Anti-Freezing Electrolytes in Aqueous Multivalent Metal-Ion Batteries: Progress, Challenges, and Optimization Strategies.

Aqueous rechargeable multivalent metal-ion batteries (ARMMBs) have attracted considerable attention due to their high capacity, high energy density, and low cost. However, their performance is often limited by low temperature operation, which requires the development of anti-freezing electrolytes. In this review, we summarize the anti-freezing mechanisms and optimization strategies of anti-freezing electrolytes for aqueous batteries (especially for Zn-ion batteries). Besides, we investigate the possible interactions and side reactions between electrolytes and electrodes. We also analyze the problems between electrolytes and electrodes at low temperature, and propose possible solutions. The research progress in the field of low temperature energy storage for aqueous Mg-ion, Ca-ion, and Al-ion batteries, and the challenges faced in their anti-freezing electrolytes are investigated in detail. Last but not least, the outlook on the energy storage applications of ARMMBs is provided to guide the future research.

Highly Anisotropic Second-Order Nonlinear Optical Effects in the Chiral Lead-Free Perovskite Spiral Microplates.

Chiral metal halide perovskites with intrinsic asymmetric structures have drawn increased research interest for the application of second-order nonlinear optics (NLO). However, designing chiral perovskites with the features of a large NLO coefficient, high laser-induced damage thresholds (LDT), and environmental friendliness remains a major challenge. Herein, we have synthesized two chiral hybrid bismuth halides: (R/S-MBA)4Bi2Br10 spiral structure microplates, templated by chiral (R/S)-methylbenzylamine (R/S-MBA). The as-grown chiral lead-free perovskite spiral microplates exhibit a recorded second harmonic generation (SHG) effect with a large effective second-order NLO coefficient (deff) of 11.9 pm V-1 and a high LDT of up to 59.2 mJ cm-2. More importantly, the twisted screw structures show competitive circular polarization sensitivity at 1200 nm with an anisotropy factor (gSHG-CD) of 0.58, which is about 3 times higher than that of reported Pb-based chiral perovskites. These findings provide a new platform to design multifunctional lead-free chiral perovskites for nonlinear photonic applications.

Altering the CO2 Electroreduction Pathways Towards C1 or C2+ Products via Engineering the Strength of Interfacial Cu-O Bond.

Copper (Cu)-based catalysts have established their unique capability for yielding wide value-added products from CO2. Herein, we demonstrate that the pathways of the electrocatalytic CO2 reduction reaction (CO2RR) can be rationally altered toward C1 or C2+ products by simply optimizing the coordination of Cu with O-containing organic species (squaric acid (H2C4O4) and cyclohexanehexaone (C6O6)). It is revealed that the strength of Cu-O bonds can significantly affect the morphologies and electronic structures of derived Cu catalysts, resulting in the distinct behaviors during CO2RR. Specifically, the C6O6-Cu catalysts made up from organized nanodomains shows a dominant C1 pathway with a total Faradaic efficiency (FE) of 63.7 % at -0.6 V (versus reversible hydrogen electrode, RHE). In comparison, the C4O4-Cu with an about perfect crystalline structure results in uniformly dispersed Cu-atoms, showing a notable FE of 65.8 % for C2+ products with enhanced capability of C-C coupling. The latter system also shows stable operation over at least 10 h with a high current density of 205.1 mA cm-2 at -1.0 VRHE, i.e., is already at the boarder of practical relevance. This study sheds light on the rational design of Cu-based catalysts for directing the CO2RR reaction pathway.

Cation Substitution Strategy for Developing Perovskite Oxide with Rich Oxygen Vacancy-Mediated Charge Redistribution Enables Highly Efficient Nitrate Electroreduction to Ammonia.

