Subsequent Target Research Articles

Pyroptosis is involved in the immune microenvironment regulation of unexplained recurrent miscarriage.

Unexplained recurrent miscarriage (URM) is a common pregnancy complication with few effective therapies. Moreover, little is known regarding the role of pyroptosis in the regulation of the URM immune microenvironment. To address this issue, gene expression profiles of publicly available placental datasets GSE22490 and GSE76862 were downloaded from the Gene Expression Omnibus database. Pyroptosis-related differentially expressed genes were identified and a total of 16 differentially expressed genes associated with pyroptosis were detected, among which 1 was upregulated and 15 were downregulated. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses indicated that the functionally enriched modules and pathways of these genes are closely related to immune and inflammatory responses. Four hub genes were identified: BTK, TLR8, NLRC4, and TNFSF13B. BTK, TLR8, and TNFSF13B were highly connected with immune cells, according to the correlation analysis of four hub genes and 20 different types of immune cells (p < 0.05). The four hub genes were used as research objects to construct the interaction networks. Chorionic villus tissue was used for quantitative real-time polymerase chain reaction and western blot to confirm the expression levels of hub genes, and the results showed that the expression of the four hub genes was significantly decreased in the chorionic villus tissue in the URM group. Collectively, the present study indicates that perhaps pyroptosis is essential to the diversity and complexity of the URM immune microenvironment, and provides a theoretical basis and research ideas for subsequent target gene verification and mechanism research.

Target mimicry: The interplay of LncRNAs and MiRNAs in the complex network of abiotic stress responses in Oryza sativa

Initially dismissed as ‘junk DNA’, genomic progress highlights the essential role of non-coding regulatory RNAs (ncRNAs) in gene expression. Long non-coding RNA (lncRNAs) and microRNA (miRNAs) play a vital role in regulating diverse plant biological processes. Despite extensive identification and characterization through bioinformatics and RNA-seq, only a limited subset of lncRNAs has been functionally studied. In this particular research, a total of twenty-six drought induced lncRNAs were selected from RNA-seq dataset of Oryza sativa for in depth analysis including in silico promoter analysis and the examination of their expression patterns under abiotic stress conditions, with the aim of unravelling their intricate regulatory mechanisms. Promoter analysis revealed their responsiveness to both ABA-independent and dependent pathways, indicating potential roles in pathway-regulated responses. Notably, three lncRNAs- OsDlnc12, OsDlnc22, and OsDlnc24 found to act as ceRNA (competing endogenous RNAs) for Osa-miR169 family members, Osa-miR2864.2, and Osa-miR1437b-3p respectively. Subsequent target prediction revealed that Osa-miR169 family members target OsNF-YA3, OsNF-YA7, and HAP2, Osa-miR2864.2 targets OsERD-15, and Osa-miR1437b-3p targets OsCYP450, OsAQP, and OsPRX precursor genes. Furthermore, qRT-PCR was employed to investigate the expression patterns and target mimicry mechanisms of selected lncRNAs under various abiotic stresses, revealing temporally regulated expressions. This finding provides insights into plant regulatory mechanisms under environmental stress, emphasizing the potential importance of lncRNAs in these processes.

A multi-timescale optimization method for integrated energy systems with carbon capture and accounting

