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Background: Frizzled (FZD) family works with the ligand Wnt protein to mediate downstream signals in the Wnt signaling pathway, while playing a superior role in the body. Therefore, this study was carried out to detect the expression patterns of FZD4 at the gene and protein levels. Methods: 10 developing and postnatal geese dorsal skin of Anser anser (n=5) and Anser cygnoides (n=5). Quantitative real-time polymerase chain reaction (qPCR) and western blotting were used to detect relative expression levels of FZD4. Feather follicles membrane localization was examined using immunohistochemistry. Student’s t-test and one-way ANOVA were used for statistical analysis. Result: The results revealed that the FZD4 was expressed differently at mRNA and protein levels at six stages. Furthermore, the positive expression sites of FZD4 protein were gradually transferred from dermal papilla; pulp; follicle wall and feather sheath. These results suggest that FZD4 is expressed in the growth and development of feather follicles for Anser anser and Anser cygnoides.

Background: In recent years, it has been reported that melatonin plays an important role in the development of skin hair follicles in mammals. The down we often use is mainly from the skin feather follicles of geese, but their production is not high. Therefore, the study of skin feather follicles is of great significance. Methods: The skin feather follicle tissues of goose dorsal skin at three developmental nodes (E14, E18 and E28) were collected; the differential expression and distribution of melatonin receptor genes were detected by fluorescence quantitative PCR, Western Blot and immunohistochemical methods, respectively. The results showed that melatonin receptor gene was expressed in varying degrees at three time points of goose embryonic skin feather follicle development. The expression sites and Protein expression level of Mel1a and RORα were regularly changed by immunohistochemistry and Western Blot. Result: Our research showed that melatonin receptor genes can play an important regulatory role in the development of goose skin feather follicles.

Edge computing was motivated by the vision of new edge-native applications that are compute-intensive, bandwidth-hungry, and latency-sensitive. We show how infrastructure deployed for such futuristic applications can also benefit virtual machine (VM)-encapsulated Windows or Linux closed-source legacy applications. We present a new capability for legacy applications called edge-based virtual desktop infrastructure (EdgeVDI), and discuss example use cases that it enables.

Real-time video analytics on small autonomous drones poses several difficult challenges at the intersection of wireless bandwidth, processing capacity, energy consumption, result accuracy, and timeliness of results. In response to these challenges, this paper describes four strategies to build adaptive computer vision pipelines for domains such as search-and-rescue, surveillance, and wildlife conservation. Our experimental results show that a judicious combination of drone-based processing and edge-based processing can save substantial wireless bandwidth and thus improve scalability, without compromising result accuracy or latency.

The generation of high-quality training data has become the key bottleneck in the use of deep learning across many domains. In this paper, we describe Eureka, an interactive system that leverages edge computing and early discard to greatly improve the productivity of experts in the construction of a labeled dataset. Our experimental results show that Eureka reduces the labeling effort needed to construct a training set by two orders of magnitude relative to a brute-force approach.

A Sequence OLAP (S-OLAP) system provides a platform on which pattern-based aggregate (PBA) queries on a sequence database are evaluated. In its simplest form, a PBA query consists of a pattern template $T$ and an aggregate function $F$ . A pattern template is a sequence of variables, each is defined over a domain. Each variable is instantiated with all possible values in its corresponding domain to derive all possible patterns of the template. Sequences are grouped based on the patterns they possess. The answer to a PBA query is a sequence cuboid (s-cuboid), which is a multidimensional array of cells. Each cell is associated with a pattern instantiated from the query's pattern template. The value of each s-cuboid cell is obtained by applying the aggregate function $F$ to the set of data sequences that belong to that cell. Since a pattern template can involve many variables and can be arbitrarily long, the induced s-cuboid for a PBA query can be huge. For most analytical tasks, however, only iceberg cells with very large aggregate values are of interest. This paper proposes an efficient approach to identifying and evaluating iceberg cells of s-cuboids. Experimental results show that our algorithms are orders of magnitude faster than existing approaches.

Wingless-types/beta-catenin (Wnt/β-catenin) signaling pathway is one of the most extensively studied transcriptional cascades involved in various types of organogenesis including embryonic and postnatal development. Downy feather quantity is primarily affected by follicular development and gene regulations. This research was aimed to investigate the role of catenin beta-1(CTNNB1) and lymphoid enhancerbinding factor-1 (LEF1) on feather follicles development at different developmental stages. Fluorescence quantitative PCR, Western-blot and immunohistochemical methods were used in Anser cygnoides and Anser anser embryos (E12, E13 E18, and E28) and after birth gosling stages (G18, G48, G88) for gene expression analysis. CTNNB1 and LEF1 genes were expressed in Anser cygnoides and Anser anser at different embryonic and after-birth gosling developmental stages and the expression levels were significantly different in different stages (p

Latency-sensitive edge-native applications may be the key to commercial success of edge infrastructure. However, success in the form of widespread deployment of such applications poses its own challenges. These applications are edge-dependent by definition, and therefore cannot simply fail over to the cloud if the edge is overloaded. In this paper, we propose an adaptation-based strategy to allow scaling up the number of concurrent edge-native applications on a resource-limited cloudlet and wireless network. We demonstrate up to 40% reduction in offered load with minimal impact on latency on a variety of cognitive assistance tasks over non-adaptive approaches. Our approach is able to gracefully degrade and maintain quality of service for a subset of applications in the face of severely loaded conditions.