The electrocatalytic nitrate (NO3-) reduction reaction (eNITRR) is a promising method for ammonia synthesis. However, its efficacy is currently limited due to poor selectivity, largely caused by the inherent complexity of the multiple-electron processes involved. To address these issues, oxygen-vacancy-rich LaFe0.9M0.1O3-δ (M = Co, Ni, and Cu) perovskite submicrofibers have been designed from the starting material LaFeO3-δ (LF) by a B-site substitution strategy and used as the eNITRR electrocatalyst. Consequently, the LaFe0.9Cu0.1O3-δ (LF0.9Cu0.1) submicrofibers with a stronger Fe-O hybridization, more oxygen vacancies, and more positive surface potential exhibit a higher ammonia yield rate of 349 ± 15 µg h-1 mg-1cat. and a Faradaic efficiency of 48 ± 2% than LF submicrofibers. The COMSOL Multiphysics simulations demonstrate that the more positive surface of LF0.9Cu0.1 submicrofibers can induce NO3- enrichment and suppress the competing hydrogen evolution reaction. By combining a variety of in situ characterizations and density functional theory calculations, the eNITRR mechanism is revealed, where the first proton-electron coupling step (*NO3 + H+ + e- → *HNO3) is the rate-determining step with a reduced energy barrier of 1.83 eV. This work highlights the positive effect of cation substitution in promoting eNITRR properties of perovskites and provides new insights into the studies of perovskite-type electrocatalytic ammonia synthesis catalysts.

Enhancing Local CO2 Adsorption by L-histidine Incorporation for Selective Formate Production Over the Wide Potential Window.

Electrochemical carbon dioxide reduction reaction (CO2 RR) to produce valuable chemicals is a promising pathway to alleviate the energy crisis and global warming issues. However, simultaneously achieving high Faradaic efficiency (FE) and current densities of CO2 RR in a wide potential range remains as a huge challenge for practical implements. Herein, we demonstrate that incorporating bismuth-based (BH) catalysts with L-histidine, a common amino acid molecule of proteins, is an effective strategy to overcome the inherent trade-off between the activity and selectivity. Benefiting from the significantly enhanced CO2 adsorption capability and promoted electron-rich nature by L-histidine integrity, the BH catalyst exhibits excellent FEformate in the unprecedented wide potential windows (>90 % within -0.1--1.8 V and >95 % within -0.2--1.6 V versus reversible hydrogen electrode, RHE). Excellent CO2 RR performance can still be achieved under the low-concentration CO2 feeding (e.g., 20 vol.%). Besides, an extremely low onset potential of -0.05 VRHE (close to the theoretical thermodynamic potential of -0.02 VRHE ) was detected by in situ ultraviolet-visible (UV-Vis) measurements, together with stable operation over 50 h with preserved FEformate of ≈95 % and high partial current density of 326.2 mA cm-2 at -1.0 VRHE .

The diurnal emission of floral scent in Oncidium hybrid orchid is controlled by CIRCADIAN CLOCK ASSOCIATED 1 (CCA1) through the direct regulation on terpene synthase.

BACKGROUND: To adapt the periodic fluctuation of environmental factors, plants are subtle to monitor the natural variation for the growth and development. The daily activities and physiological functions in coordination with the natural variation are regulated by circadian clock genes. The circadian emission of floral scents is one of the rhythmic physiological activities controlled by circadian clock genes. Here, we study the molecular mechanism of circadian emission pattern of ocimene and linalool compounds in Oncidium Sharry Baby (Onc. SB) orchid. RESULTS: GC-Mass analysis revealed that Onc. SB periodically emitted ocimene and linalool during 6 to 14 o'clock daily. Terpene synthase, one of the key gene in the terpenoid biosynthetic pathway is expressed in coordination with scent emission. The promoter structure of terpene synthase revealed a circadian binding sequence (CBS), 5'-AGATTTTT-3' for CIRCADIAN CLOCK ASSOCIATED1 (CCA1) transcription factor. EMSA data confirms the binding affinity of CCA1. Transactivation assay further verified that TPS expression is regulated by CCA1. It suggests that the emission of floral scents is controlled by CCA1. CONCLUSIONS: The work validates that the mechanism of circadian emission of floral scents in Onc. Sharry Baby is controlled by the oscillator gene, CCA1(CIRCADIAN CLOCK ASSOCIATED 1) under light condition. CCA1 transcription factor up-regulates terpene synthase (TPS) by binding on CBS motif, 5'-AGATTTTT-3' of promoter region to affect the circadian emission of floral scents in Onc. SB.

Constructing Interfacial Boron-Nitrogen Moieties in Turbostratic Carbon for Electrochemical Hydrogen Peroxide Production.

