Miracle-dz/UniFAR-FTUs huggingface dataset:
Differential DNA Methylation of THOR and hTAPAS in the Regulation of hTERT and the Diagnosis of Cancer

Simple Summary Because of its high prevalence of >45% in 9 out of 11 (82%) cancer types screened, THOR hypermethylation has been suggested to be a frequent telomerase-activating mechanism in hTERT-expressing tumor types, e.g., in cancers of the prostate, breast, blood, colon, lung, bladder, and brain. In this prime example, we present detailed DNA methylation profiles in urothelial cancer that reveal the exact positions of the most differentially methylated CpG dinucleotides within the THOR region in order to design an efficient Methylation-Specific PCR (MSPCR) approach for diagnostic and prognostic purposes. Furthermore, our data suggest an epigenetic mechanism regulating hTERT expression through the methylation status of THOR and lncRNA hTAPAS. Abstract Background: Although DNA methylation in the gene promoters usually represses gene expression, the TERT hypermethylated oncological region (THOR) located 5′ of the hTERT gene is hypermethylated when hTERT is expressed in diverse cancer types, including urothelial cancer (UC). Methods: Comprehensive MeDIP and DNA methylation array analyses complemented by the technically independent method of bisulfite genomic sequencing were applied on pathologically reviewed and classified urothelial carcinoma specimens and healthy urothelial tissue samples to reveal the methylation status of THOR in detail. Results: The detailed DNA methylation profiles reveal the exact positions of differentially methylated CpG dinucleotides within THOR in urothelial cancer and provide evidence ofa diverging role of methylation of these CpGs in the regulation of hTERT. In particular, our data suggest a regulating mechanism in which THOR methylation acts on hTERT expression through epigenetic silencing of the lncRNA hTERT antisense promoter-associated (hTAPAS), which represses hTERT. Conclusions: These findings precisely define the most differentially methylated CpGs of THOR in early urothelial cancer, enabling optimal design of Methylation-Specific PCR (MSPCR) primers to reliably probe these methylation differences for diagnostic and prognostic purposes. In addition, this strategy presents a prime example that is also applicable to many other malignancies. Finally, the first evidence for the underlying epigenetic mechanism regulating hTERT expression through the methylation status of THOR is provided.
Further Understanding of Neuro-Immune Interactions in Allergy: Implications in Pathophysiology and Role in Disease Progression

Abstract The complicated interaction between the central and the autonomic (sympathetic, parasympathetic, and enteric) nervous systems on the one hand and the immune system and its components, on the other hand, seems to substantially contribute to allergy pathophysiology, uncovering an under-recognized association that could have diagnostic and therapeutic potentials. Neurons connect directly with and regulate the function of many immune cells, including mast cells, the cells that have a leading role in allergic disorders. Proinflammatory mediators such as cytokines, neurotrophins, chemokines, and neuropeptides are released by immune cells, which stimulate sensory neurons. The release of neurotransmitters and neuropeptides caused by the activation of these neurons directly impacts the functional activity of immune cells and vice versa, playing a decisive role in this communication. Successful application of Pavlovian conditioning in allergic disorders supports the existence of a psychoneuroimmunological interplay in classical allergic hypersensitivity reactions. Activation of neuronal homeostatic reflexes, like sneezing in allergic rhinitis, coughing in allergic asthma, and vomiting in food allergy, offers additional evidence of a neuroimmunological interaction that aims to maintain homeostasis. Dysregulation of this interaction may cause overstimulation of the immune system that will produce profound symptoms and exaggerated hemodynamic responses that will lead to severe allergic pathophysiological events, including anaphylaxis. In this article, we have systematically reviewed and discussed the evidence regarding the role of the neuro-immune interactions in common allergic clinical modalities like allergic rhinitis, chronic rhinosinusitis, allergic asthma, food allergy, atopic dermatitis, and urticaria. It is essential to understand unknown – to most of the immunology and allergy experts – neurological networks that not only physiologically cooperate with the immune system to regulate homeostasis but also pathogenetically interact with more or less known immunological pathways, contribute to what is known as neuroimmunological inflammation, and shift homeostasis to instability and disease clinical expression. This understanding will provide recognition of new allergic phenotypes/endotypes and directions to focus on specialized treatments, as the era of personalized patient-centered medicine, is hastening apace.
A Report of Haemophilus influenzae Bacteremia With Acute Pelvic Inflammatory Disease