As the goal of “double carbon”, integrated energy systems aiming at the development of low-carbon economy are developing rapidly, and carbon capture and other emission reduction technologies are gradually gaining more extensive development space. For controlling carbon emissions and enhance the consumption of renewable energy. This work proposes to introduce carbon capture technology in the framework of integrated energy system and optimize the energy dispatching of integrated energy system in multiple time scales, and design a multi-time scale optimization model of integrated energy system with carbon capture. Based on the basic architecture of a low-carbon integrated energy system, this study analyzes the power characteristics of each unit of the integrated energy system, which consists of thermal power units, gas turbines, electric boilers, batteries, gas storage, heat storage, etc. By studying the energy conversion and storage processes of each unit, a power model of each unit of the integrated energy system is established. On this basis, the relationship between carbon emissions and unit output of thermal power units and gas turbines was studied, and a carbon emission model for the energy supply unit in the comprehensive energy system was established. At the same time, in order to solve the problem of carbon emission reduction under the day ahead scheduling plan of the integrated energy system, considering the emission reduction goals and system operation security factors, the study analyzed the economic model and carbon emission model of the integrated energy system, established the day ahead low-carbon scheduling model of the integrated energy system, and reasonably planned the output of each unit that can achieve the carbon emission reduction goals on the premise of meeting the balance of supply and demand. The innovation of the research method of this paper is that this paper establishes a multi time scale rolling optimization model under the emission reduction goal of the integrated energy system. Based on the day ahead scheduling scheme obtained in the day ahead low-carbon scheduling phase, the day ahead plan is first revised through 4 h rolling scheduling in the day; Then, with the goal of minimizing the adjustment amount, fine tune the unit output within 15 minutes to provide a daily output plan for subsequent low-carbon emission reduction targets. The outcomes indicate that in the practical application, the carbon emission of the optimized model in the peak hour 11:00 to 12:00 phase is 118 tons, which is 7 tons less than the 125 tons of the traditional model. In summary, it demonstrates that the studied multi-timescale optimization model of integrated energy system with carbon capture has good application. We have studied and analyzed the low-carbon implementation mechanism of coordinated cooperation in multiple time scales, and constructed a multi time scale rolling optimization model, laying a theoretical foundation for subsequent low-carbon scheduling research. This enables the system to formulate more accurate and reasonable scheduling plans, while improving the low-carbon performance and economic benefits of the system, providing reference for the low-carbon development of the power system.

Discovery of the sEH Inhibitor Epoxykynin as a Potent Kynurenine Pathway Modulator.

Disease-related phenotypic assays enable unbiased discovery of novel bioactive small molecules and may provide novel insights into physiological systems and unprecedented molecular modes of action (MMOA). Herein, we report the identification and characterization of epoxykynin, a potent inhibitor of the soluble epoxide hydrolase (sEH). Epoxykynin was discovered by means of a cellular assay monitoring modulation of kynurenine (Kyn) levels in BxPC-3 cells upon stimulation with the cytokine interferon-γ (IFN-γ) and subsequent target identification employing affinity-based chemical proteomics. Increased Kyn levels are associated with immune suppression in the tumor microenvironment and, thus, the Kyn pathway and its key player indoleamine 2,3-dioxygenase 1 (IDO1) are appealing targets in immuno-oncology. However, targeting IDO1 directly has led to limited success in clinical investigations, demonstrating that alternative approaches to reduce Kyn levels are in high demand. We uncover a cross-talk between sEH and the Kyn pathway that may provide new opportunities to revert cancer-induced immune tolerance.

A Clutter Removal Method Based on the F-K Domain for Ground-Penetrating Radar in Complex Scenarios

Ground-penetrating radar (GPR) is a classic geophysical exploration method that utilizes the emission and reception of electromagnetic waves to non-destructively detect target objects in the target medium. It has been widely applied in various fields such as pipeline detection, cavity detection, and rebar detection. However, GPR systems are susceptible to environmental clutter interference, which poses challenges for data interpretation and subsequent processing. In this paper, the separability of clutter and target signal in the frequency-wavenumber (F-K) domain is validated through modeling, leading to the proposal of a comprehensive clutter removal method based on the F-K domain for complex scenarios. The direct coupling wave is initially eliminated by applying a peak matching mean subtraction filter, which avoids the artifacts. Subsequently, the F-K domain transformation is performed and surface clutter undulations are effectively removed using a method based on singular value decomposition and k-means clustering. Finally, an angle filter with Gaussian tapering at the edges is designed based on physical models to efficiently eliminate linear interference without undesired ringing interference. The commonly used clutter removal algorithms, including mean subtraction (MS), singular value decomposition (SVD), robust principal component analysis (RPCA), and traditional F-K filtering methods, are compared with the proposed algorithm on both the numerical simulated data and actual GPR data. The results from visual and quantitative analysis confirm that our proposed method is more effective than current commonly used clutter suppression algorithms. We have successfully enhanced the Signal-to-Clutter Ratio (SCR) of the GPR data, resulting in an Improvement Factor (IF) of 30.63 dB, 23.59 dB, and 30.60 dB for simulated data, experimental data, and TU1208 public data, respectively. The detection capability of buried targets is enhanced, thereby establishing a solid foundation for subsequent data interpretation and target identification.

