Miracle-dz/UniFAR-FTUs huggingface dataset:
Intestinal barrier dysfunction mediates Whipple's disease immune reconstitution inflammatory syndrome (IRIS)

Classical Whipple's disease (CWD) affects the gastrointestinal tract and causes chronic diarrhea, malabsorption, and barrier dysfunction with microbial translocation (MT). Immune reconstitution inflammatory syndrome (IRIS) is a serious complication during antimicrobial treatment of CWD. The pathomechanisms of IRIS have not been identified and mucosal barrier integrity has not been studied in patients with IRIS CWD.
Diversity of mechanisms to control bacterial GTP homeostasis by the mutually exclusive binding of adenine and guanine nucleotides to IMP dehydrogenase

IMP dehydrogenase(IMPDH) is an essential enzyme that catalyzes the rate‐limiting step in the guanine nucleotide pathway. In eukaryotic cells, GTP binding to the regulatory domain allosterically controls the activity of IMPDH by a mechanism that is fine‐tuned by post‐translational modifications and enzyme polymerization. Nonetheless, the mechanisms of regulation of IMPDH in bacterial cells remain unclear. Using biochemical, structural, and evolutionary analyses, we demonstrate that, in most bacterial phyla, (p)ppGpp compete with ATP to allosterically modulate IMPDH activity by binding to a, previously unrecognized, conserved high affinity pocket within the regulatory domain. This pocket was lost during the evolution of Proteobacteria, making their IMPDHs insensitive to these alarmones. Instead, most proteobacterial IMPDHs evolved to be directly modulated by the balance between ATP and GTP that compete for the same allosteric binding site. Altogether, we demonstrate that the activity of bacterial IMPDHs is allosterically modulated by a universally conserved nucleotide‐controlled conformational switch that has divergently evolved to adapt to the specific particularities of each organism. These results reconcile the reported data on the crosstalk between (p)ppGpp signaling and the guanine nucleotide biosynthetic pathway and reinforce the essential role of IMPDH allosteric regulation on bacterial GTP homeostasis.
Quantification of the NA dependent change of shape in the image formation of a z‐polarized fluorescent molecule using vectorial diffraction simulations

The point spread function of a fixed fluorophore with its dipole axis colinear to the optical axis appears donut‐shaped when seen through a microscope, and its light distribution in the pupil plane is radially polarized. Yet other techniques, such as photolithography, report that this same light distribution in the pupil plane appears as a solid spot. How can this same distribution lead to a spot in one case but a donut in the other? Here, we show how the tube lens of the system plays a critical role in determining this shape. Using a vectorial treatment of image formation, we simulate the relative contributions of both longitudinal and radial components to the image of a dipole emitter and thus show how the donut (typically reported for z‐polarized single molecule fluorescence microscopy) transforms into a solid spot (as commonly reported for photolithography) as the numerical aperture of the tube lens increases. We find that the transition point occurs around 0.7 NA, which is significantly higher than used for most microscopy systems and lower than for common photolithography systems, thus resolving the seeming paradox of dipole shape.
Dimeric assembly of human Suv3 helicase promotes its RNA unwinding function in mitochondrial RNA degradosome for RNA decay

