Miracle-dz/UniFAR-FTUs huggingface dataset:
What Does Cultural Competence Mean to Preservice School Librarians? A Critical Discourse Analysis

In order to provide culturally responsive instruction to all students, school library professionals need to recognize the various discourses around cultural competence that exist in the field of library and information science (LIS) and understand the broader meanings that are attached to these discourses. This study presents an evaluation of the underlying ideologies that are embedded in the textual responses of a group of LIS students reporting on their perceived levels of cultural competence preparation.
The Effect of Using Light Stemming for Arabic Text Classification

Arabic is one of the Semitic languages in antiquity and one of the six official languages of the UN. Also, Arabic classification plays a significant and essential role in modern applications. There is a big difference between handling English text and Arabic text classification; preprocessing is also challenging for Arabic text. This paper presents the implementation of a Naïve Bayes classifier for Arabic text with and without stemmer. A set of four categories and 800 documents were used from the Text Retrieval Conference (TREC) 2001 dataset. The results showed that Naïve Bayes with light stemmer achieves better results than Naïve Bayes without stemmer. The findings of the classifier accuracy by employing stemmer and without stemmer are as preprocessing. It reveals that the accuracy resulted from the light stemmer was better than the classifier without stemmer detection, which Naïve Bayes Classification with light stemmer got 35.0745 higher than the Naïve Bayes Classification 33.831% without stemmer. After contrasting them, the stemmer got better accuracy than the classifier. Keywords—Arabic language; light stemming; information retrieval; Naïve Bayes classification
Keyword Assisted Embedded Topic Model

By illuminating latent structures in a corpus of text, topic models are an essential tool for categorizing, summarizing, and exploring large collections of documents. Probabilistic topic models, such as latent Dirichlet allocation (LDA), describe how words in documents are generated via a set of latent distributions called topics. Recently, the Embedded Topic Model (ETM) has extended LDA to utilize the semantic information in word embeddings to derive semantically richer topics. As LDA and its extensions are unsupervised models, they aren't defined to make efficient use of a user's prior knowledge of the domain. To this end, we propose the Keyword Assisted Embedded Topic Model (KeyETM), which equips ETM with the ability to incorporate user knowledge in the form of informative topic-level priors over the vocabulary. Using both quantitative metrics and human responses on a topic intrusion task, we demonstrate that KeyETM produces better topics than other guided, generative models in the literature\footnoteCode for this work can be found at \urlhttps://github.com/bahareharandizade/KeyETM .
Economic model for obtaining cyclodextrins from commercial cgtase

A repetitive batch process was employed followed by membrane ultrafiltration system to produce low-cost cyclodextrins (CDs) using commercial enzymes Toruzyme® cyclomaltodextrin glucanotransferase (CGTase) and its kinetic parameters were determined. The ultrafiltration system enabled the removalof inhibitory products from the reaction medium, allowing the enzyme to be recovered for reuse. A 10 kDa membrane was used to separate the different CDs produced by the CGTase. The substrates evaluated were maltodextrin, corn starch and cassava starch at 5, 10 and 15% (w/V), in the presence and absence of 10% (V/V) ethanol. After reaction for 132 h, 10% (w/V) cassava starch in the presence of ethanol provided the best results with 32.1 mg/mL of β-CD. Maximum production occurred after 72 h of reaction, with a yield of 87.4% of β-CD and an α-CD, β-CD and γ-CD production ratio of 1:1:0.08 g, respectively. When eight repetitive batches of 72 h followed by ultrafiltration and crystallization of β-CD were performed, 2.1 g of precipitate was obtained with a purity of 67.6% β-CD. The supernatant from the crystallization process was lyophilized and resulted in 35.3% α-CD. The developed model can be used industrially for the production of low cost CDs from easily obtained raw material.
Preparation and Evaluation of Alum Precipitate and Oil Adjuvant Multivalent Vaccines Against Clostridium perfringens

