Research on cross-modal emotion recognition based on multi-layer semantic fusion.

Multimodal emotion analysis involves the integration of information from various modalities to better understand human emotions. In this paper, we propose the Cross-modal Emotion Recognition based on multi-layer semantic fusion (CM-MSF) model, which aims to leverage the complementarity of important information between modalities and extract advanced features in an adaptive manner. To achieve comprehensive and rich feature extraction from multimodal sources, considering different dimensions and depth levels, we design a parallel deep learning algorithm module that focuses on extracting features from individual modalities, ensuring cost-effective alignment of extracted features. Furthermore, a cascaded cross-modal encoder module based on Bidirectional Long Short-Term Memory (BILSTM) layer and Convolutional 1D (ConV1d) is introduced to facilitate inter-modal information complementation. This module enables the seamless integration of information across modalities, effectively addressing the challenges associated with signal heterogeneity. To facilitate flexible and adaptive information selection and delivery, we design the Mask-gated Fusion Networks (MGF-module), which combines masking technology with gating structures. This approach allows for precise control over the information flow of each modality through gating vectors, mitigating issues related to low recognition accuracy and emotional misjudgment caused by complex features and noisy redundant information. The CM-MSF model underwent evaluation using the widely recognized multimodal emotion recognition datasets CMU-MOSI and CMU-MOSEI. The experimental findings illustrate the exceptional performance of the model, with binary classification accuracies of 89.1% and 88.6%, as well as F1 scores of 87.9% and 88.1% on the CMU-MOSI and CMU-MOSEI datasets, respectively. These results unequivocally validate the effectiveness of our approach in accurately recognizing and classifying emotions.

[Establishment and analysis of an early prognosis model of patients with acute kidney injury in intensive care unit].

OBJECTIVE: To establish a predictive model for the progression of acute kidney injury (AKI) to stage 3 AKI (renal failure) in the intensive care unit (ICU), so as to assist physicians to make early and timely decisions on whether to intervene in advance. METHODS: A retrospective analysis was conducted. Thirty-eight patients with AKI admitted to the intensive care medicine of the Third People's Hospital of Henan Province from January 2018 to May 2023 were enrolled. Patient data including acute physiology and chronic health evaluation II (APACHE II) upon admission, serum creatinine (SCr), blood urea nitrogen (BUN), daily urine output during hospitalization, and the timing of continuous renal replacement therapy (CRRT) intervention were recorded. Based on clinically collected pathological data, standardized creatinine value ratio mean polynomial fitting models were established as the first criterion for judging the progression to stage 3 AKI after data cleansing, screening, and normalization. Additionally, standardized creatinine value ratio index fitting models were established as the second criterion for predicting progression to stage 3 AKI. RESULTS: A total of 38 AKI patients were included, including 25 males and 13 females. The average age was (58.45±12.94) years old. The APACHE II score was 24.13±4.17 at admission. The intervention node was (4.42±0.95) days. Using a dual regression model approach, statistical modeling was performed with a relatively small sample size of statistical data samples, yielding a scatter index non-linear regression model for standardized creatinine value ratio data relative to day "n", with y = 1.246 2x1.164 9 and an R2 of 0.860 1, indicating reasonable statistical fitting. Additionally, a quadratic non-linear regression model was obtained for the mean standardized creatinine value ratio relative to day "n", with y = -0.260 6x2+3.010 7x-1.612 and an R2 of 0.998 9, indicating an excellent statistical fit. For example, using a baseline SCr value of 66 µmol/L for a healthy individual, the dual regression model predicted that the patient would progress to stage 3 AKI within 3-5 days. This prediction was consistent when applied to other early intervention renal injury patients. CONCLUSIONS: The established model effectively predicts the time interval of the progression of AKI to stage 3 AKI (renal failure), which assist intensive care physicians to intervene AKI as early as possible to prevent disease progression.

Auxin homeostasis is maintained by sly-miR167-SlARF8A/B-SlGH3.4 feedback module in the development of locular and placental tissues of tomato fruits.

