Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of prostate cancer cell line supernatant on functional polarization in macrophages.

OBJECTIVES We aimed on effect of supernatant derived from prostate cancer cell line PC-3 on M1/M2 functional polarization in macrophages. BACKGROUND Cytokines play an important role in carcinogenesis. Most of them are produced by macrophages. Macrophages are divided into groups M1 or M2. Classical phenotype macrophages M1 support pro-inflammatory effects and produce pro-inflammatory cytokines, such as interleukin 6 (IL-6), interleukin 12 (IL-12), tumor necrosis factor α (TNF-α). Macrophages exhibiting a phenotype M2 secrete anti-inflammatory cytokines, e. g. interleukin 10 (IL-10), transforming growth factor β (TGF-β). METHODS Peripheral blood monocytes were cultivated for 7 days and during this time went through a differentiation into macrophages. Macrophages were stimulated for 24 hours by lipopolysaccharide (LPS) as a positive control and cultivated with supernatant for another 24 hours. RESULTS Macrophages cultivated without LPS and without supernatant were used as negative control. Relative expression of IL-6, IL-10, IL-12 and TNF-α was measured by Quantitative real-time PCR. Expression of pro-inflammatory cytokines was lower in macrophages with supernatant compared to positive control. CONCLUSION Expression of pro-inflammatory cytokines was lower in macrophages with supernatant (MΦ+sup) compared to positive control (MΦ+LPS). Effect of the supernatant on expression of IL-6, IL-10, IL-12 and TNF-α was not confirmed (Tab. 1, Fig. 5, Ref. 15).
Cost Comparison of Lambda Architecture Implementations for Transportation Analytics using Public Cloud Software as a Service

Lambda architecture has gained high relevance for big data analytics by offering mixed and coordinated data processing: real time processing for fast data streams and batch processing for large workloads with high latency. However, concrete implementations over cloud infrastructures and cost comparisons are still not being sufficiently analyzed. This paper presents a cost comparison of Lambda architecture implementations using Software as a Service (SaaS) to support IT decision makers when streaming-analytics solutions must be implemented. To do that, a case study of transportation analytics is developed on three public cloud providers: Google Cloud Platform, Microsoft Azure, and Amazon Web Services Cloud. The evaluation is carried out by comparing deployment, configuration, development, and performance costs in a publictransportation delay-monitoring case study assessing various concurrency scenarios.
Occupational Therapist‐Delivered Cognitive‐Behavioral Therapy for Knee Osteoarthritis: A Randomized Pilot Study

OBJECTIVE. This study assessed the feasibility and preliminary efficacy of an online‐assisted, occupational therapist‐delivered, cognitive‐behavioral therapy intervention to promote physical function in patients with knee osteoarthritis (KOA). METHOD. Fifty‐seven participants with KOA were randomized 2:1 to the Engage program (eight clinic‐based sessions supported by online modules) or usual care. Using analysis of covariance, we estimated Engage's effect on physical function (Western Ontario and McMaster Universities Osteoarthritis Index's Physical Function subscale [WOMAC‐PF]) at 6 mo. RESULTS. Data were analyzed on 46 completers. Engage was associated with a small effect (η2 = 0.01) on the WOMAC‐PF. More Engage participants than controls reported much or very much improvement (45% vs. 13%; p = .03). Satisfaction was high, and 30 of 31 participants attended six sessions or more. CONCLUSION. An online‐supported cognitive‐behavioral program for people with KOA delivered by occupational therapists is feasible and may contribute to improved physical function. &NA; A web‐supported cognitive‐behavioral program for people with osteoarthritis delivered by occupational therapists was found to be feasible and may contribute to improved physical function.
Decoupling Strategy and Generation in Negotiation Dialogues