The electrochemical oxygen reduction reaction (ORR) provides a green route for decentralized H2 O2 synthesis, where a structure-selectivity relationship is pivotal for the control of a highly selective and active two-electron pathway. Here, we report the fabrication of a boron and nitrogen co-doped turbostratic carbon catalyst with tunable B-N-C configurations (CNB-ZIL) by the assistance of a zwitterionic liquid (ZIL) for electrochemical hydrogen peroxide production. Combined spectroscopic analysis reveals a fine tailored B-N moiety in CNB-ZIL, where interfacial B-N species in a homogeneous distribution tend to segregate into hexagonal boron nitride domains at higher pyrolysis temperatures. Based on the experimental observations, a correlation between the interfacial B-N moieties and HO2 - selectivity is established. The CNB-ZIL electrocatalysts with optimal interfacial B-N moieties exhibit a high HO2 - selectivity with small overpotentials in alkaline media, giving a HO2 - yield of ≈1787 mmol gcatalyst -1 h-1 at -1.4 V in a flow-cell reactor.

Weight gain after diagnosis of gestational diabetes mellitus and its association with adverse pregnancy outcomes: a cohort study.

BACKGROUND: Gestational diabetes mellitus (GDM) and excessive body weight are two key risk factors for adverse perinatal outcomes. However, it is not clear whether restricted gestational weight gain (GWG) is favorable to reduce the risk for adverse pregnancy and neonatal outcomes in women with GDM. Therefore, this study aimed to assess the association of GWG after an oral glucose tolerance test with maternal and neonatal outcomes. METHODS: This prospective cohort study assessed the association of GWG after an oral glucose tolerance test (OGTT) with pregnancy and neonatal outcomes in 3126 women with GDM, adjusted for age, pre-pregnancy body mass index, height, gravidity, parity, adverse history of pregnancy, GWG before OGTT, blood glucose level at OGTT and late pregnancy. The outcomes included the prevalence of pregnancy-induced hypertension (PIH) and preeclampsia, large for gestational age (LGA), small for gestational age, macrosomia, low birth weight, preterm birth, and birth by cesarean section. GDM was diagnosed according to the criteria established by the International Association of Diabetes and Pregnancy Study Groups. RESULTS: GWG after OGTT was positively associated with risk for overall adverse pregnancy outcomes (adjusted odds ratio [aOR] = 1.72, 95% confidence interval [CI] = 1.50-1.97), LGA (aOR = 1.29, 95%CI = 1.13-1.47), macrosomia (aOR = 1.24, 95%CI = 1.06-1.46) and birth by cesarean section (aOR = 1.91, 95%CI = 1.67-2.19) in women with GDM. Further analyses revealed that a combination of excessive GWG before OGTT and after OGTT increased the risk of PIH and preeclampsia, LGA, macrosomia, and birth by cesarean section compared with adequate GWG throughout pregnancy. In contrast, GWG below the Institute of Medicine guideline after OGTT did not increase the risk of adverse perinatal outcomes despite GWG before OGTT. CONCLUSION: Excessive GWG after OGTT was associated with an elevated risk of adverse pregnancy outcomes, while insufficient GWG after OGTT did not increase the risk of LBW. Restricting GWG after diagnosis of GDM in women with excessive GWG in the first half of pregnancy may be beneficial to prevent PIH and preeclampsia, LGA, macrosomia, and birth by cesarean section.

Correction: OVATE Family Protein 8 Positively Mediates Brassinosteroid Signaling through Interacting with the GSK3-like Kinase in Rice.

[This corrects the article DOI: 10.1371/journal.pgen.1006118.].

A Low-Latency and Energy-Efficient Neighbor Discovery Algorithm for Wireless Sensor Networks.

Wireless sensor networks have been widely adopted, and neighbor discovery is an essential step to construct the networks. Most existing studies on neighbor discovery are designed on the assumption that either all nodes are fully connected or only two nodes compose the network. However, networks are partially connected in reality: some nodes are within radio range of each other, while others are not. Low latency and energy efficiency are two common goals, which become even more challenging to achieve at the same time in partially connected networks. We find that the collision caused by simultaneous transmissions is the main obstruction of achieving the two goals. In this paper, we present an efficient algorithm called Panacea to address these challenges by alleviating collisions. To begin with, we design Panacea-NCD (Panacea no collision detection) for nodes that do not have a collision detection mechanism.

