Miracle-dz/UniFAR-FTUs huggingface dataset:
A Dpagt1 Missense Variant Causes Degenerative Retinopathy without Myasthenic Syndrome in Mice

Congenital disorders of glycosylation (CDG) are a heterogenous group of primarily autosomal recessive mendelian diseases caused by disruptions in the synthesis of lipid-linked oligosaccharides and their transfer to proteins. CDGs usually affect multiple organ systems and vary in presentation, even within families. There is currently no cure, and treatment is aimed at ameliorating symptoms and improving quality of life. Here, we describe a chemically induced mouse mutant, tvrm76, with early-onset photoreceptor degeneration. The recessive mutation was mapped to Chromosome 9 and associated with a missense mutation in the Dpagt1 gene encoding UDP-N-acetyl-D-glucosamine:dolichyl-phosphate N-acetyl-D-glucosaminephosphotransferase (EC 2.7.8.15). The mutation is predicted to cause a substitution of aspartic acid with glycine at residue 166 of DPAGT1. This represents the first viable animal model of a Dpagt1 mutation and a novel phenotype for a CDG. The increased expression of Ddit3, and elevated levels of HSPA5 (BiP) suggest the presence of early-onset endoplasmic reticulum (ER) stress. These changes were associated with the induction of photoreceptor apoptosis in tvrm76 retinas. Mutations in human DPAGT1 cause myasthenic syndrome-13 and severe forms of a congenital disorder of glycosylation Type Ij. In contrast, Dpagt1tvrm76 homozygous mice present with congenital photoreceptor degeneration without overt muscle or muscular junction involvement. Our results suggest the possibility of DPAGT1 mutations in human patients that present primarily with retinitis pigmentosa, with little or no muscle disease. Variants in DPAGT1 should be considered when evaluating cases of non-syndromic retinal degeneration.
The theory of response-adaptive randomization in clinical trials

Dedication. Preface. 1. Introduction. 1.1 Randomization in clinical trials. 1.2 Response-adaptive randomization in a historical context. 1.3 Outline of the book. 1.4 References. 2. Fundamental Questions of response-Adaptive Randomization. 2.1 Optimal allocation. 2.2 The realtionship between power and response-adaptive randomization. 2.3 The relationship for K > 2 treatments. 2.4 Asymptotically best procedures. 2.5 References. 3. Likelihood-based Inference. 3.1 Data structure and Likelihood. 3.2 Asymptotic properties of maximum likelihood estimators. 3.4 Conclusion. 3.5 References. 4. Procedures Based on Urn Models. 4.1 Generalized Friedman's urn. 4.2 The class of ternary urn models. 4.3 References. 5. Procedures Based on Sequential Estimation. 5.1 Examples. 5.2 Properties of procedures based on sequential estimation for K = 2. 5.3 Notation and conditions for the general framework. 5.4 Asymptotic results and some examples. 5.5 Proving the main theorems. 5.6 References. 6. Sample Size Calculation. 6.1 Power of a randomization procedure. 6.2 Three types of sample size. 6.3 Examples. 6.4 References. 7. Additional Considerations. 7.1 The effect of delayed response. 7.2 Continuous responses. 7.3 Multiple (K > 2) treatments. 7.4 Accommodating heterogeneity. 7.5 References. 8. Implications for the Practice of Clinical Trials. 8.1 Standards. 8.2 Binary response. 8.3 Continuous responses. 8.4 The effect of delayed response. 8.5 Conclusions. 8.6 References. 9. Incorporating Covariates. 9.1 Introduction and examples. 9.2 General framework and asymptotic results. 9.3 Generalized linear models. 9.4 Two treatments with binary responses. 9.5 Conclusions. 9.6 References. 10. Conclusions and Open Problems. 10.1 Conclusions. 10.2 Open problems. 10.3 References. Appendix A: Supporting Technical Material. A.1 Some matrix theory. A.2 Jordan decomposition. A.3 Matrix recursions. A.4 Martingales. A.5 Cramer-Wold device. A.6 Multivariate martingales. A.7 Multivariate Taylor's expansion. A.8 References. Appendix B: Proofs. B.1 Proofs theorems in Chapter 4. B.2 Proof of theorems in Chapter 5. B.3 Proof of theorems in Chapter 7. B.4 References. Author Index. Subject Index.
Projected changes in field workability of agricultural machinery operations for upland crop production with +4 K warming in Hokkaido, Japan