Enterotoxaemia is one of the hazardous diseases of the livestock. In Pakistan prophylaxis failure is due to the vaccination with type D monovalent vaccine. There is a need to develop a cost eff ective multivalent vaccine against enterotoxaemia using characterized toxinotypes isolated from field. Indigenously (Punjab, Pakistan) characterized Clostridium perfringens toxinotypes A (MW551947.1), B (MW332247.1) and D (MW332258.1) (n=1 each) were used. These toxinotypes were used to produce higher amount of alpha, beta and epsilon toxin units under culture conditions. Colony forming units (CFU) of each bacterium was determined through the standard plate count method and 106CFU/mL bacteria were used for vaccine dose. Monovalent, bivalent and multivalent oil adjuvant and alum precipitate vaccines were prepared. Formulated vaccines were passed the stability, sterility and safety test. Bacterin plus toxoid oil adjuvant vaccine produced higher (868.25±3.54 IU/mL) antibody titer at 28th day post vaccination in rabbits and 100% protection was observed after challenge. Multivalent bacterin plus toxoid oil adjuvant vaccine was used in field trials. Increased antibody response was detected after 4 months in sheep (1294.81±1.90 IU/mL) and goats (1091.85±2.51 IU/mL). During the experimental and field trials commercial vaccine did not produced higher antibody titer. Multivalent bacterin plus toxoid oil adjuvant vaccine proved as an excellent candidate for vaccination of animals against C. perfringens diseases, and it produced specific and efficient immune response to be used in field.
A novel Toxoplasma IMC sutures-associated protein regulates suture protein targeting and colocalizes with membrane trafficking machinery

The cytoskeleton of Toxoplasma gondii is composed of the inner membrane complex (IMC) and an array of underlying microtubules that provide support at the periphery of the parasite. Specific subregions of the IMC carry out distinct roles in replication, motility, and host cell invasion. Building on our previous in vivo biotinylation (BioID) experiments of the IMC, we identify here a novel protein that localizes to discrete punctae that are embedded in the parasite’s cytoskeleton along the IMC sutures. Gene knockout analysis shows that loss of the protein results in defects in cytoskeletal suture protein targeting, cytoskeletal integrity, parasite morphology, and host cell invasion. We then use deletion analyses to identify a domain in the N-terminus of the protein that is critical for both localization and function. Finally, we use the protein as bait for in vivo biotinylation which identifies several other proteins that colocalize in similar spot-like patterns. These putative interactors include several proteins that are implicated in membrane trafficking and are also associated with the cytoskeleton. Together, this data reveals an unexpected link between the IMC sutures and membrane trafficking elements of the parasite and suggests that the sutures punctae are likely a portal for trafficking cargo across the IMC.
Fairness Embedded Adaptive Recommender System: A Conceptual Framework

In the current fast paced and constantly changing environment, companies should ensure that their way of interacting with user is both relevant and highly adaptive. In order to stay competitive, companies should invest in state-ofthe-art technologies that optimize the relationship with the user using increasingly available data. The most popular applications used to develop user relationship are Recommender Systems. The vast majority of the traditional recommender system considers recommendation as a static procedure and focus on a specific type of recommendation, being not very agile in adapting to new situations. Also, when implementing a Recommender System there is the need to ensure fairness in the way decisions are made upon customer data. In this paper, it is proposed a novel Reinforcement Learning-based recommender system that is highly adaptive to changes in customer behavior and focuses on ensuring both producer and consumer fairness, Fairness Embedded Adaptive Recommender System (FEARS). The approach overcomes Reinforcement Learning’s main drawback in recommendation area by using a small, but meaningful action space. Also, there are presented two fairness metrics, their calculation and adaptation for usage with Reinforcement Learning, this way ensuring that the system gets to the optimal trade-off between personalization and fairness. Keywords—Algorithmic fairness; reinforcement learning; recommender systems; system adaptability
AMH (Anti- Mullerian Hormone) in Relation to High FSH (Follicle Stimulating Hormone) in Female Subfertility: A Cross Sectional Analysis

