Domestication and selection footprints in Persian walnuts (Juglans regia).

Walnut (Juglans) species are economically important hardwood trees cultivated worldwide for both edible nuts and high-quality wood. Broad-scale assessments of species diversity, evolutionary history, and domestication are needed to improve walnut breeding. In this study, we sequenced 309 walnut accessions from around the world, including 55 Juglans relatives, 98 wild Persian walnuts (J. regia), 70 J. regia landraces, and 86 J. regia cultivars. The phylogenetic tree indicated that J. regia samples (section Dioscaryon) were monophyletic within Juglans. The core areas of genetic diversity of J. regia germplasm were southwestern China and southern Asia near the Qinghai-Tibet Plateau and the Himalayas, and the uplift of the Himalayas was speculated to be the main factor leading to the current population dynamics of Persian walnut. The pattern of genomic variation in terms of nucleotide diversity, linkage disequilibrium, single nucleotide polymorphisms, and insertions/deletions revealed the domestication and selection footprints in Persian walnut. Selective sweep analysis, GWAS, and expression analysis further identified two transcription factors, JrbHLH and JrMYB6, that influence the thickness of the nut diaphragm as loci under selection during domestication. Our results elucidate the domestication and selection footprints in Persian walnuts and provide a valuable resource for the genomics-assisted breeding of this important crop.

A Millimeter-Wave Broadband Multi-Mode Substrate-Integrated Gap Waveguide Traveling-Wave Antenna with Orbit Angular Momentum.

Orbit angular momentum (OAM) has been considered a new dimension for improving channel capacity in recent years. In this paper, a millimeter-wave broadband multi-mode waveguide traveling-wave antenna with OAM is proposed by innovatively utilizing the transmitted electromagnetic waves (EMWs) characteristic of substrate-integrated gap waveguides (SIGWs) to introduce phase delay, resulting in coupling to the radiate units with a phase jump. Nine "L"-shaped slot radiate elements are cut in a circular order at a certain angle on the SIGW to generate spin angular momentum (SAM) and OAM. To generate more OAM modes and match the antenna, four "Π"-shaped slot radiate units with a 90° relationship to each other are designed in this circular array. The simulation results show that the antenna operates at 28 GHz, with a -10 dB fractional bandwidth (FBW) = 35.7%, ranging from 25.50 to 35.85 GHz and a VSWR ≤ 1.5 dB from 28.60 to 32.0 GHz and 28.60 to 32.0 GHz. The antenna radiates a linear polarization (LP) mode with a gain of 9.3 dBi at 34.0~37.2 GHz, a l = 2 SAM-OAM (i.e., circular polarization OAM (CP-OAM)) mode with 8.04 dBi at 25.90~28.08 GHz, a l = 1 and l = 2 hybrid OAM mode with 5.7 dBi at 28.08~29.67 GHz, a SAM (i.e., left/right hand circular polarization (L/RHCP) mode with 4.6 dBi at 29.67~30.41 GHz, and a LP mode at 30.41~35.85 GHz. In addition, the waveguide transmits energy with a bandwidth ranging from 26.10 to 38.46 GHz. Within the in-band, only a quasi-TEM mode is transmitted with an energy transmission loss |S21| ≤ 2 dB.

Quaternary Ammonium Salts: Insights into Synthesis and New Directions in Antibacterial Applications.

The overuse of antibiotics has led to the emergence of a large number of antibiotic-resistant genes in bacteria, and increasing evidence indicates that a fungicide with an antibacterial mechanism different from that of antibiotics is needed. Quaternary ammonium salts (QASs) are a biparental substance with good antibacterial properties that kills bacteria through simple electrostatic adsorption and insertion into cell membranes/altering of cell membrane permeability. Therefore, the probability of bacteria developing drug resistance is greatly reduced. In this review, we focus on the synthesis and application of single-chain QASs, double-chain QASs, heterocyclic QASs, and gemini QASs (GQASs). Some possible structure-function relationships of QASs are also summarized. As such, we hope this review will provide insight for researchers to explore more applications of QASs in the field of antimicrobials with the aim of developing systems for clinical applications.

Genome-wide association analysis of 101 accessions dissects the genetic basis of shell thickness for genetic improvement in Persian walnut (Juglans regia L.).