Sexually-transmitted organisms that frequently originate from the flora of the lower genital tract are often implicated in pelvic inflammatory disease (PID). Haemophilus influenzae, a pathogen found primarily in the upper respiratory tract, has been rarely associated with PID. Here we report a case of a young woman with PID whose blood cultures grew H. influenzae biotype II, a reminder that the endometrium can be the source of systemic H. influenzae infection when no typical primary focus is found.
Transcriptomics insights into interpreting AMD-GWAS discoveries for biological and clinical applications

Genome-wide association studies (GWAS) have been successful in identifying genetic risk factors for a large number of complex diseases, including age-related macular degeneration (AMD), which is a highly heritable complex disease affecting millions of elderly individuals. However, the progress of elucidating the functional relevance of genetic findings in AMD has been slow, as most risk factors are non-coding, and we have little insight into the causal genes and disease mechanisms. In the last few years, gene expression regulation is emerging as a dominant mechanism through which GWAS risk variants lead to the disease. The purpose of this review is to provide an overview of how transcriptome studies can help in identifying the genes, pathways and therapeutic targets underlying GWAS discoveries in AMD. These approaches help pave the road for mechanistic understanding of GWAS findings and drive translational advances that will lead to improved AMD management and treatment.
An Anthropomorphic Robotic Finger With Innate Human-Finger-Like Biomechanical Advantages Part II: Flexible Tendon Sheath and Grasping Demonstration

The human hand has a fantastic ability to interact with various objects in the dynamic unstructured environment of our daily activities. We believe that this outstanding performance benefits a lot from the unique biological features of the hand musculoskeletal system. In Part I of this article, a bio-inspired anthropomorphic robotic finger was developed, based on which two human-finger-like biomechanical advantages were elaborately investigated, including the anisotropic variable stiffness associated with the ligamentous joints and the enlarged feasible force space associated with the reticular extensor mechanisms. In Part II, the fingertip force-velocity characteristics resulting from the flexible tendon sheath are studied. It indicates that the fingertip force–velocity workspace can be greatly augmented owing to the self-adaptive morphing of the flexible tendon sheaths, showing the average improvement of 41.2% theoretically and 117.5% experimentally compared with the results of 2 mm, 4 mm, and 6 mm size rigid tendon sheaths. Grasping tests and comparisons are then conducted with four three-fingered robotic hands (one with the robotic finger proposed in Part I, one with hinge joints, one with linear extensors, and one with rigid tendon sheaths) and the human hands of six subjects to handle various objects on flat, rough, and soft surfaces. The results show that the novel bio-inspired design in this research could improve the grasping success rates of the robotic hand. Compared with the grasping test results from the robotic hand with the bio-inspired robotic finger proposed in Part I, the overall grasping performance of a robotic hand with hinge joints, linear extensors, and rigid tendon sheaths decreases by 10%, 6%, and 17%, respectively. The results have also shown that with the embedded biomechanical advantages, even without complex control and sensory systems, the robotic fingers can achieve very comparable performance to human fingers in the grasping demonstrations presented, indicating average 94% of the success rate achieved by the human fingers. Successfully demonstrating 14 of 16 grasp types in the Cutkoskey taxonomy further shows the human-finger-like grasping capability of the proposed robotic fingers.
Cystatin C-Based eGFR Predicts Post-Treatment Kidney Prognosis in Patients with Severe Obstructive Nephropathy

