Genomic analysis of Cobetia sp. D5 reveals its role in marine sulfur cycling.

Dimethylsulfoniopropionate (DMSP) is one of the most abundant sulfur-containing organic compounds on the earth, which is an important carbon and sulfur source and plays an important role in the global sulfur cycle. Marine microorganisms are an important group involved in DMSP metabolism. The strain Cobetia sp. D5 was isolated from seawater samples in the Yellow Sea area of Qingdao during an algal bloom. There is still limited knowledge on the capacity of DMSP utilization of Cobetia bacteria. The study reports the whole genome sequence of Cobetia sp. D5 to understand its DMSP metabolism pathway. The genome of Cobetia sp. D5 consists of a circular chromosome with a length of 4,233,985 bp and the GC content is 62.56%. Genomic analysis showed that Cobetia sp. D5 contains a set of genes to transport and metabolize DMSP, which can cleave DMSP to produce dimethyl sulphide (DMS) and 3-Hydroxypropionyl-Coenzyme A (3-HP-CoA). DMS diffuses into the environment to enter the global sulfur cycle, whereas 3-HP-CoA is catabolized to acetyl CoA to enter central carbon metabolism. Thus, this study provides genetic insights into the DMSP metabolic processes of Cobetia sp. D5 during a marine algal bloom, and contributes to the understanding of the important role played by marine bacteria in the global sulfur cycle.

Fine-Grained Cross-Modal Semantic Consistency in Natural Conservation Image Data from a Multi-Task Perspective.

Fine-grained representation is fundamental to species classification based on deep learning, and in this context, cross-modal contrastive learning is an effective method. The diversity of species coupled with the inherent contextual ambiguity of natural language poses a primary challenge in the cross-modal representation alignment of conservation area image data. Integrating cross-modal retrieval tasks with generation tasks contributes to cross-modal representation alignment based on contextual understanding. However, during the contrastive learning process, apart from learning the differences in the data itself, a pair of encoders inevitably learns the differences caused by encoder fluctuations. The latter leads to convergence shortcuts, resulting in poor representation quality and an inaccurate reflection of the similarity relationships between samples in the original dataset within the shared space of features. To achieve fine-grained cross-modal representation alignment, we first propose a residual attention network to enhance consistency during momentum updates in cross-modal encoders. Building upon this, we propose momentum encoding from a multi-task perspective as a bridge for cross-modal information, effectively improving cross-modal mutual information, representation quality, and optimizing the distribution of feature points within the cross-modal shared semantic space. By acquiring momentum encoding queues for cross-modal semantic understanding through multi-tasking, we align ambiguous natural language representations around the invariant image features of factual information, alleviating contextual ambiguity and enhancing model robustness. Experimental validation shows that our proposed multi-task perspective of cross-modal momentum encoders outperforms similar models on standardized image classification tasks and image-text cross-modal retrieval tasks on public datasets by up to 8% on the leaderboard, demonstrating the effectiveness of the proposed method. Qualitative experiments on our self-built conservation area image-text paired dataset show that our proposed method accurately performs cross-modal retrieval and generation tasks among 8142 species, proving its effectiveness on fine-grained cross-modal image-text conservation area image datasets.

Exosomes Derived from Human Palatal Mesenchymal Cells Mediate Intercellular Communication During Palatal Fusion by Promoting Oral Epithelial Cell Migration.

