Miracle-dz/UniFAR-FTUs huggingface dataset:
Novel in situ gelling vaginal sponges of sildenafil citrate-based cubosomes for uterine targeting

Abstract Sildenafil citrate (SIL), a type 5-specific phosphodiesterase inhibitor, demonstrates valuable results in the management of infertility in women; however, the absence of vaginal dosage form in addition to the associated oral adverse effects minimize its clinical performance. The present study is concerned with SIL uterine targeting following intravaginal administration via optimization of cubosomal in situ gelling sponges (CIS). An emulsification method was employed for preparation of cubosomal dispersions incorporating glyceryl monooleate as a lipid phase and poloxamer 407 as a surfactant with or without polyvinyl alcohol as a stabilizer. Cubosomes were estimated regarding entrapment efficiency (EE%), particle size, and in vitro drug release. Chitosan (2% w/w) was incorporated into the optimum formulation and then lyophilized into small sponges. For the CIS, in vivo histopathological and pharmacokinetic studies were conducted on female Wistar rats and compared with intravaginal free SIL sponges (FIS) and oral SIL solution. SIL-loaded cubosomes showed EE% ranging between 32.15 and 72.01%, particle size in the range of 150.81–446.02 nm and sustained drug release over 8 h. Histopathological study revealed a significant enlargement in endometrial thickness with congestion and dilatation of endometrial blood vessels in intravaginal CIS compared to intravaginal FIS and oral-treated groups. The pharmacokinetic study demonstrated higher AUC0–∞ and Cmax with oral administration compared to intravaginal CIS or intravaginal FIS indicating potential involvement of first uterine pass effect after intravaginal administration. Finally, intravaginal CIS could be considered as a promising platform for SIL uterine targeting with minimized systemic exposure and side effects.
A Dual Reconfigurable Printed Antenna: Design Concept and Experimental Realization

In this article, we demonstrate the design concept and experimental realization of a dual reconfigurable printed antenna. A single coplanar waveguide (CPW)-fed printed monopole antenna can provide a reconfigurable ultrawideband (UWB)-notched or narrow-band (NB) response, depending on the status of the switches and the position of the split ring resonators (SRRs) in the CPW feed region of the antenna. The rectilinear movement of the superstrate, printed with various pairs of SRRs of differing dimensions, loaded in the feed region of the antenna, provides the reconfigurable functionalities. The position of the superstrate is controlled by a commercial servo motor programmed by a microcontroller. The antenna, along with the additional accessories required to actuate the mechanical movement, are fabricated and characterized using impedance and radiation pattern measurements. The measured results for both reconfigurable cases provide a good correspondence with the simulated results and the theoretical estimation. The proposed antenna can be used as a reconfigurable communicating antenna module of the cognitive radio (CR) system.
Structurally Sparsified Backward Propagation for Faster Long Short-Term Memory Training

Exploiting sparsity enables hardware systems to run neural networks faster and more energy-efficiently. However, most prior sparsity-centric optimization techniques only accelerate the forward pass of neural networks and usually require an even longer training process with iterative pruning and retraining. We observe that artificially inducing sparsity in the gradients of the gates in an LSTM cell has little impact on the training quality. Further, we can enforce structured sparsity in the gate gradients to make the LSTM backward pass up to 45% faster than the state-of-the-art dense approach and 168% faster than the state-of-the-art sparsifying method on modern GPUs. Though the structured sparsifying method can impact the accuracy of a model, this performance gap can be eliminated by mixing our sparse training method and the standard dense training method. Experimental results show that the mixed method can achieve comparable results in a shorter time span than using purely dense training.
Deep Learning for Biometrics