A discrepancy between serum concentrations of cystatin C (CysC) and creatinine (sCr) has been reported in patients with acute obstructive nephropathy. However, the usefulness of CysC for predicting the recovery of kidney function in patients with severe obstructive nephropathy remains unclear. We examined the predictability of the estimated glomerular filtration rate calculated with CysC or sCr (eGFRcys or eGFRcreat) for the post-treatment recovery of kidney function. We retrospectively collected patients with severe obstructive nephropathy (eGFRcreat < 30 mL/min/1.73 m2) whose baseline sCr and CysC were measured between 48 h before and 24 h after the release of urinary tract obstruction (UTO). The primary outcome was recovery from severe eGFRcreat depression (i.e., eGFRcreat ≥ 30 mL/min/1.73 m2) 7 days after the release of UTO. We calculated the area under the curve (AUC) of the receiver operating characteristic (ROC) curve for the relationship between eGFRcys or eGFRcreat and recovery. Thirty-four patients (20 males) with a median age of 76 years were eligible. We identified 20 recovery cases. The AUCs of the ROC curves (95% confidence interval) for eGFRcys and eGFRcreat were 0.81 (0.66–0.96) and 0.53 (0.32–0.73), respectively. These results imply cystatin C-based eGFR may help predict kidney prognosis in patients with severe obstructive nephropathy.
AN ORDER LEVEL INVENTORY MODEL UNDER TWO LEVEL STORAGE SYSTEM WITH FUZZY DEMAND

Abstract: Deterministic inventory model with two levels of storage has been studied by numerous authors. In this paper we developed a fuzzy inventory model with two ware houses (one is the existing storage known as own warehouse (OW) and the other is hired on rental basis known as rented warehouse (RW). The model allows constant levels of item deterioration in both houses. The stock is transferred from RW to OW in continuous release pattern and the associated transportation cost is taken into account. To make the model more realistic in nature, fuzzy demand has been considered. Using α-cut for defuzzification, the total variable cost per unit time is derived. Therefore, the problem is reduced to crisp annual costs. The multi-objective model is solved by Global Criteria Method supported by GRG(Generalized Reduced Gradient) Technique, which is illustrated by a numerical example. Keywords: Fuzzy demand, deterioration, defuzzification, warehouse MSC: 90B05.
Deep Expectation-Maximization for Joint MIMO Channel Estimation and Signal Detection

To overcome the influence of channel estimation error on signal detection, this paper presents a model-driven deep learning method for joint channel estimation and signal detection in multiple-input multiple-output (MIMO) wireless communication systems. To improve the robustness of the method, we use the Student's t-distribution to model the environment noise, and model the joint problem probabilistically by taking the channel state information (CSI) as a latent variable, then derive a generalized expectation maximization (GEM) algorithm to fit the model. In GEM, the expectation step is used for robust CSI estimation while the maximization step detects the transmitted signal with the estimated CSI. To reduce the computational complexity of GEM, we unfold it into a deep neural network. The network contains only a few trainable parameters, which is easy to train and has low space complexity. Based on our experimental results, we find that the proposed GEM algorithm outperforms a variety of signal detection algorithms which take the CSI estimation and signal detection as independent modules. Further, we find empirically that the proposed GEM network outperforms state-of-the-art deep learning based joint channel estimation and signal detection methods and has a good generalization ability against the number of antennas, the modulation mode, the level of signal-to-noise ratio and channel correlation.
Emergent Climate Change Impacts on the Soil C and N Cycles in the Mojave Desert

The Mojave Desert has warmed >2°C, and aridified, in the past 50 years, making it a strategic location to investigate climate change impacts on arid soil processes. We resampled a climosequence of soils in the Mojave first sampled in 1973 and compared current soil properties to those 45+ years earlier. Radiocarbon changes revealed that C is cycling rapidly through the soils, particularly near the surface, with a temperature‐sensitive decomposition rate (Arrhenius Ea = 66 kJ/mol). Significant decreases in soil C/N ratios and increases in δ15N values occurred, suggestive of enhanced rates of soil C and N cycling and their losses. Covariation between changes in soil radiocarbon, δ13C, δ15N, and C/N point toward emerging chemical impacts on the coupled C and N cycles in response to climate change.
Bioengineering 3D Neural Networks Using Magnetic Manipulations