Purpose: Exosomes are important "messengers" in cell-cell interactions, but their potential effects on palatal fusion are still unknown. This study aimed to explore the role and mechanism of exosomes derived from palatal mesenchymal cells in epithelial-mesenchymal communication during palatogenesis. Methods: The expression of exosome marker CD63 and CD81 in palatal cells during palatogenesis was detected by immunofluorescence staining. After being purified from the supernatant of human embryonic palatal mesenchymal (HEPM) cells, exosomes (HEPM-EXO) were characterized by nanoparticle tracking analysis (NTA), transmission electron microscopy (TEM) and Western blot. HEPM-EXO were co-cultured with human immortalized oral epithelial cells (HIOEC). The effects of HEPM-EXO on the cell proliferation, migration, apoptosis and epithelial-mesenchymal transition (EMT) of HIOEC were evaluated. The proteins encapsulated in HEPM-EXO were analyzed by proteomic analysis. Results: The extensive expression of CD63 and CD81 in palatal epithelial and mesenchymal cells were continuously detected during E12.5~E14.5, suggesting that exosomes were involved in the process of palatal fusion. The expression of CD63 was also observed in the acellular basement membrane between the palatal epithelium and the mesenchyme in vivo, and HEPM-EXO could be internalized by HIOEC in vitro, suggesting that exosomes are potent to diffuse through the cellular tissue boundary to mediate palatal cell-cell communication. Exposure of HEPM-EXO to HIOEC substantially inhibited the proliferation and stimulated the migration of HIOEC, but had no significant effect on cell apoptosis and EMT. Proteomic analysis revealed the basic characteristics of the proteins in HEPM-EXO and that exosomal THBS1 may potentially regulate the cell behaviors of HIOEC, which needs further verification. Gene ontology (GO) analysis uncovered that the proteins highly expressed in HEPM-EXO are closely related to wound healing, implying a promising therapeutic opportunity of HEPM-EXO in tissue injury treatment with future studies. Conclusion: HEPM-EXO mediated cell-cell communication by regulating cell proliferation and migration of oral epithelial cells during palatogenesis.

Shared molecular mechanisms and transdiagnostic potential of neurodevelopmental disorders and immune disorders.

The co-occurrence and familial clustering of neurodevelopmental disorders and immune disorders suggest shared genetic risk factors. Based on genome-wide association summary statistics from five neurodevelopmental disorders and four immune disorders, we conducted genome-wide, local genetic correlation and polygenic overlap analysis. We further performed a cross-trait GWAS meta-analysis. Pleotropic loci shared between the two categories of diseases were mapped to candidate genes using multiple algorithms and approaches. Significant genetic correlations were observed between neurodevelopmental disorders and immune disorders, including both positive and negative correlations. Neurodevelopmental disorders exhibited higher polygenicity compared to immune disorders. Around 50%-90% of genetic variants of the immune disorders were shared with neurodevelopmental disorders. The cross-trait meta-analysis revealed 154 genome-wide significant loci, including 8 novel pleiotropic loci. Significant associations were observed for 30 loci with both types of diseases. Pathway analysis on the candidate genes at these loci revealed common pathways shared by the two types of diseases, including neural signaling, inflammatory response, and PI3K-Akt signaling pathway. In addition, 26 of the 30 lead SNPs were associated with blood cell traits. Neurodevelopmental disorders exhibit complex polygenic architecture, with a subset of individuals being at a heightened genetic risk for both neurodevelopmental and immune disorders. The identification of pleiotropic loci has important implications for exploring opportunities for drug repurposing, enabling more accurate patient stratification, and advancing genomics-informed precision in the medical field of neurodevelopmental disorders.

A Novel Nomogram for Predicting Early Rebleeding After Endoscopic Treatment of Esophagogastric Variceal Hemorrhage.

BACKGROUND: Early rebleeding is a significant complication of endoscopic treatment for esophagogastric variceal hemorrhage (EGVH). However, a reliable predictive model is currently lacking. AIMS: To identify risk factors for rebleeding within 6 weeks and establish a nomogram for predicting early rebleeding after endoscopic treatment of EVGH. METHODS: Demographic information, comorbidities, preoperative evaluation, endoscopic features, and laboratory tests were collected from 119 patients who were first endoscopic treatment for EGVH. Independent risk factors for early rebleeding were determined through least absolute shrinkage and selection operator logistic regression. The discrimination, calibration, and clinical utility of the nomogram were assessed and compared with the model for end-stage liver disease (MELD), Child-Pugh, and albumin-bilirubin (ALBI) scores using receiver-operating characteristic (ROC) curves, calibration plots, and decision curve analyses (DCA). RESULTS: Early rebleeding occurred in 39 patients (32.8%) within 6 weeks after endoscopic treatment. Independent early rebleeding factors included gastric variceal hemorrhage (GVH), concomitant hepatocellular carcinoma (HCC), international normalized ratio (INR), and creatinine. The nomogram demonstrated exceptional calibration and discrimination capability. The area under the curve for the nomogram was 0.758 (95% CI 0.668-0.848), and it was validated at 0.71 through cross-validation and bootstrapping validation. The DCA and ROC curves demonstrated that the nomogram outperformed the MELD, Child-Pugh, and ALBI scores. CONCLUSIONS: Compared with existing prediction scores, the nomogram demonstrated superior discrimination, calibration, and clinical applicability for predicting rebleeding in patients with EGVH after endoscopic treatment. Therefore, it may assist clinicians in the early implementation of aggressive treatment and follow-up.