BACKGROUND: Understanding the underlying genetic mechanisms that drive phenotypic variations is essential for enhancing the efficacy of crop improvement. Persian walnut (Juglans regia L.), which is grown extensively worldwide, is an important economic tree fruit due to its horticultural, medicinal, and material value. The quality of the walnut fruit is related to the selection of traits such as thinner shells, larger filling rates, and better taste, which is very important for breeding in China. The complex quantitative fruit-related traits are influenced by a variety of physiological and environmental factors, which can vary widely between walnut genotypes. RESULTS: For this study, a set of 101 Persian walnut accessions were re-sequenced, which generated a total of 906.2 Gb of Illumina sequence data with an average read depth of 13.8× for each accession. We performed the genome-wide association study (GWAS) using 10.9 Mb of high-quality single-nucleotide polymorphisms (SNPs) and 10 agronomic traits to explore the underlying genetic basis of the walnut fruit. Several candidate genes are proposed to be involved in walnut characteristics, including JrPXC1, JrWAKL8, JrGAMYB, and JrFRK1. Specifically, the JrPXC1 gene was confirmed to participate in the regulation of secondary wall cellulose thickening in the walnut shell. CONCLUSION: In addition to providing considerable available genetic resources for walnut trees, this study revealed the underlying genetic basis involved in important walnut agronomic traits, particularly shell thickness, as well as providing clues for the improvement of genetic breeding and domestication in other perennial economic crops.

Localization with Transfer Learning Based on Fine-Grained Subcarrier Information for Dynamic Indoor Environments.

Indoor localization provides robust solutions in many applications, and Wi-Fi-based methods are considered some of the most promising means for optimizing indoor fingerprinting localization accuracy. However, Wi-Fi signals are vulnerable to environmental variations, resulting in data across different times being subjected to different distributions. To solve this problem, this paper proposes an across-time indoor localization solution based on channel state information (CSI) fingerprinting via multi-domain representations and transfer component analysis (TCA). We represent the format of CSI readings in multiple domains, extending the characterization of fine-grained information. TCA, a domain adaptation method in transfer learning, is applied to shorten the distribution distances among several CSI readings, which overcomes various CSI distribution problems at different time periods. Finally, we present a modified Bayesian model averaging approach to integrate the multi-domain outcomes and give the estimated positions. We conducted test-bed experiments in three scenarios on both personal computer (PC) and smartphone platforms in which the source and target fingerprinting data were collected across different days. The experimental results showed that our method outperforms state-of-the-art methods in localization accuracy.

Follower: A Novel Self-Deployable Action Recognition Framework.

Deep learning technology has improved the performance of vision-based action recognition algorithms, but such methods require a large number of labeled training datasets, resulting in weak universality. To address this issue, this paper proposes a novel self-deployable ubiquitous action recognition framework that enables a self-motivated user to bootstrap and deploy action recognition services, called FOLLOWER. Our main idea is to build a "fingerprint" library of actions based on a small number of user-defined sample action data. Then, we use the matching method to complete action recognition. The key step is how to construct a suitable "fingerprint". Thus, a pose action normalized feature extraction method based on a three-dimensional pose sequence is designed. FOLLOWER is mainly composed of the guide process and follow the process. Guide process extracts pose action normalized feature and selects the inner class central feature to build a "fingerprint" library of actions. Follow process extracts the pose action normalized feature in the target video and uses the motion detection, action filtering, and adaptive weight offset template to identify the action in the video sequence. Finally, we collect an action video dataset with human pose annotation to research self-deployable action recognition and action recognition based on pose estimation. After experimenting on this dataset, the results show that FOLLOWER can effectively recognize the actions in the video sequence with recognition accuracy reaching 96.74%.

Gene correlation network analysis to identify regulatory factors in sepsis.