Controlling nerve cells to form pre‐designed 3D neural networks that recapitulate the intricate neural interconnectivity in the brain is essential for developing neuronal interfaces and new regeneration approaches. Here, nerve cells within 3D biomaterials are dynamically localized using nano‐based magnetic manipulations. Nerve cells are transformed into magnetic units and their organizational layout is manipulated using external magnetic field gradients. Iron oxide nanoparticles are incorporated into both Pheochromocytoma cell‐line 12 (PC12) cells and primary mice cortical neurons and the magnetized cells are subjected to multiple magnetic fields using pre‐designed magnetic arrays. Their movement is controlled inside multi‐layered 3D collagen scaffolds, which simulate the innate properties of in‐vivo tissue structures. Via these magnetic manipulations, functional 3D microarchitectures of neural networks are created. In light of the clustered and layered structure of the mammalian central nervous system, this strategy paves the way to creating customized 3D tissue architectures for bioengineering applications, enabling a broad range of advanced implementations and providing efficient models for investigating cellular and tissue behavior.
Experimental Investigation on the Mechanical Characteristics of Cement-Based Mortar Containing Nano-Silica, Micro-Silica, and PVA Fiber

This paper investigates bending and compressive strengths as mechanical characteristics of cement-based repair mortar containing nano-silica (NS) and micro-silica (SF) as cement replacements particles and polyvinyl alcohol (PVA) fibers. The mentioned materials were added to the mortar in three different conditions, including single (just one material), binary (mixture of two admixtures), and ternary (mixture of all three admixtures) modes. The use of PVA fibers, nano-silica and micro-silica in the triple combination of a cement-based mortar is the primary objective of the current research. In total, 28 mix designs with various percentages of particles and fiber were employed in the current study, and 112 different specimens were prepared to conduct the experimental research. The compressive and flexural strength results have been selected as the criteria for obtaining the optimum mix design for each condition. In order to specify the mechanical characteristics of specimens, a compressive test was carried out according to ACI 318, and the three-point bending test was utilized according to BS EN 1015-11. The results obtained from this study show that the mixture containing 10% silica fume (SF10) can be considered the optimum mix design for the single-mode condition. For such a mix design, a flexural strength increase of 27% and a compressive strength improvement of 48% were determined in comparison to the reference mixture design. The mixture containing nano-silica at 2% and silica fume at 8% (NS2SF8) was the optimum mix design in the binary mode condition. With the current mix design, a flexural strength improvement of 24% and a compressive strength increase of 49% in a 28-day specimen were recorded. Finally, under the ternary mode condition, a flexural strength enhancement of 3.5% and a compressive strength improvement of 4.6% were obtained. Additionally, the mixture design containing a PVA content of 0.75% and an SF content of 10% (PVA0.75SF10) was considered optimum.
The Parametric Lambda-Calculus: a Metamodel for Computation

I. Syntax. - 1. The Parametric ? -Calculus. - 2. The Call-by-Name ? -Calculus. - 3. The Call-by-Value ? -Calculus. - 4. Further Reading. - II. Operational Semantics. - 5. Parametric Operational Semantics. - 6. Call-by-Name Operational Semantics. - 7. Call-by-Value Operational Semantics. - 8. Operational Extensionality. - 9. Further Reading. - III. Denotational Semantics. - 10. -Models. - 11. Call-by-Name Denotational Semantics. - 12. Call-by-Value Denotational Semantics. - 13. Filter ? -Models and Domains. - 14. Further Reading. - IV. Computational Power. - 15. Preliminaries. - 16. Representing Functions.
Generalized Vectorization, Cross-Products, and Matrix Calculus