Optimization of TROSY- and anti-TROSY-based 15N CPMG relaxation dispersion experiments through phase cycling.

CPMG relaxation dispersion studies of biomolecular dynamics on the µs-ms timescale can provide detailed kinetic, thermodynamic, and structural insights into function. Frequently, the 15N spin serves as the probe of choice, as uniform incorporation of the 15N isotope is facile and cost-effective, and the interpretation of the resulting data is often relatively straightforward. In conventional CPMG relaxation dispersion experiments the application of CPMG pulses with constant radiofrequency (RF) phase can lead to artifactual dispersion profiles that result from off-resonance effects, RF field inhomogeneity, and pulse miscalibration. The development of CPMG experiments with the [0013]-phase cycle has significantly reduced the impact of pulse imperfections over a greater bandwidth of frequency offsets in comparison to constant phase experiments. Application of 15N-TROSY-based CPMG schemes to studies of the dynamics of large molecules is necessary for high sensitivity, yet the correct incorporation of the [0013]-phase cycle is non-trivial. Here we present TROSY- and anti-TROSY-based 15N CPMG experiments with the [0013]-phase cycling scheme and demonstrate, through comprehensive numerical simulations and experimental validation, enhanced resistance to pulse imperfections relative to traditional schemes utilizing constant phase CPMG pulses. Notably, exchange parameters derived from the new experiments are in good agreement with those obtained using other, more established, 15N-based CPMG approaches.

Variability of BMP-2 content in DBM products derived from different long bone.

Demineralized bone matrix (DBM) has been regarded as an ideal bone substitute as a native carrier of bone morphogenetic proteins (BMPs) and other growth factors. However, the osteoinductive properties diverse in different DBM products. We speculate that the harvest origin further contributing to variability of BMPs contents in DBM products besides the process technology. In the study, the cortical bone of femur, tibia, humerus, and ulna from a signal donor were prepared and followed demineralizd into DBM products. Proteins in bone martix were extracted using guanidine-HCl and collagenase, respectively, and BMP-2 content was detected by sandwich enzyme-linked immunosorbent assay (ELISA). Variability of BMP-2 content was found in 4 different DBM products. By guanidine-HCl extraction, the average concentration in DBMs harvested from ulna, humerus, tibia, and femur were 0.613 ± 0.053, 0.848 ± 0.051, 3.293 ± 0.268, and 21.763 ± 0.344, respectively (p < 0.05), while using collagenase, the levels were 0.089 ± 0.004, 0.097 ± 0.004, 0.330 ± 0.012, and 1.562 ± 0.008, respectively (p < 0.05). In general, the content of BMP-2 in long bones of Lower limb was higher than that in long bones of upper limb, and GuHCl had remarkably superior extracted efficiency for BMP-2 compared to collagenase. The results suggest that the origin of cortical bones harvested to fabricate DBM products contribute to the variability of native BMP-2 content, while the protein extracted method only changes the measured values of BMP-2.

Advances in the Application of Artificial Intelligence in Fetal Echocardiography.

Congenital heart disease is a severe health risk for newborns. Early detection of abnormalities in fetal cardiac structure and function during pregnancy can help patients seek timely diagnostic and therapeutic advice, and early intervention planning can significantly improve fetal survival rates. Echocardiography is one of the most accessible and widely used diagnostic tools in the diagnosis of fetal congenital heart disease. However, traditional fetal echocardiography has limitations due to fetal, maternal, and ultrasound equipment factors and is highly dependent on the skill level of the operator. Artificial intelligence (AI) technology, with its rapid development utilizing advanced computer algorithms, has great potential to empower sonographers in time-saving and accurate diagnosis and to bridge the skill gap in different regions. In recent years, AI-assisted fetal echocardiography has been successfully applied to a wide range of ultrasound diagnoses. This review systematically reviews the applications of AI in the field of fetal echocardiography over the years in terms of image processing, biometrics, and disease diagnosis and provides an outlook for future research.