The locular gel, produced by the placenta, is important for fruit flavor and seed development in tomato. However, the mechanism underlying locule and placenta development is not fully understood yet. Here, we show that two SlARF transcription factors, SlARF8B and SlARF8A (SlARF8A/B), promote the development of locular and placenta tissues. The expression of both SlARF8A and SlARF8B is repressed by sly-microRNA167 (sly-miR167), allowing for the activation of auxin downstream genes. In slarf8a, slarf8b, and slarf8a/b mutants, the auxin (IAA) levels are decreased, whereas the levels of inactive IAA conjugates including IAA-Ala, IAA-Asp, and IAA-Glu are increased. We further find that SlARF8B directly inhibits the expression of SlGH3.4, an acyl acid amino synthetase that conjugates the amino acids to IAA. Disruption of such auxin balance by the increased expression of SlGH3.4 or SlGH3.2 results in defective locular and placental tissues. Taken together, our findings reveal an important regulatory module constituted by sly-miR167-SlARF8A/B-SlGH3.4 during the development of locular and placenta tissues of tomato fruits.

Large kernel convolution YOLO for ship detection in surveillance video.

At present, ship detectors have many problems, such as too many hyperparameter, poor recognition accuracy and imprecise regression boundary. In this article, we designed a large kernel convolutional YOLO (Lk-YOLO) detection model based on Anchor free for one-stage ship detection. First, we discuss the introduction of large size convolution kernel in the residual module of the backbone network, so that the backbone network has a stronger feature extraction capability. Second, in order to solve the problem of conflict regression and classification fusion under the coupling of detection heads, we split the detection head into two branches, so that the detection head has better representation ability for different branches of the task and improves the accuracy of the model in regression tasks. Finally, in order to solve the problem of complex and computationally intensive anchor hyperparameter design of ship data sets, we use anchor free algorithm to predict ships. Moreover, the model adopts an improved sampling matching strategy for both positive and negative samples to expand the number of positive samples in GT (Ground Truth) while achieving high-quality sample data and reducing the imbalance between positive and negative samples caused by anchor. We used NVIDIA 1080Ti GPU as the experimental environment, and the results showed that the mAP@50 Reaching 97.7%, mAP@.5:.95 achieved 78.4%, achieving the best accuracy among all models. Therefore, the proposed method does not need to design the parameters of the anchor, and achieves better detection efficiency and robustness without hyperparameter input.

A-RetinaNet: A novel RetinaNet with an asymmetric attention fusion mechanism for dim and small drone detection in infrared images.

To solve the problems of texture lacking and resolution coarseness in the detection of dim and small drone targets in infrared images, we propose a novel RetinaNet with an asymmetric attention fusion mechanism for dim and small drone detection. First, we propose a super-resolution texture-enhancement network as an effective solution for the lack of texture-related information on small infrared targets. The network generates super-resolution images and enhances the texture features of the targets. Second, considering the inadequacy of feature pyramids in the feature fusion stage, we use an asymmetric attention fusion mechanism to constitute an asymmetric attention fusion pyramid network for cross-layer feature fusion in a bidirectional manner; it achieves high-quality semantic and location detail information interaction between scale features. Third, a global average pooling layer is employed to capture global spatial-sensitive information, thus effectively identifying features and achieving classification. Experiments were conducted by using a publicly available infrared image dim-small drone target detection dataset; the results show that the proposed method achieves an AP of 95.43% and a recall of 80.6%, which is a significant improvement over the current mainstream target detection algorithms.

Studying the Organization of the Actin Cytoskeleton in the Multicellular Trichomes of Tomato.

Trichomes are unique polarized cells of the plant epidermis that provide an excellent model system for studying the plant cytoskeleton. Unlike Arabidopsis trichomes that are unicellular with a typical three-branch shape, the trichomes in tomato (Solanum lycopersicum) are multicellular with additional morphology and function diversity. Technically, it is hard to image tomato trichomes at the subcellular level because of their size and because they can be easily damaged. Here, we describe the methods we have used for the visualization and quantification of cytoskeletal arrangements in tomato trichomes which are at different developmental stages, using both live-cell imaging and phalloidin staining after fixation.