BACKGROUND AND OBJECTIVES: Sepsis is a leading cause of mortality and morbidity in the intensive care unit. Regulatory mechanisms underlying the disease progression and prognosis are largely unknown. The study aimed to identify master regulators of mortality-related modules, providing potential therapeutic target for further translational experiments. METHODS: The dataset GSE65682 from the Gene Expression Omnibus (GEO) database was utilized for bioinformatic analysis. Consensus weighted gene co-expression netwoek analysis (WGCNA) was performed to identify modules of sepsis. The module most significantly associated with mortality were further analyzed for the identification of master regulators of transcription factors and miRNA. RESULTS: A total number of 682 subjects with various causes of sepsis were included for consensus WGCNA analysis, which identified 27 modules. The network was well preserved among different causes of sepsis. Two modules designated as black and light yellow module were found to be associated with mortality outcome. Key regulators of the black and light yellow modules were the transcription factor CEBPB (normalized enrichment score = 5.53) and ETV6 (NES = 6), respectively. The top 5 miRNA regulated the most number of genes were hsa-miR-335-5p (n = 59), hsa-miR-26b-5p (n = 57), hsa-miR-16-5p (n = 44), hsa-miR-17-5p (n = 42), and hsa-miR-124-3p (n = 38). Clustering analysis in 2-dimension space derived from manifold learning identified two subclasses of sepsis, which showed significant association with survival in Cox proportional hazard model (p = 0.018). CONCLUSIONS: The present study showed that the black and light-yellow modules were significantly associated with mortality outcome. Master regulators of the module included transcription factor CEBPB and ETV6. miRNA-target interactions identified significantly enriched miRNA.

UAV-Assisted Low-Consumption Time Synchronization Utilizing Cross-Technology Communication.

Wireless sensor networks (WSNs) have been used in many fields due to its wide applicability. In this kind of network, each node is independent of each other and has its own local clock and communicates wirelessly. Time synchronization plays a vital role in WSNs and it can ensure accuracy requirements for coordination and data reliability. However, two key challenges exist in large-scale WSNs that are severe resource constraints overhead and multihop time synchronization errors. To address these issues, this paper proposes a novel unmanned aerial vehicle (UAV)-assisted low-consumption time synchronization algorithm based on cross-technology communication (CTC) for a large-scale WSN. This algorithm uses a UAV to send time synchronization data packets for calibration. Moreover, to ensure coverage and a high success rate for UAV data transmission, we use CTC for time synchronization. Without any relays, a high-power time synchronization packet can be sent by a UAV to achieve the time synchronization of low-power sensors. This algorithm can achieve accurate time synchronization with almost zero energy consumption for the sensor nodes. Finally, we implemented our algorithm with 30 low-power RF-CC2430 ZigBee nodes and a Da Jiang Innovations (DJI) M100 UAV on a 1 km highway and an indoor site. The results show that time synchronization can be achieved accurately with almost zero energy consumption for the sensor nodes, and the time synchronization error is less than 30 µs in 99% of cases.

A Novel Mine Cage Safety Monitoring Algorithm Utilizing Visible Light.

The mine cage has an important role in the production of coal mines. It has many safety problems in the transportation of people, such as overloading of personnel and illegal outreach of human limbs. However, the harsh mine environment makes it very difficult to monitor personnel overload and limb extension. To solve these two problems, we propose a novel safety monitoring algorithm of the mine cage based on visible light. With visible light technology, our algorithm cleverly utilizes the existing underground lighting equipment (i.e., miner's headlamp and the miner's lamp deployed on the mine cage) as the transmitter to broadcast the light beacons representing unique identity information through visible light frequency modulation. Next, cheap photodiodes deployed in the mine cage are used as the receiver to perceive the modulated optical signals. Then we use the frequency matching method for personnel counting and the frequency power comparison method for illegal limb extension monitoring. Moreover, a novel method of monitoring the delineated safe area of the mine cage is also proposed to ensure that all the miners are in the delineated safe area. Finally, we conducted extensive experiments with a simulated mine cage model. Results show that our algorithm has superior performance. With the photodiode SD5421-002, the accuracy of personnel overload judgment and safe area monitoring of our algorithm can reach 99%, and the accuracy of limb extension monitoring is more than 96%.

A Novel Gesture Recognition System Based on CSI Extracted from a Smartphone with Nexmon Firmware.