Clinical characteristics and cognitive function in bipolar disorder patients with different onset symptom.

Background: In recent years, studies on the clinical features and cognitive impairment of patients with different first-episode types of bipolar disorder have received increasing attention. The patients with bipolar disorder may present with different symptoms at first onset. The aim of this study is to assess the cognitive functions of a patient's index episode of bipolar disorder, depression or mania, on risk factors of effecting on cognitive functions. Method: One hundred sixty eight patients with bipolar disorder diagnosed for the first time were enrolled in the study. All patients were divided into two groups according to their index episode of bipolar disorder, either depression or mania. Seventy three patients of the cohort had an index episode mania and 95 patients had initial symptoms of depression. Demographic and clinical disease characteristic data of all enrolled patients were collected. Meanwhile, 75 healthy controls were included. Demographic data of controls were collected. The cognitive functions of all patients and controls were detected by continuous performance test (CPT), digital span test (DST) and Wisconsin card sorting test (WCST). The main cognitive functions data were compared among the mania group, depression group and control group. The relevant risk factors affecting cognitive function were analyzed. Results: (1) Most patients with bipolar disorder had an index episode depression (56.55% vs. 43.45%). Compared with the depression group, the mania group had later age of onset [(24.01 ± 4.254) vs. (22.25 ± 6.472), t = 2. 122, p = 0.035]. The education level of patient groups was lower than control group (p < 0.001). (2) The healthy control group's DST, WCST and CPT scores were better than the patient groups (All p < 0.05). The mania group's DST (forward, reverse, sum), WCST (total responses, completed classifications, correct responses, incorrect responses, percentage of correct responses, completed the number of responses required for classification, the percentage of conceptualization level, the number of persistent responses, non-persistent errors), CPT (2 digit score, 3 digit score, 4 digit score) was better than the depression group (p < 0.05). (3) In mania group, correlation analysis showed that all CPT parameter, inverse digit span, and the sum of DST was negatively correlated with the education level (All p < 0.05). The CPT-4 digit score was negatively correlated with onset age (p < 0.05). In the WCST, the number of correct responses, the percentage of correct responses and the percentage of conceptualization level were positively correlated with the BRMS score (All p < 0.05). The number of false responses and persistent responses were negatively correlated with the BRMS score (All p < 0.05). The number of persistent errors and percentage of persistent errors was positively correlated with education years (All p < 0.05). In depression group, there was a positive correlation between inverse digit span and the education level (p < 0.05). Conclusion: In our study, there were cognitive impairments in attention, memory, and executive function of patients with different onset syndromes of bipolar disorder. Compared with the mania group, the degree of cognitive impairments in bipolar patients with the depressive episode was more severe. The risk factors affecting cognitive impairments included the age of onset, education level, number of hospitalizations and severity of illness.

[Application of Fe3[KG-*2/5]O4/Mulberry Stem Biochar Effects on Soil Arsenic Species and Rice Arsenic Content].

The passivation effect of Fe3O4/mulberry pole biochar (Fe-MBC) prepared at different carbonization temperatures on soil available arsenic content was studied through soil culture experiments, and Fe-MBC-800 (prepared by carbonization at 800℃) with good passivation effect was selected and characterized. The effects of 1%-7% (mass fraction of biochar to soil) Fe-MBC-800, MBC-800, and Fe3O4 on soil pH value, soil electrical conductivity, soil arsenic form, rice biomass, and total arsenic (As) content in rice were studied using a pot experiment. The results showed that:①Fe-MBC-800 successfully loaded Fe3O4, and its main functional groups were C=O double bond, O-H bond, C-O bond, and Fe-O bond. The specific surface areas of Fe-MBC-800, MBC-800, and Fe3O4 were 209.659 m2·g-1, 517.714 m2·g-1, and 68.025 m2·g-1, respectively. ②The addition of Fe-MBC-800 could increase the soil pH value, decrease the soil EC value, increase the content of residual arsenic in soil, and reduce the content of water-soluble arsenic and available arsenic in the soil. Under the treatment using 7% Fe-MBC-800 (ω) amendments, the content of water-soluble arsenic and available arsenic in the soil decreased by 81.6% and 56.33%, respectively. ③When the addition ratio of Fe-MBC-800 in the soil was 5%-7%, it could promote the growth of rice plants, increase rice biomass, and reduce the bioaccumulation of arsenic by between 62.5% and 68.75%.