Background: Aged women who have infrequent menstruation and raised FSH are likely to have diminished ovarian reserve. Serum AMH is a novel marker of ovarian reserve but the test is expensive. The objective of this study is to determine serum AMH level in women who have high FSH and see how AMH and FSH correlate in this group of women. Methods: We did an observational cross sectional study of 87 infertile women, age 30-39 years, with oligomenorrhoea and high FSH (>10IU/L). Serum FSH was measured on day 2-4 and AMH was measured on any day of menstrual cycle. The women were divided into three groups based on FSH: 10-20 mIU/mL, 21-40 mIU/mL and >40 mIU/mL. Correlation of AMH with FSH was analyzed in all participants as well as within different groups. Results: The mean serum FSH D(2-4) was found 38.8±22.4 mIU/ml, range (min-max) (10.2-93.5) mIU/mL. Not all but 85.1% women had low (≤1.0 ng/ml) serum AMH level. Mean serum AMH was 0.74±0.59 ng/mL, range (min-max) (0.90-2.9 ng/ml). The serum AMH decreased in groups with higher FSH. The one way ANOVA shows that there is significant difference in AMH values between the groups. There is moderate (r = -0.537) negative correlation of AMH with FSH. The negative correlation of AMH with FSH is strongest (r = 0.735) in the group having FSH in the range 10-20mIU/mL. The correlation is comparatively less (r = 0.519, r = 0.616) in groups with higher FSH. Conclusion: Not all the subfertile women with infrequent menstruation and FSH>10 mIU/mL have lower AMH (<1 mg/ml). A significant negative moderate correlation exist between AMH and FSH. The correlation is relatively stronger in the FSH range 10-20 mIU/mL.
THE DESIGN AND SIMULATION OF MPPT CONTROLLED DC-DC BOOST CONVERTER AS SOLAR BATTERY CHARGER

In this paper, the design of MPPT controlled DCDC boost converter for PV charger application is presented. Duty cycle of the designed converter, which operates in 50 KHz, is controlled directly by Incremental conductance algorithm in order to track maximum power point of the PV panel that is changing in response of variation irradiance of sunny day. The designed converter is used as interface unit for matching 24V battery bank (20AH, 12V, C/5) with CANADIAN SOLAR CS5C-90 panel. The overall system is built and validated by using MATLAB SIMULINK. The simulation is performed under two cases of environmental conditions which are slow and fast changing conditions. In addition, the performance of simulated MPPT system is measured in term of tracking efficiency, conversion efficiency, and response time. The simulation results show that the proposed converter has averaged tracking efficiency of 98% and 97.55% and averaged conversion efficiency of 98.52% and 98.24% for slow and fast changing conditions respectively, and response time in the range of 70 ms, which is measured over the simulation time of 2.5s for both conditions. These results express the high performance and effective operation of designed converter, as compared with other systems in literature.
Association between soluble biomarkers - microbial translocation, inflammation and cardiovascular risk in HIV- infected individuals: a systematic review

Microbial translocation is associated with the increased risk of cardiovascular disease in HIV-infected individuals. There is scarce information regarding the possible associations between the biomarkers of microbial translocation, inflammation and cardiovascular risk that can be evaluated in clinical laboratories using plasma or serum samples. This systematic review was conducted according to the PRISMA protocol in order to verify the most used soluble biomarkers of microbial translocation, inflammation and cardiovascular risk, as well as possible associations between them, in HIV-infected individuals. A search was performed using the Medline, Scopus and Web of Science databases to identify existing studies regarding the relationship between microbial translocation biomarkers, inflammation and cardiovascular risk in HIV-infected patients. Eleven articles that presented soluble biomarkers of microbial translocation (LPS, rDNA, sCD14, LBP and EndoCAb) were selected. The most frequently evaluated soluble biomarker was sCD14, followed by LPS; the latter were associated with some lipid profile parameters. This systematic review considered soluble blood biomarkers that can be utilized in laboratory diagnosis. The aim was to identify the interconnection between microbial translocation, inflammation and cardiovascular risk. Despite the fact that a large number of inflammation and cardiovascular risk biomarkers have been previously reported, it was noted that important markers involved in the pathophysiology of cardiovascular diseases need to be included in future research.
Towards Cost-Effective and Elastic Cloud Database Deployment via Memory Disaggregation