Human Suv3 is a unique homodimeric helicase that constitutes the major component of the mitochondrial degradosome to work cooperatively with exoribonuclease PNPase for efficient RNA decay. However, the molecular mechanism of how Suv3 is assembled into a homodimer to unwind RNA remains elusive. Here, we show that dimeric Suv3 preferentially binds to and unwinds DNA–DNA, DNA–RNA, and RNA–RNA duplexes with a long 3′ overhang (≥10 nucleotides). The C‐terminal tail (CTT)‐truncated Suv3 (Suv3ΔC) becomes a monomeric protein that binds to and unwinds duplex substrates with ~six to sevenfold lower activities relative to dimeric Suv3. Only dimeric Suv3, but not monomeric Suv3ΔC, binds RNA independently of ATP or ADP, and is capable of interacting with PNPase, indicating that dimeric Suv3 assembly ensures its continuous association with RNA and PNPase during ATP hydrolysis cycles for efficient RNA degradation. We further determined the crystal structure of the apo‐form of Suv3ΔC, and SAXS structures of dimeric Suv3 and PNPase–Suv3 complex, showing that dimeric Suv3 caps on the top of PNPase via interactions with S1 domains, and forms a dumbbell‐shaped degradosome complex with PNPase. Overall, this study reveals that Suv3 is assembled into a dimeric helicase by its CTT for efficient and persistent RNA binding and unwinding to facilitate interactions with PNPase, promote RNA degradation, and maintain mitochondrial genome integrity and homeostasis.
The return of the rings: Evolutionary convergence of aromatic residues in the intrinsically disordered regions of RNA‐binding proteins for liquid–liquid phase separation

Aromatic residues appeared relatively late in the evolution of protein sequences to stabilize the globular proteins' folding core and are less in the intrinsically disordered regions (IDRs). Recent advances in protein liquid–liquid phase separation (LLPS) studies have also shown that aromatic residues in IDRs often act as “stickers” to promote multivalent interactions in forming higher‐order oligomers. To study how general these structure‐promoting residues are in IDRs, we compared levels of sequence disorder in RNA binding proteins (RBPs), which are often found to undergo LLPS, and the human proteome. We found that aromatic residues appear more frequently than expected in the IDRs of RBPs and, through multiple sequence alignment analysis, those aromatic residues are often conserved among chordates. Using TDP‐43, FUS, and some other well‐studied LLPS proteins as examples, the conserved aromatic residues are important to their LLPS‐related functions. These analyses suggest that aromatic residues may have contributed twice to evolution: stabilizing structured proteins and assembling biomolecular condensates.
Unconformity‐covering pillow lava dated at 2.14 Ga: Challenging the “stable‐shelf” Minas Supergroup of the Quadrilátero Ferrífero, Minas Gerais, Brazil

We report on the occurrence of pillow lava in the upper part of the banded‐iron‐formation (BIF) sequence of the Quadrilátero Ferrífero, famous for hosting world‐class iron‐ore deposits in Minas Gerais, Brazil. A pillow‐lava domain is found as an outlier within the BIF sequence, known as the Itabira iron formation or the Itabira Group. The pillow lava occurs as lateritized, indurated crusts in a sequence of ochreous itabirite, the weathered expression of dolomitic itabirite, a metamorphosed BIF rock containing dolomite. About 90 zircon grains, extracted from two samples of lateritized pillow lava, an indurated pillow rim and a friable pillow core, yielded Archaean to Palaeoproterozoic ages between 3.37 and 2.14 Ga, with age clusters at 2.98, 2.72, 2.25, and 2.14 Ga, and minor clusters at 3.37, 3.26, 3.16, and 2.82 Ga. The youngest grains, all exhibiting perfectly euhedral crystals, defined a Concordia age at 2,141 ± 6 Ma, interpreted as the age of subaqueous volcanism. This young zircon age and the perfectly euhedral zircon crystals suggest that the pillow‐lava domain represents an outlier of Palaeoproterozoic volcanic rocks, which covered the unconformity between the Itabira Group and the overlying Piracicaba Group. The finding of pillow lava lends support to previous, but usually ignored works, which described metavolcanic rocks in spatial association with the Itabira Group, and provides evidence for subaqueous volcanism that challenges the prevailing view of a stable‐shelf or passive‐margin setting for the geological evolution of the Minas Supergroup.
Design of an Interactive Multidisciplinary Residential Summer Program for Recruitment of High School Females to Engineering