Molecular Basis and Evolutionary Origin of 1-Nitronaphthalene Catabolism in Sphingobium sp. Strain JS3065.

Nitrated polycyclic aromatic hydrocarbons (nitro-PAHs) enter the environment from natural sources and anthropogenic activities. To date, microorganisms able to mineralize nitro-PAHs have not been reported. Here, Sphingobium sp. strain JS3065 was isolated by selective enrichment for its ability to grow on 1-nitronaphthalene as the sole carbon, nitrogen, and energy source. Analysis of the complete genome of strain JS3065 indicated that the gene cluster encoding 1-nitronaphthalene catabolism (nin) is located on a plasmid. Based on the genetic and biochemical evidence, the nin genes share an origin with the nag-like genes encoding naphthalene degradation in Ralstonia sp. strain U2. The initial step in degradation of 1-nitronaphthalene is catalyzed by a three-component dioxygenase, NinAaAbAcAd, resulting in formation of 1,2-dihydroxynaphthalene which is also an early intermediate in the naphthalene degradation pathway. Introduction of the ninAaAbAcAd genes into strain U2 enabled its growth on 1-nitronaphthalene. Phylogenic analysis of NinAc suggested that an ancestral 1-nitronaphthalene dioxygenase was an early step in the evolution of nitroarene dioxygenases. Based on bioinformatic analysis and enzyme assays, the subsequent assimilation of 1,2-dihydroxynaphthalene seems to follow the well-established pathway for naphthalene degradation by Ralstonia sp. strain U2. This is the first report of catabolic pathway for 1-nitronaphthalene and is another example of how expanding the substrate range of Rieske type dioxygenase enables bacteria to grow on recalcitrant nitroaromatic compounds. IMPORTANCE Nitrated polycyclic aromatic hydrocarbons (nitro-PAHs) have been widely detected in the environment and they are more toxic than their corresponding parent PAHs. Although biodegradation of many PAHs has been extensively described at genetic and biochemical levels, little is known about the microbial degradation of nitro-PAHs. This work reports the isolation of a Sphingobium strain growing on 1-nitronaphthalene and the genetic basis for the catabolic pathway. The pathway evolved from an ancestral naphthalene catabolic pathway by a remarkably small modification in the specificity of the initial dioxygenase. Data presented here not only shed light on the biochemical processes involved in the microbial degradation of globally important nitrated polycyclic aromatic hydrocarbons, but also provide an evolutionary paradigm for how bacteria evolve a novel catabolic pathway with minimal alteration of preexisting pathways for natural organic compounds.

Multimodal Emotion Recognition Based on Cascaded Multichannel and Hierarchical Fusion.

Humans express their emotions in a variety of ways, which inspires research on multimodal fusion-based emotion recognition that utilizes different modalities to achieve information complementation. However, extracting deep emotional features from different modalities and fusing them remain a challenging task. It is essential to exploit the advantages of different extraction and fusion approaches to capture the emotional information contained within and across modalities. In this paper, we present a novel multimodal emotion recognition framework called multimodal emotion recognition based on cascaded multichannel and hierarchical fusion (CMC-HF), where visual, speech, and text signals are simultaneously utilized as multimodal inputs. First, three cascaded channels based on deep learning technology perform feature extraction for the three modalities separately to enhance deeper information extraction ability within each modality and improve recognition performance. Second, an improved hierarchical fusion module is introduced to promote intermodality interactions of three modalities and further improve recognition and classification accuracy. Finally, to validate the effectiveness of the designed CMC-HF model, some experiments are conducted to evaluate two benchmark datasets, IEMOCAP and CMU-MOSI. The results show that we achieved an almost 2%∼3.2% increase in accuracy of the four classes for the IEMOCAP dataset as well as an improvement of 0.9%∼2.5% in the average class accuracy for the CMU-MOSI dataset when compared to the existing state-of-the-art methods. The ablation experimental results indicate that the cascaded feature extraction method and the hierarchical fusion method make a significant contribution to multimodal emotion recognition, suggesting that the three modalities contain deeper information interactions of both intermodality and intramodality. Hence, the proposed model has better overall performance and achieves higher recognition efficiency and better robustness.

