Miracle-dz/UniFAR-FTUs huggingface dataset:
An empirical comparison of sampling techniques for matrix column subset selection

Column subset selection (CSS) is the problem of selecting a small portion of columns from a large data matrix as one form of interpretable data summarization. Leverage score sampling, which enjoys both sound theoretical guarantee and superior empirical performance, is widely recognized as the state-of-the-art algorithm for column subset selection. In this paper, we revisit iterative norm sampling, another sampling based CSS algorithm proposed even before leverage score sampling, and demonstrate its competitive performance under a wide range of experimental settings. We also compare iterative norm sampling with several of its other competitors and show its superior performance in terms of both approximation accuracy and computational efficiency. We conclude that further theoretical investigation and practical consideration should be devoted to iterative norm sampling in column subset selection.
Human Serum Promotes Candida albicans Biofilm Growth and Virulence Gene Expression on Silicone Biomaterial

Objectives Systemic candidal infections are a common problem in hospitalized patients due to central venous catheters fabricated using silicone biomaterial (SB). We therefore evaluated the effect of human serum on C. albicans biofilm morphology, growth, and the expression of virulence-related genes on SB in vitro. Methods We cultivated C. albicans SC5314 (wild-type strain, WT) and its derivative HLC54 (hyphal mutant, HM) for 48 h in various conditions, including the presence or absence of SB discs, and human serum. The growth of planktonic and biofilm cells of both strains was monitored at three time points by a tetrazolium salt reduction assay and by scanning electron microscopy. We also analyzed by RT-PCR its expression of the virulence-related genes ALS3, HWP1, EAP1, ECE1, SAP1 - SAP10, PLB1, PLB2, PLC and PLD. Results At each time point, planktonic cells of WT strain cultured in yeast nitrogen base displayed a much higher expression of EAP1 and HWP1, and a moderately higher ALS3 expression, than HM cells. In planktonic cells, expression of the ten SAP genes was higher in the WT strain initially, but were highly expressed in the HM strain by 48 h. Biofilm growth of both strains on SB was promoted in the presence of human serum than in its absence. Significant upregulation of ALS3, HWP1, EAP1, ECE1, SAP1, SAP4, SAP6 - SAP10, PLB1, PLB2 and PLC was observed for WT biofilms grown on serum-treated SB discs for at least one time point, compared with biofilms on serum-free SB discs. Conclusions Human serum stimulates C. albicans biofilm growth on SB discs and upregulates the expression of virulence genes, particularly adhesion genes ALS3 and HWP1, and hydrolase-encoding genes SAP, PLB1 and PLB2. This response is likely to promote the colonization of this versatile pathogen within the human host.
Advances in the Study of Extracellular Vesicles for Bone Regeneration

Promoting the efficiency of bone regeneration in bone loss diseases is a significant clinical challenge. Traditional therapies often fail to achieve better therapeutic outcomes and shorter treatment times. However, in recent years, extracellular vesicles (EVs) have gained significant attention due to their exceptional osteogenic function in bone regeneration and superior therapeutic effects compared to traditional cell therapy. EVs have emerged as a promising therapy for tissue defect regeneration due to their various physiological functions, such as regulating the immune response and promoting tissue repair and regeneration. Moreover, EVs have good biocompatibility, low immunogenicity, and long-term stability, and can be improved through pretreatment and other methods. Studies investigating the mechanisms by which extracellular vesicles promote bone regeneration and applying EVs from different sources using various methods to animal models of bone defects have increased. Therefore, this paper reviews the types of EVs used for bone regeneration, their sources, roles, delivery pathways, scaffold biomaterials, and applications.
MeCP2 deficiency down-regulates specific nuclear proteins that could be partially recovered by valproic acid in vitro