It is challenging for cloud-native relational databases to meet the ever-increasing needs of scaling compute and memory resources independently and elastically. The recent emergence of memory disaggregation architecture, relying on high-speed RDMA network, offers opportunities to build cost-effective and elastic cloud-native databases. There exist proposals to let unmodified applications run transparently on disaggregated systems. However, running relational database kernel atop such proposals experiences notable performance degradation and time-consuming failure recovery, offsetting the benefits of disaggregation. To address these challenges, in this paper, we propose a novel database architecture called LegoBase, which explores the co-design of database kernel and memory disaggregation. It pushes the memory management back to the database layer for bypassing the Linux I/O stack and re-using or designing (remote) memory access optimizations with an understanding of data access patterns. LegoBase further splits the conventional ARIES fault tolerance protocol to independently handle the local and remote memory failures for fast recovery of compute instances. We implemented LegoBase atop MySQL. We compare LegoBase against MySQL running on a standalone machine and the state-of-the-art disaggregation proposal Infiniswap. Our evaluation shows that even with a large fraction of data placed on the remote memory, LegoBase's system performance in terms of throughput (up to 9.41% drop) and P99 latency (up to 11.58% increase) is comparable to the monolithic MySQL setup, and significantly outperforms (1.99x-2.33x, respectively) the deployment of MySQL over Infiniswap. Meanwhile, LegoBase introduces an up to 3.87x and 5.48x speedup of the recovery and warm-up time, respectively, over the monolithic MySQL and MySQL over Infiniswap, when handling failures or planned re-configurations.
A Comparison Analysis on Decision Tree Algorithms

Decision trees are widely utilized in many ﬁelds for systematic prediction concerning classiﬁcation and regression problems. One of the leading works of decision tree techniques is Classiﬁcation and Regression Trees (CART) by Breiman et al. (1984). Some later proposals indicate the greedy nature and limitations of CART, seeking to minimize misclassiﬁcation and the solving time for decision tree optimization. A new formulation for learning the optimal classiﬁcation tree is proposed by Verwer and Zhang (2019) as a binary linear model called BinOCT. In this report, we compare BinOCT with CART, Logistic regression, and Random Forests to examine if BinOCT actually achieves better accuracy within shorter solving time as claimed in Verwer and Zhang (2019). We perform our study on both small and large datasets, since the formulation size of BinOCT is said to be largely independent from the training data size. Our experiment results show little but no signiﬁcant improvements of BinOCT compared to CART, which fail to agree with Verwer and Zhang (2019). All four of our selected algorithms work better on smaller datasets with fewer number of features where Logistic Regression seems to be more stable and performs well on all datasets. For future direction, we are interested in exploring hyper-parameters tuning as well as the performance of BinOCT on large real-world datasets.
The Strategic Choice of Peers in M&A Valuations

We examine the strategic choice of peer comparables in fairness opinions (FOs) used in M&amp;A valuations. Using a hand-collected sample of peer comparable analyses and a regulatory shock to appraisal lawsuit risk, we show that target-sought FOs employ lower-valued peers when litigation risk is higher. Cross-sectionally, the bias is stronger when the target CEO is retained and owns fewer shares in the target. Although the selection of downward-biased peers reduces appraisal lawsuit likelihood, it is also associated with lower premiums. We conclude that FO valuations are driven, at least in part, by the strategic motive to mitigate litigation risk.
FORECASTING GOLD FUTURES PRICES CONSIDERING THE BENCHMARK INTEREST RATES

This study uses the benchmark interest rate of the Federal Open Market Committee (FOMC) to predict gold futures prices. For the predictions, we used the support vector machine (SVM) (a machine-learning model) and the long short-term memory (LSTM) deep-learning model. We found that the LSTM method is more accurate than the SVM method. Moreover, we applied the Boruta algorithm to demonstrate that the FOMC benchmark interest rates correlate with gold futures.
ON THE BOLTZMANN EQUATION FOR LONG-RANGE INTERACTIONS AND THE LANDAU APPROXIMATION IN PLASMA

We de ne a suitable weak formulation of the Boltzmann equation with a cross-section singular in both the relative velocity and the angular variables. This formulation allows us to extend the DiPerna-Lions theory to physically realistic long-range interactions, and to prove the validity of the Landau approximation in plasma physics assuming only natural physical bounds.
Magnesium Metabolism in Chronic Alcohol-Use Disorder: Meta-Analysis and Systematic Review