Aerobic Degradation of the Antidiabetic Drug Metformin by Aminobacter sp. Strain NyZ550.

Metformin is becoming one of the most common emerging contaminants in surface and wastewater. Its biodegradation generally leads to the accumulation of guanylurea in the environment, but the microorganisms and mechanisms involved in this process remain elusive. Here, Aminobacter sp. strain NyZ550 was isolated and characterized for its ability to grow on metformin as a sole source of carbon, nitrogen, and energy under oxic conditions. This isolate also assimilated a variety of nitrogenous compounds, including dimethylamine. Hydrolysis of metformin by strain NyZ550 was accompanied by a stoichiometric accumulation of guanylurea as a dead-end product. Based on ion chromatography, gas chromatography-mass spectrometry, and comparative transcriptomic analyses, dimethylamine was identified as an additional hydrolytic product supporting the growth of the strain. Notably, a microbial mixture consisting of strain NyZ550 and an engineered Pseudomonas putida PaW340 expressing a guanylurea hydrolase was constructed for complete elimination of metformin and its persistent product guanylurea. Overall, our results not only provide new insights into the metformin biodegradation pathway, leading to the commonly observed accumulation of guanylurea in the environment, but also open doors for the complete degradation of the new pollutant metformin.

The Atmospheric Vertical Detection of Large Area Regions Based on Interference Signal Denoising of Weighted Adaptive Kalman Filter.

In comparison with traditional space infrared spectroscopy technology, the interference signals of a large focal plane array (FPA) can be used to obtain spectra over a larger area range and rapidly achieve large-scale coverage of hyperspectral remote sensing. However, the low signal-to-noise ratio of the interference signals limits the application of spectral data, especially when atmospheric detection occurs in the long-wavelength infrared (LWIR) band. In this paper, we construct an LWIR hyperspectral system of a Fourier transform spectrometer composed of a HgCdTe photovoltaic IR FPA and a Michelson interferometer. The LWIR interference signals are obtained by a high-frequency oversampling technique. We use the Kalman filter (KF) and its improved weighted adaptive Kalman filter (WAKF) to reduce the noise of multiple measured data of each pixel. The effect of overshoot and ringing artifacts on the objective signals is reduced by the WAKF. The applicability is studied by the interference signals from the different sampling frequencies and different pixels. The effectiveness is also verified by comparing the spectra of denoised interferograms with the reference spectrum. The experimental results show that the WAKF algorithm has excellent noise suppression, and the standard deviation of the interferogram can be reduced by 39.50% compared with that of KF. The WAKF is more advantageous in improving the signal-to-noise ratio of the interferogram and spectra. The results indicate that our system can be applied to atmospheric vertical detection and hyperspectral remote sensing over large area ranges because our denoised technique is suitable for large LWIR FPA.

Biodegradation of 3-Chloronitrobenzene and 3-Bromonitrobenzene by Diaphorobacter sp. Strain JS3051.