Real-time video analytics on small autonomous drones poses several difficult challenges at the intersection of wireless bandwidth, processing capacity, energy consumption, result accuracy, and timeliness of results. In response to these challenges, we describe four strategies to build an adaptive computer vision pipeline for search tasks in domains such as search-and-rescue, surveillance, and wildlife conservation. Our experimental results show that a judicious combination of drone-based processing and edge-based processing can save substantial wireless bandwidth and thus improve scalability, without compromising result accuracy or result latency.

We show how edge-based early discard of data can greatly improve the productivity of a human expert in assembling a large training set for machine learning. This task may span multiple data sources that are live (e.g., video cameras) or archival (data sets dispersed over the Internet). The critical resource here is the attention of the expert. We describe Eureka, an interactive system that leverages edge computing to greatly improve the productivity of experts in this task. Our experimental results show that Eureka reduces the labeling effort needed to construct a training set by two orders of magnitude relative to a brute-force approach.

Accurate, up-to-date maps of transient traffic and hazards are invaluable to drivers, city managers, and the emerging class of self-driving vehicles. We present LiveMap, a scalable, automated system for acquiring, curating, and disseminating detailed, continually-updated road conditions in a region. LiveMap leverages in-vehicle cameras, sensors, and processors to crowd-source hazard detection without human intervention. We build a real-time simulation framework that allows a mix of real and simulated components to be tested together at scale. We demonstrate that LiveMap can work well at city scales within the limits of today's cellular network bandwidth. We also show the feasibility of accurate, in-vehicle, computer-vision-based hazard detection.

Cooperative transmission can significantly reduce the power consumption associated with long distance transmission in wireless sensor networks (WSNs). In this paper, we analyze the optimal power consumption of cluster-based multi-hop transmission for a cooperative WSN. With specific Quality of Service (QoS) constraints on delay and channel capacity, we show that the power optimization problem of the whole network has no closed-form solution in a slow flat Rayleigh fading environment. Thus we propose a dynamic power control and optimization (DPCO) scheme that can jointly determine the optimal number of cooperative sensors and their transmission power. We further propose a channel approximation algorithm that can significantly reduce the computational complexity of the DPCO scheme.

Recently, Amazon has announced Redshift, a Parallel-Database-as-a Service (PDaaS). Redshift adopts the "virtual cluster" approach to implement multitenancy, which has the merit of hard isolation among tenants (i.e., tenants do not interfere even when sharing resources). However, that benefit comes with poor resource utilization due to the significant redundancy incurred in the resources. In this demonstration, we present Thrifty, a Parallel-Database-as-a-Service operated using the "shared-process" approach. Compared with Redshift, each tenant in Thrifty does not occupy an exclusive amount of resource but share the database processes together, leading to better resource utilization. To avoid contention among tenants, Thrifty uses a proper cluster design, a tenant placement scheme, and a query routing mechanism to achieve soft isolation. In the demonstration, an attendee will be invited to register with Thrifty as a tenant to rent a parallel database instance. Then the attendee will be allowed to view the dashboard of a Thrifty's administrator. Next, the attendee will be invited to control (e.g., increase) the workload of the tenant so as to see how Thrifty carries out online re- consolidation and elastic scaling.

Scan and lookup are two core operations in main memory column stores. A scan operation scans a column and returns a result bit vector that indicates which records satisfy a filter. Once a column scan is completed, the result bit vector is converted into a list of record numbers, which is then used to look up values from other columns of interest for a query. Recently there are several in-memory data layout proposals that aim to improve the performance of in-memory data processing. However, these solutions all stand at either end of a trade-off --- each is either good in lookup performance or good in scan performance, but not both. In this paper we present ByteSlice, a new main memory storage layout that supports both highly efficient scans and lookups. ByteSlice is a byte-level columnar layout that fully leverages SIMD data-parallelism. Micro-benchmark experiments show that ByteSlice achieves a data scan speed at less than 0.5 processor cycle per column value --- a new limit of main memory data scan, without sacrificing lookup performance. Our experiments on TPC-H data and real data show that ByteSlice offers significant performance improvement over all state-of-the-art approaches.