This book presents the reader with new operators and matrices that arise in the area of matrix calculus. The properties of these mathematical concepts are investigated and linked with zero-one matrices such as the commutation matrix. Elimination and duplication matrices are revisited and partitioned into submatrices. Studying the properties of these submatrices facilitates achieving new results for the original matrices themselves. Different concepts of matrix derivatives are presented and transformation principles linking these concepts are obtained. One of these concepts is used to derive new matrix calculus results, some involving the new operators and others the derivatives of the operators themselves. The last chapter contains applications of matrix calculus, including optimization, differentiation of log-likelihood functions, iterative interpretations of maximum likelihood estimators and a Lagrangian multiplier test for endogeneity.
A Bayesian Based Unrolling Approach to Single-Photon Lidar Imaging through Obscurants

In this paper, we propose a deep learning model for 3D single-photon Lidar imaging through obscurants, i.e., in the presence of a high and non-uniform background. The proposed method unrolls the iterative steps of a Bayesian based-algorithm into the layers of a deep neural network. To deal with imaging through obscurants, the method first unmix signal and background photons in a pre-processing step. Following this, the method builds on multiscale information to improve robustness to noise and uses the attention framework for scale selection within the network. Experimental results on simulated and real underwater data demonstrate that our method can estimate accurate depth maps in challenging situations with a high non-uniform background. Compared to state-of-the-art deep learning methods, the proposed method enables an estimation of parameters uncertainties, suitable for decision making.
Energy Games with Resource-Bounded Environments

An energy game is played between two players, modeling a resource-bounded system and its environment. The players take turns moving a token along a finite graph. Each edge of the graph is labeled by an integer, describing an update to the energy level of the system that occurs whenever the edge is traversed. The system wins the game if it never runs out of energy. Different applications have led to extensions of the above basic setting. For example, addressing a combination of the energy requirement with behavioral specifications, researchers have studied richer winning conditions, and addressing systems with several bounded resources, researchers have studied games with multi-dimensional energy updates. All extensions, however, assume that the environment has no bounded resources. We introduce and study both-bounded energy games (BBEGs), in which both the system and the environment have multi-dimensional energy bounds. In BBEGs, each edge in the game graph is labeled by two integer vectors, describing updates to the multi-dimensional energy levels of the system and the environment. A system wins a BBEG if it never runs out of energy or if its environment runs out of energy. We show that BBEGs are determined, and that the problem of determining the winner in a given BBEG is decidable iff both the system and the environment have energy vectors of dimension 1. We also study how restrictions on the memory of the system and/or the environment as well as upper bounds on their energy levels influence the winner and the complexity of the problem.
Biology-Inspired Geometric Representation of Probability and Applications to Completion and Options’ Pricing

Geometry constitutes a core set of intuitions present in all humans, regardless of their language or schooling [1]. Could brain's built in machinery for processing geometric information take part in uncertainty representation? For decades already traders have been citing the price of uncertainty based FX optional contracts in terms of implied volatility, a dummy variable related to the standard deviation, instead of pricing with units of money. This work introduces a methodology for geometric representation of probability in terms of implied volatility and attempts to find ways to approximate certain probability distributions using intuitive geometric symmetry. In particular, it is shown how any probability distribution supported on $\mathbb{R}_{+}$ and having finite expectation may be represented with a planar curve whose geometric characteristics can be further analyzed. Log-normal distributions are represented with circles centered at the origin. Certain non-log-normal distributions with bell-shaped density profiles are represented by curves that can be closely approximated with circles whose centers are translated away from the origin. Only three points are needed to define a circle while it represents the candidate probability density approximating the distribution along the entire $\mathbb{R}_{+}$. Just three numbers: scaling and translations along the $x$ and $y$ axes map one circle to another. It is possible to introduce equivalence classes whose member distributions can be obtained by transitive actions of geometric transformations on any of corresponding representations. Approximate completion of probability with non-circular shapes and cases when probability is supported outside of $\mathbb{R}_{+}$ are considered too. Proposed completion of implied volatility is compared to the vanna-volga method.