In the recent past, deep learning methods have demonstrated remarkable success for supervised learning tasks in multiple domains including computer vision, natural language processing, and speech processing. In this article, we investigate the impact of deep learning in the field of biometrics, given its success in other domains. Since biometrics deals with identifying people by using their characteristics, it primarily involves supervised learning and can leverage the success of deep learning in other related domains. In this article, we survey 100 different approaches that explore deep learning for recognizing individuals using various biometric modalities. We find that most deep learning research in biometrics has been focused on face and speaker recognition. Based on inferences from these approaches, we discuss how deep learning methods can benefit the field of biometrics and the potential gaps that deep learning approaches need to address for real-world biometric applications.
Efficient k-means on GPUs

k-Means is a versatile clustering algorithm widely-used in practice. To cluster large data sets, state-of-the-art implementations use GPUs to shorten the data to knowledge time. These implementations commonly assign points on a GPU and update centroids on a CPU. We show that this approach has two main drawbacks. First, it separates the two algorithm phases over different processors, which requires an expensive data exchange between devices. Second, even when both phases are computed on the GPU, the same data are read twice per iteration, leading to inefficient use of memory bandwidth. In this paper, we describe a new approach that executes k-means in a single data pass per iteration. We propose a new algorithm to updates centroids that allows us to perform both phases efficiently on GPUs. Thereby, we remove data transfers within each iteration. We fuse both phases to eliminate artificial synchronization barriers, and thus compute k-means in a single data pass. Overall, we achieve up to 20x higher throughput compared to the state-of-the-art approach.
Kernel-based regression of non-causal systems for inverse model feedforward estimation

Inversion-based feedforward control enables high performance for industrial motion systems. To this end, accurate knowledge of the inverse system is required, and non-causal control actions must be enabled. The aim of this paper is to accurately identify non-causal inverse models in view of high feedforward control performance. The developed method employs kernel-based regularization to minimize the mean squared error of the estimate. The performance benefits of the presented approach are demonstrated on an industrial printing system, including non-causal feedforward control actions.
Bone Morphogenetic Protein (BMP)-3b Gene Depletion Causes High Mortality in a Mouse Model of Neonatal Hypoxic-Ischemic Encephalopathy

Bone morphogenetic proteins (BMPs) are a group of proteins that induce the formation of bone and the development of the nervous system. BMP-3b, also known as growth and differentiation factor 10, is a member of the BMPs that is highly expressed in the developing and adult brain. BMP-3b is therefore thought to play an important role in the brain even after physiological neurogenesis has completed. BMP-3b is induced in peri-infarct neurons in aged brains and is one of the most highly upregulated genes during the initiation of axonal sprouting. However, little is known about the role of BMP-3b in neonatal brain injury. In the present study, we aimed to describe the effects of BMP-3b gene depletion on neonatal hypoxic-ischemic encephalopathy, which frequently results in death or lifelong neurological disabilities, such as cerebral palsy and mental retardation. BMP-3b knockout and wild type mice were prepared at postnatal day 12. Mice of each genotype were divided into sham-surgery, mild hypoxia-ischemia (HI), and severe HI groups (n = 12–45). Mice in the HI groups were subjected to left common carotid artery ligation followed by 30 min (mild HI) or 50 min (severe HI) of systemic hypoxic insult. A battery of tests, including behavioral tests, was performed, and the brain was then removed and evaluated at 14 days after insult. Compared with wild type pups, BMP-3b knockout pups demonstrated the following characteristics. (1) The males exposed to severe HI had a strikingly higher mortality rate, and as many as 70% of the knockout pups but none of the wild type pups died; (2) significantly more hyperactive locomotion was observed in males exposed to severe HI; and (3) significantly more hyperactive rearing was observed in both males and females exposed to mild HI. However, BMP-3b gene depletion did not affect other parameters, such as cerebral blood flow, cylinder test and rotarod test performance, body weight gain, brain weight, spleen weight, and neuroanatomical injury. The results of this study suggest that BMP-3b may play a crucial role to survive in severe neonatal hypoxic-ischemic insult.
Cloud Federation and Distribution

