Miracle-dz/UniFAR-FTUs huggingface dataset:
A Comparative Approach to Assessing the Functional and Structural Characteristics of Human FKBP52 in the Regulation of Steroid Hormone Receptor Signaling Pathway

VI TABLE OF CONTENTS . X LIST OF TABLES . XII GLOSSARY OF KEY TERMS . XIV CHAPTER 1: INTRODUCTION . 1 1.1 ANDROGEN RECEPTOR IN DISEASE . 2 1.1.2 Androgen Insensitivity Syndrome (AIS) . 2 1.1.3 Spinal Bulbar Muscular Atrophy (SBMA) . 3 1.1.4 Prostate Cancer . 5 1.2 TREATMENTS . 8 1.2.1 CYP17 Inhibitors: Abiraterone (Zytiga) . 8 1.2.2 MDV3100: Enzalutamide (Xtandi) . 9 1.2.3 Hsp90 Inhibitors:17-AAG (Tanespimycin) and 17-DMAG (Alvespimycin) . 11 1.3 ANDROGEN RECEPTOR SIGNALING PATHWAY . 13 1.3.1 Androgen Receptor . 13 1.3.2 Steroid Hormone Receptor Complex . 18 1.3.3 FK506 Binding Proteins . 24 1.3.4 FKBP51 and FKBP52 . 25 1.4 CURRENT RESEARCH FOCUS . 34 1.5 DISSERTATION GOAL . 35 CHAPTER 2: FUNCTIONAL AND STRUCTURAL COMPARISON OF HUMAN AND DANIO RERIO FKBP52 REVEALS A NOVEL DOMAIN IMPORTANT FOR HFKBP52 FUNCTION . 37 2.1 RATIONALE . 38 2.2 MATERIALS AND METHODS . 39 2.2.1 Construction of FKBP52 And DrFKBP52 Chimeras . 39 2.2.2 In Vitro Protein Binding Assays . 40 2.2.3 Yeast Assays for Hormone-Induced Reporter Gene Expression . 40 2.2.4 Western Blot of Yeast Lysates . 41 2.3 RESULTS . 42 2.3.1 Danio rerio FKBP52: A Comparative Model for Human FKBP52 Characterization . 42 2.3.2 Functional Assessment Of Chimera Constructs In Yeast And Mammalian Cells . 53 2.3.3 Characterization of the FK2 Domain . 57 2.4 DISCUSSION . 60 CHAPTER 3: FUNCTIONAL RANDOM MUTAGENESIS OF HFKBP52 IN COMPARISON TO DANIO RERIO FKBP52 . 62 3.1 RATIONALE . 63 3.2 EXPERIMENTAL DESIGN . 63 3.2.1 Yeast Strains and Β-Galactosidase Reporter Assays . 63 3.2.2 Site-Directed Mutant Plasmids . 64 3.2.3 Selection for FKBP51 Potentiation Mutants and Analysis . 64 3.2.4 Yeast Plasmid Extraction . 65 3.2. 5 Hormone-Induced Gene Expression In Mouse Cells . 66 3.2. 6 Western Blot . 67 3.3 RESULTS . 68 3.3.1 Designing and Optimizing the Gain-of-Function Random Mutagenesis Screening Strain . 68 3.3.2 Genetic Screen for Danio rerio FKBP52 Random Mutants that Up-Regulate AR-P723S Activity . 73 3.3.3 Characterization of DrFKBP52 Mutants . 80 3.3.4 Characterization of Chimera ZHZ-150_253 in Combination With A111V . 83 3.4 DISCUSSION . 92 CHAPTER 4: FKBP HOMOLOGY MODELING . 95 4.1 RATIONALE . 96 4.2 MATERIAL AND METHODS . 97 4.2.1 Template Recognition and Initial Alignment . 97 xi 4.2.2 Alignment Correlation . 97 4.2.3 I-TASSER Homology Modeling . 98 4.3 RESULTS . 98 4.3.1 FKBP Sequence Alignments . 98 4.3 DISSCUSION . 109 REFERENCES . 111 CURRICULUM VITA . 121
Tinea capitis:A tropical disease of hygienic concern among primary school children in an urban community in Nigeria

