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Abstract Background Medical dispute is a global public health issue, which has been garnering increasing attention. In this study, we used machine learning (ML) method to establish a dispute prediction model and explored the clinical-application efficiency of this model in effectively reducing the occurrence of medical disputes. Methods Retrospective study of All disputes filed by Gansu Medical Mediation Committee from 2019 to 2021 and patients with the same hospital level as that of the dispute group and hospitalization year were randomly selected as the control group in 1:1 ratio. SPSS software was used for univariate feature selection of the 14 factors that may cause disputes, and factors with statistical differences were selected. The data were divided into training and test sets in a 7:3 ratio. Six ML models were selected, and Python was used to establish a dispute prediction model. The area under the curve (AUC) of the receiver operating characteristic curve (ROC), sensitivity, specificity, accuracy, precision, average precision (AP), and F1 score were used to characterize the fitting and accuracy of the models, while decision curve analysis (DCA) was used to evaluate their clinical utility. Results A total of 1189 patients in the dispute and control groups were extracted. Following 11 influencing factors were selected: the inpatient department, doctor title, patient age, patient gender, patient occupation, payment method, hospitalization days, hospitalization times, discharge method, blood transfusion volume, and hospitalization espenses. Compared to other models, the AUC (0.945, 95% CI 0.913–0.981), Sensitivity (0.887), Accuracy (0.887), AP (0.834), and F1 score (0.880) of the random forest model were higher than those of other models, while the DCA curve indicated its high clinical benefits. Conclusions Inpatient department, hospitalization expenses, and discharge type are the primary influencing factors of dispute. Random forest exhibited high dispute prediction and clinical-application value and is expected to be promoted for offline dispute prediction.

Robotic surgery, also known as robotic-assisted surgery (RAS), has rapidly evolved during the last decade. RAS systems are developed to assist surgeons to perform complex minimally invasive surgeries, and necessitate augmented interfaces for precise execution of these image-guided procedures. Extended Reality (XR) technologies, augmenting the real-world perception via integrating digital contents, show promise in enhancing RAS efficacy in various studies. Despite multiple reviews on technological and medical aspects, the crucial elements of human-robot interaction (HRI) and user experience (UX) remain underexplored. This review fills this gap by elucidating HRI dynamics within XR-aided RAS systems, emphasizing their impact on UX and overall surgical outcomes. By synthesizing existing literature, this systematic review study identifies challenges and opportunities, paving the way for improved XR-enhanced robotic surgery, ultimately enhancing patient care and surgical performance.

Abstract Background The 11th revision of the World Health Organization’s International Classification of Diseases (ICD-11) includes a new disorder, complex posttraumatic stress disorder (CPTSD), the diagnostic applicability of which has not been discussed sufficiently in Chinese culture. The network approach to psychopathology enables investigation of the structure of disorders at the symptom level, which allows for analysis of direct symptom interactions. The main objectives of the present study were to explore CPTSD symptom structure and identify key symptoms in CPTSD among young adults in China. Methods The present study collected a large, stratified sample of Beijing university students (1368), ranging from 18 to 25 years old, the majority of whom (65.4%) were female. CPTSD symptoms were assessed using the International Trauma Questionnaire (ITQ). A regularized partial correlation network and Bayesian network were applied to estimate the network structure and the upstream symptoms of CPTSD, respectively. Results The regularized partial correlation network showed that the high central symptoms were feelings of failure and hypervigilance, while the bridge symptom between posttraumatic stress disorder (PTSD) and disturbance in self-organization (DSO) domains was long-term upset. The Bayesian network showed that external avoidance and hypervigilance symptoms were upstream in CPTSD symptoms. Conclusions Hypervigilance is a central symptom that can be predictive of other symptoms of CPTSD. While feeling of failure is also a highly central symptom, it may be influenced by other symptoms. In the diagnosis and intervention of CPTSD, more attention should be given to hypervigilance symptoms.

Purpose: Investigate the oxylipin profiles in the aqueous humor of primary open-angle glaucoma (POAG) patients. Methods: Aqueous humor samples were collected from 17 POAG patients and 15 cataract subjects and subjected to a liquid chromatography/mass spectrometry (LC-MS) analysis to detect the oxylipins. The prediction potential of the differential abundant oxylipins was assessed by the receiver operating characteristic (ROC) curves. Pathway and correlation analyses on the oxylipins and clinical and biochemical parameters were also conducted. Results: The LC-MS analysis detected a total of 76 oxylipins, of which 29 oxylipins reached the detection limit. The multivariate analysis identified five differential abundant oxylipins, 15-keto-prostaglandin F2 alpha (15-kPGF2α), Leukotriene B4 (LTB4), 12,13-Epoxyoctadecenoic acid (12,13-Epome), 15-Hydroxyeicosatetraenoic acid (15-HETE) and 11-Hydroxyeicosatetraenoic acid (11-HETE). The five oxylipins are enriched in the arachidonic acid metabolism and linoleic acid metabolism pathways. Pearson correlation analysis showed that 11-HETE was positively correlated with intraocular pressure and central corneal thickness and negatively with cup/disk area ratio in the POAG patients. In addition, 15-kPGF2α was moderately and positively correlated with the mean deviation (MD) of visual field defect, and LTB4 was moderately and negatively correlated with macular thickness. Conclusions: This study revealed the oxylipin profile in the aqueous humor of POAG patients. Oxylipins involved in the arachidonic acid metabolism pathway could play a role in POAG, and anti-inflammatory therapies could be potential treatment strategies for POAG.

Learning English as a second language (ESL) has garnered significant attention from researchers and practitioners over the past few decades, with numerous ESL learning outcomes examined in the literature. However, self-sustained learning (SSL), a crucial factor in promoting students’ sustained learning and development within a sustainable society, has long been overlooked. To deepen the understanding of SSL, especially in the context of ESL in China, this study examined the direct and indirect effects of intercultural communicative skills, language mindset, and positive L2 self on sustained English learning among 1238 Chinese college students through descriptive statistics and a Structural Equation Modeling (SEM) mediation analysis. The results indicated that Chinese college students exhibited a strong language mindset and positive L2 self. They also scored high in intercultural communicative skills and long-term self-sustained English learning. SEM analysis showed that, after controlling for students’ demographic characteristics, both intercultural communicative skills and language mindset positively predicted positive L2 self and self-sustained English learning. Moreover, intercultural communicative skills and language mindset had significant and positive indirect effects on self-sustained English learning through positive L2 self, underscoring the significant mediating role of positive L2 self in the relationships between intercultural communicative skills, language mindset, and self-sustained English learning. These findings suggest that, to promote self-sustained learning among English learners, instructors should enhance students’ intercultural communicative skills, foster a growth language mindset, and cultivate positive language learning beliefs.