Classification of Central Nervous System Tumors Histologically Diagnosed in a Single Center of China 2003-2019.

OBJECTIVE: The classification of central nervous system (CNS) tumors has changed greatly. The Central Brain Tumor Registry of the United States (CBTRUS) and other institutions have analyzed the incidence rate and characteristics of primary CNS tumors. However, there are limited studies analyzing the incidence rate and characteristics of CNS tumors in China. To better understand CNS tumors in China, we summarized all primary CNS tumors diagnosed pathologically in a single center from 2003 to 2019. METHODS: All patients with primary CNS tumors who underwent neurosurgery at our hospital from January 2003 to December 2019 were included in this study. The data were collected from the hospital information system, including diagnosis time, age, gender, anatomic sites, and pathologic results. RESULTS: A total of 17,226 cases of primary CNS tumors were retrospectively analyzed in this study. Among all cases, the major tumor types included meningiomas, tumors of neuroepithelial tissue, and pituitary adenomas. Most tumors of neuroepithelial tissue were glioblastoma and astrocytoma. Most tumors of neuroepithelial tissue were located in the frontal lobe. However, grade 4 tumors of neuroepithelial tissue were more common in the temporal lobe. The median age of all patients was 46 years. The incidence of CNS tumors was higher in women than in men. CONCLUSIONS: Based on this data set, we analyzed various parameters of CNS tumors and found that grade 4 tumors of neuroepithelial tissue were more common in the temporal lobe, which were rarely reported in previous articles.

Dimethylsulfoniopropionate and its catabolites are important chemical signals mediating marine microbial interactions.

Dimethylsulfoniopropionate (DMSP) is a ubiquitous organosulfur compound with key ecological roles in marine environments. This paper offers a brief insight into the mechanisms, environmental diversity, and importance of DMSP-mediated marine microbial interactions, including algae-microzooplankton interactions, bacteria-microzooplankton interactions, and algae-bacteria interactions. We also highlight current challenges that warrant further investigation.

A novel class of xylanases specifically degrade marine red algal ß1,3/1,4-mixed-linkage xylan.

Xylans are polysaccharides composed of xylose and include ß1,4-xylan, ß1,3-xylan, and ß1,3/1,4-mixed-linkage xylan (MLX). MLX is widely present in marine red algae and constitutes a significant organic carbon in the ocean. Xylanases are hydrolase enzymes that play an important role in xylan degradation. While a variety of ß1,4-xylanases and ß1,3-xylanases involved in the degradation of ß1,4-xylan and ß1,3-xylan have been reported, no specific enzyme has yet been identified that degrades MLX. Herein, we report the characterization of a new MLX-specific xylanase from the marine bacterium Polaribacter sp. Q13 which utilizes MLX for growth. The bacterium secretes xylanases to degrade MLX, among which is Xyn26A, an MLX-specific xylanase that shows low sequence similarities (<27%) to ß1,3-xylanases in the glycoside hydrolase family 26 (GH26). We show that Xyn26A attacks MLX precisely at ß1,4-linkages, following a ß1,3-linkage toward the reducing end. We confirm that Xyn26A and its homologs have the same specificity and mode of action on MLX, and thus represent a new xylanase group which we term as MLXases. We further solved the structure of a representative MLXase, AlXyn26A. Structural and biochemical analyses revealed that the specificity of MLXases depends critically on a precisely positioned ß1,3-linkage at the -2/-1 subsite. Compared to the GH26 ß1,3-xylanases, we found MLXases have evolved a tunnel-shaped cavity that is fine-tuned to specifically recognize and hydrolyze MLX. Overall, this study offers a foremost insight into MLXases, shedding light on the biochemical mechanism of bacterial degradation of MLX.

Structural and molecular basis for urea recognition by Prochlorococcus.