ions There are several traditional abstractions for programming distributed applications in general, which also play a role in cloud datacenter communication: ● Sockets: these are typical for programming unicast interaction (one-to-one) as well as simple one-to-many interaction ● Publish/subscribe: this is a common high-level abstraction for multicasting. Producers are termed ​publishers​ and consumers ​subscribe​ to specific messages. Figure 7 illustrates entities of a publish/subscribe system. Several variants for outlining messages of interest can be distinguished: o Topic-based publish/subscribe (TPS): here, publishers explicitly post messages under specific ​topics​, whilst subscribers delineate topics of interest. Topics can exhibit a “flat” name-space or be in different relationships to each other such as hierarchical. o Content-based publish/subscribe (CPS): here subscriptions are expressed based on predicates on message content ("filters"). Most systems thus support messages with some form of property-value pairs which are used for filtering. ● Message queuing: these systems are the most common implementation of one-of-many communication. Typically consumers pull messages from queues, but this can also be replaced by push-style communication. Sockets typically are bound to IP addresses, which given the problems with such addresses outside of clouds makes them unsuited for inter-cloud communication.
Generalized Robust Bayesian Committee Machine for Large-scale Gaussian Process Regression

In order to scale standard Gaussian process (GP) regression to large-scale datasets, aggregation models employ factorized training process and then combine predictions from distributed experts. The state-of-the-art aggregation models, however, either provide inconsistent predictions or require time-consuming aggregation process. We first prove the inconsistency of typical aggregations using disjoint or random data partition, and then present a consistent yet efficient aggregation model for large-scale GP. The proposed model inherits the advantages of aggregations, e.g., closed-form inference and aggregation, parallelization and distributed computing. Furthermore, theoretical and empirical analyses reveal that the new aggregation model performs better due to the consistent predictions that converge to the true underlying function when the training size approaches infinity.
DEAD box protein DDX1 promotes colorectal tumorigenesis through transcriptional activation of the LGR5 gene

DDX1, a member of the DEAD box RNA helicase family, plays a critical role in testicular tumors. However, it remains to be clarified whether DDX1 is involved in other types of malignant tumors such as colorectal cancer. We disrupted the DDX1 gene in a human colorectal cancer cell line LoVo using the CRISPR/Cas9‐mediated gene‐targeting system. DDX1‐KO LoVo cells exhibited a much slower growth rate, produced fewer colonies in soft agar medium, and generated smaller solid tumors in nude mice than parental LoVo cells. Such phenotypes of the DDX1‐KO cells were mostly reversed by exogenous expression of DDX1. These results indicate that DDX1 is required for tumorigenicity of colorectal cancer cells. In the DDX1‐KO cells, the cancer stem cell marker genes LGR5, CD133, ALDH1 and SOX2 were markedly suppressed. Among them, expression of LGR5, which is essential for tumorigenicity of colorectal cancer cells, was restored in the DDX1‐transfected DDX1‐KO cells. Consistently, the DDX1‐KO cells lost sphere‐forming capacity in a DDX1‐dependent fashion. Reporter and chromatin immunoprecipitation assays revealed that DDX1 directly bound to the −1837 to −1662 region of the enhancer/promoter region of the human LGR5 gene and enhanced its transcription in LoVo cells. Repression of LGR5 by DDX1 knockdown was observed in 2 other human colorectal cancer cell lines, Colo320 and SW837. These results suggest that LGR5 is a critical effector of DDX1 in colorectal cancer cells. The DDX1‐LGR5 axis could be a new drug target for this type of malignant cancer.
Multicriterial evaluation of process planning of a new product in the stage of its design

The objective of this study is to propose to the designer a multi-criteria evaluation approach of manufacture of a new product during its design phase. The approach is based on methods of predicting of the manufacturing quality, cost and time, and a method of decision support to make a multi-criteria choice. The manufacturing quality is determined by the Composite Process Capability Indicator (CCP), the manufacturing cost is calculated from the ABC method, the manufacturing time is estimated using CAM software. And to make a multi-criteria choice of the concept, the AHP tool is used for a joint assessment of the manufacturing performance indicators.
A landscape of conservation philanthropy for U.S. land trusts