We consider negotiation settings in which two agents use natural language to bargain on goods. Agents need to decide on both high-level strategy (e.g., proposing $50) and the execution of that strategy (e.g., generating “The bike is brand new. Selling for just $50! ”). Recent work on negotiation trains neural models, but their end-to-end nature makes it hard to control their strategy, and reinforcement learning tends to lead to degenerate solutions. In this paper, we propose a modular approach based on coarse dialogue acts (e.g., propose(price=50)) that decouples strategy and generation. We show that we can flexibly set the strategy using supervised learning, reinforcement learning, or domain-specific knowledge without degeneracy, while our retrieval-based generation can maintain context-awareness and produce diverse utterances. We test our approach on the recently proposed DEALORNODEAL game, and we also collect a richer dataset based on real items on Craigslist. Human evaluation shows that our systems achieve higher task success rate and more human-like negotiation behavior than previous approaches.
High Speed Efficient Multiplier Design using Reversible Gates

Now-$a$-days, reversible logic is getting huge interest among the IC designer because it consumes less power. Reversible logic has been found in applications like Digital signal processing, DNA and quantum computing and high speed VLSI design. The implementation of reversible logic contains number of reversible logic gates. In this work, a multiplier is designed using HNG gate. An efficient high speed multiplier has been proposed using verilog coding and Cadence 180 nm technology is used for its implementation. Compared to the existing multiplier, the proposed multiplier consumes less power and comparatively less quantum cost. Hence, the proposed multiplier results in less power consumption without sacrificing the speed.
Inflammation and NF-κB Signaling in Prostate Cancer: Mechanisms and Clinical Implications

Prostate cancer is a highly prevalent form of cancer that is usually slow-developing and benign. Due to its high prevalence, it is, however, still the second most common cause of death by cancer in men in the West. The higher prevalence of prostate cancer in the West might be due to elevated inflammation from metabolic syndrome or associated comorbidities. NF-κB activation and many other signals associated with inflammation are known to contribute to prostate cancer malignancy. Inflammatory signals have also been associated with the development of castration resistance and resistance against other androgen depletion strategies, which is a major therapeutic challenge. Here, we review the role of inflammation and its link with androgen signaling in prostate cancer. We further describe the role of NF-κB in prostate cancer cell survival and proliferation, major NF-κB signaling pathways in prostate cancer, and the crosstalk between NF-κB and androgen receptor signaling. Several NF-κB-induced risk factors in prostate cancer and their potential for therapeutic targeting in the clinic are described. A better understanding of the inflammatory mechanisms that control the development of prostate cancer and resistance to androgen-deprivation therapy will eventually lead to novel treatment options for patients.
Deep Learning for Household Load Forecasting—A Novel Pooling Deep RNN

The key challenge for household load forecasting lies in the high volatility and uncertainty of load profiles. Traditional methods tend to avoid such uncertainty by load aggregation (to offset uncertainties), customer classification (to cluster uncertainties) and spectral analysis (to filter out uncertainties). This paper, for the first time, aims to directly learn the uncertainty by applying a new breed of machine learning algorithms—deep learning. However, simply adding layers in neural networks will cap the forecasting performance due to the occurrence of over-fitting. A novel pooling-based deep recurrent neural network is proposed in this paper which batches a group of customers’ load profiles into a pool of inputs. Essentially the model could address the over-fitting issue by increasing data diversity and volume. This paper reports the first attempts to develop a bespoke deep learning application for household load forecasting and achieved preliminary success. The developed method is implemented on Tensorflow deep learning platform and tested on 920 smart metered customers from Ireland. Compared with the state-of-the-art techniques in household load forecasting, the proposed method outperforms ARIMA by 19.5%, SVR by 13.1% and classical deep RNN by 6.5% in terms of RMSE.
One month of hyperglycemia alters spectral responses of the zebrafish photopic electroretinogram