The use of agricultural machines for field operations is often restricted by the soil status, which is determined by the meteorological conditions several days before the operation. While a projected rise in air temperature caused by climate change may promote evapotranspiration and incur positive impacts on the use of agricultural machines, intensified rainfall may have negative effects. Here, we provide probabilistic estimates of climate change impacts on meteorological constraints on the field workability of agricultural machines in Hokkaido, Japan. Analyses based on historical meteorological data and machinery operation logs recorded at two agricultural research stations revealed that operations were concentrated on days with a smaller antecedent precipitation index ( API ) . A simple derivative that reflects a maximum API value for conducting a certain field operation, termed threshold API, was proposed to evaluate meteorological constraints on field operations. The threshold API values for operations that were vulnerable to soil conditions ( e.g., sowing and soil preparation ) and/or in fields with poor drainage soil were small; therefore, this threshold should be a reasonable and quantitative measure of the meteorological constraint on field workability. Using 1 km gridded API values under historical and future climates calculated from a large ensemble dataset of daily mean air temperature and precipitation, we separated workable and unworkable days from June to October based on a threshold value of 5 mm and calculated changes in the monthly numbers of total workable days and consecutive unworkable days. While there were slight negative effects in several of the southern and central regions under a 4-K warmer future climate, positive changes prevailed in the total workable days and consecutive unworkable days in the other regions and months. The present results facilitate a probabilistic discussion of changes in agricultural calendars and suggest that climate change may extend the agricultural season in Hokkaido.
Si/SiGe quantum dot with superconducting single-electron transistor charge sensor

We report a robust process for fabrication of surface-gated Si/SiGe quantum dots (QDs) with an integrated superconducting single-electron transistor (SSET) charge sensor. A combination of a deep mesa etch and AlOx backfill is used to reduce gate leakage. After the leakage current is suppressed, Coulomb oscillations of the QD and the current-voltage characteristics of the SSET are observed at a temperature of 0.3 K. Coupling of the SSET to the QD is confirmed by using the SSET to perform sensing of the QD charge state.
Using One-Step Acid Leaching for the Recovering of Coal Gasification Fine Slag as Functional Adsorbents: Preparation and Performance

Coal gasification fine slag (FS), a kind of by-product of coal chemical industry, was recovered for the preparation of functional adsorbents by acid leaching process, which was orthogonally optimized by HCl, HNO3, HF, HAc, and H2SO4. Methylene blue (MB) was used to evaluate the performance of functional adsorbents. The results demonstrated that 57.6% of the leaching efficiency (RLE) and 162.94 mg/g of adsorption capacity (CAC) of MB were achieved under the optimal conditions of HNO3 of 2.0 mol/L, acid leaching time of 2.0 h, and acid leaching temperature of 293K. The detections on X-ray Diffraction (XRD), Scanning Electron Microscope (SEM), Fourier Transform Infrared Spectroscopy (FTIR), and BET surface area (SBET) indicated that the synthesized functional adsorbents were characterized by mesoporous materials. The good fitting of adsorption process using pseudo-second-order and Langmuir models demonstrated that the chemisorption contributed to MB removal. The results of thermodynamics further revealed that the adsorption process of MB occurred spontaneously due to the exothermic properties. The work is expected to develop a novel and cost-effective strategy for the safe disposal of FS, and potentially offer an alternative pathway to increase the additional value for the coal chemical industry.
Geometry and operator theory on quaternionic Hilbert spaces

In this article, we study the geometry and operator theory on quaternionic Hilbert spaces. As it is well-known, Cowen--Douglas operators are a class of non-normal operators related to complex geometry on complex Hilbert spaces. Our purpose is to generalize this concept on quaternionic Hilbert spaces. At the beginning, we study a class of complex holomorphic curves which naturally induce complex vector bundles as sub-bundles in the product space of the base space and a quaternionic Hilbert space. Then we introduce quaternionic Cowen--Douglas operators and give their quaternion unitarily equivalent invariant related to the geometry of the holomorphic curves.
Smart Battery Technology for Lifetime Improvement