Finding ways to increase financial support is critical to conservation efforts. We used conservation fundraising data, unprecedented in their resolution, to reveal spatial patterns in philanthropic giving to a major land protection organization in the United States. We also quantified the relationship between the amount of effort devoted to fundraising and donations received. Around 40% of the variation in the propensity to give and overall value of gifts was explained by sociodemographic and other predictors. For example, education level had greater predictive capacity than income, political views, and other factors often considered important. Fundraising effort was strongly predictive of the amount donated in an area. Our model estimated a doubling of funds raised with a 5‐fold increase of effort. Conservation organizations could use our statistical framework to inform efforts aimed at increasing philanthropic giving by identifying locations with large model residuals. An example application of our framework showed an almost 40% increase (US$200 million) in fundraising revenue for the case‐study conservation organization.
WIDEBAND MASSIVE MIMO CHANNEL ESTIMATION VIA SEQUENTIAL ATOMIC NORM MINIMIZATION

The recently introduced atomic norm minimization (ANM) framework for parameter estimation is a promising candidate towards low-overhead channel estimation in wireless communications. However, previous works on ANM-based channel estimation evaluated performance on channels with artificially imposed channel path separability, which cannot be guaranteed in practice. In addition, direct application of the ANM framework for massive MIMO channel estimation is computationally infeasible due to the large signal dimensions. In this paper, a low-complexity ANM-based channel estimator for wideband massive MIMO is proposed, consisting of two sequential steps, the first step estimating the channel over the angle-of-arival/spatial domain and the second step over the delay/frequency domain. Its mean squared error performance is analytically characterized in terms of tight lower bounds. It is shown that the proposed algorithm achieves excellent performance that is close to the best that can be achieved by any unbiased channel estimator in the regime of low to moderate number of channel paths, without any restrictions on their separability.
Sparse iterative learning control (SPILC): When to sample for resource-efficiency?

Iterative learning control enables the determination of optimal command inputs by learning from measured data of previous tasks. The aim of this paper is to address the negative impact of trial-varying disturbances that contaminate these measurements, both in terms of resource-efficient implementations and performance degradation. The proposed method is an optimal framework for ILC that enforces sparsity and related structure on the command signal. This is achieved through a convex relaxation relying on ℓ1 regularization. The approach is demonstrated on a benchmark motion system, confirming substantial extensions compared to earlier results.
Heterogeneous subgraph features for information networks

Networks play an increasingly important role in modelling real-world systems due to their utility in representing complex connections. For predictive analyses, the engineering of node features in such networks is of fundamental importance to machine learning applications, where the lack of external information often introduces the need for features that are based purely on network topology. Existing feature extraction approaches have so far focused primarily on networks with just one type of node and thereby disregarded the information contained in the topology of heterogeneous networks, or used domain specific approaches that incorporate node labels based on external knowledge. Here, we generalize the notion of heterogeneity and present an approach for the efficient extraction and representation of heterogeneous subgraph features. We evaluate their performance for rank- and label-prediction tasks and explore the implications of feature importance for prominent subgraphs. Our experiments reveal that heterogeneous subgraph features reach the predictive power of manually engineered features that incorporate domain knowledge. Furthermore, we find that heterogeneous subgraph features outperform state-of-the-art neural node embeddings in both tasks and across all data sets.
Free Dietary Choice and Free-Range Rearing Improve the Product Quality, Gait Score, and Microbial Richness of Chickens

