Infection with the human immunodeficiency virus (HIV) has grown to be an important humanitarian problem influencing people worldwide. After a long period of research and practice, antiretroviral ...therapy (ART) and immunotherapy have gradually become the two main methods of treating HIV. However, there are still many questions to be explored regarding the comparison of these two treatments and the future direction of their development. Therefore, the aim of this study was to compare ART therapy and immunotherapy in HIV treatment and to explore their differences in terms of viral suppression, immune recovery, and patient quality of life. The potential and challenges of these two treatments in the future are envisioned.A thorough literature review and analysis were used to thoroughly assess research advancements in immunotherapy and antiretroviral therapy. ART entails long-term medication compliance and runs the risk of developing drug resistance. Immunotherapy has a lot of potential for inducing the immune system to manufacture particular antibodies and cellular immunological responses, but research is still being done to determine its usefulness and safety. The effectiveness of antiretroviral medication in preventing HIV infection and preserving patients’ health has been astounding, but issues with long-term drug use and drug resistance need to be addressed. While immunotherapy, a young science, presents prospects to create novel treatments, more study is required to guarantee their efficacy.Future HIV treatment may take advantage of both ideologies’ strengths to create a more effective combination therapy strategy with the ultimate objective to enhance HIV control and elimination.
Remote sensing images are featured by massiveness, diversity and complexity. These features put forward higher requirements for the speed and accuracy of remote sensing image retrieval. The ...extraction method plays a key role in retrieving remote sensing images. Deep metric learning (DML) captures the semantic similarity information between data points by learning embedding in vector space. However, due to the uneven distribution of sample data in remote sensing image datasets, the pair-based loss currently used in DML is not suitable. To improve this, we propose a novel distribution consistency loss to solve this problem. First, we define a new way to mine samples by selecting five in-class hard samples and five inter-class hard samples to form an informative set. This method can make the network extract more useful information in a short time. Secondly, in order to avoid inaccurate feature extraction due to sample imbalance, we assign dynamic weight to the positive samples according to the ratio of the number of hard samples and easy samples in the class, and name the loss caused by the positive sample as the sample balance loss. We combine the sample balance of the positive samples with the ranking consistency of the negative samples to form our distribution consistency loss. Finally, we built an end-to-end fine-tuning network suitable for remote sensing image retrieval. We display comprehensive experimental results drawing on three remote sensing image datasets that are publicly available and show that our method achieves the state-of-the-art performance.
In recent years, semantic segmentation has made significant progress in visual place recognition (VPR) by using semantic information that is relatively invariant to appearance and viewpoint, ...demonstrating great potential. However, in some extreme scenarios, there may be semantic occlusion and semantic sparsity, which can lead to confusion when relying solely on semantic information for localization. Therefore, this paper proposes a novel VPR framework that employs a coarse-to-fine image matching strategy, combining semantic and appearance information to improve algorithm performance. First, we construct SemLook global descriptors using semantic contours, which can preliminarily screen images to enhance the accuracy and real-time performance of the algorithm. Based on this, we introduce SemLook local descriptors for fine screening, combining robust appearance information extracted by deep learning with semantic information. These local descriptors can address issues such as semantic overlap and sparsity in urban environments, further improving the accuracy of the algorithm. Through this refined screening process, we can effectively handle the challenges of complex image matching in urban environments and obtain more accurate results. The performance of SemLook descriptors is evaluated on three public datasets (Extended-CMU Season, Robot-Car Seasons v2, and SYNTHIA) and compared with six state-of-the-art VPR algorithms (HOG, CoHOG, AlexNet_VPR, Region VLAD, Patch-NetVLAD, Forest). In the experimental comparison, considering both real-time performance and evaluation metrics, the SemLook descriptors are found to outperform the other six algorithms. Evaluation metrics include the area under the curve (AUC) based on the precision-recall curve, Recall@100%Precision, and Precision@100%Recall. On the Extended-CMU Season dataset, SemLook descriptors achieve a 100% AUC value, and on the SYNTHIA dataset, they achieve a 99% AUC value, demonstrating outstanding performance. The experimental results indicate that introducing global descriptors for initial screening and utilizing local descriptors combining both semantic and appearance information for precise matching can effectively address the issue of location recognition in scenarios with semantic ambiguity or sparsity. This algorithm enhances descriptor performance, making it more accurate and robust in scenes with variations in appearance and viewpoint.