Tinea capitis is the most common dermatophyte infection in school age children in developing countries and is known to impact negatively on their health and well-being. This study determined the prevalence of T. capitis and its relationship with hygiene among primary school pupils in Ile-Ife. This descriptive cross-sectional study recruited randomly selected pupils from public and private schools in Ile-Ife from March to April 2017. The data was collected with the use of a pre–tested interviewer-administered questionnaire on hygiene followed by physical examination. Children with a clinical diagnosis of tinea capitis had scalp and hair scrapings for microscopy and culture. Data were analyzed using descriptive and inferential statistics. A total of 420 pupils participated, 240 (57.1%) males and 180 (42.9%) females aged between 4 and 16 years. The overall prevalence rate of T. Capitis infection was 21.7% with etiological agents consisting of Epidemophyton floccosum (12.0%), Microsporum gypseum (7.7%) and Trichophyton mentagrophytes (7.7%) while 33% grew Aspergillus niger (a contaminant) and no organism was isolated in 36 (39.6%) clinically diagnosed cases. The infection was prevalent among pupils sharing combs, had close contact with animals, playing with sand and lower socio-economic status. School type and personal hygiene were the major determinants of tinea capitis infection. Prevalence of T. capitis infection among school children was high and the infection is associated with poor level of hygiene and low socio-economic status. Health promotion and health education interventions are needed to promote good hygiene, early identification and treatment of the infection. Key words: Tinea capitis, primary school children, poverty, hygiene.
An Alternative Way to Relieve Neuropathic Pain After Total Knee Replacement by Genicular Nerve Radio-Frequency Ablation

Total Knee Replacement (TKR) is a treatment of knee osteoarthritis to alleviate pain symptoms and improve physical functioning when other conservative treatments have failed. However, some patients still complaining of persistent post surgical pain (PPSP) after TKR. Although the cause of PPSP is not known, it seems that some of the patients with PPSP after TKR suffer from neuropathic pain. Recently genicular nerve radio-frequency ablation has emerged as an alternative intervention for patients at high risk who are unable to undergo surgical intervention. We have shown from our case, a 69-year old lady who had right total knee replacement presented with neuropathic knee pain has good symptom relieves after undergone radio-frequency ablation.
A survey of pipelined workflow scheduling: Models and algorithms

A large class of applications need to execute the same workflow on different datasets of identical size. Efficient execution of such applications necessitates intelligent distribution of the application components and tasks on a parallel machine, and the execution can be orchestrated by utilizing task, data, pipelined, and/or replicated parallelism. The scheduling problem that encompasses all of these techniques is called pipelined workflow scheduling, and it has been widely studied in the last decade. Multiple models and algorithms have flourished to tackle various programming paradigms, constraints, machine behaviors, or optimization goals. This article surveys the field by summing up and structuring known results and approaches.
Value of Cardiac Biomarkers in the Early Diagnosis of Takotsubo Syndrome

Background: Bedside diagnosis between Takotsubo syndrome (TTS) and ST elevation (STEMI) and non-ST elevation (NSTEMI) myocardial infarction remains challenging. We sought to determine a cardiac biomarker profile to enable their early distinction. Methods: 1100 patients (TTS n = 314, STEMI n = 452, NSTEMI n = 334) were enrolled in two centers. Baseline clinical and biological characteristics were compared between groups. Results: At admission, cut-off values of BNP (B-type natriuretic peptide)/TnI (Troponin I) ratio of 54 and 329 distinguished respectively STEMI from NSTEMI, and NSTEMI from TTS. Best differentiation was obtained by the use of BNP/TnI ratio at peak (cut-of values of 6 and 115 discriminated respectively STEMI from NSTEMI, and NSTEMI from TTS). We developed a score including five parameters (age, gender, history of psychiatric disorders, LVEF, and BNP/TnI ratio at admission) enabling good distinction between TTS and STEMI (77% specificity and 92% sensitivity, AUC 0.93). For the distinction between TTS and NSTEMI, a four variables score (gender, history of psychiatric disorders, LVEF, and BNP at admission) achieved a good diagnostic performance (89% sensitivity, 85% specificity, AUC 0.94). Conclusion: A distinctive cardiac biomarker profile enables at an early stage a differentiation between TTS and ACS. A four (NSTEMI) or five variables score (STEMI) permitted a better discrimination.
Transfer Learning with Deep Convolutional Neural Network (CNN) for Pneumonia Detection using Chest X-ray