Sulfur hexafluoride (SF6) is a typical fluorine gas with excellent insulation and arc extinguishing properties that has been widely used in large-scale power equipment. The detection of SF6 gas in high-power electrical equipment is a necessary measure to ensure the reliability and safety of power grid operation. A failure of SF6 insulated electrical equipment, such as discharging or overheating conditions, can cause SF6 gas decomposition, resulting in various decomposition products. The decomposed gases inside the equipment decrease the insulating properties and are toxic. The leakage of SF6 can also decrease the insulating properties. Therefore, it is crucial to monitor the leakage of SF6 decomposed gases from electrical equipment. Quantitative testing of decomposition products allows us to assess the insulation state of the equipment, identify internal faults, and maintain the equipment. This review comprehensively introduces the decomposition formation mechanism of SF6 gas and the current detection technology of decomposition products from the aspects of principle and structure, materials, test effect, and practicability. Finally, the development trends of SF6 and decomposition gas detection technology for the reliability and safety of power grid operation are prospected.

Abstract This paper investigates a novel wireless information surveillance scheme assisted by reconfigurable intelligent surface (RIS) beamforming and artificial noise jamming cooperation, aiming at monitoring the information sent by an access point (AP) to a suspicious illegal user (SIU). It is assumed that the AP adopt the fixed maximum ratio transmission (MRT) precoding scheme, which is not affected by the information monitoring party. The goal of this paper is to maximize the effective information monitoring rate by jointly optimizing the RIS phase shifts, the receive beamforming vector of the legitimate receiver (LR), and the transmit beamforming vector along with jamming power of the jamming antenna (JA). The resultant optimization problem is non‐convex, and its optimization variables are highly coupled in the objective function and constraints. To tackle this difficulty, the optimization variables are optimized under the alternate optimization (AO) framework. Especially, the intractable RIS phase shifts are optimized by using Riemannian manifold optimization (RMO) algorithm under the penalty dual decomposition (PDD) framework and the semidefinite relaxation (SDR) technique, respectively. Numerical results verify the effectiveness of the proposed algorithms, and also demonstrate the superiority of the designed wireless information surveillance scheme over other benchmark schemes.

Evidence suggests that sex differences commonly occur in trauma-related disorders. The current study aims to explore sex differences in complex posttraumatic stress disorder (CPTSD) symptom networks among Chinese young adults with childhood trauma. The current study utilized a representative sample of college students in Beijing and included 1416 participants (409 men and 907 women) who had childhood trauma experience. CPTSD symptoms were evaluated using the International Trauma Questionnaire. Regularized partial correlation network analysis and Bayesian network analysis were used to estimate the network structure and possible causality of CPTSD symptoms for both sexes. Male and female CPTSD symptom networks had differences in strength centrality and bridge centrality. Nightmares and feelings of failure had the highest strength centrality, and long-term upset and nightmares had the highest bridge centrality for men. Hypervigilance and feelings of failure had the highest strength centrality, and long-term upset and exaggerated startle response had the highest bridge centrality for women. The current study provides the first evidence of sex differences in the CPTSD symptom network among Chinese young adults with childhood trauma. Young men and women differed in highly central symptoms, which may speak to sex specificity in the main manifestations of CPTSD symptoms.

Abstract The beam direction constrained problem is one of the important issues to be solved in millimeter-wave (mmWave) wideband communications when serving multi-user with squint beams whose direction varies with frequency. In this paper, we improve the number of simultaneous users served by collaboratively transmitting squint beams among multi-subarray at the base station (BS) end in a downlink multi-user line-of-sight (LoS) scenario, and reduce the interference among co-channel squint beams by a beam domain approach. The optimization problem of maximizing the number of users served in the system by transmitting beams in the two-dimensional beamspace of the planar antenna subarray is proposed and its suboptimal algorithm is given. Finally, the feasibility of the proposed method and the performance of the proposed algorithm are verified by numerical simulations.

ABSTRACT Fecal microbiota transplantation (FMT) targeting gut microbiota has recently been applied to the treatment of ulcerative colitis (UC). However, preliminary trials showed that only a subset of patients responded to FMT, and the heterogeneity in donor gut microbiota probably played important roles in patients’ responses, implying the significance of matching an appropriate donor to a specified patient. We developed a strategy to build a donor-recipient matching model to guide rational donor selection for UC in FMT. We collected and uniformly reanalyzed 656 fecal 16S rRNA gene sequencing samples (350 from UC patients and 306 from healthy subjects) from 9 studies. Significantly lower α-diversity indexes were observed in UC patients by random effects model. Thirty-four bacterial genera and 34 predicted pathways were identified with significant odds ratios and classification potentials for UC patients. Based on six bacterial indicators, including richness, overall distance, genera, and pathways (beneficial and harmful), the analytic hierarchy process-based donor-recipient matching model was set to rank and select appropriate donors for patients with UC. Finally, the model showed favorable classification powers (>70%) for FMT effectiveness in two previous clinical trials. This study revealed the dysbiosis of fecal bacterial diversity, composition, and predicted pathways of patients with UC by meta-analysis and hereby developed a donor-recipient matching strategy to guide donor selection for UC in FMT. This strategy can also be applied to other diseases associated with gut microbiota. IMPORTANCE Modulation of gut microbiota by FMT from donors has been applied to the treatment of UC and yielded variable effectiveness in clinical trials. One possibility is that this variable effectiveness was related to donor selection, as a patient’s response to FMT may rely on the capability of the used donor’s microbiota to restore the specific gut disturbances of the patient. However, the biggest issues on the practical level are what should be considered in the selection process and how to set up such a donor-recipient matching model. In this study, we presented a bacterial profile-based donor-recipient matching strategy to guide donor selection for UC in FMT by first meta-analysis of 656 fecal 16S rRNA gene sequencing samples from 9 studies to identify significant indicators and then setting up the model by an analytic hierarchy process. The applicability and accuracy of this model were verified in the data sets from two previous FMT clinical studies. Our data indicate that the donor-recipient matching model built in this study enables researchers to rationally select donors for UC patients in FMT clinical practice, although it needs more samples and prospective trials for validation. The strategy adopted in this study to leverage existing data sets to build donor-recipient matching models for precision FMT is feasible for other diseases associated with gut microbiota.