Deep learning based multi-target detection for roads

The vehicle target detection algorithm based on deep learning has gradually become a research hotspot in this field. In recent years, with the significant breakthrough of deep learning in the field of target recognition, the vehicle target detection algorithm based on deep learning has gradually become a research hotspot in this field. For the task of vehicle target detection, this paper first briefly introduces the process of traditional target detection algorithms and some optimization methods. It summarizes the development process of YOLO, the current mainstream one-stage vehicle target detection algorithm, and the process of Faster R-CNN, the second-stage vehicle target detection algorithm, and its improvement. Then the characteristics of several types of representative convolutional neural network algorithms are analyzed in chronological development order. Finally, it looks forward to t he future research direction of vehicle target detection algorithms, and also provides new ideas for the optimization of the subsequent vehicle target detection algorithms, which have good engineering application value. Provides algorithmic support for the underlying logic of autonomous driving.

Hostile activism: Hostile tactics or hostile hedge funds?

AbstractIn this article, we examine reputation building by activist hedge funds and provide two new findings regarding hostile activism. First, we find evidence of a permanent reputation effect to hostile activism. Activist hedge funds that have engaged in hostile tactics receive on average a 3% higher cumulative abnormal return (CAR) [−10, +10] on their subsequent nonhostile campaigns compared to hedge funds that have not engaged in hostile tactics. This abnormal return is positively related to the level of the hostile reputation of the activist hedge fund. Second, we find that activist hedge funds with higher hostile reputations modify their nonhostile activism style to engage hostile‐like targets and pursue hostile‐like objectives but withhold the use of explicitly hostile tactics. These findings imply that (1) hedge funds can build a hostile reputation using their past engagement tactics and (2) market participants perceive and value such reputation as evidenced by the higher announcement return observed in subsequent targets.

The Preparatory Activation of Guidance Templates for Visual Search and of Target Templates in Non-Search Tasks.

Representations of task-relevant object attributes (attentional templates) control the adaptive selectivity of visual processing. Previous studies have demonstrated that templates involved in the guidance of attention during visual search are activated in a preparatory fashion prior to the arrival of visual search displays. The current study investigated whether such proactive mechanisms are also triggered in non-search tasks, where attentional templates do not mediate the guidance of attention towards targets amongst distractors but are still necessary for subsequent target recognition processes. Participants either searched for colour-defined targets among multiple distractors or performed two other non-search tasks where imperative stimuli appeared without competing distractors (a colour-based Go/NoGo task, and a shape discrimination task where target colour was constant and could thus be ignored). Preparatory activation of colour-selective templates was tracked by measuring N2pc components (markers of attention allocation) to task-irrelevant colour singleton probes flashed every 200 ms during the interval between target displays. As expected, N2pcs were triggered by target-coloured probes in the search task, indicating that a corresponding guidance template was triggered proactively. Critically, clear probe N2pcs were also observed in the Go/NoGo task, and even in the shape discrimination task in an attenuated fashion. These findings demonstrate that the preparatory activation of feature-selective attentional task settings is not uniquely associated with the guidance of visual search but is also present in other types of visual selection tasks where guidance is not required.

Crisis-related stimuli do not increase the emotional attentional blink in a general university student population