Pneumonia is a life-threatening disease, which occurs in the lungs caused by either bacterial or viral infection. It can be life-endangering if not acted upon at the right time and thus the early diagnosis of pneumonia is vital. The paper aims to automatically detect bacterial and viral pneumonia using digital x-ray images. It provides a detailed report on advances in accurate detection of pneumonia and then presents the methodology adopted by the authors. Four different pre-trained deep Convolutional Neural Network (CNN): AlexNet, ResNet18, DenseNet201, and SqueezeNet were used for transfer learning. A total of 5247 chest X-ray images consisting of bacterial, viral, and normal chest x-rays images were preprocessed and trained for the transfer learning-based classification task. In this study, the authors have reported three schemes of classifications: normal vs. pneumonia, bacterial vs. viral pneumonia, and normal, bacterial, and viral pneumonia. The classification accuracy of normal and pneumonia images, bacterial and viral pneumonia images, and normal, bacterial, and viral pneumonia were 98%, 95%, and 93.3%, respectively. This is the highest accuracy, in any scheme, of the accuracies reported in the literature. Therefore, the proposed study can be useful in more quickly diagnosing pneumonia by the radiologist and can help in the fast airport screening of pneumonia patients.
Effect of different stages of fruit maturity and size grading on seed quality in jatropha (Jatropha curcas)

The seed quality of Jatropha harvested at different stages of fruit maturity was evaluated to determine the right stage of harvesting for getting high quality seeds at Karaikal, Puducherry (UT) in the year 2011. One hundred freshly opened flowers were tagged at anthesis in 25 numbers of two year old trees. The fruit set in each tagged flower was recorded for seven weeks, consecutively. The results revealed that, only 59 per cent of the flowers set fruits and four different colour changes in fruits, viz. light green (Stage I), lemon yellow (Stage II), dark brown (Stage III) and dark brown with hairline cracks (Stage IV) were observed during maturation at 21, 28, 42 and 49 days after anthesis (DAA), respectively. Observation on seed germination indicated that the seeds obtained from stage III was on par with stage IV (68 per cent) and excelled stage I (4 per cent) and stage II (56 per cent). Between stages III and IV, the seeds obtained from stage III was found to be superior to stage IV, attributed due to higher vigour index and hence considered as the stage of physiological maturity for Jatropha. The larger sized capsules had excelled medium and smaller sized in terms of both vigour and viability and emphasized the significance of size grading for elite seedling production in Jatropha.
Towards Robust Classification with Image Quality Assessment

Recent studies have shown that deep convolutional neural networks (DCNN) are vulnerable to adversarial examples and sensitive to perceptual quality as well as the acquisition condition of images. These findings raise a big concern for the adoption of DCNN-based applications for critical tasks. In the literature, various defense strategies have been introduced to increase the robustness of DCNN, including re-training an entire model with benign noise injection, adversarial examples, or adding extra layers. In this paper, we investigate the connection between adversarial manipulation and image quality, subsequently propose a protective mechanism that doesnt require re-training a DCNN. Our method combines image quality assessment with knowledge distillation to detect input images that would trigger a DCCN to produce egregiously wrong results. Using the ResNet model trained on ImageNet as an example, we demonstrate that the detector can effectively identify poor quality and adversarial images.
Constraints on the Circumnuclear Disk through Free–Free Absorption in the Nucleus of 3C 84 with KaVA and KVN at 43 and 86 GHz

The nearby bright radio galaxy 3C 84 at the center of the Perseus cluster is an ideal target to explore the jet in an active galactic nucleus and its parsec-scale environment. The recent research of Fujita & Nagai revealed the existence of the northern counter-jet component (N1) located 2 mas north from the central core in very long baseline interferometer (VLBI) images at 15 and 43 GHz and they are explained by the free–free absorption (FFA) due to an ionized plasma foreground. Here we report a new quasi-simultaneous observation of 3C 84 with the Korean VLBI Network (KVN) at 86 GHz and the KVN and VLBI Exploration of Radio Astrometry Array (KaVA) at 43 GHz in 2016 February. We succeeded the first detection of N1 at 86 GHz and the data show that N1 still has an inverted spectrum between 43 and 86 GHz with its spectral index α ( ) of 1.19 ± 0.43, while the approaching lobe component has a steep spectrum with an index of −0.54 ± 0.30. Based on the measured flux asymmetry between the counter and approaching lobes, we constrain the averaged number density of the FFA foreground ne as . Those results suggest that the observational properties of the FFA foreground can be explained by the dense ionized gas in the circumnuclear disk and/or assembly of clumpy clouds at the central ∼1 pc region of 3C 84.
Can Fermi surface nesting alone drive the charge-density-wave transition in monolayer vanadium diselenide?