Abstract Background Faecal microbiota transplantation (FMT) is an effective therapy for recurrent Clostridium difficile infections and chronic gastrointestional infections. However, the risks of FMT and the selection process of suitable donors remain insufficiently characterized. The eligibility rate for screening, underlying microbial basis, and core ethical issues of stool donors for FMT are yet to be elucidated in China. Results The potential stool donors were screened from December 2017 to December 2019 with the help of an online survey, clinical assessments, and stool and blood testing. Bioinformatics analyses were performed, and the composition and stability of gut microbiota in stool obtained from eligible donors were dynamically observed using metagenomics. Meanwhile, we build a donor microbial evaluation index (DoMEI) for stool donor screening. In the screening process, we also focused on ethical principles and requirements. Of the 2071 participants, 66 donors were selected via the screening process (3.19% success rate). Although there were significant differences in gut microbiota among donors, we found that the changes in the gut microbiota of the same donor were typically more stable than those between donors over time. Conclusions DoMEI provides a potential reference index for regular stool donor re-evaluation. In this retrospective study, we summarised the donor recruitment and screening procedure ensuring the safety and tolerability for FMT in China. Based on the latest advances in this field, we carried out rigorous recommendation and method which can assist stool bank and clinicians to screen eligible stool donor for FMT.

Abstract This article investigates using a phased linear antenna array instead of the planar array to circumvent the problem that two frequency squint steering main beams cannot cover any two beam directions simultaneously. First, we approximate the donut-shaped main beam of the linear array by means of multiple pencil-shaped main beams of a virtual planar array for matching the steering main beam of the linear array with the multi-path sparse scattering channel model mathematically and give a method for calculating the number of antenna elements of the virtual array. Second, we cope with possible inter-user interference on a single squint main beam of the linear array in some scenarios by means of the power-domain non-orthogonal multiple access (PD-NOMA) technique, making it possible to support communication with two users on a single squint main beam at the base station (BS) side. The feasible domain of PD-NOMA is given when a single antenna is used for both the BS and the user end, assuming a two-user successive interference cancellation (SIC) decoding power ratio limit. Third, three algorithms are given for serving multi-user at the BS via squint beams of the linear array. Finally, numerical results show that the second proposed algorithm supporting PD-NOMA pairing within a single donut-shaped squint main beam significantly increases the number of simultaneous users served within a single cellular system.

Abstract The dominant video captioning methods employ the attentional encoder–decoder architecture, where the decoder is an autoregressive structure that generates sentences from left‐to‐right. However, these methods generally suffer from the exposure bias issue and neglect the guidance of future output contexts obtained from the right‐to‐left decoding. Here, the authors propose a new symmetric bidirectional decoder for video captioning. The authors first integrate the self‐attentive multi‐head attention and bidirectional gated recurrent unit for capturing the long‐term semantic dependencies in videos. The authors then apply one single decoder to generate accurate descriptions from left‐to‐right and right‐to‐left simultaneously. The decoder in each decoding direction performs two cross‐attentive multi‐head attention modules to consider both the past hidden states from the same decoding direction and the future hidden states from the reverse decoding direction at each time step. A symmetric semantic‐guided gated attention module is specially devised to adaptively suppress the irrelevant or misleading contents in the past or future output contexts and retain the useful ones for avoiding under‐description. Experimental evaluations on two widely applied benchmark datasets: Microsoft research video to text and Microsoft video description corpus, demonstrate that the authors' proposed method obtains substantially state‐of‐the‐art performance, which validates the superiority of the bidirectional decoder.

Abstract Cooperation between the mobile edge computing (MEC) and the mobile cloud computing (MCC) in offloading computing could improve quality of service (QoS) of user equipments (UEs) with computation-intensive tasks. In this paper, in order to minimize the expect charge, we focus on the problem of how to offload the computation-intensive task from the resource-scarce UE to access point’s (AP) and the cloud, and the density allocation of APs’ at mobile edge. We consider three offloading computing modes and focus on the coverage probability of each mode and corresponding ergodic rates. The resulting optimization problem is a mixed-integer and non-convex problem in the objective function and constraints. We propose a low-complexity suboptimal algorithm called Iteration of Convex Optimization and Nonlinear Programming (ICONP) to solve it. Numerical results verify the better performance of our proposed algorithm. Optimal computing ratios and APs’ density allocation contribute to the charge saving.

Temporal sentence grounding aims to ground a query sentence into a specific segment of the video. Previous methods follow the common equally-spaced frame selection mechanism for appearance and motion modeling, which fails to consider redundant and distracting visual information. There is also no guarantee that all meaningful frames can be obtained. Moreover, this task needs to detect the location clues precisely from both spatial and temporal dimensions, but the relationship between spatial-temporal semantic information and query sentence is still unexplored in existing methods. Inspired by human thinking patterns, we propose a Coarse-to-Fine Spatial-Temporal Relationship Inference (CFSTRI) network to progressively localize fine-grained activity segments. Firstly, we present a coarse-grained crucial frame selection module, where the query-guided local difference context modeling from adjacent frames helps discriminate all the coarse boundary locations relevant to the sentence semantics, and the soft assignment vector of locally aggregated descriptors are employed to enhance the representation of selected frames. Then, we develop a fine-grained spatial-temporal relationship matching module to refine the coarse boundaries, which disentangles the spatial and temporal semantic information from query sentence to guide the excavation of visual grounding clues of corresponding dimensions. Furthermore, we devise a gated graph convolution network to incorporate the spatial-temporal semantic information by leveraging a gate operation to highlight frames referred to by the query sentence from spatial and temporal dimensions, and propagate fused information on the graph. Extensive experiments on two benchmark datasets demonstrate that our CFSTRI significantly outperforms most state-of-the-art methods.

Abstract Background The anti-immunological rejection therapy for small-for-size syndrome (SFSS) after live donor liver transplantation (LDLT) play a central role in keeping graft survival. The hepatocyte number and grafts function has undergone real-time changes with the proliferation and apoptosis of the grafts after reperfusion. Lacking an accurate and effective treatment regiments or indicators to guide the use of immunosuppressive drugs in SFS liver transplantation has made immunotherapy after SFS liver transplantation an urgent problem to be solved. Herein, we established small-for-size (SFS) and normal size liver transplantation model in rats to explore the effective indicators in guiding immunotherapy, to find an effective way for overcoming SFSS. Methods Lewis rats (donors) and BN rats (recipients) were used to mimic allograft liver transplantation and treated with tacrolimus. Local graft immune response was analyzed through haematoxylin and eosin and immunohistochemistry. Flow cytometry was used to assess the overall immune status of recipient. The pharmacokinetics mechanism of immunosuppressive drugs was explored through detecting CYP3A2 expression at mRNA level and protein levels. Results The results showed the local immune reaction of SFS grafts and systemic immune responses of recipient were significantly increased compared with those in normal size grafts and their recipient at four days after liver transplantation. Regression equation was used to regulate the tacrolimus dose which not only controlled tacrolimus serum concentration effectively but alleviated liver damage and improved survival rate. Conclusions This study showed that AST level and tacrolimus serum concentrations are effective indicators in guiding immunotherapy. Regression equation (TD = − 0.494TC-0.0035AST + 260.487) based on AST and tacrolimus serum concentration can be used as a reference for adjustment of immunotherapy after SFS liver transplantation, which is applicable in clinical practice.