The need to inspire and recruit female high school students to study engineering is well known. Texas Tech University (TTU) reports the female population is 18%, similar to the national average. Outreach programs are a common means of exposing students to different engineering disciplines and a variety of employment opportunities for engineers. Traditional forms of outreach programs such as seminars, information sessions or research activities are often less interactive and less student-centered. A week-long summer program was implemented at TTU to provide a holistic design experience to expose students to a higher education environment. The program consisted of interactive discipline specific lessons, a multidisciplinary group project, professional development sessions, and recreational activities. The goals and design of the summer program, Engineering – Get Into Real Learning (E-GIRL), aimed to interest and recruit junior and senior high school females to pursue engineering by introducing six engineering disciplines through interactive, problem-based learning. Further, the camp aimed to provide female students with a positive experience and a chance to develop personal, interpersonal and technical engineering skills. Using a flipped classroom structure, students were given supplemental readings prior to each engineering discipline’s discipline specific lesson providing background knowledge for each activity. Students were tasked to work in groups on an open-ended project applying knowledge of the six disciplines introduced throughout the program. Problem-based learning through the assigned project allowed students to develop skills such as teamwork, oral communication, time management and project management. During the final program session, students gave an oral presentation to peers, parents and program instructors detailing their design solutions to a real-world problem. Evaluation instruments of the outreach program’s design included preand post-questionnaires for assessment of the interactive sessions, and their impact on the development of engineering skills and the understanding of information presented. Assessment results led to the conclusion that students were able to identify areas of improvement for self-development of engineering skills, exhibited better comprehension of engineering as a career, and distinguished among various disciplines of engineering. Responses provided by students led to the conclusion that the implementation of the presented residential outreach program curriculum achieved the goals of educating and exciting female students about engineering careers.
Exploring the role of machine learning in Email Filtering

Spam, commonly known by various names as spam, commercial, or junk email, is the process or practice of sending some unsolicited bulk email messages to a randomly selected group of recipients, often to promote the sale of something. Phishing messages try to get your private and personal information by disguising it as legitimate messages from multiple trusted sources. They take you to many fraudulent websites, to fill in and use the private information that can be used for identity theft and many other things. Spam spreads across the Internet quickly because the value and transaction cost of electronic communications is much lower than that of any other and different type of communication. Spam is more than just annoying. It can be dangerous, especially if it is part of a phishing message. There are many agents used to filter spam through different methods to identify and mark an incoming message as spam, ranging from whitelisting, mail header analysis, content scanning, etc. With all the spam every day. This is not because the filters are not powerful enough, but rather because of the total and rapid adoption of all new technologies by spammers and the inflexibility of spam with changes. Therefore, modern machine learning algorithms offer many huge possibilities to help many organizations extract business value from the wealth and amount of data available today. All developers when starting the machine learning process rely on their knowledge and experience with statistics and the creation of many successful models. In this paper, we look at machine learning spam filtering strategies currently in use, as well as a few in development. We have discussed the use of three commonly used machine learning algorithms, namely C 4.5 decision tree, Naïve Bayes, and Multilayer Perceptron.
CartaGenie: Context-Driven Synthesis of City-Scale Mobile Network Traffic Snapshots

Mobile network traffic data offers unprecedented opportunities for innovative studies within and beyond networking. However, progress is hindered by the very limited access that the research community at large has to the real-world mobile network data that is needed to develop and dependably test mobile traffic data-driven solutions. As a contribution to overcome this barrier, we propose CartaGenie, a generator of realistic mobile traffic snapshots at city scale. Taking a deep generative modeling approach and through a tailored conditional generator design, CartaGenie can synthesize high-fidelity and artifact-free spatial traffic snapshots using only contextual information about the target geographical region that is easily found in public repositories. Hence, CartaGenie allows researchers to create their own realistic datasets of spatial traffic from open data about their region of interest. Experiments with real-world mobile traffic measurements collected in multiple metropolitan areas show that CartaGenie can produce dependable network traffic loads for areas where no prior traffic information is available, significantly outperforming a comprehensive set of benchmarks. Moreover, tests with practical case studies demonstrate that the synthetic data generated by CartaGenie is as good as real data in supporting diverse research-oriented mobile traffic data-driven applications.
Denoisereg: Unsupervised Joint Denoising and Registration of Time-Lapse Live Cell Microscopy Images Using Deep Learning