Halonitrobenzenes are toxic chemical intermediates used widely for industrial synthesis of dyes and pesticides. Bacteria able to degrade 2- and 4-chloronitrobenzene have been isolated and characterized; in contrast, no natural isolate has been reported to degrade meta-halonitrobenzenes. In this study, Diaphorobacter sp. strain JS3051, previously reported to degrade 2,3-dichloronitrobenzene, grew readily on 3-chloronitrobenzene and 3-bromonitrobenzene, but not on 3-fluoronitrobenzene, as sole sources of carbon, nitrogen, and energy. A Rieske nonheme iron dioxygenase (DcbAaAbAcAd) catalyzed the dihydroxylation of 3-chloronitrobenzene and 3-bromonitrobenzene, resulting in the regiospecific production of ring-cleavage intermediates 4-chlorocatechol and 4-bromocatechol. The lower activity and relaxed regiospecificity of DcbAaAbAcAd toward 3-fluoronitrobenzene is likely due to the higher electronegativity of the fluorine atom, which hinders it from interacting with E204 residue at the active site. DccA, a chlorocatechol 1,2-dioxygenase, converts 4-chlorocatechol and 4-bromocatechol into the corresponding halomuconic acids with high catalytic efficiency, but with much lower Kcat/Km values for fluorocatechol analogues. The results indicate that the Dcb and Dcc enzymes of Diaphorobacter sp. strain JS3051 can catalyze the degradation of 3-chloro- and 3-bromonitrobenzene in addition to 2,3-dichloronitrobenzene. The ability to utilize multiple substrates would provide a strong selective advantage in a habitat contaminated with mixtures of chloronitrobenzenes. IMPORTANCE Halonitroaromatic compounds are persistent environmental contaminants, and some of them have been demonstrated to be degraded by bacteria. Natural isolates that degrade 3-chloronitrobenzene and 3-bromonitrobenzene have not been reported. In this study, we report that Diaphorobacter sp. strain JS3051 can degrade 2,3-dichloronitrobenzene, 3-chloronitrobenzene, and 3-bromonitrobenzene using the same catabolic pathway, whereas it is unable to grow on 3-fluoronitrobenzene. Based on biochemical analyses, it can be concluded that the initial dioxygenase and lower pathway enzymes are inefficient for 3-fluoronitrobenzene and even misroute the intermediates, which is likely responsible for the failure to grow. These results advance our understanding of how the broad substrate specificities of catabolic enzymes allow bacteria to adapt to habitats with mixtures of xenobiotic contaminants.

H and HL synergistically regulate jasmonate-triggered trichome formation in tomato.

The development of trichomes, which protect plants against herbivores, is affected by various stresses. In tomato, previous studies showed that stress triggered JA signaling influences trichome formation, but the underlying mechanism is not fully resolved. Here, we found two C2H2 zinc finger proteins synergistically regulate JA-induced trichome formation in tomato. The naturally occurring mutations in H and its close homolog H-like gene in a spontaneous mutant, LA3172 cause severely affected trcihome development. Compared with respective single mutant, h/hl double mutant displayed more severe trichome defects in all tissues. Despite the partially redundant function, H and HL genes regulate the trichome formation in the spatially distinct manner, with HL more involved in hypocotyls and leaves, while H more involved in stems and sepals. Furthermore，the activity of H/HL is essential for JA-triggered trichome formation. JA signaling inhibitor SlJAZ2 represses the activity of H and HL via physical interaction, resulting in the activation of THM1, a negative regulator of trichome formation. Our results provide novel insight into the mechanism of the trichome formation in response to stress induced JA signaling in tomato.

NSD-SSD: A Novel Real-Time Ship Detector Based on Convolutional Neural Network in Surveillance Video.

With the rapid development of the marine industry, intelligent ship detection plays a very important role in the marine traffic safety and the port management. Current detection methods mainly focus on synthetic aperture radar (SAR) images, which is of great significance to the field of ship detection. However, these methods sometimes cannot meet the real-time requirement. To solve the problems, a novel ship detection network based on SSD (Single Shot Detector), named NSD-SSD, is proposed in this paper. Nowadays, the surveillance system is widely used in the indoor and outdoor environment, and its combination with deep learning greatly promotes the development of intelligent object detection and recognition. The NSD-SSD uses visual images captured by surveillance cameras to achieve real-time detection and further improves detection performance. First, dilated convolution and multiscale feature fusion are combined to improve the small objects' performance and detection accuracy. Second, an improved prediction module is introduced to enhance deeper feature extraction ability of the model, and the mean Average Precision (mAP) and recall are significant improved. Finally, the prior boxes are reconstructed by using the K-means clustering algorithm, the Intersection-over-Union (IoU) is higher, and the visual effect is better. The experimental results based on ship images show that the mAP and recall can reach 89.3% and 93.6%, respectively, which outperforms the representative model (Faster R-CNN, SSD, and YOLOv3). Moreover, our model's FPS is 45, which can meet real-time detection acquirement well. Hence, the proposed method has the better overall performance and achieves higher detection efficiency and better robustness.