ABSTRACT Prolonged hyperglycemia can alter retinal function, ultimately resulting in blindness. Adult zebrafish adults exposed to alternating conditions of 2% glucose/0% glucose display a 3× increase in blood sugar levels. After 4 weeks of treatment, electroretinograms (ERGs) were recorded from isolated, perfused, in vitro eyecups. Control animals were exposed to alternating 2% mannitol/0% mannitol (osmotic control) or to alternating water (0% glucose/0% glucose; handling control). Two types of ERGs were recorded: (1) native ERGs measured using white-light stimuli and medium without synaptic blockers; and (2) spectral ERGs measured with an AMPA/kainate receptor antagonist, isolating photoreceptor-to-ON-bipolar-cell synapses, and a spectral protocol that separated red (R), green (G), blue (B) and UV cone signals. Retinas were evaluated for changes in layer thickness and for the inflammatory markers GFAP and Nf-κB (RelA or p65). In native ERGs, hyperglycemic b- and d-waves were lower in amplitude than the b- and d-waves of mannitol controls. Alteration of waveshape became severe, with b-waves becoming more transient and ERG responses showing more PIII-like (a-wave) characteristics. For spectral ERGs, waveshape appeared similar in all treatment groups. However, a1- and b2-wave implicit times were significantly longer, and amplitudes were significantly reduced, in response to hyperglycemic treatment, owing to the functional reduction in signals from R, G and B cones. Nf-κB increased significantly in hyperglycemic retinas, but the increase in GFAP was not significant and retinal layer thickness was unaffected. Thus, prolonged hyperglycemia triggers an inflammatory response and functional deficits localized to specific cone types, indicating the rapid onset of neural complications in the zebrafish model of diabetic retinopathy. Summary: Zebrafish can be used to examine diabetic complications, including vision loss. Here, in zebrafish, we show that prolonged (4 week) hyperglycemia causes an inflammatory response associated with functional deficits localized to specific cone types.
Robust Face Recognition Approaches Using PCA, ICA, LDA Based on DWT, and SVM Algorithms

Face Recognition represents one of the attracting research areas. It has drawn the attention of many researchers due to its varying applications such as database identification, identity authentication, surveillance and security access control human computer interaction. … In this order, different face recognition algorithms are proposed. These methods are based on the face representations. In order to use the most important information in the face representation, we propose to exploit the concept of 2D-DWT for the image compression as a preprocessing for feature extraction. In fact, the DWT is performed at different scales and orientations so it is sensitive to strong lighting conditions and facial details. Based on the DWT advantages, the LL sub-band of the processed image is used as input image for feature extraction process based on ICA, PCA, LDA and SVM algorithms. The importance of the proposed approaches is evaluated by experimental and comparative studies tested on AT&T database.
Bioinformatic evidence of widespread priming in type I and II CRISPR-Cas systems

ABSTRACT CRISPR-Cas systems provide bacteria and archaea with adaptive immunity against invading genetic elements, such as plasmids, bacteriophages and archaeal viruses. They consist of cas genes and CRISPR loci, which store genetic memories of previously encountered invaders as short sequences termed spacers. Spacers determine the specificity of CRISPR-Cas defence and immunity can be gained or updated by the addition of new spacers into CRISPR loci. There are two main routes to spacer acquisition, which are known as naïve and primed CRISPR adaptation. Naïve CRISPR adaptation involves the de novo formation of immunity, independent of pre-existing spacers. In contrast, primed CRISPR adaptation (priming) uses existing spacers to enhance the acquisition of new spacers. Priming typically results in spacer acquisition from locations near the site of target recognition by the existing (priming) spacer. Primed CRISPR adaptation has been observed in several type I CRISPR-Cas systems and it is potentially widespread. However, experimental evidence is unavailable for some subtypes, and for most systems, priming has only been shown in a small number of hosts. There is also no current evidence of priming by other CRISPR-Cas types. Here, we used a bioinformatic approach to search for evidence of priming in diverse CRISPR-Cas systems. By analysing the clustering of spacers acquired from phages, prophages and archaeal viruses, including strand and directional biases between subsequently acquired spacers, we demonstrate that two patterns of primed CRISPR adaptation dominate in type I systems. In addition, we find evidence of a priming-like pathway in type II CRISPR-Cas systems.
The Impacts of Land Use Patterns on Water Quality in a Trans-Boundary River Basin in Northeast China Based on Eco-Functional Regionalization