OsOFP19 modulates plant architecture by integrating the cell division pattern and brassinosteroid signaling.

Characterization of OVATE family proteins (OFPs) has revealed that they exert functions by interacting with different types of transcription factor. However, the molecular bases of these processes are poorly understood. Here, we report that OsOFP19 negatively modulates brassinosteroid (BR) response and integrates it with the cell division pattern to affect plant architecture, including grain shape, through interaction with both DWARF AND LOW-TILLERING (DLT) and Oryza sativa homeobox1 (OSH1). Overexpression of OsOFP19 caused a semi-dwarf stature with thicker leaves and stronger culms and roots, which result from an increase in cell layers in the sub-epidermal tissue. Further studies revealed that OsOFP19 interacts with OSH1, and that this interaction mutually enhances the transcriptional activity of these proteins and leads to a transition from anticlinal to periclinal cell division. Furthermore, DLT interacts with both OsOFP19 and OSH1 and acts as an antagonist in the two interactions. Therefore, OsOFP19, OSH1 and DLT form a functional complex which plays a pivotal role in modulating BR signaling and determining the cell division pattern during plant growth and development.

OVATE Family Protein 8 Positively Mediates Brassinosteroid Signaling through Interacting with the GSK3-like Kinase in Rice.

OVATE gene was first identified as a key regulator of fruit shape in tomato. OVATE family proteins (OFPs) are characterized as plant-specific transcription factors and conserved in Arabidopsis, tomato, and rice. Roles of OFPs involved in plant development and growth are largely unknown. Brassinosteroids (BRs) are a class of steroid hormones involved in diverse biological functions. OsGKS2 plays a critical role in BR signaling by phosphorylating downstream components such as OsBZR1 and DLT. Here we report in rice that OsOFP8 plays a positive role in BR signaling pathway. BL treatment induced the expression of OsOFP8 and led to enhanced accumulation of OsOFP8 protein. The gain-of-function mutant Osofp8 and OsOFP8 overexpression lines showed enhanced lamina joint inclination, whereas OsOFP8 RNAi transgenic lines showed more upright leaf phenotype, which suggest that OsOFP8 is involved in BR responses. Further analyses indicated that OsGSK2 interacts with and phosphorylates OsOFP8. BRZ treatment resulted in the cytoplasmic distribution of OsOFP8, and bikinin treatment reduced the cytoplasmic accumulation of OsOFP8. Phosphorylation of OsOFP8 by OsGSK2 is needed for its nuclear export. The phospphorylated OsOFP8 shuttles to the cytoplasm and is targeted for proteasomal degradation. These results indicate that OsOFP8 is a substrate of OsGSK2 and the function of OsOFP8 in plant growth and development is at least partly through the BR signaling pathway.

Risk factors for abnormal postpartum glucose outcome in women with gestational diabetes mellitus diagnosed by modified The International Association of the Diabetes and Pregnancy Study Groups criteria.

AIM: To characterize postpartum glycemic outcome and related risk factors in women diagnosed with gestational diabetes mellitus (GDM) by modified The International Association of the Diabetes and Pregnancy Study Groups (IADPSG) criteria. METHODS: This is a cohort study of 583 patients with GDM diagnosed by modified IADPSG criteria for Chinese women from 2016 to 2017. According to their oral glucose tolerance tests (OGTT) results at 6-12 weeks postpartum, the subjects were categorized into normal glucose tolerance (NGT) and abnormal glucose tolerance (AGT) groups using the World Health Organization criteria. Multivariate pregestational and gestational factors were compared between the NGT and AGT groups. RESULTS: A total of 174 (29.9%) and 17 (2.9%) subjects were found to have AGT and diabetes, respectively. Multivariate logistic regression analysis showed that elevated 2 h postprandial plasma glucose (PPG) at the diagnosis of GDM (odds ratio [OR], 1.485; 95% confidence interval [CI], 1.253-1.760) and multigravida (OR, 2.187; 95% CI, 1.152-4.150) were independent predictors of AGT in GDM women. Subjects with elevated OGTT 2 h PPG at gestational 24-28 weeks had a 2.254-fold increased risk (95% CI, 1.439-3.530) of developing AGT. Presence of multigravida further increased the risk to 7.329 (95% CI, 2.879-18.659). Women with two or three elevated glucose levels at OGTT had higher risk for postpartum dysglycemia. There was a robust and continuous association of OGTT 2 h PPG at gestational 24-28 weeks with abnormal postpartum glycemic outcomes. CONCLUSION: In GDM women, OGTT 2 h PPG at gestational 24-28 weeks appear to confer a continuously increased risk for postpartum dysglycemia, which is further increased by the presence of multigravida. Multigravida and women with two or three elevated glucose levels during OGTT have higher risks of impaired postpartum glucose metabolism.