We develop algorithms to control the scope of an infection spread on a network by allocating a fixed immunization budget to edges of the graph. We assume that the infection propagates according to an independent cascade model and interventions operate by reducing the propensities of edges to transmit the infection. We formulate this problem as a constrained min-max optimization problem with respect to the placements of interventions and the location of the worst-case seed nodes. However, the result is a challenging bilevel mixed integer optimization problem. Furthermore, gradients of the objective function with respect to the continuous variables are unavailable in closed form. We employ tools from derivative-free optimization and stochastic optimization to optimize the objective by iterating between the outer minimization and inner maximization problems. In the inner loop, we use a weighted degree discount (WDD) method to select the seed set of the influence maximization problem. In the outer loop, we utilize two methods: a sample-based simultaneous perturbation Nelder-Mead (SBSP-NM) algorithm and a simultaneous perturbation stochastic approximation (SPSA) algorithm. We perform simulations on synthetic graphs and three larger-scale real-world datasets and illustrate the computational feasibility of our algorithms and their efficacy at controlling epidemic spreads.

The integration of massive multiple-input and multiple-output (MIMO) enabled heterogeneous cellular networks (HCNs) and Internet of Things (IoT) is a promising treatment for future networking paradigms. In such networks, the human-to-human (H2H) devices and IoT devices have some distinct service requirements. Specifically, the former mainly concentrates on downlink throughput, but the latter pays more attention to uplink power consumption. Based on this, we design a device association mechanism (scheme) to achieve a tradeoff between these two performance metrics under devices' association requirements. Through some appropriate adjustments of weighting parameters, different types of devices can improve their performance metrics of interest. At last, such a scheme is formulated as a network-wide logarithmic utility maximization problem in a nonlinear and combinatorial form. To solve it, we develop two types of association algorithms, whose primary difference lies in the treatment of weighting parameters. Then, we show the corresponding convergence and computation complexity analyses for them. Finally, we investigate the impacts of weighting parameters, the BS power and the number of antennas on some certain performance metrics of designed algorithms and other existing one. The simulation results show that the designed algorithms can meet different devices' association requirements by properly adjusting weighting parameters.

Millimeter-wave (mmWave) cellular systems leverage the hybrid analog-digital structure to balance the costs and the capacity performance by using fewer Radio Frequency (RF) chains and the spatial sparsity of the channel. Besides, a more broadband system is the natural outgrowth of the gigantic frequency resource in mmWave band. However, it becomes precarious when the hybrid structure is extended to the wideband systems as the wideband phase shifters are used in the array. In this article, a multi-wideband planar array structure using the concept of Sierpinski carpet fractal is proposed, which exploits the beam squinting property instead of forming several independent beams through an existing hybrid structure. Moreover, multi-subarray can form a larger antenna array without additional costs of phase shifters for the wider range of values. First, we derive several properties of the steering beams from a wideband linear array. Then, we exploit the beam squinting property of the planar arrays employing wideband ideal phase shifters to form multi-beam. The functionality of the phase shifters in an array is simplified by a set of wideband cells connected in series. The maximum cell number needed in the array is derived, and during this derivation, we find a flaw of the beam squinting method. Finally, we present numerical results on the performance of the proposed arrays with the proposed algorithm, based on a cellular scenario of one base station (BS) and multi-user, where the BS has four subarrays and each user has one subarray.

Satellite communication plays an important role in future wireless network. In this article, we investigate the outage performance of the integrated satellite-terrestrial relay network (ISTRN) with relay selection based adaptive decode-and-forward (RSADF), where the relay selection is based on outdated channel state information (CSI), and the multiple-antenna relays conduct maximum-ratio receiving and transmitting for decoding and forwarding, respectively. Particularly, we first obtain a novel closed-form expression for the outage probability of the considered ISTRN with RSADF. Furthermore, to get further insights of the effect of key system parameters, e.g., the number of relays, the number of antennas, the CSI outdating level, and the channel fading characteristics, on the system performance, we conduct asymptotic analysis for three typical high average signal-to-noise-ratio (SNR) scenarios, i.e., 1) all satellite and terrestrial links are with high average SNR, 2) the satellite-to-relay link and the relay-to-user link are with high average SNR, and 3) only the relay-to-user link is with high average SNR. The derived high SNR approximations of outage probability explicitly reveal the impact of key system parameters in the form of diversity gain and coding gain. Finally, numerical simulations are presented to validate the analytical results.

Periodontitis is one of the most prevalent oral inflammatory diseases leading to teeth loss and oral health problems in adults. Periodontitis mainly affects periodontal tissue by affecting the host immune system and bone homeostasis. Moreover, periodontitis is associated with various systemic diseases. Diabetes is a metabolic disease with systemic effects. Both periodontitis and diabetes are common inflammatory diseases, and comorbidity of two diseases is linked to exacerbation of the pathophysiology of both diseases. Since bacterial dysbiosis is mainly responsible for periodontitis, antibiotics are widely used drugs to treat periodontitis in clinics. However, the outcomes of antibiotic treatments in periodontitis are not satisfactory. Therefore, the application of anti-inflammatory drugs in combination with antibiotics could be a treatment option for periodontitis-diabetes comorbidity. Anti-diabetic drugs usually have anti-inflammatory properties and have shown beneficial effects on periodontitis. Sulfonylureas, insulin secretagogues, are the earliest and most widely used oral hypoglycemic drugs used for type-2 diabetes. Studies have found that sulfonylurea drugs can play a certain role in the mitigation of periodontitis and inflammation. This article reviews the effects of sulfonylurea drugs on the mitigation of periodontitis-diabetes comorbidity-related inflammation, bone loss, and vascular growth as well as the involved molecular mechanisms. We discuss the possibility of a new application of sulfonylureas (old drug) to treat periodontitis-diabetes comorbidity.

Type 2 diabetes mellitus (T2DM) is a complex disorder comprehensively influenced by genetic and environmental risk, and research increasingly has indicated the role of microbial dysbiosis in T2DM pathogenesis. However, studies comparing the microbiome characteristics between T2DM and healthy controls have reported inconsistent results. To further identify and describe the characteristics of the intestinal flora of T2DM patients, we performed a systematic review and meta-analysis of stool microbial profiles to discern and describe microbial dysbiosis in T2DM and to explore heterogeneity among 7 studies (600 T2DM cases, 543 controls, 1143 samples in total). Using a random effects model and a fixed effects model, we observed significant differences in beta diversity, but not alpha diversity, between individuals with T2DM and controls. We identified various operational taxonomic unit (OTUs) and bacterial genera with significant odds ratios for T2DM. The T2DM signatures derived from a single study by stepwise feature selection could be applied in other studies. By training on multiple studies, we improved the detection accuracy and disease specificity for T2DM. We also discuss the relationship between T2DM-enriched or T2DM-depleted genera and probiotics and provide new ideas for diabetes prevention and improvement.