HOMEODOMAIN PROTEIN8 mediates jasmonate-triggered trichome elongation in tomato.

Plant hormones can adjust the physiology and development of plants to enhance their adaptation to biotic and abiotic challenges. Jasmonic acid (JA), one of the immunity hormones in plants, triggers genome-wide transcriptional changes in response to insect attack and wounding. Although JA is known to affect the development of trichomes, epidermal appendages that form a protective barrier against various stresses, it remains unclear how JA interacts with developmental programs that regulate trichome development. In this study, we show that JA affects trichome length in tomato by releasing the transcriptional repression mediated by Jasmonate ZIM (JAZ) proteins. We identified SlJAZ4, a negative regulator preferentially expressed in trichomes, as the critical component in JA signaling in tomato trichomes. We also identified a homeodomain-leucine zipper gene, SlHD8, as the downstream regulator of JA signaling that promotes trichome elongation. SlHD8 is also highly expressed in trichomes and physically interacts with SlJAZ4. Loss-of-function mutations in SlHD8 caused shorter trichomes, a phenotype that was only partially rescued by methyl jasmonate treatment. Our dual-luciferase and chromatin immunoprecipitation-quantitative PCR assays revealed that SlHD8 regulates trichome elongation by directly binding to the promoters of a set of cell-wall-loosening protein genes and activating their transcription. Together, our findings define SlHD8-SlJAZ4 as a key module mediating JA-induced trichome elongation in tomato.

Mediation of JA signalling in glandular trichomes by the woolly/SlMYC1 regulatory module improves pest resistance in tomato.

Almost all plants form trichomes, which protect them against insect herbivores by forming a physical barrier and releasing chemical repellents. Glandular trichomes produce a variety of specialized defensive metabolites, including volatile terpenes. Previous studies have shown that the defence hormone jasmonic acid (JA) affects trichome development and induces terpene synthases (TPSs) but the underlying molecular mechanisms remain unclear. Here, we characterized a loss-of-function allele of the HD-ZIP IV transcription factor woolly (wo) and analysed its role in mediating JA signalling in tomato. We showed that knockout of wo led to extensive trichome defects, including structural and functional changes in type VI glandular trichomes, and a dramatic reduction in terpene levels. We further found that wo directly binds to TPS gene promoters to recruit SlMYC1, a JA signalling modulator, and that together these transcription factors promote terpene biosynthesis in tomato trichomes. The wo/SlMYC1 regulatory module is inhibited by SlJAZ2 through a competitive binding mechanism, resulting in a fine-tuned JA response in tomato trichomes. Enhanced expression of SlMYC1 substantially increased terpene levels and improved tomato resistance to spider mites. Interestingly, we also found that SlMYC1 plays an additional role in glandular cell division and expansion in type VI trichomes, independent of JA. Together, our results reveal a novel, JA-mediated regulatory mechanism that promotes insect resistance in tomato.

Rational design of genetically encoded reporter genes for optical imaging of apoptosis.

Apoptosis is a process in which cells are genetically regulated to cause a series of changes in morphology and metabolic activity, which ultimately lead to cell death. Apoptosis plays a vital role in the entire life cycle of an organism. Too much or too little apoptosis can cause a variety of diseases. Therefore, efficient and convenient methods for detecting apoptosis are necessary for clinical treatment and drug development. Traditional methods for detecting apoptosis may cause damage to the body during sample collection, such as for flow cytometry analysis. So it is necessary to monitor apoptosis without invasion in vivo. Optical imaging technique provides a more sensitive and economical way for apoptosis visualization. A subset of engineered reporter genes based on fluorescent proteins or luciferases are currently developed to monitor the dynamic changes in apoptotic markers, such as activation of caspases and exposure of phosphatidylserine on the surface of dying cells. These reporters detect apoptosis when cells have not undergone significant morphological changes, providing conditions for early diagnosis of tumors. In addition, these reporters show considerable value in high-throughput screening of apoptosis-related drugs and evaluation of their efficacy in treating tumors. In this review, we will discuss the recent research progress in the optical imaging of apoptosis based on the genetically encoded reporter genes.

