Miracle-dz/UniFAR-FTUs huggingface dataset:
Advanced Alzheimer’s Disease Patients Show Safe, Significant, and Persistent Benefit in 6-Month Bryostatin Trial

Background: In pre-clinical studies, Bryostatin, MW (molecular weight) 904, has demonstrated synaptogenic, anti-apoptotic, anti-amyloid, and anti-tau tangle efficacies. Objective: To identify AD patients who show significant cognitive benefit versus placebo when treated in a trial with chronic Bryostatin dosing. Methods: In this 6-month 122 AD patient Bryostatin trial, there were two cohorts: the Moderate Cohort (MMSE, Mini-Mental Status Exam: 15-18) and the Moderately Severe Cohort (MMSE 10-14) as pre-specified secondary endpoints. Patient randomization was stratified by baseline SIB to insure balance in baseline cognitive ability between treatment arms. Results: With no safety events noted by the data safety and monitoring board, the Moderately Severe (MMSE 10-14) Bryostatin-treated patients were significantly improved above the placebo patients for Weeks #13 through Week #42. After two cycles of 7 x i.v. Bryostatin doses over a 26-week period, the 10-14 Cohort Severe Impairment Battery (SIB), measured every 2 weeks, showed significant benefit using a Mixed Model Repeated Measures model (MMRM, 2-tailed, p < 0.05) for Weeks #13 through #42, even 16 weeks after dosing completion by Week #26. Placebo 10-14 patients showed no benefit, declining to negative 12.8 points by Week #42. Trend analyses confirmed the MMRM data for this Cohort, with a significant downward slope (equivalent to Cognitive Decline) for the placebo group, p < 0.001, 2-tailed, but no significant decline for the Bryostatin-treated group (p = 0.409, NS), treatment versus placebo p < 0.007. The Moderate Cohort patients showed no significant benefit. Conclusions: The Bryostatin-treated MMSE 10-14 patients showed no significant cognitive decline throughout the 10-month trial, versus placebo patients’ decline of -12.8 SIB points.
Successful Management of Right Ventricular Failure After Emergent Transcatheter Mitral Valve Edge-to-Edge Repair With Inhaled Nitric Oxide: A Case Report

Mitral regurgitation (MR) induces left ventricular failure and pulmonary hypertension (PH) and can lead to right ventricular (RV) failure. Inhaled nitric oxide (iNO) decreases pulmonary vessel resistance. iNO has been used in patients with PH and RV failure. We present a case with cardiogenic shock due to severe degenerative MR. The patient underwent emergent transcatheter mitral valve edge-to-edge repair (TEER). Despite TEER had been successfully performed, hemodynamics did not improve due to RV failure. Administration of iNO improved hemodynamics. This case suggests administration of iNO could be an effective option for RV failure after TEER.
Volcanic hazard studies for the Yucca Mountain project

Volcanic hazard studies are ongoing to evaluate the risk of future volcanism with respect to siting of a repository for disposal of high-level radioactive waste at the Yucca Mountain site. Seven Quaternary basaltic volcanic centers are located a minimum distance of 12 km and a maximum distance of 47 km from the outer boundary of the exploration block. The conditional probability of disruption of a repository by future basaltic volcanism is bounded by the range of 10{sup {minus}8} to 10{sup {minus}10} yr{sup {minus}1}. These values are currently being reexamined based on new developments in the understanding of the evaluation of small volume, basaltic volcanic centers including: (1) Many, perhaps most, of the volcanic centers exhibit brief periods of eruptive activity separated by longer periods of inactivity. (2) The centers may be active for time spans exceeding 10{sup 5} yrs, (3) There is a decline in the volume of eruptions of the centers through time, and (4) Small volume eruptions occurred at two of the Quaternary centers during latest Pleistocene or Holocene time. We classify the basalt centers as polycyclic, and distinguish them from polygenetic volcanoes. Polycyclic volcanism is characterized by small volume, episodic eruptions of magma of uniform composition over time spans of 10{sup 3} to 10{sup 5} yrs. Magma eruption rates are low and the time between eruptions exceeds the cooling time of the magma volumes. 25 refs., 2 figs.
Innovation Adoption Research in Healthcare: Understanding Context and Embracing Complexity