Applications of lithium-ion batteries are widespread, ranging from electric vehicles to energy storage systems. In spite of nearly meeting the target in terms of energy density and cost, enhanced safety, lifetime, and second-life applications, there remain challenges. As a result of the difference between the electric characteristics of the cells, the degradation process is accelerated for battery packs containing many cells. The development of new generation battery solutions for transportation and grid storage with improved performance is the goal of this paper, which introduces the novel concept of Smart Battery that brings together batteries with advanced power electronics and artificial intelligence (AI). The key feature is a bypass device attached to each cell that can insert relaxation time to individual cell operation with minimal effect on the load. An advanced AI-based performance optimizer is trained to recognize early signs of accelerated degradation modes and to decide upon the optimal insertion of relaxation time. The resulting pulsed current operation has been proven to extend lifetime by up to 80% in laboratory aging conditions. The Smart Battery unique architecture uses a digital twin to accelerate the training of performance optimizers and predict failures. The Smart Battery technology is a new technology currently at the proof-of-concept stage.
Design of OCSRR-Based Differential Microwave Sensor for Microfluidic Applications

In this article, a high-sensitivity differential microwave sensor based on open comprehensive split-ring resonator (OCSRR) is proposed to extract the complex dielectric constant of liquid samples. An OCSRR structure is etched on the metal plate attached to the upper layer of the substrate. In addition, the outer ring of OCSRR adopts a hexagonal structure to reduce the original capacitance of OCSRR, and the inner ring of OCSRR adopts a rectangular structure to facilitate optimization and obtain the highest electric field intensity. The polydimethylsiloxane (PDMS) microfluidic channel is placed on the side of the outer ring with high electric field intensity and injects different concentrations of water–ethanol mixture. During resonance, the electric field is concentrated along the slit where the microfluidic channel is located. When the liquid sample is injected, the corresponding reflection coefficient changes. The designed sensor is fabricated and tested, and the experimental results are in good agreement with the simulation results. Compared with the previous similar sensors, the sensor can suppress the influence of environmental factors and has an average high sensitivity of 0.88%.
More sums of Hilbert frames

In this paper we have some new results on sums of Hilbert space frames and Riesz bases. We also have a correction for some results in "S. Obeidat et al., Sums of Hilbert space frames, J. Math. Anal. Appl. 351 (2009) 579-585. "
Fault Statistics and Diagnosis Techniques in the Power System

In this paper, fault statistics, diagnosis and short circuit analysis are based on the statistical approach and transient analysis in the power system. Quickest approaches of the fault clearance and power restoration are both taken into account by the unparallel activities for the un-interrupted power at the consumer ends where faults in the power system are treated by events or variables of the probability function to assess the modeling and objective of the paper
Physics beyond the standard model inspired by extra space dimensions

The aim of this thesis has been to actively participate in the ongoing devel¬ opments in the area of quantum field theories formulated in space-times with more than four dimensions. The outline is as following: First the shortcom¬ ings of the Standard Model are discussed and it is argued why field theories with extra dimensions are believed to improve on these deficits. As most extra dimensional models are non-renormalizable the sensible discussion of loop-corrections is essential. To do so a formalism widely used in finite tem¬ perature field theory is applied to theories with compactified extra space dimensions. In this framework loop-corrections in 5D QED and 5D nonabelian gauge theory are considered. Contrary to the standard treatment loop integrals are evaluated in momentum space for the four infinite and in position space for the compactified dimension. We calculate mass corrections to the Kaluza-Klein tower of the gauge bosons and the electron. In the case of the orbifold compactification there exist extra operators that are local¬ ized on the fixed points under the action of the discrete symmetry group. The nature of the divergencies is discussed and it is pointed out where a proper regularization mechanism is required. All calculations are carried out analytically using a computer algebra system. An appropriate regularization mechanism can be provided by deconstruction. Although deconstructed theories are renormalizable strongly interact¬ ing gauge theories in four dimensions, they can mimic a compact extra dimen¬ sion in the low-energy limit. By properly identifying the parameters of both theories, deconstruction can serve as a gauge invariant (lattice-)regularization for the higher dimensional model. The low-energy limit of the deconstructed model is described by a non-linear sigma model. To perform calculations one can thus utilize the methods of chiral perturbation theory developed for low-energy QCD and theories with a heavy Higgs boson. The method is explained and applied to the case at hand. Introducing counterterms that cancel the divergencies it is possible to perform loop calculations that yield manifestly finite results in every order of perturbation theory. In general they will however depend on the unknown coefficients of the counterterms.
A CNN Ensemble Based on a Spectral Feature Refining Module for Hyperspectral Image Classification