Genetically Encoded Reporter Genes for MicroRNA Imaging in Living Cells and Animals.

MicroRNAs (miRNAs) are small non-coding RNAs that regulate gene expression by base paring with the complementary sequences of the target mRNAs, and then exert their function through degrading mRNA or inhibiting protein translation. They play a significant role as a regulatory factor in biological processes of organism development, cell proliferation, differentiation, and cell death. Some of the traditional methods for studying miRNAs, such as northern blot, real-time PCR, or microarray, have been extensively used to investigate the biological properties and expression patterns of miRNAs. However, these methods often require considerable time, cell samples, and the design of effective primers or specific probes. Therefore, in order to gain a deeper understanding of the role of miRNAs in biological processes and accelerate the clinical application of miRNAs in the field of disease treatment, non-invasive, sensitive, and efficient imaging methods are needed to visualize the dynamic expression of miRNAs in living cells and animals. In this study, we reviewed the recent progress in the genetically encoded reporter genes for miRNA imaging.

A spatio-temporal analysis to identify the drivers of malaria transmission in Bhutan.

At a time when Bhutan is on the verge of malaria elimination, the aim of this study was to identify malaria clusters at high geographical resolution and to determine its association with local environmental characteristics. Malaria cases from 2006-2014 were obtained from the Vector-borne Disease Control Program under the Ministry of Health, Bhutan. A Zero-Inflated Poisson multivariable regression model with a conditional autoregressive (CAR) prior structure was developed. Bayesian Markov chain Monte Carlo (MCMC) simulation with Gibbs sampling was used to estimate posterior parameters. A total of 2,062 Plasmodium falciparum and 2,284 Plasmodium vivax cases were reported during the study period. Both species of malaria showed seasonal peaks with decreasing trend. Gender and age were not associated with the transmission of either species of malaria. P. falciparum increased by 0.7% (95% CrI: 0.3%, 0.9%) for a one mm increase in rainfall, while climatic variables (temperature and rainfall) were not associated with P. vivax. Insecticide treated bed net use and residual indoor insecticide coverage were unaccounted for in this study. Hot spots and clusters of both species were isolated in the central southern part of Bhutan bordering India. There was significant residual spatial clustering after accounting for climate and demographic variables.

Computational fluid dynamics approaches to drag and wake of a long-line mussel dropper under tidal current.

Hydrodynamic effects of mussel farms have attracted increased research attentions in recent years. The understanding of the hydrodynamic impacts is essential for predicting the sustainability of mussel farms. A large mussel farm includes thousands of mussel droppers, and the combined drag on the mussel droppers is sufficient to possibly affect the longevity of the entire long-lines. This article intends to study the drag and wake of an individual long-line mussel dropper using computational fluid dynamics approaches. Two equivalent rough cylinders, namely, Curved-Model and Sharp-Model, have been utilized to simulate the mussel dropper, and each rough cylinder is assigned with surface roughness. The porosity is not considered in this article due to its complexity from inhalant and exhalant of mussels. Two-dimensional laminar simulations are conducted at Reynolds number from 10 to 200, and three-dimensional large eddy simulations are conducted at subcritical Reynolds number ranging from 3900 to 105. The results show that larger drag coefficients and Strouhal numbers are attributed to surface roughness and sharp crowns on the rough cylinder. The obtained drag coefficient ranges from 1.1 to 1.2 with respect to the diameter of the mussel dropper and the peak value of the tidal velocities. Wakes behind rough cylinders fluctuate more actively compared to those of smooth cylinders. This research work provides new insight for further investigations on hydrodynamic interactions between fluid and mussel droppers.