Nitrogen (N) is an essential element for microbial growth and metabolism. The growth and reproduction of microorganisms in more than 75% of areas of the ocean are limited by N. Prochlorococcus is numerically the most abundant photosynthetic organism on the planet. Urea is an important and efficient N source for Prochlorococcus. However, how Prochlorococcus recognizes and absorbs urea still remains unclear. Prochlorococcus marinus MIT 9313, a typical Cyanobacteria, contains an ABC-type transporter, UrtABCDE, which may account for the transport of urea. Here, we heterologously expressed and purified UrtA, the substrate-binding protein of UrtABCDE, detected its binding affinity toward urea, and further determined the crystal structure of the UrtA/urea complex. Molecular dynamics simulations indicated that UrtA can alternate between "open" and "closed" states for urea binding. Based on structural and biochemical analyses, the molecular mechanism for urea recognition and binding was proposed. When a urea molecule is bound, UrtA undergoes a state change from open to closed surrounding the urea molecule, and the urea molecule is further stabilized by the hydrogen bonds supported by the conserved residues around it. Moreover, bioinformatics analysis showed that ABC-type urea transporters are widespread in bacteria and probably share similar urea recognition and binding mechanisms as UrtA from P. marinus MIT 9313. Our study provides a better understanding of urea absorption and utilization in marine bacteria.

Balancing "on" and "off" response of hydroxyl groups to a nanozyme-catalyzing system for the construction of an ultra-sensitive and selective "signal-on" detection platform for dopamine.

Targeting the functional groups present in analytes by nanozyme-catalyzed systems is a promising strategy to construct sensitive and selective platforms for the sensing of specific analytes. Herein, various groups (-COOH, -CHO, -OH, and -NH2) on benzene were introduced in an Fe-based nanozyme system with MoS2-MIL-101(Fe) as the model peroxidase nanozyme, H2O2 as the oxidizing agent, and TMB as the chromogenic substrate, and the effects of these groups at both a low concentration and high concentration were further investigated. It was found that the hydroxyl group-based substance catechol showed an "on" effect at a low concentration to increase the catalytic rate and enhance the absorbance signal, whereas an "off" effect at a high concentration with a decreased absorbance signal. Based on these results, the "on" mode and "off" mode for the biological molecule dopamine, a type of catechol derivative, were proposed. In the control system, MoS2-MIL-101(Fe) catalyzed the decomposition of H2O2 to produce ROS, which further oxidized TMB. In the "on" mode, the hydroxyl groups of dopamine could combine with the Fe(iii) site of the nanozyme to lower its oxidation state, resulting in higher catalytic activity. In the "off" mode, the excess dopamine could consume ROS, which inhibited the catalytic process. Under the optimal conditions, by balancing the "on" and "off" modes, the "on" mode for the detection of dopamine was found to have better sensitivity and selectivity. The LOD was as low as 0.5 nM. This detection platform was successfully applied for the detection of dopamine in human serum with satisfactory recovery. Our results can pave the way for the design of nanozyme sensing systems with sensitivity and selectivity.

A nomogram for predicting major gastrointestinal bleeding in patients treated with rivaroxaban.

BACKGROUND: Rivaroxaban is a direct oral anticoagulant with the highest risk of anticoagulant-induced major gastrointestinal bleeding (MGIB). Currently, there is a lack of tools to identify patients at high risk of rivaroxaban-induced MGIB. OBJECTIVE: To establish a nomogram model to predict the risk of MGIB in patients receiving rivaroxaban. METHODS: Demographic information, comorbidities, concomitant medications, and laboratory test results were collected from 356 patients (178 diagnosed with MGIB) who were taking rivaroxaban between January 2013 and June 2021. Univariate and multivariate logistic regression analyses were used to identify the independent predictors of MGIB, and a nomogram was constructed based on these predictors. A receiver operating characteristic curve, Brier score, calibration plot, decision curve, and internal validation was used to evaluate the calibration, discrimination, and clinical usefulness of the nomogram. RESULTS: Age, haemoglobin level, platelet count, creatinine level, prior peptic ulcer disease, prior bleeding, prior stroke, proton pump inhibitor use, and antiplatelet agent use were independent predictors of rivaroxaban-induced MGIB. These risk factors were used to establish the nomogram. The area under the curve of the nomogram was 0.833 (95%CI, 0.782-0.866), the Brier score was 0.171, the internal validation accuracy was 0.73, and the kappa value was 0.46. CONCLUSION: The nomogram demonstrated good discrimination, calibration, and clinical applicability. Therefore, it could accurately predict the risk of MGIB in patients treated with rivaroxaban.