In the study of hyperspectral image classification based on machine learning theory and techniques, the problems related to the high dimensionality of the images and the scarcity of training samples are widely discussed as two main issues that limit the performance of the data-driven classifiers. These two issues are closely interrelated, but are usually addressed separately. In our study, we try to kill two birds with one stone by constructing an ensemble of lightweight base models embedded with spectral feature refining modules. The spectral feature refining module is a technique based on the mechanism of channel attention. This technique can not only perform dimensionality reduction, but also provide diversity within the ensemble. The proposed ensemble can provide state-of-the-art performance when the training samples are quite limited. Specifically, using only a total of 200 samples from each of the four popular benchmark data sets (Indian Pines, Salinas, Pavia University and Kennedy Space Center), we achieved overall accuracies of 89.34%, 95.75%, 93.58%, and 98.14%, respectively.
A Shifted View of Fundamental Physics

We speculate on the role of relativistic versions of delayed differential equa- tions in fundamental physics. Relativistic invariance implies that we must consider both advanced and retarded terms in the equations, so we refer to them as shifted equations. The shifted Dirac equation has some novel properties. A tentative formulation of shifted Einstein-Maxwell equations naturally incorporates a small but nonzero cosmological con- stant.
Transducer finite aperture effects in sound transmission near leaky Lamb modes in elastic plates at normal incidence

The interaction of ultrasonic waves with fluid-embedded viscoelastic plates, pipes, and shells, have been subject to extensive theoretical and experimental studies over several decades. In normal-incidence through-transmission measurements of a water-embedded solid plate using ultrasonic piezoelectric transducer sound fields, significant deviations from plane wave theory have recently been observed. To quantitatively describe such measured phenomena, finite element modeling (FEM), also combined with an angular spectrum method (ASM), have been used for three-dimensional (3D) simulation of the voltage-to-sound-pressure signal propagation through the electro-acoustic measurement system consisting of the piezoelectric transducer, the water-embedded steel plate, and the fluid regions at both sides of the plate. The observed phenomena of frequency downshift of the plate resonance, increased sound pressure level through the plate, and beam narrowing / widening, are ascribed to the finite angular spectrum of the beam, that excites a region of negative and zero group velocity for the leaky Lamb mode in question.
Dynamic Delegation-based Privacy Preserving in IoT Architectures

The management of smart devices in IoT environments can benefit from the flexibility offered by delegation-based authorization. Dynamic delegation improves the system making the process more efficient and autonomous. Privacy preservation in that kind of scenario has, however, serious problems. This paper proposes an architecture based on delegation-based authorization to dynamic D2D delegation. A proof of concept demonstrates how a smart device can delegate its permissions to another smart device with the same abilities when it is required. Moreover, the architecture focus on preserving the user's privacy by requesting his consent when delegation involves access to personal data. Different levels of data sensitivity are considered and it works as a boundary to the system to recognize when consent is required and different levels of care should be taken into account depending on the data level sensitivity. Finally, a receipt is generated as proof of the consent providing the user's control over his data and transparency about the delegation process.
Polytropic and Chaplygin f(T)-gravity models

We reconstruct different f(T)-gravity models corresponding to a set of dark energy scenarios containing the polytropic, the standard Chaplygin and the generalized Chaplygin gas models. We also derive the equation of state parameter of the selected f(T)-gravity models and obtain the necessary conditions for crossing the phantom-divide line.
MSIM: A Highly Parallel Near-Memory Accelerator for MinHash Sketch