Chronic alcohol-use disorder has been imputed as a possible cause of dietary magnesium depletion. The purpose of this study was to assess the prevalence of hypomagnesemia in chronic alcohol-use disorder, and to provide information on intracellular magnesium and on its renal handling. We carried out a structured literature search up to November 2020, which returned 2719 potentially relevant records. After excluding non-significant records, 25 were retained for the final analysis. The meta-analysis disclosed that both total and ionized circulating magnesium are markedly reduced in chronic alcohol-use disorder. The funnel plot and the Egger’s test did not disclose significant publication bias. The I2-test demonstrated significant statistical heterogeneity between studies. We also found that the skeletal muscle magnesium content is reduced and the kidney’s normal response to hypomagnesemia is blunted. In conclusion, magnesium depletion is common in chronic alcohol-use disorder. Furthermore, the kidney plays a crucial role in the development of magnesium depletion.
Empowering the Emission of Upconversion Nanoparticles for Precise Subcellular Imaging

Upconversion nanoparticles (UCNPs) are a class of inorganic fluorophores that follow the anti-Stokes mechanism, to which the wavelength of emission is shorter than absorption. This unique optical behavior generates relatively long-lived intermediate energy levels of lanthanides that stabilize the excitation state in the fluorescence process. Longer-wavelength light sources, e.g., near-infrared (NIR), penetrate deeper into biological materials such as tissue and cells that provide a larger working space for cell biology applications and imaging, whereby UCNPs have recently gained increasing interest in medicine. In this report, the emission intensity of a gadolinium-based UCNP was screened by changing the concentrations of the constituents. The optimized condition was utilized as a luminescent nanoprobe for targeting the mitochondria as a distinguished subcellular organelle within differentiated neuroblastoma cells. The main goal of this study is to illustrate the targeting process within the cells in a native state using modified UCNPs. Confocal microscopy on the cells treated with the functionalized UCNPs indicated a selective accumulation of UCNPs after immunolabeling. To tackle the insolubility of as-synthesized particles in water-based media, the optimized UCNPs were surface-coated with polyamidoamine (PAMAM) dendrimers that due to peripheral amino groups are suitable for functionalizing with peptides and antibodies. Ultimately, we concluded that UCNPs are potentially versatile and ideal tools for NIR bioimaging and capable of making adequate contrast against biomaterials to be detectable in electron microscopy (EM) imaging.
Specificity of the HIV-1 Protease on Substrates Representing the Cleavage Site in the Proximal Zinc-Finger of HIV-1 Nucleocapsid Protein

To explore the sequence context-dependent nature of the human immunodeficiency virus type 1 (HIV-1) protease’s specificity and to provide a rationale for viral mutagenesis to study the potential role of the nucleocapsid (NC) processing in HIV-1 replication, synthetic oligopeptide substrates representing the wild-type and modified versions of the proximal cleavage site of HIV-1 NC were assayed as substrates of the HIV-1 protease (PR). The S1′ substrate binding site of HIV-1 PR was studied by an in vitro assay using KIVKCF↓NCGK decapeptides having amino acid substitutions of N17 residue of the cleavage site of the first zinc-finger domain, and in silico calculations were also performed to investigate amino acid preferences of S1′ site. Second site substitutions have also been designed to produce “revertant” substrates and convert a non-hydrolysable sequence (having glycine in place of N17) to a substrate. The specificity constants obtained for peptides containing non-charged P1′ substitutions correlated well with the residue volume, while the correlation with the calculated interaction energies showed the importance of hydrophobicity: interaction energies with polar residues were related to substantially lower specificity constants. Cleavable “revertants” showed one residue shift of cleavage position due to an alternative productive binding mode, and surprisingly, a double cleavage of a substrate was also observed. The results revealed the importance of alternative binding possibilities of substrates into the HIV-1 PR. The introduction of the “revertant” mutations into infectious virus clones may provide further insights into the potential role of NC processing in the early phase of the viral life-cycle.