Modern CPUs have word width of 64 bits but real data values are usually represented using bits fewer than a CPU word. This underutilization of CPU at register level has motivated the recent development of bit-parallel algorithms that carry out data processing operations (e.g., filter scan) on CPU words packed with data values (e.g., 8 data values are packed into one 64-bit word). Bit-parallel algorithms fully unleash the intra-cycle parallelism of modern CPUs and they are especially attractive to main-memory column stores whose goal is to process data at the speed of the “bare metal”. Main-memory column stores generally focus on analytical queries, where aggregation is a common operation. Current bit-parallel algorithms, however, have not covered aggregation yet. In this paper, we present a suite of bit-parallel algorithms to accelerate all standard aggregation operations: SUM, MIN, MAX, AVG, MEDIAN, COUNT. The algorithms are designed to fully leverage the intra-cycle parallelism in CPU cores when aggregating words of packed values. Experimental evaluation shows that our bit-parallel aggregation algorithms exhibit significant performance benefits compared with non-bit-parallel methods.

To investigate the inhibitory effect of trichostatin A (TSA) on the proliferation of HepG2 cells and explore the underlying mechanism.HepG2 cells exposed to different concentrations of TSA for 24, 48, or 72 h were examined for cell growth inhibition using a cell counting kit, changes in cell cycle distribution with flow cytometry, cell apoptosis with annexin V-FTIC/PI double staining, and cell morphology changes under inverted microscope. The expressions of beta-catenin, HDAC1, HDAC3, H3K9, cyclinD1 and Bax proteins in the exposed cells were detected by Western blotting, and the expressions of HDAC1 and HDAC3 mRNAs by quantitative fluorescent PCR.Exposure to TSA caused significant dose- and time-dependent inhibition of HepG2 cell proliferation (P0.05) and resulted in increased cell percentage in G0/G1 and G2/M phases and decreased cell percentage in S phase. The apoptotic index in the control group was (6.22 ± 0.25)%, which increased to (7.17 ± 0.20)% and (18.14 ± 0.42)% after exposure to 250 and 500 nmol/L TSA, respectively. Exposure to 250 and 500 nmol/L TSA also caused cell morphology changes with numerous floating cells. The expressions of beta-catenin, H3K9 and Bax proteins were significantly increased and CyclinD1, HDAC1, and HDAC3 protein expressions decreased in TSA-treated cells, but the expressions of HDAC1 and HDAC3 mRNAs showed no significant changes.TSA can inhibit the proliferation of HepG2 cells and induce cell cycle arrest and apoptosis by inhibiting HDAC activity, promoting histone acetylation, and activating Wnt/beta-catenin signaling pathway.

Multigrade asphalt cement (MAC) is a liquefied binder material by chemical method and is widely applied in heavy traffic pavement environments. Its high construction temperature restricts its application in low temperature. The latest Evotherm product (Evotherm warm-mixed asphalt, EWMA) was added into MAC to produce a composite binder. Dense graded hot-mixed mixture AC-20 was selected as the studied aim, and Marshall method was used when the mixture was designed. MAC added in different dosages of EWMA (EWMA-MAC) was mixed with aggregate at 180°C and compacted at temperatures of 155°C, 140°C, and 125°C. The air voids of EWMA-MAC mixture with 0.7% EWMA compacted at 155°C and 140°C were very close to those of MAC mixture compacted at 170°C. Water stability, high and low temperature properties of EWMA-MAC mixture with 0.7% EWMA compacted at 155°C, 140°C, and 125°C were measured and discussed. The field tested results showed that EWMA technology can improve the workability of MAC mixture at low temperature and obtain good early pavement performance.

Cell selection is an important issue in femtocell networks, which can balance the utilization of the whole network. In this paper, we investigate cell selection problem in a two-tier femtocell network that contains a micro base station (MBS) and several femtocells with different access methods and coverage areas. We propose the evolutionary game model to describe the dynamics of the cell selection process and consider the evolutionary equilibrium as the solution. In order to achieve the evolutionary equilibrium, we introduce the reinforcement learning algorithm that can help distributed individual users make selection decisions independently. With their own knowledge of the past, the users can learn to achieve the evolutionary equilibrium without complete knowledge of other users. Finally, the performance of the evolutionary game and reinforcement learning algorithm is analyzed, and simulation results show the convergence and effectiveness of the proposed algorithm.

A joint access control and subchannel allocation (JACSA) scheme is proposed in this paper for femtocell-based machine-to-machine (M2M) network to provide better communication services. As short-range and cost-beneficial eNodeBs, femtocells can improve indoor coverage and data transmission, and can further be used for M2M communication. There are two challenges for femtocell network, access control and truth-telling. Femtocell machine-type communication devices (FMD) select femtocell access points (FAPs) according to the reported channel capacities, while the true values are private information of each femtocell. Therefore, selfish FAPs have incentive to report larger capacities to win greater opportunity to be selected. To solve the aforementioned two problems, a JACSA scheme based on the Arrow-d'Aspremont-Gerard-Varet (AGV) is proposed both in open access and hybrid access scenarios, and formula derivations are given. We prove that, compared with the optimal subchannel allocation (OSA) scheme, JACSA scheme has the feature of achieving near optimal performances with much lower computational complexity. Furthermore, we compare the allocation results for open access and hybrid access in the proposed JACSA scheme. Finally, simulations are performed, and the results verify the availability of our proposed scheme.