Crises such as natural disasters or pandemics negatively impact the mental health of the affected community, increasing rates of depression, anxiety, or stress. It has been proposed that this stems in part from crisis-related stimuli triggering negative reactions that interrupt daily life. Given the frequency and prominence of crisis events, it is crucial to understand when crisis-related stimuli involuntarily capture attention and trigger increased stress and distraction from obligations. The emotional attentional blink (EAB) paradigm—in which emotional distractors hinder report of subsequent targets in streams of rapidly displayed stimuli—allows examination of such attentional capture in a rapidly changing dynamic environment. EABs are typically observed with generally disturbing stimuli, but stimuli related to personal traumas yield similar or greater effects, indicating strong attentional capture by stimuli related to individual trauma history. The current study investigated whether a similar comparable or increased crisis-related EAB exists within a community affected by large-scale crisis. Specifically, effects of conventional emotional distractors and distractors related to recent crises were compared using EABs in university students without a mental health diagnosis. Experiment 1 used images related to Hurricane Harvey, evaluating a crisis 4 years prior to data collection. Experiment 2 used words related to the COVID pandemic, evaluating an ongoing crisis at the time of data collection. In both experiments, the conventional EAB distractors yielded strong EABs, while the crisis-related distractors yielded absent or weak EABs in the same participants. This suggests that crisis-related stimuli do not have special potency for capturing attention in the general university student population. More generally, crises affecting communities do not necessarily yield widespread, strong reactivity to crisis-related stimuli.

UBE4B regulates p27 expression in A549 NSCLC cells through regulating the interaction of HuR and the p27 5’ UTR

Ubiquitination factor E4B (UBE4B) has a tumor-promoting effect, demonstrated by its aberrant expression in various types of cancers, and in vitro studies have shown that the retardation of cancer cell proliferation can be induced by targeting UBE4B. However, the molecular pathways through which UBE4B exerts its oncogenic activities have not yet been clearly identified and existing knowledge is limited to p53 and its subsequent downstream targets. In this study, we demonstrated that UBE4B regulates p27 expression in A549 cells via the cap-independent translation pathway following treatment with rapamycin and cycloheximide (CHX). Subsequently, we identified that UBE4B regulates p27 translation by regulating the interaction between human antigen R (HuR) and the p27 internal ribosomal entry site (IRES). First, UBE4B interacts with HuR, which inhibits p27 translation through the IRES. Secondly, the interaction between HuR and the p27 IRES was diminished by UBE4B depletion and enhanced by UBE4B overexpression. Finally, HuR depletion-induced growth retardation, accompanied by p27 accumulation, was restored by UBE4B overexpression. Collectively, these results suggest that the oncogenic properties of UBE4B in A549 cells are mediated by HuR, suggesting the potential of targeting the UBE4B-HuR-p27 axis as a therapeutic strategy for lung cancer.

Extracellular vesicles and microvilli in the immune synapse.

T cell receptor (TCR) binding to cognate antigen on the plasma membrane of an antigen-presenting cell (APC) triggers the immune synapse (IS) formation. The IS constitutes a dedicated contact region between different cells that comprises a signaling platform where several cues evoked by TCR and accessory molecules are integrated, ultimately leading to an effective TCR signal transmission that guarantees intercellular message communication. This eventually leads to T lymphocyte activation and the efficient execution of different T lymphocyte effector tasks, including cytotoxicity and subsequent target cell death. Recent evidence demonstrates that the transmission of information between immune cells forming synapses is produced, to a significant extent, by the generation and secretion of distinct extracellular vesicles (EV) from both the effector T lymphocyte and the APC. These EV carry biologically active molecules that transfer cues among immune cells leading to a broad range of biological responses in the recipient cells. Included among these bioactive molecules are regulatory miRNAs, pro-apoptotic molecules implicated in target cell apoptosis, or molecules triggering cell activation. In this study we deal with the different EV classes detected at the IS, placing emphasis on the most recent findings on microvilli/lamellipodium-produced EV. The signals leading to polarized secretion of EV at the synaptic cleft will be discussed, showing that the IS architecture fulfills a fundamental task during this route.

High serum prevalence of autoreactive IgG antibodies against peripheral nerve structures in patients with neurological post-COVID-19 vaccination syndrome.