Simple Summary The worldwide demand for productivity and quality meat, eggs, and other animal products is increasing. More and more people are expressing concerns relating to product quality and animal welfare. Our study aimed to provide scientific knowledge regarding how welfare factors contribute to quantity and quality of chicken. We used 400 Beijing You chickens to compare welfare factors by providing free dietary choice under cage rearing, and further comparing cage rearing with the free-range rearing system. Results showed that under cage rearing, free dietary choice of mealworms and fresh grass contributed to better meat quality, gait score and foot pad dermatitis than the conventional cage feeding and rearing system. This also gave rise to higher values of blood platelets and a richer gut microbial composition. As compared to caged chickens, free-range chickens developed better meat quality, gait score, and feather conditions, as well as a richer microbial composition. Our work provides a comprehensive understanding of welfare factors under both cage and free-range systems, and also broadens knowledge of health-related gut microbial composition in chickens. Abstract Poultry welfare has been extensively studied; however, there is a lack of rigorous scientific knowledge relating to the different aspects of welfare factors and how this may contribute to the production quantity and product quality as well as the welfare of chickens. Therefore, we conducted an integrated study to compare welfare factors in chickens by providing free dietary choice under cage rearing, and further comparing cage rearing with free-range rearing. One hundred chickens each were allocated to a cage rearing group with conventional feeding (CC), a cage rearing group with free dietary choice of mealworms (FDM), a cage rearing group with free dietary choice of mealworms and fresh grass (FDMG), and a free-range rearing system group with free dietary choice of mealworms and fresh grass (FRMG). Results showed that under cage rearing, free dietary choice contributed to better meat quality and gait score, higher values of blood platelets, and a richer gut microbial composition, but poorer egg production than CC chickens. As compared to FDMG, FRMG chickens showed better meat quality, gait score, and feather conditions, as well as a richer gut microbial composition; however, they had poorer egg production and a poorer foot pad and foot feather condition. We conclude that free dietary choice and free-range rearing systems improve the product quality, gait score, and microbial richness of chickens.
Treadmill Running Ameliorates Destruction of Articular Cartilage and Subchondral Bone, Not Only Synovitis, in a Rheumatoid Arthritis Rat Model

We analyzed the influence of treadmill running on rheumatoid arthritis (RA) joints using a collagen-induced arthritis (CIA) rat model. Eight-week-old male Dark Agouti rats were randomly divided into four groups: The control group, treadmill group (30 min/day for 4 weeks from 10-weeks-old), CIA group (induced CIA at 8-weeks-old), and CIA + treadmill group. Destruction of the ankle joint was evaluated by histological analyses. Morphological changes of subchondral bone were analyzed by μ-CT. CIA treatment-induced synovial membrane invasion, articular cartilage destruction, and bone erosion. Treadmill running improved these changes. The synovial membrane in CIA rats produced a large amount of tumor necrosis factor-α and Connexin 43; production was significantly suppressed by treadmill running. On μ-CT of the talus, bone volume fraction (BV/TV) was significantly decreased in the CIA group. Marrow star volume (MSV), an index of bone loss, was significantly increased. These changes were significantly improved by treadmill running. Bone destruction in the talus was significantly increased with CIA and was suppressed by treadmill running. On tartrate-resistant acid phosphate and alkaline phosphatase (TRAP/ALP) staining, the number of osteoclasts around the pannus was decreased by treadmill running. These findings indicate that treadmill running in CIA rats inhibited synovial hyperplasia and joint destruction.
Collective Household Welfare and Intra-household Inequality

We investigate the relationship between the individual and household indirect utility functions in the context of a collective household model. Our analysis produces new results that explain how the rule governing the distribution of resources among household members is related to the measurement of household welfare and intra-household inequality. We show that in a collective model of private consumption, income shares are equal to the product of two weights: the Pareto weight and a distribution weight reflecting income effects across individuals. For a weighted Bergsonian representation of household utility and general assumptions about individual preferences, we derive the associated household welfare functions and intra-household inequality measures belonging to a family of entropy indexes. We illustrate our findings with an empirical application that estimates a collective demand system to recover associated individual and household welfare functions along with the measures of intra-household inequality. This is the first application that estimates the Pareto weight and examines its role within a measure of income dispersion among household members.