MeCP2, the major causative factor of Rett syndrome and related phenotypes including autism, is a two-face nuclear modulator acting via transcriptional and chromatin remodeling mechanisms. This study investigated the expression of several nuclear proteins and their dependence on MeCP2 dose and presence of the Rett causative R306C mutation. To this end, we developed in vitro models representing MeCP2 deficiency induced by siRNAs, and cells expressing the R306C mutation. Using an extended antibody microarray validated by specific assays, revealed that MeCP2 dose was correlated with specific nuclear proteins profiles including the BRM/SNF2 component of SWI/SNF complex, PRMT1 methyl transferase and HDAC2. Furthermore, while exposing the MeCP2 knock-down system to therapeutic concentrations of valproic acid (VPA), a known HDACs inhibitor, we observed a partial restoration of MeCP2 expression levels. Exposure to VPA also increased the levels of BRM, as well as of BDNF, an important co-factor in MeCP2-mediated pathway. Our findings provide additional evidence of diverse mechanisms of MeCP2 function as transcriptional repressor and activator of specific genes. As it has been recently demonstrated that post-natal restoration of MeCP2 deficiency may reverse neurological defects in a mouse model of Rett syndrome, we suggest to study the restorative effect of HDAC inhibitors in MeCP2-deficient mouse model.
A Web service-based communication architecture for smartphone/WPAN sensor ensembles

There are many applications that use low power wireless sensors to monitor people's vital signs or the environment around people. For mobile applications, smartphones with Internet access have become a commonly used tool to make this data remotely accessible. However, the yet presented architectures are quite complex and lack flexibility. Our aim was to investigate how existing communication architectures for smartphone/WPAN sensor ensembles can be simplified and to provide a transparent solution where the smartphone doesn't require knowledge about sensors for which it serves as an Internet gateway. We therefore present an IP-based communication architecture, that allows applications to easily integrate remote sensor data of mobile users based on existing Internet standards. In particular, our solution provides simple Web service-based APIs and does not require the smartphone to process sensor data. Instead, the smartphone becomes a transparent gateway as it only forwards IP packets.
Using domain specific language for large screen game interaction

High-resolution display provides the advantage to video games by making gaming experience more realistic. Wall-size display extends the viewing experience of playing game to the entire visible zone with the immersive display experience. In this paper, we propose a new Domain Specific Language for wall-size game interaction naming it SDGi. This new programming language translates tracking signal of a spatial pointer into pointing input that are native to game operating systems. Motion of the illuminated pointer is used to mirror conventional mouse input to such a Single Display Groupware, thus eliminating the need for any extra or expensive hardware or a new software toolkit. Furthermore, the grammar of this domain specific language can be customized to create user defined pointer motions in 3D space so that all common pointing input can be simulated from using a pointer-based interaction to wall-size display.
Environmental Risk Assessment of a Hydrocracker Unit in Abadan Oil Refinery Using the EFMEA Analysis

Background: The aim of risk assessment is to use a rational method to analyze risks and to identify the possible hazards and outcomes for people, equipment, materials, and the environment. Methods: The criteria and indices for assessing the environmental, health, and safety risks of the hydrocracker unit were determined by a Delphi questionnaire in this study. Then, an Environmental Failure Mode and Effects Analysis was applied to evaluate, score, and rank the risks based on their probability of occurrence, severity, probability of detection, extent of pollution, and potential of recycling. Results: According to the results of the Delphi process, 19 out of the 22 items were identified as the main criteria in the environmental, health, and safety risk evaluation of the hydrocracker unit at the Abadan Oil Refinery. However, the results indicated that 67% of the risks associated with the life cycle operation were low while 33% of them were high in terms of intensity. In contrast, 75% of the risks associated with control room operators were low and 25% were high in terms of intensity. On the other hand, 64, 7, and 29% of the risks associated with the activities of site employees were low, moderate, and high in terms of intensity, respectively, while the corresponding figures were 14, 29, and 57% in the case of risks associated with repairs. Conclusion: Based on the results of techniques of environmental failure modes and effects analysis (EFMEA) and Delphi, appropriate methods can be used to identify and reduce risks in similar industries.
Therapeutic alliance and behavior inhibition