Erratum: Wang, Y. et al., A Privacy Preserving Scheme for Nearest Neighbor Query. Sensors 2018, 18, 2440.

The authors wish to make the following erratum to this paper [...].

Low-Power Distributed Data Flow Anomaly-Monitoring Technology for Industrial Internet of Things.

. In recent years, the industrial use of the internet of things (IoT) has been constantly growing and is now widespread. Wireless sensor networks (WSNs) are a fundamental technology that has enabled such prevalent adoption of IoT in industry. WSNs can connect IoT sensors and monitor the working conditions of such sensors and of the overall environment, as well as detect unexpected system events in a timely and accurate manner. Monitoring large amounts of unstructured data generated by IoT devices and collected by the big-data analytics systems is a challenging task. Furthermore, detecting anomalies within the vast amount of data collected in real time by a centralized monitoring system is an even bigger challenge. In the context of the industrial use of the IoT, solutions for monitoring anomalies in distributed data flow need to be explored. In this paper, a low-power distributed data flow anomaly-monitoring model (LP-DDAM) is proposed to mitigate the communication overhead problem. As the data flow monitoring system is only interested in anomalies, which are rare, and the relationship among objects in terms of the size of their attribute values remains stable within any specific period of time, LP-DDAM integrates multiple objects as a complete set for processing, makes full use of the relationship among the objects, selects only one "representative" object for continuous monitoring, establishes certain constraints to ensure correctness, and reduces communication overheads by maintaining the overheads of constraints in exchange for a reduction in the number of monitored objects. Experiments on real data sets show that LP-DDAM can reduce communication overheads by approximately 70% when compared to an equivalent method that continuously monitors all objects under the same conditions.

A Fine-Grained Video Encryption Service Based on the Cloud-Fog-Local Architecture for Public and Private Videos.

With the advancement of cloud computing and fog computing, more and more services and data are being moved from local servers to the fog and cloud for processing and storage. Videos are an important part of this movement. However, security issues involved in video moving have drawn wide attention. Although many video-encryption algorithms have been developed to protect local videos, these algorithms fail to solve the new problems faced on the media cloud, such as how to provide a video encryption service to devices with low computing power, how to meet the different encryption requirements for different type of videos, and how to ensure massive video encryption efficiency. To solve these three problems, we propose a cloud-fog-local video encryption framework which consists of a three-layer service model and corresponding key management strategies, a fine-grain video encryption algorithm based on the network abstract layer unit (NALU), and a massive video encryption framework based on Spark. The experiment proves that our proposed solution can meet the different encryption requirements for public videos and private videos. Moreover, in the experiment environment, our encryption algorithm for public videos reaches a speed of 1708 Mbps, and can provide a real-time encryption service for at least 42 channels of 4K-resolution videos.

A Practical Neighbor Discovery Framework for Wireless Sensor Networks.

Neighbor discovery is a crucial operation frequently executed throughout the life cycle of a Wireless Sensor Network (WSN). Various protocols have been proposed to minimize the discovery latency or to prolong the lifetime of sensors. However, none of them have addressed that all the critical concerns stemming from real WSNs, including communication collisions, latency constraints and energy consumption limitations. In this paper, we propose Spear, the first practical neighbor discovery framework to meet all these requirements. Spear offers two new methods to reduce communication collisions, thus boosting the discovery rate of existing neighbor discovery protocols. Spear also takes into consideration latency constraints and facilitates timely adjustments in order to reduce the discovery latency. Spear offers two practical energy management methods that evidently prolong the lifetime of sensor nodes. Most importantly, Spear automatically improves the discovery results of existing discovery protocols, on which no modification is required. Beyond reporting details of different Spear modules, we also present experiment evaluations on several notable neighbor discovery protocols. Results show that Spear greatly improves the discovery rate from 33.0% to 99.2%, and prolongs the sensor nodes lifetime up to 6.47 times.