Patients suffering from neurological symptoms after COVID-19 vaccination (post-COVID-19 vaccination syndrome (PCVS)) have imposed an increasing challenge on medical practice, as diagnostic precision and therapeutic options are lacking. Underlying autoimmune dysfunctions, including autoantibodies, have been discussed in neurological disorders after SARS-CoV-2 infection and vaccination. Here, we describe the frequency and targets of autoantibodies against peripheral nervous system tissues in PCVS. Sera from 50 PCVS patients with peripheral neurological symptoms after COVID-19 vaccination and 35 vaccinated healthy controls were used in this study. IgG autoreactivity was measured via indirect immunofluorescence assays on mouse sciatic nerve teased fibers. The frequencies of autoantibodies were compared between groups using Fisher's exact test. Serum anti-ganglioside antibodies were measured in ganglioside blots. Autoantibody target identification was performed using immunoprecipitation coupled to mass spectrometry. Subsequent target confirmation was conducted via cell-based assays and ELISA. Compared with controls, PCVS patients had a significantly greater frequency of autoantibodies against peripheral nervous system structures (9/50(18%) vs 1/35(3%); p=0.04). Autoantibodies bound to paranodes (n=5), axons (n=4), Schmidt-Lanterman incisures (n=2) and Schwann cell nuclei (n=1). Conversely, antibodies against gangliosides were absent in PCVS patients. Target identification and subsequent confirmation revealed various subunits of neurofilaments as well as DFS-70 as autoantibody epitopes. Our data suggest that autoantibodies against nervous system tissue could be relevant in PCVS patients. Autoantibodies against neurofilaments and cell nuclei with so far non-established links to this disease spectrum should be further elucidated to determine their biomarker potential.

Inflachromene ameliorates Parkinson's disease by targeting Nrf2-binding Keap1.

Parkinson's disease (PD) is the most common neurodegenerative disease characterized by movement disorder. Despite current therapeutic efforts, PD progression and the loss of dopaminergic neurons in the substantia nigra remain challenging to prevent due to the complex and unclear molecular mechanism involved. We adopted a phenotype-based drug screening approach with neuronal cells to overcome these limitations. In this study, we successfully identified a small molecule with a promising therapeutic effect for PD treatment, called inflachromene (ICM), through our phenotypic screening strategy. Subsequent target identification using fluorescence difference in two-dimensional gel electrophoresis (FITGE) revealed that ICM ameliorates PD by targeting a specific form of Keap1. This interaction led to upregulating various antioxidants, including HO-1, NQO1, and glutathione, ultimately alleviating PD symptoms. Furthermore, ICM exhibited remarkable efficacy in inhibiting the loss of dopaminergic neurons and the activation of astrocytes and microglia, which are critical factors in PD pathology. Our findings suggest that the phenotypic approach employed in this study identified that ICM has potential for PD treatment, offering new hope for more effective therapeutic interventions in the future.

Willed Attentional Selection of Visual Features: An EEG Study.

Visual selective attention studies generally tend to apply cuing paradigms to instructively direct observers' attention to certain locations, features or objects. However, in real situations, attention in humans often flows spontaneously without any specific instructions. Recently, a concept named "willed attention" was raised in visuospatial attention, in which participants are free to make volitional attention decisions. Several ERP components during willed attention were found, along with a perspective that ongoing alpha activity may bias the subsequent attentional choice. However, it remains unclear whether similar neural mechanisms exist in feature- or object-based willed attention. Here, we included choice cues and instruct cues in a feature-based selective attention paradigm, allowing participants to freely choose or to be instructed to attend a color for the subsequent target detection task. Pre-cue ongoing alpha oscillations, cue-evoked potentials and target-related steady-state visual evoked potentials (SSVEPs) were simultaneously measured as markers of attentional processing. As expected, SSVEP responses were similarly modulated by attention between choice and instruct cue trials. Similar to the case of spatial attention, a willed-attention component (Willed Attention Component, WAC) was isolated during the cue-related choice period by comparing choice and instruct cues. However, pre-cue ongoing alpha oscillations did not predict the color choice (yellow vs blue), as indicated by the chance level decoding accuracy (50%). Overall, our results revealed both similarities and differences between spatial and feature-based willed attention, and thus extended the understanding toward the neural mechanisms of volitional attention.

Mindfulness and mechanisms of attention in a neutral and palatable food context.