Abstract The study examined whether the quality of the therapeutic alliance is associated with a tendency toward behavior inhibition. Inhibition was measured by the frequency with which the clients used the word “not” and by their verbal productivity. In treatment sessions with low-quality therapeutic alliance, clients tended to use the word “not” more often and speak fewer words. Results suggest that it could be useful for therapists to use indexes of behavior inhibition as problem markers in the therapeutic alliance.
Normalization and Automatic Recognition of Assamese Vowels

Vowel normalization is a procedure of finding out the intermediate parameter from varied forms of utterance produced by different speakers of a particular language or same vowel token uttered by same speaker at different times. Every speaker has their own speech parameters and the variation of these parameters is the primary cause of speaker to speaker variation of speech sounds. The significant parameters that distinguish one vowel from the other are its formant values. In this paper an automatic vowel recognition procedure is described where the eight standard Assamese vowels are analyzed through Formant detection and Normalization. Here a standard approach is adopted for Normalization of vowels based on two Formants F1 and F2. A Vowel Recognition System based on Formant Estimation is developed and a preliminary recognition test for isolated vowels shows an expected recognition rate of 84-96% for arbitrarily selected speakers. General Terms Speech processing, Phonetics
Effect of Hydrocortisone on Liver Mitochondria Proteome of Rats

Every liver mitochondria protein sample from the normal rats and rats infected with hydrocortisone was labeled with Cy3 or Cy5 randomly. Each Cy3-labeled sample and Cy5-labeled sample were mixed on the same 2-D gel along with a Cy2-labeled mixture of all samples as an internal standard. The gels were respectively imaged by Typhoon 9400 using different emission filter and spot-features were analyzed by DeCyder5.0 software in each paired normal/disease comparison. 11 different expression proteins were selected, in which 7 proteins were successfully identified. In their proteins, the expression of Glucose regulated protein (grp75), heat shock protein 60, carbamoyl-phosphate synthetase 1, ATP synthetase, sarcosine dehydrogenase, chaperone protein, dimethylglycine dehydrogenase increased while the expression of adenylate kinas4 decreased. These result showed that the metabolism of substance was affected by hydrocortisone. The expression of heat shock protein and Glucose regulated protein increased, which contributed to protect the cell from damage. The metabolism of protein increased and the energy metabolism was alleviated to increase the output of ATP. However, the stability of energy metabolism and the ability of self-regulation were disturbed when the rats were injected with hydrocortisone.
Parallel software implementation of recursive multidimensional digital filters for point-target detection in cluttered infrared scenes

A technique for the enhancement of point targets in clutter is described. The local 3-D spectrum at each pixel is estimated recursively. An optical flow-field for the textured background is then generated using the 3-D autocorrelation function and the local velocity estimates are used to apply high-pass velocity-selective spatiotemporal filters, with finite impulse responses (FIRs), to subtract the background clutter signal, leaving the foreground target signal, plus noise. Parallel software implementations using a multicore central processing unit (CPU) and a graphical processing unit (GPU) are investigated.
Iterated Prisoner ’ s Dilemma : A review

Much debate has centered on the nature of social dilemmas. In environmental issues, trade-wars, negotiations between countries and social interactions, there are many scenarios where involved players may choose altruistic, cooperative action or an alternative selfish behaviour. Games, particularly the prisoner’s dilemma and variations have been used to model and analyse such scenarios. In this paper, an overview of research in this domain is presented. The iterated prisoner’s dilemma (IPD) is discussed with attention payed to evolution of strategies in this environment, the effect of noise in the IPD and finally Nplayer versions.
Indonesian media framing against Trump in the 2020 presidential election

The presidential election of the United States has always captured the world's attention. This is because America is a superpower in the global political arena. Therefore, the figure who is elected to occupy the White House has always impacted the global political scene for four years. Donald Trump, President of America 2017-2021, has had a tremendous impact on the global political scene with some controversial policies. Trump protectionism policies triggered the trade war between America and China, greatly impacting the global economy. This article looks at the sentiment of national media framing in Indonesia (Kompas, The Jakarta Post, and Republika) against the Donald Trump in the 2020 presidential election, which Trump lost to Joe Biden. In this research, the writer reads news related to the presidential election of the U.S. from September 2020 to November 2020 from the three national media outlets in Indonesia to see the framing. In this framing analysis, we wanted to determine whether the framing was positive, negative, or neutral when making news about Trump's figure. The results of this study indicate that the highest percentage of mainstream media framing in Indonesia is negative. Trump has been widely reported to have negative sentiment toward the media in Indonesia. News outlets with positive sentiment are the lowest against Trump. This means that the national media in Indonesia also does not like Trump's controversial leadership style.
Applicability of 99mTc-HL91, a putative hypoxic tracer, to detection of tumor hypoxia.

