA device was implanted in all patients (Watchman device – 19 pts, Amplatzer Amulet LAAO device – 6 pts). 5 of 8 QoL parameters improved significantly (p <0, 05) at 12 month follow-up compared to respective baseline values. The mean SF36q scale increased from 50, 3 points to 65, 25 points after 12 months. There was no significant corelation between the indications for procedure, sex or type of implanted device. Conclusions: Transcatheter LAAC resulted in improvement of the quality of life, questionnaire at 12-month
ALKALOID DRUGS PRODUCTION BY METABOLIC ENGINEERING OF MICROBES AND THEIR MEDICAL APPLICATIONS: A REWIEW

Alkaloids based pharmaceutical products are mainly produced by some plants species and further proceeds to fermentation in bioreactors while due to some limitations; it’s not a well defined way for alkaloids production. The most important alkaloids are benzylisoquinoline alkaloids, ergot alkaloids, quinoline alkaloids, monoterpene indole alkaloids, tetrahydroisoquinoline alkaloids, phenylethylisoquinoline alkaloids, phenylethylamines alkaloids and pyrroloquinoline alkaloids. While endophytic fungi in plants are also good sources of alkaloids drugs, which showed excellent biological and medical properties including anticancer, insecticidal, cytotoxic and antimicrobial activities. Now days, plant pathways, in order to generate alkaloid-derived pharmaceutical products, are introduced into microbes. Many genes coding for specific enzymes, involved in biosynthesis of alkaloids, are sequenced and introduce into microbes by recombinant DNA technology. In this review, we examine main sources of alkaloids based drugs and advance methods for their better production. Furthermore, we investigate challenges, which cause hindrance in microbial production of alkaloids-derived drugs that must be defeated.
Machine Learning for Diagnosis of Alzheimer’s Disease and Early Stages

: According to the WHO, approximately 50 million people worldwide have dementia and there are nearly 10 million new cases every year. Alzheimer’s disease is the most common form of dementia and may contribute to 60–70% of cases. It has been proved that early diagnosis is key to promoting early and optimal management. However, the early stage of dementia is often overlooked and patients are typically diagnosed when the disease progresses to a more advanced stage. The objective of this contribution is to predict Alzheimer’s early stages, not only dementia itself. To carry out this objective, different types of SVM and CNN machine learning classiﬁers will be used, as well as two different feature selection algorithms: PCA and mRMR. The different experiments and their performance are compared when classifying patients from MRI images. The newness of the experiments conducted in this research includes the wide range of stages that we aim to predict, the processing of all the available information simultaneously and the Segmentation routine implemented in SPM12 for preprocessing. We will make use of multiple slices and consider different parts of the brain to give a more accurate response. Overall, excellent results have been obtained, reaching a maximum F1 score of 0.9979 from the SVM and PCA classiﬁer.
Workflow Scheduling Strategies for Achieving Fault Tolerance in Cloud: A Review

: Cloud computing is increasingly used amidst researchers for scientific workflows to perform high throughput computing and data analysis. cloud are realizing the vision of utility by delivering computing resources as services. There are many workflow scheduling algorithms focuses on makespan time, cost and reliability etc. Numerous research studies have investigated fault-tolerant aspect of the workflow management system in different distributed systems. In this study, we analyze these efforts and provide an in-depth taxonomy of them. We present the ontology of faults and fault-tolerant techniques then position the existing workflow management systems with respect to the taxonomies and the techniques.
Tumour Grading and Discrimination based on Class Assignment and Quantitative Texture Analysis Techni

Medical imaging represents the utilisation of technology in biology for the purpose of noninvasively revealing the internal structure of the organs of the human body. It is a way to improve the quality of the patient’s life through a more precise and rapid diagnosis, and with limited side-effects, leading to an effective overall treatment procedure. The main objective of this thesis is to propose novel tumour discrimination techniques that cover both micro and macro-scale textures encountered in computed tomography (CT) and digital microscopy (DM) modalities, respectively. Image texture can provide significant information on the (ab)normality of tissue, and this thesis expands this idea to tumour texture grading and classification. The fractal dimension (FD) as a texture measure was applied to contrast enhanced CT lung tumour images in an aim to improve tumour grading accuracy from conventional CT modality, and quantitative performance analysis showed an accuracy of 83.30% in distinguishing between advanced (aggressive) and early stage (non-aggressive) malignant tumours. A different approach was adopted for subtype discrimination of brain tumour DM images via a set of statistical and model-based texture analysis algorithms. The combined Gaussian Markov random field and run-length matrix texture measures outperformed all other combinations, achieving an overall class assignment classification accuracy of 92.50%. Also two new histopathological multiresolution approaches based on applying the FD as the best bases selection for discrete wavelet packet transform, and when fused with the Gabor filters’ energy output improved the accuracy to 91.25% and 95.00%, respectively. While noise is quite common in all medical imaging modalities, the impact of noise on the applied texture measures was assessed as well. The developed lung and brain texture analysis techniques can improve the physician’s ability to detect and analyse pathologies leading for a more reliable diagnosis and treatment of disease. This work has been cited in [1-3].
On effective personalized music retrieval via exploring online user behaviors