The relationships between land use patterns and water quality in trans-boundary watersheds remain elusive due to the heterogeneous natural environment. We assess the impact of land use patterns on water quality at different eco-functional regions in the Songhua River basin during two hydrological seasons in 2016. The partial least square regression indicated that agricultural activities associated with most water quality pollutants in the region with a relative higher runoff depth and lower altitude. Intensive grazing had negative impacts on water quality in plain areas with low runoff depth. Forest was related negatively with degraded water quality in mountainous high flow region. Patch density and edge density had major impacts on water quality contaminants especially in mountainous high flow region; Contagion was related with non-point source pollutants in mountainous normal flow region; landscape shape index was an effective indicator for anions in some eco-regions in high flow season; Shannon’s diversity index contributed to degraded water quality in each eco-region, indicating the variation of landscape heterogeneity influenced water quality regardless of natural environment. The results provide a regional based approach of identifying the impact of land use patterns on water quality in order to improve water pollution control and land use management.
Learning To Split and Rephrase From Wikipedia Edit History

Split and rephrase is the task of breaking down a sentence into shorter ones that together convey the same meaning. We extract a rich new dataset for this task by mining Wikipedia’s edit history: WikiSplit contains one million naturally occurring sentence rewrites, providing sixty times more distinct split examples and a ninety times larger vocabulary than the WebSplit corpus introduced by Narayan et al. (2017) as a benchmark for this task. Incorporating WikiSplit as training data produces a model with qualitatively better predictions that score 32 BLEU points above the prior best result on the WebSplit benchmark.
Error Reduction of The Low-Frequency Sampling Method for Measuring The Current, Voltage And Power of a Cage Induction Motor

The low-frequency sampling method consists in sampling a periodic signals (in this case sinusoidal) at intervals much greater than its period. Nevertheless, it is possible to obtain all relevant electrical information such as active, reactive power and rms values. The low-frequency sampling method allows a significant reduction of computing power and, consequently, enables on-line power and RMS values calculation by a simple PLC microcontroller without overloading it and enabling its normal operation as a controller. The article presents a way of reducing measurement error of the low-frequency sampling method.
Effects of Smoking Cessation Intervention Education Program Based on Blended Learning among Nursing Students in South Korea

Objectives This study was conducted to evaluate whether a “smoking cessation intervention” education program based on blended learning, was effective in improving nursing students’ perceived competence and motivation to perform a smoking cessation intervention for smokers. Methods A quasi-experimental, pretest–posttest design was conducted. The smoking cessation intervention education program based on blended learning, was administered to the experimental group (n = 23) in 5 sessions, consisting of 2 courses of an e-learning program and 1 course of a face-to-face learning program per session. The control group (n = 21) received the opportunity to participate in an e-learning program as well as receiving material of a face-to-face learning program, after completion of the smoking cessation intervention education program. Results The experimental group showed significant differences in autonomous motivation (t = −6.982, p < 0.001), controlled motivation (t = −3.729, p = 0.001), and perceived competence compared to the control group (t = −3.801, p < 0.001). Conclusion This study showed that a smoking cessation intervention education program adopting blended learning, was significantly effective in enhancing nursing students’ autonomous motivation and perceived competence to conduct a smoking cessation intervention. Further studies are needed to confirm longitudinal effects of this program.
Association of Daily Workload, Wellness, and Injury and Illness During Tours in International Cricketers.

PURPOSE To examine the relationship between player internal workloads, daily wellness monitoring, and injury and illness in a group of elite adolescent cricketers during overseas competitions. METHODS A total of 39 male international adolescent cricketers (17.5 [0.8] y) took part in the study. Data were collected over 5 tours across a 3-y period (2014-2016). Measures of wellness were recorded and daily training loads were calculated using session rating of perceived exertion. The injury and illness status of each member of the squad was recorded daily. Acute and chronic workloads were calculated using 3-d and 14-d moving averages. Acute workloads, chronic workloads, and acute chronic workload ratios were independently modeled as fixed effects predictor variables. RESULTS In the subsequent week, a high 3-d workload was significantly associated with an increased risk of injury (relative risk = 2.51; CI = 1.70-3.70). Similarly, a high 14-d workload was also associated with an increased risk of injury (relative risk = 1.48; CI = 1.01-2.70). Individual differences in the load-injury relationship were also found. No clear relationship between the acute chronic workload ratios and injury risk was found, but high chronic workloads combined with high or low acute chronic workload ratios showed an increased probability of injury compared with moderate chronic workloads. There were also trends for sleep quality and cold symptoms worsening the week before an injury occurred. CONCLUSION Although there is significant individual variation, short-term high workloads and change in wellness status appear to be associated with injury risk.
Architectural Pattern of Progressive Web and Background Synchronization