In this paper, we propose a novel gesture recognition system based on a smartphone. Due to the limitation of Channel State Information (CSI) extraction equipment, existing WiFi-based gesture recognition is limited to the microcomputer terminal equipped with Intel 5300 or Atheros 9580 network cards. Therefore, accurate gesture recognition can only be performed in an area relatively fixed to the transceiver link. The new gesture recognition system proposed by us breaks this limitation. First, we use nexmon firmware to obtain 256 CSI subcarriers from the bottom layer of the smartphone in IEEE 802.11ac mode on 80 MHz bandwidth to realize the gesture recognition system's mobility. Second, we adopt the cross-correlation method to integrate the extracted CSI features in the time and frequency domain to reduce the influence of changes in the smartphone location. Third, we use a new improved DTW algorithm to classify and recognize gestures. We implemented vast experiments to verify the system's recognition accuracy at different distances in different directions and environments. The results show that the system can effectively improve the recognition accuracy.

ARS: Adaptive Robust Synchronization for Underground Coal Wireless Internet of Things.

Clock synchronization is still a vital and challenging task for underground coal wireless internet of things (IoT) due to the uncertainty of underground environment and unreliability of communication links. Instead of considering on-demand driven clock synchronization, this paper proposes a novel Adaptive Robust Synchronization (ARS) scheme with packets loss for mine wireless environment. A clock synchronization framework that is based on Kalman filtering is first proposed, which can adaptively adjust the sampling period of each clock and reduce the communication overhead in single-hop networks. The proposed scheme also solves the problem of outliers in data packets with time-stamps. In addition, this paper extends the ARS algorithm to multi-hop networks. Additionally, the upper and lower bounds of error covariance expectation are analyzed in the case of incomplete measurement. Extensive simulations are conducted in order to evaluate the performance. In the simulation environment, the clock accuracy of ARS algorithm is improved by 7.85% when compared with previous studies for single-hop networks. For multi-hop networks, the proposed scheme improves the accuracy by 12.56%. The results show that the proposed algorithm has high scalability, robustness, and accuracy, and can quickly adapt to different clock accuracy requirements.

Optimization on conditions of podophyllotoxin-loaded liposomes using response surface methodology and its activity on PC3 cells.

The purpose of this study was to optimize the preparation conditions of podophyllotoxin liposomes (PPT-Lips), and to investigate their effects on PC3 cells. PPT-Lips were prepared by using a thin-film dispersion method. In order to achieve maximum drug encapsulation efficiency (EE), the process and formulation variables were optimized by response surface methodology (RSM). The optimum preparation conditions were cholesterol to lecithin ratio of 3.6:40 (w/w), lipid to drug ratio of 15.8:1 (w/w), and the ultrasonic intensity of 35% (total power of 400 W). The experimental EE of PPT-Lips was 90.425%, which was consistent with the theoretically predicted value. The characterization studies showed that PPT-Lips were well-dispersible spherical particles with an average size of 106 nm and a zeta potential of -10.1 mV. A gradual and time-dependent pattern of PPT from liposomes was found in in vitro drug release with a cumulative release amount up to 70.3% in 24 h. Results of cell viability experiments on PC3 cells demonstrated that PPT-Lips exhibited more effective anticancer activity in comparison with free PPT. Therefore, PPT-Lips represent an efficient and promising drug delivery system for PPT.

Two macamide extracts relieve physical fatigue by attenuating muscle damage in mice.

BACKGROUND: Macamides, the main active components contained in maca, have attracted increasing attention due to their various bioactivities. In this study, crude macamide extract (CME) and purified macamide extract (PME) were prepared by enzyme-assisted extraction and macroporous resin separation, and the anti-fatigue effects of CME and PME were evaluated in a forced swimming model. RESULTS: The composition analysis results revealed that both CME and PME mainly contain eight kinds of macamide. Based on the results of a weight-loaded forced swimming test, compared with a control group, CME and and PME groups could prolong exhaustive swimming time, increase levels of liver glycogen (LG) and muscle glycogen (MG), accelerate fatty acid oxidation in serum to provide energy, eliminate the accumulation of blood lactic acid (BLA) and blood urea nitrogen (BUN), and decrease the serum biomarkers for muscle damage, such as lactate dehydrogenase (LDH) and creatine kinase (CK). Histological analysis also indicated that CME and PME attenuated damage to skeletal muscle and the myocardium in mice during exercise. CONCLUSION: Two macamide extracts have a beneficial effect on relieving physical fatigue by attenuating the damage of skeletal muscle and myocardium during exercise, and a better effect was observed in the PME group. © 2018 Society of Chemical Industry.