Image registration is important for analysing time-lapse live cell microscopy images. However, this is challenging due to significant image noise and complex cell movement. We propose a novel end-to-end trainable deep neural network for joint denoising and affine registration of temporal live cell microscopy images. Our network is trained unsupervised, and only a single network is required for both tasks which reduces overfitting. Our experiments show that the proposed network performs better than deep affine registration without denoising, and better than sequential deep denoising and affine registration. In combination with deep non-rigid registration, we outperform state-of-the-art non-rigid registration methods.
Accessible, Affordable and Low-Risk Lungs Health Monitoring in Covid-19: Deep Cascade Reconstruction from Degraded LR-ULDCT

We present deep cascade reconstruction of degraded low-resolution ultra-low-dose computed tomography (LR-ULDCT) chest images to restored and super-resolved (SR) ULDCT as accessible, affordable, and relatively less hazardous recourse for lungs health monitoring in COVID-19; when compared to relatively less available, costly, and high radiation dose high-resolution CT (HRCT). The degraded LR-ULDCT is first restored with unsupervised dictionary-based deep residual learning network that handles degradations along with Poisson noise found in CT data. The restored version is given to SR network that increases its spatial resolution by minimizing adversarial loss between LR-ULDCT and reconstructed SR-ULDCT within minimax game. It is then fed for segmentation which is achieved by additional block of convolution, Leaky-ReLU, and batch-normalization in U-Net. Thus restored segmented SR-ULDCT estimates presence of ground glass opacity and facilitates monitoring of lungs health at par HRCT. Comparative experiments and ablation study are presented using synthetic and real COVID-19 data.
Aporia & Epiphany in Context: Computer Game Agency in Baldur’s Gate II & Heroes of Might & Magic IV

This thesis studies computer games from a recipient’s perspective by doing an analysis of how an implied player is expected by game design to take action in computer games. From the assumption that the player of computer games is cast in the role as problem solver, the thesis aims to study how the player engages in the structuring of courses of action in the computer role-playing game (CRPG) Baldur’s Gate II: The Shadows of Amn and the turn-based strategy game (TBS) Heroes of Might & Magic IV. The work is based upon theoretical views from different fields. One of the central concepts utilised is agency, derived from the action theory branch of philosophy and adapted to computer environments by Janet Murray and Brenda Laurel. Coupled with Espen Aarseth’s claim that a computer game player meets aporias or roadblocks in the game that must be solved by sudden epiphanies, the concept of agency will be fruitfully developed in order to investigate the player’s participation in computer games. Parallels will be drawn between the aporia-epiphany pair and cognitive psychology’s view on problem solving in order to identify different species of problem sequences in games. Most interesting is the proposal of the concept computer game agency, which denotes a kind of player action that is motivated, intentional, and has a certain effect in that it contributes to the progression of the game. The comparative analysis of Baldur’s Gate II and Heroes IV demonstrates how an implied player traverses the game via the means of problem solving. BGII’s focus on role-play creates a very different motivation for problem solving than does HoMMIV’s focus on strategy. While BGII little by little reveals the goal of the game by letting the player traverse a chain of separately arranged problems, HoMMIV defines the goal beforehand and presents new problems within existing ones like a Chinese box. Since BGII lets the player concentrate on one problem at a time, while HoMMIV lets the player plan ahead and see problems as interrelated, it becomes clear that the games rely on different kinds of logics when it comes to how the player is expected to traverse them.
Diabetic Retinopathy Diagnostic CAD System Using 3D-Oct Higher Order Spatial Appearance Model