We demonstrate that charge-density-wave formation is possible via a purely electronic mechanism in monolayers of the transition metal dichalcogenide 1T-VSe$_2$. Via a renormalization group treatment of an extended Hubbard model we examine the competition of superconducting and density-wave fluctuations as sections of the Fermi surface are tuned to perfect nesting. We find regions of charge-density-wave order when the Heisenberg exchange interaction is comparable to the Coulomb repulsion, and $d$-wave superconductivity for purely repulsive interactions. We discuss the possible role of lattice vibrations in enhancing the effective Heisenberg exchange.
A Divide-and-Conquer Approach to the Summarization of Academic Articles

We present a novel divide-and-conquer method for the summarization of long documents. Our method processes the input in parts and generates a corresponding summary. These partial summaries are then combined in order to produce a final complete summary. Splitting the problem of long document summarization into smaller and simpler problems, reduces the computational complexity of the summarization process and leads to more training examples that at the same time contain less noise in the target summaries compared to the standard approach of producing the whole summary at once. Using a fairly simple sequence to sequence architecture with a combination of LSTM units and Rotational Units of Memory (RUM) our approach leads to state-of-the-art results in two publicly available datasets of academic articles.
VehicleNet: Learning Robust Visual Representation for Vehicle Re-Identification

One fundamental challenge of vehicle re-identification (re-id) is to learn robust and discriminative visual representation, given the significant intra-class vehicle variations across different camera views. As the existing vehicle datasets are limited in terms of training images and viewpoints, we propose to build a unique large-scale vehicle dataset (called VehicleNet) by harnessing four public vehicle datasets, and design a simple yet effective two-stage progressive approach to learning more robust visual representation from VehicleNet. The first stage of our approach is to learn the generic representation for all domains (i.e., source vehicle datasets) by training with the conventional classification loss. This stage relaxes the full alignment between the training and testing domains, as it is agnostic to the target vehicle domain. The second stage is to fine-tune the trained model purely based on the target vehicle set, by minimizing the distribution discrepancy between our VehicleNet and any target domain. We discuss our proposed multi-source dataset VehicleNet and evaluate the effectiveness of the two-stage progressive representation learning through extensive experiments. We achieve the state-of-art accuracy of $\text{86.07}\%$ mAP on the private test set of AICity Challenge, and competitive results on two other public vehicle re-id datasets, i.e., VeRi-776 and VehicleID. We hope this new VehicleNet dataset and the learned robust representations can pave the way for vehicle re-id in the real-world environments.
Precise mass and radius of a transiting super-Earth planet orbiting the M dwarf TOI-1235: a planet in the radius gap?

We report the confirmation of a transiting planet around the bright weakly active M0.5 V star TOI-1235 (TYC 4384–1735–1, V ≈ 11.5 mag), whose transit signal was detected in the photometric time series of sectors 14, 20, and 21 of the TESS space mission. We confirm the planetary nature of the transit signal, which has a period of 3.44 d, by using precise RV measurements with the CARMENES, HARPS-N, and iSHELL spectrographs, supplemented by high-resolution imaging and ground-based photometry. A comparison of the properties derived for TOI-1235 b with theoretical models reveals that the planet has a rocky composition, with a bulk density slightly higher than that of Earth. In particular, we measure a mass of Mp = 5.9 ± 0.6 M⊕ and a radius of Rp = 1.69 ± 0.08 R⊕, which together result in a density of ρp = 6.7− 1.1+ 1.3 g cm−3. When compared with other well-characterized exoplanetary systems, the particular combination of planetary radius and mass places our discovery in the radius gap, which is a transition region between rocky planets and planets with significant atmospheric envelopes. A few examples of planets occupying the radius gap are known to date. While the exact location of the radius gap for M dwarfs is still a matter of debate, our results constrain it to be located at around 1.7 R⊕ or larger at the insolation levels received by TOI-1235 b (~60 S⊕). This makes it an extremely interesting object for further studies of planet formation and atmospheric evolution.
Signal-background interference for digluon resonances at the Large Hadron Collider