Lémann Index at Diagnosis Predicts the Risk of Early Surgery in Crohn's Disease.

BACKGROUND: Identifying patients with Crohn's disease with rapid disease progress or high risk of early surgery is crucial to clinical decision making. OBJECTIVE: The aim was to evaluate the correlation between the Lémann index at diagnosis and abdominal surgery in the first year after Crohn's disease diagnosis and to find the risk factors for early surgery. DESIGN: This was a retrospective cohort study. SETTINGS: The study was conducted at a single tertiary hospital. PATIENTS: Patients diagnosed with Crohn's disease between 2013 and 2015 in our center were included. MAIN OUTCOME MEASURES: The outcome of interest was the need for an abdominal surgery within 1 year after the Lémann index evaluation at diagnosis. RESULTS: Of 212 eligible patients, 48 patients underwent abdominal surgery during follow-up. Lémann index was much higher in the surgery group (5.3 vs 2.6; p < 0.001). On tertiles of the Lémann index, the frequency of surgery grew (2.8%, 9.9%, and 55.7%; p < 0.001) as the Lémann index increased. The receiver operating characteristic curve was constructed taking into account the Lémann index for selecting patients with a high risk of surgery. Specificity, sensitivity, and area under receiver operating characteristic curve were 84.8%, 81.3%, and 0.89 of the Lémann Index at a cutoff level of 3.7. Patients with Lémann index ≥3.7 carried a higher risk of abdominal surgery (OR = 18.6; p < 0.001). Stricturing and penetrating disease were predictors for abdominal surgery, whereas antitumor necrosis factor treatment was associated with a significant reduction of surgical requirements. LIMITATIONS: This study was limited by its retrospective design. The ability of the Lémann index to predict the long-term risk of surgery was unknown. CONCLUSIONS: Lémann index at diagnosis is a reliable index to predict the risk of abdominal surgery in the first year after diagnosis of Crohn's disease. Patients with a high Lémann index might need closer follow-up or aggressive medical therapy. See Video Abstract at http://links.lww.com/DCR/A518.

Localization with Graph Diffusion Property.

Node localization is an essential issue in wireless sensor networks (WSNs). Many range-free localization methods have been proposed to satisfy the requirement of low-system cost. However, some range-free methods only depend on network connectivity, and others only utilize the proximity information attached in neighborhood ordering. To employ the strength of the above two aspects, this paper introduces a new metric system called Combined and Weighted Diffusion Distance (CWDD). CWDD is designed to obtain the relative distance among nodes based on both graph diffusion property and neighbor information. We implement our design by embedding CWDD into two well-known localization algorithms and evaluate it by extensive simulations. Results show that our design improves the localization performance in large scale and non-uniform sensor networks, which reduces positioning errors by as much as 26%.

Cooperative Game-Based Energy Efficiency Management over Ultra-Dense Wireless Cellular Networks.

Ultra-dense wireless cellular networks have been envisioned as a promising technique for handling the explosive increase of wireless traffic volume. With the extensive deployment of small cells in wireless cellular networks, the network spectral efficiency (SE) is improved with the use of limited frequency. However, the mutual inter-tier and intra-tier interference between or among small cells and macro cells becomes serious. On the other hand, more chances for potential cooperation among different cells are introduced. Energy efficiency (EE) has become one of the most important problems for future wireless networks. This paper proposes a cooperative bargaining game-based method for comprehensive EE management in an ultra-dense wireless cellular network, which highlights the complicated interference influence on energy-saving challenges and the power-coordination process among small cells and macro cells. Especially, a unified EE utility with the consideration of the interference mitigation is proposed to jointly address the SE, the deployment efficiency (DE), and the EE. In particular, closed-form power-coordination solutions for the optimal EE are derived to show the convergence property of the algorithm. Moreover, a simplified algorithm is presented to reduce the complexity of the signaling overhead, which is significant for ultra-dense small cells. Finally, numerical simulations are provided to illustrate the efficiency of the proposed cooperative bargaining game-based and simplified schemes.