Diagnoses of Diabetic Retinopathy (DR) at an early stage are of extreme importance so that the retina can be preserved and the risk of substantial damage to the retina or loss of vision is reduced. A new Computer-Aided Diagnosis (CAD) method based on Optical Coherence Tomography (OCT) scans of the retina is presented here for the detection of DR at an early stage. Utilizing an adaptive appearance-based approach that uses prior shape information, the system segments the retinal layers from the 3D-OCT scans. From the layers segmented from the B-scans volume of the OCT, novel texture features are extracted for DR diagnosis. In particular, a 2nd-order reflectivity value is calculated for each individual layer using the 2D Markov-Gibbs Random Field (2D-MGRF) model. Then, Cumulative Distribution Function (CDF) descriptors are used to represent the extracted image-derived feature using CDF’s percentiles. A feed-forward neural network is used for layer-by-layer classification of 3D volume using Gibbs energy features extracted from each individual layer. In the final stage, all twelve layers are fused with a global subject diagnosis based on a majority voting method. We evaluated a 3D-OCT system using 180 subjects using a combination of different k-fold validation techniques. The system performance for this CAD system using 4-, 5-, and 10-fold cross validation achieved accuracies of 89.4%, 91.5%, and 95.7%, respectively. In addition, our system’s ability to detect the DR early has been validated by further comparisons with the state-of-the-art deep learning networks.
Analysis of Income on the Basis of Occupation using Data Mining

Income forecasting is always the idea of knowing how many people us are around. Here we saw how to use machine learning technology to test people’s income according to their different lifestyles. The huge gap between wealth and income, especially in the United States, is a serious problem. One of the possible reasons for reducing the growing economic inequality in the world is the possibility of reducing poverty. The rule of worldwide ethical balance ensures maintainable improvement and financial steadiness of the nation. The governments of different countries are doing their best to solve this problem and find the best solutions. The point of this think about is to demonstrate the utilize of machine learning and Data mining strategies to fathom pay uniformity issues. Machine learning is classified as predicting whether a person has an annual income.
Decentralized Spatially Constrained Source-Based Morphometry

There is growing interest in extracting multivariate patterns (covarying networks) from structural magnetic resonance imaging (sMRI) data to analyze brain morphometry. Constrained source-based morphometry (constrained SBM) is a hybrid approach which provides a fully automated strategy for extracting subject-specific parameters characterizing gray matter networks. In constrained SBM, constrained independent component analysis (ICA) is used to compute maximally independent sources and statistical analysis is used to identify sources significantly associated with variables of interest. However, constrained SBM is built on the assumption that the data are locally accessible. As such, it cannot take advantage of decentralized (i.e., federated) data. While open data repositories have grown in recent years, there are various reasons (e.g., privacy concerns for rare disease data, institutional or IRB policies, etc.) that restrict a large amount of existing data to local access only. To overcome this limitation, we introduce a novel approach: decentralized constrained source-based morphometry (dcSBM). In our approach, data samples are located at different sites and each site operates the constrained ICA in a distributed manner. Finally, a master node simply aggregates result estimates from each local site and runs the statistical analysis centrally. We apply our method to UK Biobank sMRI data and validate our results by comparing to centralized constrained SBM results.
Implikasi Proksi Aset, Profitabilitas dan Jenis Industri pada Islamic Social Reporting (ISR)

Abstract Jakarta Islamic Index (JII) is a reference for investors to place their fund in Indonesia Stock Exchange which is in line with Islamic teaching. It has a specific requirements (screening process) which must be passed by one stock to be included in JII. Nevertheless, the screening process of JII composition is seen to have limitations because the process is merely based on the quantitative and qualitative criteria. Therefore, this research explores and analyzes the deeper aspects (Othman, et al., 2009) which is called as Islamic Social Reporting (ISR). ISR analyzes the compliance to Islamic teaching application from the company perspective. The research period is from the year of 2006 until 2008, while the research sample is taken from companies listed in the JII. This research adopts full interaction model to determine significant factors of ISR. Previous research did not apply the interaction model theory, i.e. the Difference in Difference (DiD) theory1. It can show meaningful existence of dummy variable with other proxies. The proxies used in this study are industry type, company size, and profitability. The results show that there are no differences among the industry type, while the company size affects the ISR. Keywords: Islamic Social Reporting, Jakarta Islamic Index, asset, profitability