In this paper, we investigate a secure transmission algorithm exploiting the vertical domain for simultaneous wireless information and power transfer (SWIPT) in a massive multiple-input multiple-output system over Rician fading channels. Each user adopts the power splitting (PS) technique to separate information and energy, which is regarded as a potential eavesdropper (ED) for the intended user. We consider the scenario, where at least one ED has the same angle of departure with the intended user. The base station (BS) applies artificial noise (AN) and deploys 3-D directional antennas to enhance the secure rate and energy transfer by adjusting the antenna tilt adaptively. We first derive the approximate ergodic achievable secrecy rate and harvested power. Based on these approximations, the optimization problem is formulated to minimize the total transmit power with individual secrecy rate and harvested power targets. We propose an iterative algorithm to jointly optimizing the BS antenna tilt, AN covariance, power allocation, and PS ratios. The numerical results verify that the approximate secrecy rate is very close to the actual values and show that the performance of the proposal approaches that of the optimal solutions from the brute-force search and outperforms the 2-D and conventional 3-D schemes, which proves the significant role of tilt adaptation in the secure SWIPT.

According to the limited communication distance and the low transmission efficiency for massive electronic tags/sensors of traditional or bistatic backscatter technology, the BackFi transmission protocol design with energy harvesting of massive smart multi-hop electronic tags/sensors was investigated in the ambient backscatter communication systems. This paper proposed the large-scale tags/sensors multi-hop communication protocols with energy harvesting under Wi-Fi architecture, where the “CTS-to-self” frame can be used to set the traditional AP/STA to the sensing status without sending, and then, the new defined frame “Multi-RTS” may send the trigger signal to massive tags or sensors with its frame field as “M-RTS” strategy, compressed bitmap, tags/sensors number, and other messages. The tags/sensors shall respond to the sounding signal and give the backscatter signal to AP using energy harvesting. It can be mentioned that the Wi-Fi standard in this paper is based on the IEEE 802.11ah. Finally, AP can collect and modulate the backscatter signal after massive tags/sensors response to the reader. It was worth noting that the multi-hop relay adopts the power splitting way to implement the communication process, where the object function was energy efficiency maximization with the parameters optimization of transmitting power and relay scaling factor. In order to solve the non-convex problem, this paper derived the approximate expression in the high SNR case and achieved the optimal solution via the Lambert W function. The simulation and analysis verified the effectiveness of the proposed schemes and the correctness of the derivations.

In this paper, for the heterogeneous cellular networks (HCNs) with non-orthogonal multiple access (NOMA), we mainly concentrate on the design of green base station (BS) assignment integrating with power allocation. Note that the power allocation is mainly used for mitigating network interference and reducing energy consumption. To attain the goal of green communications, we take account of an optimization problem with maximizing the whole energy efficiency under users' signal-to-interference-plus-noise ratio (SINR) constraints, where such efficiency represents the ratio of the total throughput of users to the total power consumption of BSs selected by them. To solve the finally formulated problem in a nonconvex and nonlinear form, we first introduce an auxiliary parameter to convert it into a tractable one whose optimal solution can be found by employing an effective iterative algorithm. After that, the whole problem, being in a coupling form, can be broken into the BS assignment and power allocation subproblems that can be optimized alternately. To solve the former, a type of highly effective algorithm is designed according to a coalitional game. In addition, an approach based on a two-sided scalable (2.s.s.) function is developed for the latter. Then, we reveal the properties of these proposed algorithms, including computation complexity and convergence. In the simulation, we mainly investigate the impacts of densities of small BSs and users on the different performance indices of the designed and existing algorithms.

This paper investigates a novel architecture of coordinated multi-point (CoMP) transmission in the cognitive satellite-terrestrial network associated with an unmanned aerial vehicle (UAV). We consider the downlink communication where the UAV and base station (BS) cooperatively serve the terrestrial user by sharing the licensed satellite network spectrum. The goal of this paper is to maximize the achievable rate of the terrestrial user by jointly optimizing BS/UAV transmit power allocation and UAV trajectory, subjecting to the interference temperature threshold imposed on satellite network as well as UAV mobility constraint. The formulated problem is shown in a complicated non-convex form, which is hard to tackle. To this end, we decompose the original problem into two sub-problems, and the block coordinate descent method is employed to solve the two sub-problems alternately. Specifically, for the first sub-problem, the optimal BS/UAV transmit power is obtained with a given UAV trajectory by using the Lagrangian dual method. For the second sub-problem, the UAV trajectory is attained with a given BS/UAV power allocation by using the successive convex approximation technique. Simulation results show that our proposed joint CoMP transmission scheme significantly improves the terrestrial network throughput compared with other benchmark schemes.

To fully utilize the system resources and enhance the user experience, two types of offloading mechanisms are designed for massive MIMO (multiple-input-multiple-out) enabled heterogeneous cellular networks (HCNs). Such mechanisms can achieve a tradeoff between energy efficiency (EE) and spectral efficiency (SE) experiences, but also have a difference on whether or not a power coordination measure is mentioned. At last, they are mathematically written as the network-wide utility maximization problems that are closely related to the SEs and EEs of associated users. In them, we introduce a crucial parameter α to adjust the EE-SE preference. It is noteworthy that the finally formulated problems are in some relatively complicated forms. To solve them, some necessary changes should be made at first, and then we can design some feasible algorithms. Specifically, we try to design a distributed algorithm for a mere offloading problem using dual decomposition (DD), and develop a two-layer iterative algorithm for the joint power coordination and offloading problem using DD and two-sided scalable (2.s.s.) function update. Regarding such these algorithms, we show the corresponding computation complexity and convergence analyses. In the simulation, we mainly investigate different network parameters on an EE-SE tradeoff for our advocated mechanisms and another existing mechanism.

In recommendation systems (RSs), nowadays, not only the traditional user-item rating matrices but also more additional information like contents, contexts, trust friends and other auxiliary information are available to enhance the performance of RS, leading to content-aware, context-aware, trust-aware RS, etc. Thus, it provides much potential to take into consideration the additional information in RS. Hence, we focus on a general low-rank matrix factorization (LRMF) model with similarity constraints and propose a decentralized algorithm based on alternating direction method of multipliers (ADMM) to relieve the computation burden in each server while preserving privacy. What's more, we utilize low-complexity skills in numerical analysis to reduce the computational complexity, based on the exploitation of the special form of the problem. Finally, simulations are performed to validate the effectiveness of our algorithms.