UNLABELLED To elucidate the applicability of 99mTc-HL91 (HL91) a putative hypoxic tracer, to the imaging of hypoxia in tumors, a biodistribution study of the tracer was performed. The intratumoral distribution of HL91 was compared with that of 14C-deoxyglucose (DG) and the expression of glucose transporter 1 (GLUT1) in an implanted tumor. METHODS Biodistribution of HL91 after intravenous injection into Wistar rats with rat mammary tumor (Walker-256) was studied by determining blood and tissue levels of radioactivity from 15 min to 6 h after injection. Dual ex vivo autoradiography was performed on sections of the tumor using HL91 (74 MBq) and DG (185 kBq). The same sections were immunohistologically analyzed with anti-GLUT1 antibody. Tumor tissue was histologically divided into areas of viable cancer cells, necrosis and granulation tissue. The viable cancer cell area was further divided into normoxic and hypoxic areas. Uptake of both tracers in each area was measured quantitatively. The intensity of GLUT1 staining (relative optical density [ROD]) in each area was evaluated by densitometry. RESULTS The uptake of HL91 in the tumor reached a maximal value (0.897 +/- 0.118% ID [injected dose], mean +/- SD, n = 5) at 120 min after intravenous injection of HL91, then gradually decreased. The tumor-to-muscle ratio continued to increase until 360 min (4.34 at 120 min, 7.01 at 240 min and 10.4 at 360 min). HL91 accumulated to significantly higher levels in the hypoxic area than those in the other tissues (P < 0.0001). Uptake of DG and expression of GLUT1 were significantly higher in the hypoxic area than in the normoxic area (P < 0.0001). In the viable cancer cell area, uptake of HL91 and expression of GLUT1 were strongly correlated (r = 0.624-0.868, mean r = 0.743, P < 0.0001), and DG uptake was moderately correlated with GLUT1 expression (r = 0.328-0.669, mean r = 0.505, P < 0.0001). CONCLUSION These results indicate that HL91 can be used to detect tumor hypoxia.
A Case-Based Framework for Leveraging NutriGenomics Knowledge and Personalized Nutrition Counseling*

NutriGenomics is the bioscience that links the way nutrients and other dietary components shape genetic activity. It builds on the success of Human Genome Project by applying systems biology methods to explain how the molecular components of food, supplements and pharmaceuticals dynami- cally influence and shape the activity of genomic subsystems, which in turn de- fine how a person can stay healthy or become ill. Applying NutriGenomics knowledge is done through Directive Genomics, which develops purposeful dietary strategies that influence gene expression at the individual level with the goal of better genetic function and health. This paper proposes a case-based framework for leveraging nutrigenomics knowledge and Directive Genomics applications. The unique features of the proposed system include a self- maintained distributed case base structure and a CBR-based nutrition counsel- ing module that can learn, adapt, and maintain its case base via the integrated distributed case bases as well as external resources.
Stochastic coupling of rainfall and biomass dynamics

A modeling scheme is presented to derive the probabilistic structure of plant biomass when subjected to stochastic precipitation conditions. Using the fact that soil moisture varies on a much shorter scale than plant biomass, analytical expressions are derived for the steady state probability distribution of the average plant biomass over a period T, which is expected to be of the order of 2 or 3 months. Analytical expressions are also given for the time‐dependent mean and variance of the biomass over a period T. The analysis is based on a simple model linking the daily dynamics of plant growth and soil moisture. The derived analytical expressions reproduce the results obtained from a full simulation of the underlying model very well. The results allow the study of the impact of different climatic scenarios regarding changes in frequency and intensity of rainfall, as well as changes in the mean seasonal temperature, on the expected value and variance of plant biomass throughout time.
A cellular automata based Izhikevich neuron model