Joint Particle Swarm Optimization of Power and Phase Shift for IRS-Aided D2D Underlaying Cellular Systems.

Device-to-device (D2D) communication is a promising wireless communication technology which can effectively reduce the traffic load of the base station and improve the spectral efficiency. The application of intelligent reflective surfaces (IRS) in D2D communication systems can further improve the throughput, but the problem of interference suppression becomes more complex and challenging due to the introduction of new links. Therefore, how to perform effective and low-complexity optimal radio resource allocation is still a problem to be solved in IRS-assisted D2D communication systems. To this end, a low-complexity power and phase shift joint optimization algorithm based on particle swarm optimization is proposed in this paper. First, a multivariable joint optimization problem for the uplink cellular network with IRS-assisted D2D communication is established, where multiple DUEs are allowed to share a CUE's sub-channel. However, the proposed problem considering the joint optimization of power and phase shift, with the objective of maximizing the system sum rate and the constraints of the minimum user signal-to-interference-plus-noise ratio (SINR), is a non-convex non-linear model and is hard to solve. Different from the existing work, instead of decomposing this optimization problem into two sub-problems and optimizing the two variables separately, we jointly optimize them based on Particle Swarm Optimization (PSO). Then, a fitness function with a penalty term is established, and a penalty value priority update scheme is designed for discrete phase shift optimization variables and continuous power optimization variables. Finally, the performance analysis and simulation results show that the proposed algorithm is close to the iterative algorithm in terms of sum rate, but lower in power consumption. In particular, when the number of D2D users is four, the power consumption is reduced by 20%. In addition, compared with PSO and distributed PSO, the sum rate of the proposed algorithm increases by about 10.2% and 38.3%, respectively, when the number of D2D users is four.

Application and Progress of Artificial Intelligence in Fetal Ultrasound.

Prenatal ultrasonography is the most crucial imaging modality during pregnancy. However, problems such as high fetal mobility, excessive maternal abdominal wall thickness, and inter-observer variability limit the development of traditional ultrasound in clinical applications. The combination of artificial intelligence (AI) and obstetric ultrasound may help optimize fetal ultrasound examination by shortening the examination time, reducing the physician's workload, and improving diagnostic accuracy. AI has been successfully applied to automatic fetal ultrasound standard plane detection, biometric parameter measurement, and disease diagnosis to facilitate conventional imaging approaches. In this review, we attempt to thoroughly review the applications and advantages of AI in prenatal fetal ultrasound and discuss the challenges and promises of this new field.

Aerobic methylation of hydrogen sulfide to dimethylsulfide in diverse microorganisms and environments.

Dimethylsulfide (DMS) is the major biosulfur source emitted to the atmosphere with key roles in global sulfur cycling and potentially climate regulation. The main precursor of DMS is thought to be dimethylsulfoniopropionate. However, hydrogen sulfide (H2S), a widely distributed and abundant volatile in natural environments, can be methylated to DMS. The microorganisms and the enzymes that convert H2S to DMS, and their importance in global sulfur cycling were unknown. Here we demonstrate that the bacterial MddA enzyme, previously known as a methanethiol S-methyltransferase, could methylate inorganic H2S to DMS. We determine key residues involved in MddA catalysis and propose the mechanism for H2S S-methylation. These results enabled subsequent identification of functional MddA enzymes in abundant haloarchaea and a diverse range of algae, thus expanding the significance of MddA mediated H2S methylation to other domains of life. Furthermore, we provide evidence for H2S S-methylation being a detoxification strategy in microorganisms. The mddA gene was abundant in diverse environments including marine sediments, lake sediments, hydrothermal vents and soils. Thus, the significance of MddA-driven methylation of inorganic H2S to global DMS production and sulfur cycling has likely been considerably underestimated.