This paper presents a literature review on innovation adoption in healthcare. Healthcare is one of the world's largest and fastest-growing industries, driven by demands such as ageing populations, increasing co-morbidity, and improving technologies. Innovation continues to be a key driver in balancing cost containment and improving quality for health systems. However, healthcare has been slow to adopt and utilize the numerous innovations available to improve patient outcomes and efficiency. Stakeholders in healthcare innovation need to understand the influences on innovation adoption to increase the success rate of implementing innovation into practice. This literature review was conducted via searches of publication databases using selected keywords regarding innovation and adoption in general, and in healthcare specifically. Publications from academic journals and grey literature were assessed based on relevance to the topic, quality, influence, and citations. Key papers, theories, findings, and conclusions in the field are discussed in this review. The review revealed that innovation adoption has been extensively studied in multiple disciplines over decades. However, mostempirical research and theory development has taken place in the context of information technologies (IT) and their adoption in various industries and sectors. Research has mainly focused on individual acceptance and adoption of technology, which is less appropriate in healthcare due to its complex organizational structures, processes, and highly skilled workforce with significant social influence. Research into organizational adoption of innovation has been conducted, but these models havegenerally been utilized less in research and practice, both in general and specifically in healthcare. Within healthcare literature, innovation adoption has been recognized as a complex and challenging issue with multiple factors influencing success. However, research and theory development have generally been more limited in this setting. The review concludes with suggestions to bring learning from disciplines with stronger theory development to the healthcare setting. A novel conceptual model specific to healthcare is posited, accounting for the complexity of the system and understanding theprocess through a holistic approach. This model should be useful to and useable by any healthcare innovation stakeholder, from clinicians, to industry, to policy makers, as well as by researchers in this field.
Early Weaning Possibly Increases the Activity of Lipogenic and Adipogenic Pathways in Intramuscular Adipose Tissue of Nellore Calves

This study aimed to evaluate by wide-expression profile analysis how early weaning at 120 days can alter the skeletal muscle metabolism of calves supplemented with a concentrated diet until the growth phase. Longissimus thoracis muscle samples were obtained by biopsy from two groups of calves, early weaned (EW; n = 8) and conventionally weaned (CW; n = 8) at two different times (120 days of age—T1 [EW] and 205 days of age—T2 [CW]). Next, differential gene expression analysis and functional enrichment of metabolic pathways and biological processes were performed. The results showed respectively 658 and 165 differentially expressed genes when T1 and T2 were contrasted in the early weaning group and when early and conventionally weaned groups were compared at T2. The FABP4, SCD1, FASN, LDLR, ADIPOQ, ACACA, PPARD, and ACOX3 genes were prospected in both comparisons described above. Given the key role of these differentially expressed genes in lipid and fatty acid metabolism, the results demonstrate the effect of diet on the modulation of energy metabolism, particularly favoring postnatal adipogenesis and lipogenesis, as well as a consequent trend in obtaining better quality cuts, as long as an environment for the maintenance of these alterations until adulthood is provided.
AMOSTRAGEM DE FOLHAS EM CAFEEIRO PARA FINS DE DIAGNOSE DO ESTADO NUTRICIONAL.

SUMMARY – Coffee is one of the main products on the list of agricultural exports, becoming one of the most important sources of foreign exchange for the Brazilian economy. Additionally, coffee has an important social role in the country, employing considerable amount of manpower due to the requirement of culture in relation to cultural practices. The leaf analysis is an important tool to help increase productivity of perennial crops in general and, particularly for coffee and is essential to know the ideal size of a leaf sample to be collected in order to optimize the manpower and reduce errors inherent in the criteria for diagnosis of nutritional status. This study aimed to determine in 8-12 years-old commercial crops of coffee, installed in Oxisol submitted to two water regimes, the size of leaf samples and the variation of sampling error for the diagnosis of nutritional status of plants coffee. A completely randomized design, consisting of four treatments was used, that consisted of leaf sampling at 5, 10, 20 and 40 coffee plants and five repetitions, to with and without irrigation system. To determination of macronutrients, the sample sheets 10 and 20 plants per unit sample with and without irrigation systems respectively, was sufficient to maintain the sample error less than 10. To determine chemical analysis of macro and micronutrients, 20 plants were enough to keep the error in estimating the sample mean less than 10% in the with and without irrigation systems, except for Cu and Mn that need more than 40 plants without irrigation system. SUMMARY – The leaf analysis is a method of evaluating the nutritional status of plants that helps fertilization programs in order to obtain higher yields in perennial crops in general, however, is indispensable to know the optimal number of plants to be sampled. The study aimed to determine cash crops of coffee, Mundo Novo cultivar subjected two water regimes, the number of plants to be sampled and the sampling error estimate for the diagnosis of nutritional status of this culture. The work comprised two studies, where samples taken from leaves in irrigated and without irrigation. For both studies we used a completely randomized design with four treatments and five repetitions that consisted of leaf collection in 5, 10, 20 and 40 coffee plants per plot. It was concluded that the plots assessed no difference in the number of plants, considering the error in estimating the sample mean less than 10% acceptable, to compose the leaf sample for macronutrients, in the system with and without irrigation, when we considered the sample of 20 plants. The sampling of the 3rd pair of leaves from the tip of the branch, the half-height of the plants in equal numbers on both sides of the row of coffee plants, 20 plants are enough to perform chemical analysis of macro and micronutrients an error in estimating the sample mean less than 10% in the system with and without irrigation, except for Cu and Mn in need of higher than 40 plants in the system without irrigation.
Lonar crater glasses and high-magnesium australites - Trace element volatilization and meteoritic contamination. [terrestrial analog for lunar craters]