Genome Assembly is an important Big Data analytics which involves massive computations for similarity searches on sequence databases. Being major component of runtime, similarity searches require careful design for scalable performance. MinHash Sketching is an extensively used data structure in Long-read genome assembly pipelines, which involves generating, randomizing and minimizing a set of hashes for all the k-mers in genome sequences. Compute-hungry MinHash sketch processing on commercially available multi-threaded CPUs suffer from the limited bandwidth of the L1-cache, which causes the CPUs to stall. Near-Data Processing (NDP) is an emerging trend in data-bound Big Data analytics to harness the low-latency, highbandwidth available within the Dual In-line Memory Modules (DIMMs). While NDP architectures have generally been utilized for memory-bound computations, MinHash sketching is a potential application that can gain massive throughput by exploiting memory Banks as higher bandwidth L1-cache.In this work, we propose MSIM, a distributed, highly parallel and efficient hardware-software co-design for accelerating MinHash Sketch processing on light-weight components placed on the DRAM hierarchy. Multiple ASIC-based Processing Engines (PEs) placed at the bank-group-level in MSIM provide highparallelism for low-latency computations. The PEs sequentially access data from all Banks within their bank-group with the help of a dedicated Address calculator, which utilizes an optimal data mapping scheme. The PEs are controlled by a custom Arbiter, which is directly activated by the host CPU using general DDR commands, without requiring any modification to the memory controller or the DIMM standard buses. MSIM requires limited area and power overheads, while displaying up-to 384.9x speedup and 1088.4x energy reduction compared to the baseline multithreaded software solution in our experiments. MSIM achieves 4.26x speedup over high-end GPU, while consuming 26.4x lesser energy. Moreover, MSIM design is highly scalable and extendable in nature.
Inland Waterway Ship Path Planning Based on Improved RRT Algorithm

Ship path planning is crucial for the shipping industry, especially for the development of autonomous ships. Many algorithms have been developed over the last few decades to solve the ship path planning problem. However, it is still challenging for ship path planning in an inland waterway. In this paper, an improved RRT algorithm for ship path planning in complex inland waterways is proposed. The improved algorithm has a path shearing and smoothing module, and the function of keeping a safe distance between a moving ship and obstacles. In addition, the algorithm has been tested in two inland waterway scenarios, and the results have confirmed its feasibility and reliability. The path planning algorithm proposed in this research seeks to reduce the risks faced by ship navigation in inland water. It has theoretical and practical significance in improving navigation safety in complex inland waters.
Absolute Configuration and Biological Evaluation of Novel Triterpenes as Possible Anti-Inflammatory or Anti-Tumor Agents

Two new compounds, ardisiapunine B (1) and ardisiapunine C (2), were isolated from Ardisia lindleyana D. Dietr. Their structures were examined using HR–ESI–MS, IR, (1D, 2D) NMR spectroscopic analyses, single–crystal X–ray diffraction, and ECD calculation. It was found that the two new compounds belong to unusual oleanane-type triterpenes, with compound 1 bearing an acetal unit and a C–13–C–18 double bond, and compound 2 bearing a C–28 aldehyde group and a C–18–C–19 double bond. The anti-inflammatory properties of compounds 1 and 2 were tested on NO production and cellular morphology using RAW264.7 cells, and their anti-tumor properties were tested on cytotoxic activities, cellular morphology, cell apoptosis, and cell cycle. The results showed that compound 1 exhibited a potent cytotoxicity against HepG2 cell lines with an IC50 of 12.40 μM. Furthermore, it is possible that compound 1 inhibits cell proliferation by blocking the cell G2/M phase and promoting cell apoptosis. Compound 2 exhibited a potential anti-inflammatory activity by decreasing the production of NO in LPS–stimulated RAW264.7 cells. Comparative analysis of the structures of compounds 1 and 2 revealed that the acetal structure and double bond positions were the main differences between them, and these are presumed to be the main reasons for the extreme differences in their cytotoxicity and anti-inflammatory activities. From these new findings, two promising lead compounds were identified for the future development of potential anti–inflammatory or anti–tumor agents.