Stability Analysis of Multi-Sensor Kalman Filtering over Lossy Networks.

This paper studies the remote Kalman filtering problem for a distributed system setting with multiple sensors that are located at different physical locations. Each sensor encapsulates its own measurement data into one single packet and transmits the packet to the remote filter via a lossy distinct channel. For each communication channel, a time-homogeneous Markov chain is used to model the normal operating condition of packet delivery and losses. Based on the Markov model, a necessary and sufficient condition is obtained, which can guarantee the stability of the mean estimation error covariance. Especially, the stability condition is explicitly expressed as a simple inequality whose parameters are the spectral radius of the system state matrix and transition probabilities of the Markov chains. In contrast to the existing related results, our method imposes less restrictive conditions on systems. Finally, the results are illustrated by simulation examples.

Achieving Passive Localization with Traffic Light Schedules in Urban Road Sensor Networks.

Localization is crucial for the monitoring applications of cities, such as road monitoring, environment surveillance, vehicle tracking, etc. In urban road sensor networks, sensors are often sparely deployed due to the hardware cost. Under this sparse deployment, sensors cannot communicate with each other via ranging hardware or one-hop connectivity, rendering the existing localization solutions ineffective. To address this issue, this paper proposes a novel Traffic Lights Schedule-based localization algorithm (TLS), which is built on the fact that vehicles move through the intersection with a known traffic light schedule. We can first obtain the law by binary vehicle detection time stamps and describe the law as a matrix, called a detection matrix. At the same time, we can also use the known traffic light information to construct the matrices, which can be formed as a collection called a known matrix collection. The detection matrix is then matched in the known matrix collection for identifying where sensors are located on urban roads. We evaluate our algorithm by extensive simulation. The results show that the localization accuracy of intersection sensors can reach more than 90%. In addition, we compare it with a state-of-the-art algorithm and prove that it has a wider operational region.

SSL: Signal Similarity-Based Localization for Ocean Sensor Networks.

Nowadays, wireless sensor networks are often deployed on the sea surface for ocean scientific monitoring. One of the important challenges is to localize the nodes' positions. Existing localization schemes can be roughly divided into two types: range-based and range-free. The range-based localization approaches heavily depend on extra hardware capabilities, while range-free ones often suffer from poor accuracy and low scalability, far from the practical ocean monitoring applications. In response to the above limitations, this paper proposes a novel signal similarity-based localization (SSL) technology, which localizes the nodes' positions by fully utilizing the similarity of received signal strength and the open-air characteristics of the sea surface. In the localization process, we first estimate the relative distance between neighboring nodes through comparing the similarity of received signal strength and then calculate the relative distance for non-neighboring nodes with the shortest path algorithm. After that, the nodes' relative relation map of the whole network can be obtained. Given at least three anchors, the physical locations of nodes can be finally determined based on the multi-dimensional scaling (MDS) technology. The design is evaluated by two types of ocean experiments: a zonal network and a non-regular network using 28 nodes. Results show that the proposed design improves the localization accuracy compared to typical connectivity-based approaches and also confirm its effectiveness for large-scale ocean sensor networks.

Quaternary ammonium salts for water treatment with balanced rate of sterilization and degradation.

The growing number of infections caused by drug-resistant bacteria which arise from the overuse of antibiotics has severely affected the normal operation of human society. The high antibacterial activity of QAS makes it promising as an alternative to antibiotics, but it suffers from secondary pollution due to its non-degradation. Here we have synthesized a class of gemini quaternary ammonium salts (GQAS) with different carbon chain lengths containing ester groups by using facile methylation reaction. Quaternary ammonium groups contribute to insert negatively charged bacterial membranes, resulting in membrane damage and bacteria death. Compared with conventional single-chain QAS, except for the more efficient antibacterial efficiency attribute to the presence of the second carbon chain, GQAS with alterable antibacterial properties can minimize the possibility of bacterial resistance and reduce the accumulation of GQAS in the environment through the introduction of degradable ester groups. GQAS is completely superior to the commercial bactericide benzalkonium chloride (BAC) in both antibacterial activity and degrade performance, which can be used as a more environmentally friendly bactericide.