A cellular automata based spiking neuron model is presented in this paper. The base of this model is inspired from map-based Izhikevich neuron model. Proposed model can be implemented efficiently with low cost arithmetic units. Model behavior and the state transitions are discussed and it is shown that this model can exhibit biological neuron-like responses. Results are verified by comparing RTL simulations and the state transition of the proposed model with other similar models.
Convenient or Marketing matters? Unveiling the Determinants of Purchasing Decisions in Tokopedia E-Commerce

This paper investigates the determinants of purchasing decision of Tokopedia e-commerce consumers with Gen-Z as a sample. This paper refers to Technology Acceptance Model (TAM) as theoretical framework, where the indicators use in this study combines both convenient matter in purchasing at e-commerce such as Artificial Intelligence (AI) and marketing matters from both digital and non-digital marketing i.e., Islamic Marketing Ethics (IME). This paper employs Structural Equation Model-Pooled Least Square (SEM-PLS). The results show that AI and Digital Marketing (DM) have positive and significant relationship to influence consumers’ decision to purchase in Tokopedia. Meanwhile, IME has positive but non-significant relationship with purchase decision. This paper argues that this is due to the respondents’ knowledge of Sharia compliance in e-commerce transaction and lack of IME indicators socialization by Tokopedia, where Tokopedia already implement some indicators of IME in their feature such as Halal Corner. The result expects to contribute to development of theoretical and practical implication, particularly on Marketing matters both digital and Islamic marketing ethics.
Unsupervised Deep Hashing with Dynamic Pseudo-Multi-Labels for Image Retrieval

Hashing has received a lot of attention in large-scale image retrieval due to its high retrieval accuracy and speed. Unsupervised deep hashing methods with pseudo-labels suffer from suboptimal performance due to low clustering accuracies, which result in unreliable generated pseudo-labels, and are highly sensitive to the number of clusters. To tackle these challenges, we propose an unsupervised deep hashing image retrieval method with dynamic pseudo-multi-labels (UDHPM). Specifically, UDHPM designs a dynamic pseudo-multi-label generation network by employing a soft clustering model to maximize the approximation to the real data distribution and continuously optimizing the pseudo-multi-labels in an end-to-end manner to provide reliable supervised information. Furthermore, UDHPM preserves the category information of dynamic pseudo-multi-labels by applying Kullback–Leibler (KL) divergence to the hashing network. In experiments on three benchmark datasets, UDHPM significantly improves retrieval performance over existing state-of-the-art unsupervised deep hashing methods. UDHPM also exhibits a degree of robustness to the number of clusters.
Seismic fault detection with convolutional neural network

Mapping fault planes using seismic images is a crucial and time-consuming step in hydrocarbon prospecting. Conventionally, this requires significant manual efforts that normally go through several iterations to optimize how the different fault planes connect with each other. Many techniques have been developed to automate this process, such as seismic coherence estimation, edge detection, and ant-tracking, to name a few. However, these techniques do not take advantage of the valuable experience accumulated by the interpreters. We have developed a method that uses the convolutional neural network (CNN) to automatically detect and map fault zones using 3D seismic images in a similar fashion to the way done by interpreters. This new technique is implemented in two steps: training and prediction. In the training step, a CNN model is trained with annotated seismic image cubes of field data, where every point in the seismic image is labeled as fault or nonfault. In the prediction step, the trained model is applied to compute fault probabilities at every location in other seismic image cubes. Unlike reported methods in the literature, our technique does not require precomputed attributes to predict the faults. We verified our approach on the synthetic and field data sets. We clearly determined that the CNN-computed fault probability outperformed that obtained using the coherence technique in terms of exhibiting clearer discontinuities. With the capability of emulating human experience and evolving through training using new field data sets, deep-learning tools manifest huge potential in automating and advancing seismic fault mapping.