With the development of commercialized autonomous vehicles (AVs), the interaction between AVs and human-driven vehicles has become increasingly important. Nevertheless, on the one hand, complex driver behaviors like distraction are hard to detect by AVs, which may lead to traffic accidents because of the late alert to the following vehicles. On the other hand, advanced techniques such as the real-time image or video processing and vehicle-to-vehicle (V2V) communications make it possible to let AVs receive monitoring signals from nearby vehicles, predict the latent risks, and make smart control to avoid the vehicles driven by distracted drivers. Hence, in this paper, we envisage a collaborative framework integrating human driver distraction monitoring, V2V communications, and AV velocity control. Then, we design the smart velocity control of AVs by taking into consideration the distraction behaviors of the drivers in the human-driven vehicles, and by formulating it as a feasible optimization problem based on model predictive control (MPC) strategies. Furthermore, we analyze the safety benefits that the collaborative framework could help improve on the condition of preserving traffic performance. Finally, we implement the contrast tests of real-time evaluation on driver distraction monitoring based on convolutional neural networks (CNNs) and perform simulations of smart velocity control strategies of the AV at avoiding the distracted driver and reducing rear-end collisions. Through the analysis and the simulations, we show our framework could increase the safety regions, reduce the rear-end collisions, and thus increase the safety of the whole transportation networks.

In this paper, a criterion for sea surface zoning based on the fractal characteristics of disturbances is demonstrated. To improve radar detection performance in heavy-tailed sea clutter, a multitude of sea clutter models and corresponding optimum and suboptimum detectors have been designed in recent years. However, there are cases where these models and detectors become insufficient to describe the coexistence of noise and sea clutter. The commonly used signal-to-clutter ratio (SCR) can hardly serve as an indicator revealing which kind of disturbances dominate in a certain area since it is difficult to decide the level of SCR at which sea clutter or noise exceeds the other. Therefore, it is necessary that a set of rules reflecting essential differences between sea clutter and noise are proposed to tell areas where sea clutter dominates, areas where sea clutter and noise coexist and areas where noise dominates. Analyzing fractal characteristics of disturbances, we consider the Hurst exponent H as a feature distinguishing sea clutter and noise from each other. A modified Sigmoid function is employed to model the variation in H with range bins, and the derivative of the function helps to formulate a set of rules for sea surface zoning.

Periodontitis is a chronic inflammatory oral disease that affects almost half of the adult population. NF-κB activator 1 (Act1) is mainly expressed in immune cells, including macrophages, and modulates immune cells’ function to regulate inflammation in inflammatory diseases. Macrophages play a vital role in the pathophysiology of periodontitis. However, the effect of macrophage-specific Act1 on periodontitis has not been investigated yet. This study aims to unravel the role of macrophage-specific Act1 on the pathophysiology of periodontitis. The expression of Act1 in healthy and periodontitis periodontal tissue was confirmed by immunohistochemistry. Macrophage-specific Act1 expression downregulated (anti-Act1) mice were developed by inserting anti-Act1 antisense oligonucleotides after the CD68 promoter of C57BL/6 mice. Ligature-induced periodontitis (LIP) was induced in anti-Act1 mice and wildtype mice. Micro-CT, histology, and TRAP staining analyzed the periodontal tissue status, alveolar bone loss, and osteoclast numbers. Immunohistochemistry, RT-qPCR, and ELISA analyzed the inflammatory cells infiltration, expression of inflammatory cytokines, and M1/M2 macrophage polarization. mRNA sequencing of in vitro bacterial lipopolysaccharide (LPS)-treated peritoneal macrophages analyzed the differentially expressed genes in anti-Act1 mice during inflammation. Anti-Act1 mice showed aggravated periodontitis and alveolar bone loss compared to wildtype. Periodontitis-affected periodontal tissue (PAPT) of anti-Act1 mice showed a higher degree of macrophage infiltration, and M1 macrophage polarization compared to wildtype. Levels of pro-inflammatory cytokines (IL-1β, IL-6, and TNFα), and macrophage activity-related factors (CCL2, CCL3, and CCL4) were robustly high in PAPT of anti-Act1 mice compared to wildtype. mRNA sequencing and KEGG analysis showed activated TNF/NF-κB signaling in LPS-treated macrophages from anti-Act1 mice. In vitro studies on LPS-treated peritoneal macrophages from anti-act1 mice showed a higher degree of cell migration and expression of inflammatory cytokines, macrophage activity-related factors, M1 macrophage-related factors, and TNF/NF-κB signaling related P-p65 protein. In conclusion, downregulation of macrophage-specific Act1 aggravated periodontitis, alveolar bone loss, macrophage infiltration, inflammation, and M1 macrophage polarization. Furthermore, LPS-treated macrophages from anti-Act1 mice activated TNF/NF-κB signaling. These results indicate the distinct role of macrophage-specific Act1 on the pathophysiology of periodontitis possibly via TNF/NF-κB signaling.

In the post-pandemic era, the two most widely-used live streaming interactive teaching platforms for adult Chinese teaching in the UK are Zoom and Blackboard. This study adopts mixed research methods, focusing on learners of different levels and types of courses from six universities, namely Confucius Institute and adult education institution in England and Wales. This study examines the advantages and disadvantages of online Chinese teaching compared with face-to-face teaching and identifies major factors affecting online teaching quality. The results suggest that although online teaching is slightly less efficient than face-to-face teaching in certain aspects at the transition stage from offline to online, the overall benefits of this approach are positive, particularly regarding: flexibility in the learning place and time, the facilitation of autonomous and active learning, convenient access to reading and learning materials, effective feedback methods, and lesson satisfaction. Disadvantages of online teaching evident in this study include: minor technical and internet issues, reduced interaction and communication between students and teachers, complexity in operating the platforms. The main factors affecting online teaching identified in this study involve: class engagement and interaction, support and feedback from teachers, learning attitude and adaptability of new learning style. Suggested coping strategies include: improving lesson designs, increasing interactive activities, providing adequate support and feedback both online and offline, stimulating active learning, increasing students’ mental adaptability of online classes, and strengthening online teaching training.