Radiochemical neutron activation analysis was used to detect six siderophile elements (Ni, Re, Os, Pd, Ir and Au) and three volatile elements (Zn, Se and Cd) in two basalts, a pumice and three impact glasses from Lonar crater, India, and in six high-magnesium australites. Significant depletions in Re and Se with respect to the parent basalts were found in the Lonar glasses. These depletions, apparently correlated with the degree of shock, are probably due to volatilization under the oxidizing conditions characteristic of earth. One of the australites is substantially enriched in siderophiles relative to the level of these elements in the other five tektites. The element patterns in the australites resemble those of the carbonaceous or cometary component of an Apollo 16 soil.
Chromatic Aberration Based Depth Estimation in a Fluid Field

One of the most challenging issues is to analyze the behavior of fluid body, and it is very difficult to capture the flow of liquid. Then, PIV (Particle Image Velocimetry) or PTV (Particle Tracking Velocimetry) are usually used in order to observe the flow in a fluid field. In PIV, tracer particles, which have some color, are thrown into the fluid body, and the scattered light reflected on the particles by irradiating laser is imaged. In fact, the frequency of the scattered light is slightly changed by the Doppler Effect, and the velocity of a particle can be measured by utilizing the fact that the frequency change is proportional to the particle velocity. On the other hand, PTV is a method to measure the particle velocity by tracking each particle in images obtained by a camera. In general, both of PIV and PTV can analyze only the 2D behavior of the particles so that some additional information is required to track the particles in 3D space such as stereo vision, de-focus of the image, and color of the particle. These systems, however, requires multiple cameras, multiple images, or multiple lights. Then, a new system has been developed, which estimates the distance from the camera to a particle by utilizing the relation between the frequencies of light reflected on the particle and the lengths to the particle. However, it is not always true that hue of color is proportional to the wavelength of light. Therefore, in this paper, we propose a depth estimation method of particle by utilizing chromatic aberration of lens and investigate the relation between the hue of color for a particle and the length from the camera. First, we measure the focal length of a particle from the camera by adjusting the height of the chromatic lens. Then, we obtain the relation between the hue of color for the particle and the length from the camera by approximating the experimental data. By analyzing the approximated calibration curve, we can clarify the relation between the hue of color and the wavelength. Finally, we can estimate the depth of the particle by using the obtained calibration curve. In addition, we measure the error between the expected value and the estimated one, and investigate the factors of the estimation error. In the end of the paper, we clarify the difficulty for the estimation of particle positions in 3D fluid field.  Manuscript received November 17, 2017; revised April 20, 2018. Journal of Image and Graphics, Vol. 6, No. 1, June 2018 59 ©2018 Journal of Image and Graphics doi: 10.18178/joig.6. 1.59-63
Machine learning enhanced optimisation of crash box design for crashworthiness analysis

The primary goal of the current study is to optimise crash box designs for automobiles. Machine learning (ML) techniques are used to build an intelligent and reliable ML framework. With the help of this framework, the crash box design can be optimised for crashworthiness analysis. The optimisation of the crash box design is resource‐intensive due to its intricate geometric design, use of a variety of materials, and extensive use of dynamic simulations to determine the ideal structural parameters through simulations. A reinforcement learning‐based (RL) optimization technique is developed for this reason. The crash box is built with nonlinear first‐order shear deformation shell elements to provide the necessary simulation data, and nonlinear elastoplastic material is used to model the dynamic impact process. The RL agents are tuned using finite element (FE) simulations and synthetic data generated by a generative adversarial network (GAN) to optimise and select the optimal model parameters for vehicle crashworthiness analysis. To estimate the correct model parameter required to fulfil specified crashworthiness metrics, the RL agent learns and optimises automatically. This method is effective in terms of resources and could be helpful in the early stages of vehicle development.
A Comprehensive Survey on Artificial Intelligence Empowered Edge Computing on Consumer Electronics