A quality detection system for the "Red Fuji" apple in Luochuan was designed for automatic grading. According to the Chinese national standard, the grading principles of apple appearance quality and ...Brix detection were determined. Based on machine vision and image processing, the classifier models of apple defect, contour, and size were constructed. And then, the grading thresholds were set to detect the defective pixel ratio t, aspect ratio lambda, and the cross-sectional diameter W.sub.p in the image of the apple. Spectral information of apples in the wavelength range of 400 nm~1000 nm was collected and the multiple scattering correction (MSC) and standard normal variable (SNV) transformation methods were used to preprocess spectral reflectance data. The competitive adaptive reweighted sampling (CARS) algorithm and the successive projections algorithm (SPA) were used to extract characteristic wavelength points containing Brix information, and the CARS-PLS (partial least squares) algorithm was used to establish a Brix prediction model. Apple defect, contour, size, and Brix were combined as grading indicators. The apple quality online grading detection platform was built, and apple's comprehensive grading detection algorithm and upper computer software were designed. The experiments showed that the average accuracy of apple defect, contour, and size grading detection was 96.67%, 95.00%, and 94.67% respectively, and the correlation coefficient R.sub.p of the Brix prediction set was 0.9469. The total accuracy of apple defect, contour, size, and Brix grading was 96.67%, indicating that the detection system designed in this paper is feasible to classify "Red Fuji" apple in Luochuan.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Objectives:
Cannabis use is proposed as a risk factor for psychosis and is associated with depressive disorders. However, the relationship between recreational cannabis use and its longitudinal ...implications on anxiety conditions is less studied. The aim of this investigation is to systematically evaluate published literature and perform a meta-analysis of the data.
Methods:
A systematic search was performed of MEDLINE, Embase, and PsychINFO from inception to May 31, 2020, in addition to a hand search. Longitudinal studies that evaluated the relationship of cannabis use and development of anxiety were included. Where applicable, adjusted odds ratios (ORs) were extracted, pooled, and evaluated using random-effects meta-analysis.
Results:
After screening of unique abstracts (n = 6835), the final evaluation included 24 studies, of which 10 reported ORs that were analyzed quantitatively. Cannabis use was significantly associated with increased odds of developing any anxiety conditions (OR = 1.25; 95% CI, 1.01 to 1.54). Cannabis use was not significantly associated with developing generalized anxiety disorder, panic disorder, or social anxiety disorder. Review of studies not reporting OR revealed mixed results but are suggestive of a link between cannabis use and increased rates/severity of anxiety.
Conclusions:
Published evidence suggests that cannabis use is likely associated with increased risk of anxiety in the long term but variability of study designs precludes declaration of a causal relationship. Awareness of this association is of relevance for both clinical practice and mental health policy implementation.
An important area in a gathering place is a region attracting the constant attention of people and has evident visual features, such as a flexible stage or an open-air show. Finding such areas can ...help security supervisors locate the abnormal regions automatically. The existing related methods lack an efficient means to find important area candidates from a scene and have failed to judge whether or not a candidate attracts people's attention. To realize the detection of an important area, this study proposes a two-stage method with a novel multi-input attention network (MAN). The first stage, called important area candidate generation, aims to generate candidate important areas with an image-processing algorithm (i.e., K-means++, image dilation, median filtering, and the RLSA algorithm). The candidate areas can be selected automatically for further analysis. The second stage, called important area candidate classification, aims to detect an important area from candidates with MAN. In particular, MAN is designed as a multi-input network structure, which fuses global and local image features to judge whether or not an area attracts people's attention. To enhance the representation of candidate areas, two modules (i.e., channel attention and spatial attention modules) are proposed on the basis of the attention mechanism. These modules are mainly based on multi-layer perceptron and pooling operation to reconstruct the image feature and provide considerably efficient representation. This study also contributes to a new dataset called gathering place important area detection for testing the proposed two-stage method. Lastly, experimental results show that the proposed method has good performance and can correctly detect an important area.
Siloxane rubber shows attractive properties of high stability, elasticity and transparency. Besides, the regulation of its properties renders it widely used in many application fields. However, most ...of the reported performance improvement methods of siloxane rubber focus on the change of chemical composition of siloxane rubber, including the design of molecular chain and the introduction of other compounds, etc. Such a strategy is still faced with many limitations in practical application. In this work, on the premise of not changing the chemical composition of siloxane rubber, we propose a facile solvothermal polymerization process to change the structure of cross-linking networks, so as to obtain the siloxane rubber with controllable mechanical properties. Compared to the normal curing method, we realized polydimethylsiloxane elastomer (PDMS) with maximum elongation of more than 3,000% (> 10 times of normally cured one) and tensile modulus lower than 0.15 MPa (< 1/10 of normally cured one). In addition to superior stretchability, it gains extra high softness, stickiness and sensitive response to organic solvents. Based on our solvothermal cured PDMS, its applications in oil collection and organic solvent sensor have been demonstrated. It is expected that this method can be readily utilized widely and shows great application potentials.