SLIT2, a member of neuronal guidance cues, has been reported to regulate inflammation and cancer progression. Periodontitis is an oral inflammatory disease that degenerates periodontal tissue, alveolar bone and tooth. This study aims to explore the expression pattern of SLIT2 in periodontitis and its role in disease progression and bone loss. Gingival tissue of 20 periodontitis patients and 20 healthy-controls was obtained. Ligature-induced periodontitis (LIP) mice-model was developed in Slit2-Tg and wild-type mice. The effect of SLIT2 on inflammation, immune cell infiltration, M1 macrophage polarization, and alveolar bone loss in periodontitis was analyzed extensively. In periodontitis-affected gingival-tissue, SLIT2 expression was 4.4-fold higher compared to healthy-volunteers. LIP enhanced SLIT2 expression in mice periodontitis-affected periodontal tissue (PAPT) and blood circulation of wild-type mice by 4. 6-, and 5.0-fold, respectively. In Slit2-Tg-mice PAPT, SLIT2 expression was 1.8-fold higher compared to wild-type mice. Micro-CT and histomorphometric analysis revealed a 1.3-fold higher cement-enamel-junction to the alveolar-bone-crest (CEJ-ABC) distance and alveolar bone loss in LIP Slit2-Tg-mice compare to LIP wild-type mice. Results from RNA-sequencing, RT-qPCR, and ELISA showed a higher expression of Cxcr2, Il-18, TNFα, IL-6, and IL-1β in Slit2-Tg-mice PAPT compared to wild-type-mice. Slit2-Tg-mice PAPT showed a higher number of osteoclasts, M1 macrophages, and the upregulation of Robo1 expression. Slit2-Tg-mice PAPT showed upregulation of M1 macrophage marker CD16/32 and osteoclastogenic markers Acp5, Ctsk, and Nfatc1, but osteogenic markers (Alp, Bglap) remained unchanged. Immunohistochemistry unveiled the higher vasculature and infiltration of leucocytes and macrophages in Slit2-Tg-mice PAPT. RNA-sequencing, GO-pathway enrichment analysis, and western blot analysis revealed the activation of the MAPK signaling pathway in Slit2-Tg mice PAPT. In conclusion, SLIT2 overexpression in periodontitis intensifies inflammation, immune cells infiltration, M1 macrophage polarization, osteoclastogenesis, and alveolar bone loss, possibly via activation of MAPK signaling, suggesting the role of SLIT2 on exacerbation of periodontitis and alveolar bone loss.

Dysbiosis of gut microbiota during the progression of HBV-related liver disease is not well understood, as there are very few reports that discuss the featured bacterial taxa in different stages. The aim of this study was to reveal the featured bacterial species whose abundances are directly associated with HBV disease progression, that is, progression from healthy subjects to, chronic HBV infection, chronic hepatitis B to liver cirrhosis. Approximately 400 fecal samples were collected, and 97 samples were subjected to 16S rRNA gene sequencing after age and BMI matching. Compared with the healthy individuals, significant gut microbiota alterations were associated with the progression of liver disease. LEfSe results showed that the HBV infected patients had higher Fusobacteria, Veillonella, and Haemophilus abundance while the healthy individuals had higher levels of Prevotella and Phascolarctobacterium. Indicator analysis revealed that 57 OTUs changed as the disease progressed, and their combination produced an AUC value of 90% (95% CI: 86–94%) between the LC and non-LC groups. In addition, the abundances of OTU51 (Dialister succinatiphilus) and OTU50 (Alistipes onderdonkii) decreased as the disease progressed, and these results were further verified by qPCR. The LC patients had the higher bacterial network complexity, which was accompanied with a lower abundance of potential beneficial bacterial taxa, such as Dialister and Alistipes, while they had a higher abundance of pathogenic species within Actinobacteria. The compositional and network changes in the gut microbiota in varied CHB stages, suggest the potential contributions of gut microbiota in CHB disease progression.

Abstract In cloud radio access network (Cloud-RAN), most previous research aims to reduce the energy consumption of the total system and ignores specific requirements of energy-saving in mobile users. For the meantime, some efforts have been made to devise efficient signal quantization techniques for fronthaul capacity restriction to improve the performance of Cloud-RAN. To improve the user-centric energy efficiency (EE), we propose two kinds of optimization algorithms in orthogonal frequency division multiple access-based Cloud-RAN in this paper, whereby Gaussian quantization and uniform scalar quantization methods are utilized. For single-RRH scenario, alternative iterative optimization algorithms are proposed to maximize users’ EE, by jointly allocating resources. Furthermore, in more general scenario for multiple RRHs, authors investigate a modified particle swarm optimization (M-PSO) algorithm to solve more complex problems, which remain non-linear and non-convex. Simulation results demonstrate the proposed schemes in this paper outperform conventional solutions and improve EE of users.

In this paper, we investigate the design of multiple panel arrays (MPAs) in line of sight (LoS) multiple-input-multiple-output (MIMO) communication systems. We utilize the spherical-wave model for the LoS channel component. The fundamental relations among key design parameters in MPAs LoS MIMO system are studied to obtain the maximum spatial multiplexing gain according to the effective degrees of freedom and channel capacity. We further analyze the performance of MPAs system when the array size and transmission distance are not matched and propose an algorithm to optimize the location of antennas. Numerical results indicate that the proposed algorithm can reduce the computational load compared with the exhaustive approach for the similar system performance. Moreover, the optimized panel antenna array is robust to the geometrical change, misplacement, and signal-to-noise-ratio.

Background/Aims: Mesenchymal stem/stromal cells (MSCs) are known to home to sites of tumor microenvironments where they participate in the formation of the tumor microenvironment and to interplay with tumor cells. However, the potential functional effects of MSCs on tumor cell growth are controversial. Here, we, from the view of bone marrow MSC-derived exosomes, study the molecular mechanism of MSCs on the growth of human osteosarcoma and human gastric cancer cells. Methods: MSCs derived from human bone marrow (hBMSCs) were isolated and cultured in complete DMEM/F12 supplemented with 10% exosome-depleted fetal bovine serum and 1% penicillin-streptomycin, cell culture supernatants containing exosomes were harvested and exosome purification was performed by ultracentrifugation. Osteosarcoma (MG63) and gastric cancer (SGC7901) cells, respectively, were treated with hBMSC-derived exosomes in the presence or absence of a small molecule inhibitor of Hedgehog pathway. Cell viability was measured by transwell invasion assay, scratch migration assay and CCK-8 test. The expression of the signaling molecules Smoothened, Patched-1, Gli1 and the ligand Shh were tested by western blot and RT-PCR. Results: In this study, we found that hBMSC-derived exosomes promoted MG63 and SGC7901 cell growth through the activation of Hedgehog signaling pathway. Inhibition of Hedgehog signaling pathway significantly suppressed the process of hBMSC-derived exosomes on tumor growth. Conclusion: Our findings demonstrated the new roles of hedgehog signaling pathway in the hBMSCs-derived exosomes induced tumor progression.

Although the massive MIMO enabled heterogeneous networks (HetNets) can intensify the spectral efficiency benefits, the proper user association and resource allocation are both crucial to achieve desirable performance since the power consumption of base station (BS) scales with the large number of antennas. This paper aims to investigate joint design of user association and power allocation with loads constraint and transmit power constraint for the massive MIMO HetNets by considering proportional fairness about the spectral efficiency under imperfect channel state information. First, we derive a closed-form lower bound on the ergodic spectral efficiency with linear zero-forcing beamforming, based on which, a mixed-integer nonlinear programming problem is formulated. It is difficult to efficiently obtain an exact solution since it is non-convex and combinational. To solve this NP-hard problem, an effective algorithm with guaranteed convergence is proposed, where the original problem is decomposed into the corresponding subproblems, which can be solved by low complexity approaches, respectively. Numerical results show that how the number of antennas and the number of BSs affect the spectral efficiency, and our proposed algorithm outperforms other algorithms in terms of the spectral efficiency and load balancing.