The Internet revolution and Moore’s Law drove the rapid expansion of connected consumer electronics. As massive data is generated by Internet of Things (IoT) devices, edge computing has been developed and applied in consumer electronics to provide agile and real-time services. In this paper, we provide an overview of artificial intelligence (AI)-empowered edge computing in consumer electronics. We start with the background of consumer electronics and artificial intelligence and outline key characteristics and challenges, which are used to evaluate the state of research in smart homes, smart buildings, smart healthcare, and intelligent diagnosis. The evaluation shows that research in AI-empowered edge computing for consumer electronics has mainly focused on latency, robustness, and reliability over the past 5 years. However, explainability and load balancing are mentioned less and have great potential for future research. Although AI-empowered edge computing for consumer electronics is a thriving and widely used research area, there are still many unsolved problems and challenges that researchers can address.
Experimental analysis of sea clutter using airborne circular scanning SAR in medium grazing angle

The theoretical statistical model of sea clutter is a key issue for maritime surveillance. In recent years, the statistical model of sea clutter has attracted much attention to design the detector well. Meanwhile, the circular scanning synthetic aperture radar (SAR) has been applied to wide surveillance of the sea surface. Therefore, the paper analyzes the validity of available sea clutter models for range Doppler images from different scan angles for moderate sea state in the medium grazing angle. The parameter estimation method for different distribution models employs the method of logarithmic cumulant (MoLC) based on Mellin transform uniformly. By the analysis of the fitting performance between the histogram of real data and the amplitude probability density function (PDF) of empirical distribution models and the goodness-of-fit (GoF) test for real data from different scan angles, it is indicated the generalized K (GK) distribution with generalized Gamma texture distribution can fit the sea clutter well for different scan angles in the medium grazing angle.
An Iterative Learning Based Direct Torque Control Strategy of DC-Biased Vernier Reluctance Machines for Torque Ripple Reduction

This article proposes an iterative learning-based direct torque control (IL-DTC) strategy for dc-biased Vernier reluctance machines (dc-VRMs) to effectively improve the torque control accuracy. Firstly, the model for dc-VRMs in stator flux synchronous rotating reference frame is proposed for DTC, considering the harmonic currents and saturation of self-inductance. Based on the model, torque ripple is implied to be suppressed by injecting third zero-sequence current (ZSC). Then, a simplified iterative learning controller (ILC) with varying gain coefficient is designed for dc-VRMs to generate the desired ZSC online. The convergence and parameter design principle of the ILC are analyzed elaborately to ensure the control accuracy. Finally, comparative experiments with conventional proportional-integral-based DTC scheme and torque ripple suppression scheme are carried out to verify the superior torque control accuracy of the proposed IL-DTC scheme.
Accurate Pose Estimation of the Texture-Less Objects With Known CAD Models via Point Cloud Matching

As the use of robot-based automation continues to expand in various production fields, achieving accurate pose estimation of texture-less objects has become increasingly significant and challenging. This article proposes an accurate pose estimation algorithm for texture-less objects with known CAD models by using point cloud matching. The algorithm mainly includes four parts, namely the offline data generation, global matching, local matching, and fine registration, respectively. To generate the reference point clouds required for coarse matching, the synthetic view rendering technique is utilized in the offline data generation step. To address the problem of feature matching ambiguity caused by incorrect correspondences in both the scene and reference point clouds, we introduce a similar view filter and a pose cluster mechanism in the global and local matching steps, respectively. Additionally, the genetic algorithm (GA) is utilized in the fine registration step, in which the incremental Euler angle modeling is introduced to avoid boundary cliffs and improve the searching accuracy and speed. Finally, a series of experiments on T-LESS dataset are conducted to demonstrate the effectiveness of our work. Compared with the state-of-the-art (SOTA) methods, the results show that our method improves the accuracy by at least 18% for single-input single-output pose estimation of texture-less objects.
Influence of sodium chloride on morphophysiological characteristics of wheat and maize plants

Aim. The effect of sodium chloride on the physiological and biochemical parameters of wheat and maize genotypes, the identification of salinity-resistant varieties was studied. Wheat and maize plants were grown in pots in the soil with the addition of 0.5 % sodium chloride. To create new salt-tolerant varieties of wheat and maize, a comparative analysis of the morphophysiological parameters plants was carried out. Methods. Morphophysiological parameters such as growth, chlorophyll and carotenoid content, photochemical activty of chloroplasts and PSII activity have been studied in wheat and maize plant. Results. The effect of salt on the amount of chlorophyll a, chlorophyll b, and carotenoids, which are the main physiological indicators, is manifested in different ways in both plants. When studying salt-tolerance of plants, differences were detected in the relative amounts of chlorophyll (a+b), carotenoids, as well as the photochemical activity of chloroplasts and efficiency of PS 2. Conclusions. Among the varieties, according to all morphophysiological parameters, the most tolerant to the action of salt was the soft wheat variety Mirbashir-128, and the corn hybrid Belaya x Gurur.