Mindfulness has been associated with benefits on cognitive processes, including attention. However, the exact relationship between mindfulness, components of attention, and the role of reward context has not yet been fully elucidated, which is relevant, especially in the context of addiction. In the current study, we specifically evaluated the relationship between dispositional mindfulness and the balance between voluntary (top-down), and stimulus-driven (bottom-up) attention. In addition, we explored whether the relationship was mediated by asymmetry of frontal brain activity, an index of approach tendencies, and varies as a function of reward context. In total, 95 participants (30 male, 65 female) with a mean age of 25.87 (SD = 7.38) participated. Resting-state electrophysiological activity was recorded using EEG, and participants were assessed on dispositional mindfulness, and performed the visuospatial cueing (VSC) task, which indexed voluntary- and stimulus-driven attention in a neutral and palatable food (reward) context. In the endogenous VSC task, a central cue signals the likely location of a subsequent target. The validity effect represents the benefit of valid cueing relative to the costs of invalid cueing in terms of response time. Dispositional mindfulness was associated with a reduced validity effect, plausibly reflecting a combination of reduced voluntary attention and increased stimulus-driven attention, irrespective of condition. The relationship between dispositional mindfulness and visuospatial attention could not be explained by asymmetry of frontal brain activity.

Spectral uncertainty analysis of grassland and its camouflage materials based on land-based hyperspectral images

Abstract Spectral uncertainty is one of the most prominent spectral characteristics of hyperspectral images. Compared to the process of remote sensing hyperspectral imaging, hyperspectral imaging under land-based imaging conditions has the characteristics of variable detection directions, random imaging times, and complex environmental conditions, resulting in increased spectral uncertainty of targets in land-based hyperspectral images. The spectral uncertainty of the target mainly refers to the phenomenon of “Same spectral reflectance but different objects” and “Same object but different spectral reflectance” which poses significant difficulties for subsequent hyperspectral image target detection and recognition. In order to analyze the spectral uncertainty of hyperspectral images in land-based applications and address the issue of spectral uncertainty in similar targets, a spectral uncertainty evaluation index based on standard deviation vector was proposed. For the overall spectral separability between different types of targets, a quantitative index based on Jaccard Distance (JD-SSI) is proposed to measure the spectral separability of different kinds of targets. The experiment focused on grassland and its four typical camouflage materials, analyzing the spectral intra class differences and inter class separability of each target with grassland. It is a fundamental work for studying the spectral characteristics of land-based hyperspectral images, providing a new approach for subsequent spectral band extraction, hyperspectral image classification, and target detection tasks.

Comprehensive Analysis of microRNA Methylation Profiles and Determination of Their Functional Significance in Colorectal Cancer: A Study Protocol

Colorectal cancer (CRC) is a leading cause of cancer-associated fatalities globally. Despite the notable progress in diagnostics and patient care, challenges persist in areas such as early detection, prognostic variable identification, metastatic disease treatment, and personalised treatment options. In CRC, microRNAs, a type of short non-coding RNA, are found to be deregulated and can significantly influence its onset and progression. However, previous microRNA research primarily focused on expression levels to ascertain their biological significance, leaving the microRNA methylation landscapes in CRC patients largely unexplored. This study aims to investigate the genome-wide methylome profiles of microRNA and clarify their roles in CRC. We will analyse the microRNA methylation profiles from our in-house data of more than 100 CRCs and correlate the differentially methylated microRNAs with clinicopathological features. To understand the biological significance of methylated microRNAs, we will perform pathway enrichment analysis and molecular dynamics simulations to examine the binding of argonaut protein to the differentially methylated microRNAs and the structural changes involved. Furthermore, we will conduct functional studies to determine the roles of selected microRNAs. CRC cell lines with hypermethylated microRNA of interest will be treated with a demethylating agent, followed by cell-based assays. Subsequent transcriptome-wide microRNA gene target identification and protein profiling will be performed to interrogate the molecular pathways affected by demethylation of the microRNA of interest. Methylation signifies a major transformation in cancer detection, as widespread epigenetic changes could potentially provide superior early-stage cancer detection and classification compared to somatic mutations. Effective CRC diagnosis ensures timely and appropriate treatment for patients, thereby improving their quality of life. The insights gained from this study could be applied to individualized health diagnostics, disease prognostication, and treatment monitoring.