The wideband underwater acoustic multipath channel can be modeled as a multi-scale multi-lag (MSML) channel because signals from different paths might experience different Doppler scales. This brings great challenge to channel parameter estimation. In this paper, we propose a novel algorithm for parameter estimation of MSML channels. This new algorithm is a modified particle swarm optimization (MPSO) algorithm, which can estimate the parameters of the Doppler scale, the time delay, and the amplitude simultaneously for each individual path. Comparing to PSO algorithm, MPSO algorithm uses a multipath list to record positions and fitness values of particles whose fitness values are selected as lbests, and uses these lbests to update particles' velocities at each iteration. As for training sequence, we employ the zero correlation zone sequence which has excellent correlation properties. Computer simulation is used to evaluate the proposed algorithm in comparison with the matching pursuit (MP)-based method and the fractional Fourier transform (FrFT)-based method. Simulation results confirm that the proposed MPSO algorithm outperforms both MP-based method and FrFT-based method in estimation accuracy as well as computation complexity.

To fully exploit both multiplexing gain and array gain of massive multiple-input-multipleoutput (MIMO), the channel state information must be obtained accurately at transmitter side (CSIT). However, conventional channel estimation solutions are not suitable for frequency-division duplexing (FDD) multiuser massive MIMO because of overwhelming pilot and feedback overhead. To reduce the pilot and feedback overhead of channel estimation in FDD systems, we propose a compressive channel estimation scheme for FDD massive MIMO systems in this paper, where the beam-blocked sparsity of massive MIMO channels in beamspace is leveraged. Particularly, we first propose a beam-blocked compressive channel estimation scheme, which can reduce the overhead for downlink training. Then, an optimal block orthogonal matching pursuit algorithm at the BS is proposed to acquire reliable CSIT from the limited number of pilots. Furthermore, an efficient algorithm for channel matrix recovery from separately quantized amplitude and phase of received signals is developed to efficiently decrease feedback load. Simulation results demonstrate that our proposed scheme outperforms conventional solutions.

This paper presents the modeling of tide−induced groundwater response using the spacetime collocation approach (SCA). The newly developed SCA begins with the consideration of Trefftz basis functions which are general solutions of the governing equation deriving from the separation of variables. The solution of the groundwater response in a coastal confined aquifer with an estuary boundary where the phase and amplitude of tide can vary with time and position is then approximated by the linear combination of Trefftz basis functions using the superposition theorem. The SCA is validated for several numerical examples with analytical solutions. The comparison of the results and accuracy for the SCA with the time−marching finite difference method is carried out. In addition, the SCA is adopted to examine the tidal and groundwater piezometer data at the Xing−Da port, Kaohsiung, Taiwan. The results demonstrate the SCA may obtain highly accurate results. Moreover, it shows the advantages of the SCA such that we only discretize by a set of points on the spacetime boundary without tedious mesh generation and thus significantly enhance the applicability.

In this study, the complete chloroplast genome of Chenopodium sp. were sequenced and annotated. The complete chloroplast genome of Chenopodium sp. was composed of circular DNA molecules with a total length of 152,068 bp. The base composition of this chloroplast genome is as follows: A (31.16%), T (31.58%), G (18.27%), and C (18.99%). The chloroplast genome contains 84 protein-coding genes, 8 ribosomal RNA genes (rRNA), and 37 transfer RNA (tRNA) genes. The taxonomic status of the Chenopodium sp. chloroplast genome exhibits a closest relationship with Chenopodium quinoa.

Phaeodactylum tricornutum is a dominant bloom forming species and potential biofuel feedstock. To control P. tricornutum bloom or to release lipids from P. tricornutum, we previously screened and identified the lytic bacterium Labrenzia sp. KD531 toward P. tricornutum. In the present study, we evaluated the lytic activity of Labrenzia sp. KD531 on microalgae and investigated its lytic mechanism. The results indicated that the lytic activity of KD531 was temperature- and pH-dependent, but light-independent. In addition to P. tricornutum, KD531 also showed lytic activity against other algal species, especially green algae. A quantitative analysis of algal cellular protein, carbohydrate and lipid content together with measurements of dry weight after exposure to bacteria-infected algal lysate indicated that the bacterium KD531 influenced the algal biomass by disrupting the algal cells. Both chemotactic analysis and microscopic observations of subsamples from different regions of formed plaques showed that KD531 could move toward and then directly contact algal cells. Direct contact between P. tricornutum and KD531 cells was essential for the lytic process.

Vehicle to everything (V2X) has been deemed a promising technology due to its potential to achieve traffic safety and efficiency. This paper considers a V2X downlink system with a simultaneous wireless information and power transfer (SWIPT) system where the base station not only conveys data and energy to two types of wireless vehicular receivers, such as one hybrid power-splitting vehicular receiver, and multiple energy vehicular receivers, but also prevents information from being intercepted by the potential eavesdroppers (idle energy vehicular receivers). Both the base station and the energy vehicular receivers are equipped with multiple antennas, whereas the information vehicular receiver is equipped with a single antenna. In particular, the imperfect channel state information (CSI) and the practical nonlinear energy harvesting (EH) model are taken into account. The non-convex optimization problem is formulated to maximize the minimum harvested energy power among the energy vehicular receivers satisfying the lowest harvested energy power threshold at the information vehicular receiver and secure vehicular communication requirements. In light of the intractability of the optimization problem, the semidefinite relaxation (SDR) technique and variable substitutions are applied, and the optimal solution is proven to be tight. A number of results demonstrate that the proposed robust secure beamforming scheme has better performance than other schemes.

Energy-efficient communications, namely, green communications, has attracted increasing attention due to energy shortage and greenhouse effect. Motivated by this, we consider the uplink energy-efficient resource allocation in multiuser massive multiple-input multiple-output (MIMO) systems. Specifically, we consider that both the number of antenna arrays at the base station (BS) and the transmit data rate at UE are adjusted adaptively to maximize the energy efficiency. Firstly, we demonstrate the existence of a unique resource allocation solution that is globally optimal by exploiting the properties of objective function. Then we develop an iterative algorithm to solve it. By transforming the originally fractional optimization problem into an equivalent subtractive form using the properties of fractional programming, we develop another efficient iterative resource allocation algorithm. Simulation results have validated the effectiveness of the proposed two algorithms and have shown that both algorithms can fast converge to a near-optimal solution in a small number of iterations.