In this paper, we study the problem of personalized text based music retrieval which takes users’ music preferences on songs into account via the analysis of online listening behaviours and social tags. Towards the goal, a novel DualLayer Music Preference Topic Model (DL-MPTM) is proposed to construct latent music interest space and characterize the correlations among (user, song, term). Based on the DL-MPTM, we further develop an effective personalized music retrieval system. To evaluate the system’s performance, extensive experimental studies have been conducted over two test collections to compare the proposed method with the state-of-the-art music retrieval methods. The results demonstrate that our proposed method significantly outperforms those approaches in terms of personalized search accuracy.
Selective Information-Driven Learning for Producing Interpretable Internal Representations in Multi-Layered Neural Networks

—This paper aims to propose a new type of information-theoretic method to control information content stored and transmitted in neural networks. To make the meaning of information concretely interpretable, we introduce the selective information and a method to control it, called “selective information-driven learning”. The new method is more suited for modeling neural learning than conventional information-theoretic measures, such as mutual information, because we can easily maximize and minimize the information, and we can interpret the meaning of information more concretely. The method was applied to the well-known wine data set. The experimental results show that the selective information could be maximized and minimized, and we could easily interpret the meaning of information in terms of the number of strong weights. In addition, the partial compression of multi-layered neural networks revealed that maximum information tended to be focused on output information, while minimum information tried to consider input information in addition to output information. Finally, collective weights, averaged over all compressed weights obtained in learning, were similar to the original correlation coefﬁcients between inputs and targets, meaning that the selective information can disentangle complicated connection weights into simple, linear, and independent ones to be easily interpreted.
Validation of interactions between aminoacyl-tRNA synthetases and cancer-associated factors using FRET-based screening system

Aminoacyl-tRNA synthetases(ARSs) are essential proteins that are involved in cellular protein synthesis and viability. They catalyse the specific amino acids to their cognate tRNAs with a high fidelity. However, previous study shows that these enzymes are multi-functional proteins that are involved in diverse cellular processes. According to the recent study, a systematic analysis of the expression of ARSs indicates that these proteins are associated with cancer. Because of the need to identify the correlation, we developed a cell-based high throughput screen method using förster resonance energy transfer (FRET) for anticancer activity in the living cells. Fluorescence resonance energy transfer (FRET) is unique in generating fluorescence signals that are sensitive to molecular conformation, association, and separation in the 1-10nm range. At first, we constructed vectors that expressed cyan fluorescent protein (CFP) tagged ARSs and yellow fluorescent protein (YFP) tagged CAGs. Then we used a confocal microscope and calculated the FRET efficiency. As a result, we found that AIMP3 and RPSA protein pair which has higher FRET efficiency than the control. To confirm this protein-protein interaction, we conducted co-immunoprecipitation and in vitro pull down assay. From the previous experiments, we identified that the AIMP3 and RPSA has a direct interaction. Accordingly, our results indicated that FRET-based validating system could be used to detect protein-protein interactions. Moreover, with our assay, it could help with the development of new anti-cancer drugs in the future.
Removing Noise From Pyrosequenced Amplicons The made this article openly available. Please share how this access benefits you. Your story matters

Background: In many environmental genomics applications a homologous region of DNA from a diverse sample is first amplified by PCR and then sequenced. The next generation sequencing technology, 454 pyrosequencing, has allowed much larger read numbers from PCR amplicons than ever before. This has revolutionised the study of microbial diversity as it is now possible to sequence a substantial fraction of the 16S rRNA genes in a community. However, there is a growing realisation that because of the large read numbers and the lack of consensus sequences it is vital to distinguish noise from true sequence diversity in this data. Otherwise this leads to inflated estimates of the number of types or operational taxonomic units (OTUs) present. Three sources of error are important: sequencing error, PCR single base substitutions and PCR chimeras. We present AmpliconNoise, a development of the PyroNoise algorithm that is capable of separately removing 454 sequencing errors and PCR single base errors. We also introduce a novel chimera removal program, Perseus, that exploits the sequence abundances associated with pyrosequencing data. We use data sets where samples of known diversity have been amplified and sequenced to quantify the effect of each of the sources of error on OTU inflation and to validate these algorithms. Results: AmpliconNoise outperforms alternative algorithms substantially reducing per base error rates for both the GS FLX and latest Titanium protocol. All three sources of error lead to inflation of diversity estimates. In particular, chimera formation has a hitherto unrealised importance which varies according to amplification protocol. We show that AmpliconNoise allows accurate estimates of OTU number. Just as importantly AmpliconNoise generates the right OTUs even at low sequence differences. We demonstrate that Perseus has very high sensitivity, able to find 99% of chimeras, which is critical when these are present at high frequencies. Conclusions: AmpliconNoise followed by Perseus is a very effective pipeline for the removal of noise. In addition the principles behind the algorithms, the inference of true sequences using Expectation-Maximization