Automated Vulnerability Discovery and Exploitation in the Internet of Things.

Recently, automated software vulnerability detection and exploitation in Internet of Things (IoT) has attracted more and more attention, due to IoT's fast adoption and high social impact. However, the task is challenging and the solutions are non-trivial: the existing methods have limited effectiveness at discovering vulnerabilities capable of compromising IoT systems. To address this, we propose an Automated Vulnerability Discovery and Exploitation framework with a Scheduling strategy, AutoDES that aims to improve the efficiency and effectiveness of vulnerability discovery and exploitation. In the vulnerability discovery stage, we use our Anti-Driller technique to mitigate the "path explosion" problem. This approach first generates a specific input proceeding from symbolic execution based on a Control Flow Graph (CFG). It then leverages a mutation-based fuzzer to find vulnerabilities while avoiding invalid mutations. In the vulnerability exploitation stage, we analyze the characteristics of vulnerabilities and then propose to generate exploits, via the use of several proposed attack techniques that can produce a shell based on the detected vulnerabilities. We also propose a genetic algorithm (GA)-based scheduling strategy (AutoS) that helps with assigning the computing resources dynamically and efficiently. The extensive experimental results on the RHG 2018 challenge dataset and the BCTF-RHG 2019 challenge dataset clearly demonstrate the effectiveness and efficiency of the proposed framework.

A brassinosteroid responsive miRNA-target module regulates gibberellin biosynthesis and plant development.

Plant growth and development are highly coordinated by hormones, including brassinosteroid (BR) and gibberellin (GA). Although much progress has been made in understanding the fundamental signaling transduction in BR and GA, their relationship remains elusive in rice. Here, we show that BR suppresses the level of OsmiR159d, which cleaves the target OsGAMYBL2 gene. The OsmiR159d-OsGAMYBL2 pair functions as an early BR-responsive module regulating the expression of BU1, a BR-regulated gene involved in BR signaling, and CPS1 and GA3ox2, two genes in GA biosynthesis, by binding to the promoters of these genes. Furthermore, OsGSK2, a key negative player in BR signaling, interacts with OsGAMYBL2 and prevents it from being degraded under 24-epibrassinolide treatment, whereas SLR1, a rice DELLA protein negatively regulating GA signaling, interacts with OsGAMYBL2 and prevents OsGAMYBL2 from binding to the target gene promoter. GA signaling induces degradation of OsGAMYBL2 and, consequently, enhances BR signaling. These results demonstrate that a BR-responsive module acts as a common component functioning in both BR and GA pathways, which connects BR signaling and GA biosynthesis, and thus coordinates the regulation of BR and GA in plant growth and development.

A prospective cohort study of early-pregnancy risk factors for gestational diabetes in polycystic ovarian syndrome.

BACKGROUND: Polycystic ovarian syndrome (PCOS) is a strong risk factor for gestational diabetes (GDM). However, the association between features of PCOS during early pregnancy and the risk of GDM is not clearly characterized. In this prospective cohort study, we seek to identify early-pregnancy risk factors for GDM in PCOS women. METHODS: Between 2011 and 2013, 248 women with PCOS were followed from their first prenatal visit to delivery. Multiple early-pregnancy metabolic factors were evaluated for their association with the risk of GDM. RESULTS: Among 248 subjects, 75 (30.2%) developed GDM. Single factor analysis identified a number of metabolic risk factors for GDM, including higher body mass index, fasting plasma glucose (FPG) and insulin resistance; abnormal cholesterol; elevated blood pressure and free androgen index; lower level of sex-hormone binding globulin (SHBG); and less gestational weight gain. Multivariate analysis showed that FPG, non-high-density lipoprotein-cholesterol and SHBG are independent predictive factors for GDM. CONCLUSIONS: Our study established strong association of multiple early-pregnancy risk factors with development of GDM in PCOS women. These risk factors are predominantly related to the regulation of glucose, lipid, and androgen metabolism. Among these factors, FPG, non-high-density lipoprotein-cholesterol, and SHBG, predict incident GDM.