Progressive Web Applications emerged as a better alternative to traditional mobile application development. Possessing the best of both native applications and web applications, PWA also offers advantages like offline loading of the application as well as services like push notifications and background synchronization to enhance user engagement. The paper provides an overview of using Progressive Web Apps as modern application development cycle. We have discussed in detail in the paper how progressiveness in a web application offers advantage over native application. The paper also discusses about the architectural pattern followed to develop progressive web apps to improve loading time of the app. API provided by the service workers have also been discussed in detail including push notifications and background synchronization to provide native app like experience. A case study has been carried out to depict the working of various progressive features.
Hippocampusjapapigu, a new species of pygmy seahorse from Japan, with a redescription of H.pontohi (Teleostei, Syngnathidae)

Abstract The pygmy seahorse Hippocampusjapapigusp. n. is described based on three specimens, 13.9–16.3 mm SL, collected from a mixed soft coral and algae reef at 11 m depth at Hachijo-jima Island, Izu Islands, Japan. The new taxon shares morphological synapomorphies with the previously described central Indo-Pacific pygmy seahorses, H.colemani, H.pontohi, H.satomiae, and H.waleananus, including extremely small size, 12 trunk rings, strongly raised continuous cleithral ring, snout spine, large spine on the eighth lateral and fifth and 12 superior trunk ridges, respectively, and unusual wing-like-protrusions immediately posterior to the head. Hippocampusjapapigusp. n. can be distinguished from all congeners by the following combination of features in the anterodorsal area of the trunk: bilaterally paired wing-like protrusions formed by a single pair of large, truncate spines projecting dorsolaterad on the first superior trunk ridge, followed by a unique elevated dorsal ridge formed by triangular bony mounds dorsally on the second to fourth superior trunk ridges. In contrast, H.pontohi possesses a pair of large truncate spines projecting strongly laterad on both the first and second superior trunk ridges followed by flat surfaces dorsally on the third and fourth superior trunk rings. The new species can be further differentiated by genetic divergence from H.pontohi (an uncorrected p-distance of 10.1% in the mitochondrial COI gene) and a striking reticulated white and brown lattice pattern on the head, trunk, and tail. Hippocampusjapapigusp. n. represents the fifth species of pygmy seahorse recorded in Japan.
An Accurate Geometrical Multi-Target Device-Free Localization Method Using Light Sensors

Device-free localization (DFL) is a technique that can locate targets wearing no devices, and has become an attractive technique for wide applications, such as intrusion detection and gesture recognition. In recent years, research on indoor localization using light sensor mainly focuses on traditional active localization methods, which require photodiodes being attached to targets. In this paper, we explore the visible light-based DFL method. In DFL, shadowed areas caused by different targets may overlap when multiple targets simultaneously exist in a small monitored area, which increases difficulties both in counting target and achieving accuracy localization. This paper presents an accurate geometrical multi-target device-free localization method, which includes a sector ring model and an adaptive multi-target positioning (AMTP) algorithm. The proposed model does well in describing the relationship between the target location and the shadowed area, which provides essential observation information for DFL. The proposed AMTP algorithm improves the accuracy of counting and locating multiple targets through updating weight map of the monitored area in each iteration. Numerous simulation results show that the proposed DFL method effectively achieves multi-target location, and outperforms the traditional geometrical localization method and the fingerprinting localization method both in counting and localization accuracy.