The effect of nanoconfinement on the rate of isothermal polymerization of methyl methacrylate (MMA) polymerization is investigated using differential scanning calorimetry. Controlled pore glass (CPG) ...with pore diameters of 13, 50, and 111 nm are used for the confinement of the reaction. Both hydrophilic and hydrophobic pore surfaces are studied. The effective reaction rate and the apparent activation energy at low conversions, prior to autoacceleration, are unchanged in hydrophobic pores. On the other hand, in hydrophilic pores, the reaction rate increases by as much as a factor of 8 in the smallest 13 nm hydrophilic pores, and the effective activation energy decreases. For both pore surfaces, the time required to reach autoacceleration decreases with decreasing pore size, with the effect much more pronounced in the hydrophilic pores. The results are consistent with a model of nanoconfined free radical polymerization which accounts for suppressed termination due to a decrease in the diffusivity of nanoconfined chains.
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Photovoltaic-powered drip irrigation is a vital approach to address the irrigation requirements in regions with limited water resources and energy deficiencies, thereby ensuring the provision of ...sustenance and horticultural produce for local inhabitants. However, the susceptibility of the drip irrigation system to clogging as well as the fluctuations in photovoltaic output can significantly impact irrigation quality. Moreover, conventional storage methods commonly employed in photovoltaic-powered drip irrigation systems, such as elevated water tanks and batteries, exhibit notable technological, economic, and environmental limitations. The present study introduces a novel photovoltaic drip irrigation technology (CAES-PVDI) that utilizes solar energy as the exclusive source of power, enabling stable and cost-effective high-quality drip irrigation. This technology actively regulates solar energy through compressed air energy storage, employing a cyclic pulse discharge method to ensure uniformity in irrigation outflow and significantly enhance the anti-clogging performance of the drip irrigation system. The proposed technology was implemented in a solar greenhouse for drip irrigation, and subsequent tests were conducted to assess its hydraulic performance and anti-clogging properties The results demonstrated that the system achieved a discharge uniformity of no less than 91.76 %. Furthermore, there was no blocked emitter in CAES-PVDI system, and the sedimentation inside the capillary tube decreased by 78.95 %-93.36 % compared to traditional drip irrigation system. In comparison to existing photovoltaic-powered drip irrigation technology, the CAES-PVDI system exhibited exceptional technical indicators and offered significant economic and environmental benefits, thereby presenting a novel approach to promote environmentally friendly and efficient operation of drip irrigation systems.
•Compressed air energy storage technology applied to photovoltaic drip irrigation.•Controlled compressed air release for intermittent cycle pulse drip irrigation.•A solar single energy supply PLC-controlled pulse drip irrigation system.•New green pulse drip irrigation system offers superior hydraulic performance.•Significantly improved clogging resistance of drip emitters.
Floral color and scent profiles vary across species, geographical locations, and developmental stages. The exclusive floral color and fragrance of
is contributed by a range of endogenous chemicals ...that distinguish it from other flowers and present amazing ornamental value. This comprehensive review explores the intricate interplay of environmental factors, chemicals and genes shaping the flower color and fragrance of
. Genetic and physiological factors control morpho-anatomical attributes as well as pigment synthesis, while environmental factors such as temperature, light intensity, and soil composition influence flower characteristics. Specific genes control pigment synthesis, and environmental factors such as temperature, light intensity, and soil composition influence flower characteristics. Physiological processes including plant hormone contribute to flower color and fragrance. Hormones, notably ethylene, exert a profound influence on varioustraits. Pigment investigations have spotlighted specific flavonoids, including kaempferol 3-O-rutinoside, quercetin, and rutin. Red tepals exhibit unique composition with cyanidin-3-O-rutinoside and cyanidin-3-O-glucoside being distinctive components. Elucidating the molecular basis of tepal color variation, particularly in red and yellow varieties, involves the identification of crucial regulatory genes. In conclusion, this review unravels the mysteries of
, providing a holistic understanding of its flower color and fragrance for landscape applications. This comprehensive review uniquely explores the genetic intricacies, chemical and environmental influences that govern the mesmerizing flower color and fragrance of
, providing valuable insights for its landscape applications. This review article is designed for a diverse audience, including plant geneticists, horticulturists, environmental scientists, urban planners, and students, offering understandings into the genetic intricacies, ecological significance, and practical applications of
across various disciplines. Its appeal extends to professionals and enthusiasts interested in plant biology, conservation, and industries dependent on unique floral characteristics.
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