We study the interference between the amplitudes for $gg \rightarrow X \rightarrow gg$, where $X$ is a new heavy digluon resonance, and the QCD background $gg \rightarrow gg$, at the Large Hadron Collider. The interference produces a large low-mass tail and a deficit of events above the resonance mass, compared to the naive pure resonance peak. For a variety of different resonance quantum numbers and masses, we evaluate the signal-background interference contribution at leading order, including showering, hadronization, and detector effects. The resulting new physics dijet mass distribution may have a shape that appears, after QCD background fitting and subtraction, to resemble an enhanced peak, a shelf, a peak/dip, or even a pure dip. We argue that the true limits on new digluon resonances are likely to differ significantly from the limits obtained when interference is neglected, especially if the branching ratio to $gg$ is less than 1.
Improving Calibration and Out-of-Distribution Detection in Medical Image Segmentation with Convolutional Neural Networks

Convolutional Neural Networks (CNNs) have shown to be powerful medical image segmentation models. In this study, we address some of the main unresolved issues regarding these models. Specifically, training of these models on small medical image datasets is still challenging, with many studies promoting techniques such as transfer learning. Moreover, these models are infamous for producing over-confident predictions and for failing silently when presented with out-of-distribution (OOD) data at test time. In this paper, we advocate for multi-task learning, i.e., training a single model on several different datasets, spanning several different organs of interest and different imaging modalities. We show that not only a single CNN learns to automatically recognize the context and accurately segment the organ of interest in each context, but also that such a joint model often has more accurate and better-calibrated predictions than dedicated models trained separately on each dataset. Our experiments show that multi-task learning can outperform transfer learning in medical image segmentation tasks. For detecting OOD data, we propose a method based on spectral analysis of CNN feature maps. We show that different datasets, representing different imaging modalities and/or different organs of interest, have distinct spectral signatures, which can be used to identify whether or not a test image is similar to the images used to train a model. We show that this approach is far more accurate than OOD detection based on prediction uncertainty. The methods proposed in this paper contribute significantly to improving the accuracy and reliability of CNN-based medical image segmentation models.
Star Formation Traced by Optical and Millimeter Hydrogen Recombination Lines and Free–Free Emissions in the Dusty Merging Galaxy NGC 3256—MUSE/VLT and ALMA Synergy

A galaxy–galaxy merger and the subsequent triggering of starburst activity are fundamental processes linked to the morphological transformation of galaxies and the evolution of star formation across the history of the universe. Both nuclear and disk-wide starbursts are assumed to occur during the merger process. However, quantifying both nuclear and disk-wide star formation activity is nontrivial because the nuclear starburst is dusty in the most active merging starburst galaxies. This paper presents a new approach to this problem: combining hydrogen recombination lines in optical, millimeter, and free–free emission. Using NGC 3256 as a case study, Hβ, H40α, and free–free emissions are investigated using the Multi Unit Spectroscopic Explorer at the Very Large Telescope of the European Southern Observatory (MUSE) and the Atacama Large Millimeter/submillimeter Array (ALMA). The Hβ image obtained by MUSE identifies star-forming regions outside the nuclear regions, suggesting a disk-wide starburst. In contrast, the H40α image obtained by ALMA identifies a nuclear starburst where optical lines are undetected due to dust extinction (AV ∼ 25). Combining both MUSE and ALMA observations, we conclude that the total star formation rate (SFR) is 49 ± 2 M⊙ yr−1 and the contributions from nuclear and disk-wide starbursts are ∼34% and ∼66%, respectively. This suggests the dominance of disk-wide star formation in NGC 3256. In addition, pixel-by-pixel analyses for disk-wide star-forming regions suggest that shock gas tracers (e.g., CH3OH) are enhanced where gas depletion time (τgas = Mgas/SFR) is long. This possibly means that merger-induced shocks regulate disk-wide star formation activities.