Improved Retinex algorithm for low illumination image enhancement in the chemical plant area

Due to the complexity of the chemical plant area at night and the harsh lighting environment, the images obtained by monitoring equipment have issues such as blurred details and insufficient contrast, which is not conducive to the subsequent target detection work. A low illumination image enhancement model based on an improved Retinex algorithm is proposed to address the above issues. The model consists of a decomposition network, an adjustment network, and a reconstruction network. In the decomposition network, a new decomposition network USD-Net is established based on U-Net, which decomposes the original image into illumination and reflection maps, enhancing the extraction of image details and low-frequency information; Using an adjustment network to enhance the decomposed lighting image, and introducing a Mobilenetv3 lightweight network and residual structure to simplify the network model and improve the contrast of the image; In the reconstruction network, the BM3D method is used for image denoising to enhance the ability to restore image detail features; The enhanced illumination and reflection images were fused based on the Retinex algorithm to achieve low illumination image enhancement in the chemical plant area. This article uses five image quality evaluation indicators, namely Peak Signal-to-Noise Ratio, Structural Similarity Index, Natural Image Quality Evaluator, Interpolation Error, and Level of Effort, to compare eight traditional or modern algorithms and evaluate three different types of datasets. The experimental results show that the improved algorithm enhances the texture details of the image, improves the contrast and saturation of the image, and has good stability and robustness, which can effectively meet the needs of low illumination image enhancement in chemical plant areas.

Beyond the discourses of policy and power: HIV, AIDS, and community perspectives on the lived pandemic

As HIV unfolded in the 1980s, HIV and AIDS were heard and seen through the lives of those whom it touched directly. Personal histories were foregrounded, centred by the inevitable mortality attached to HIV infection, the courage of circumstance in the face of illness, and the activism necessary to make a difference. In the later part of the decade, the impacts of HIV were most apparent in East Africa, where community responses and political leadership coalesced to reduce new HIV infections. The 1990s marked a turn towards centralised policy, funding, and the reification of biomedically oriented approaches and systems that are intrinsically top-down. This biomedical turn centred on foregrounding the widespread availability of low-cost antiretroviral therapy and was articulated in the 90-90-90 and subsequent targets. Biomedicalisation reinforces individualised approaches to health primarily focused on biomedical technologies and health service provision that do not rely on community or social organisation formats. Emphasis on HIV treatment, along with the promise of reduced HIV transmission through viral load suppression, contributed to limiting socio-behavioural approaches to HIV prevention. While the importance of community-led response was highlighted in the 2016 United Nations Political Declaration on Ending AIDS, community-led response has devolved towards concepts such as community-led monitoring and community-led response that frame responses in direct relation to the dominant biomedical paradigm. There are lessons in the history of the AIDS response that demonstrate that communities formulate responses to AIDS differently when there have been opportunities for community members to determine the pathways for action to address HIV independently. Older research studies offer retrospective insights into moments in the history of HIV when communities were foregrounded and highlighted the need to reconsider the current trajectory of the HIV response. Not only for history’s sake but to acknowledge that community leadership remains overwhelmed by elites. While lofty targets and goals drive the contemporary HIV response, the way forward is mired by uncertainty. HIV prevention efforts remain uneven, and millions of people living with HIV depend on access to treatment for decades to come in the context of budgetary uncertainties. Changing the course of AIDS will not be achieved if we fail to ensure that communities occupy a genuine and unambiguous place in shaping HIV response.

Do We Prepare for What We Predict? How Target Expectations Affect Preparatory Attentional Templates and Target Selection in Visual Search.

Visual search is guided by representations of target-defining features (attentional templates) that are activated in a preparatory fashion. Here, we investigated whether these template activation processes are modulated by probabilistic expectations about upcoming search targets. We tracked template activation while observers prepared to search for one or two possible color-defined targets by measuring N2pc components (markers of attentional capture) to task-irrelevant color probes flashed every 200 msec during the interval between search displays. These probes elicit N2pcs only if the corresponding color template is active at the time when the probe appears. Probe N2pcs emerged from about 600 msec before search display onset. They did not differ between one-color and two-color search, indicating that two color templates can be activated concurrently. Critically, probe N2pcs measured during two-color search were identical for probes matching an expected or unexpected color (target color probability: 80% vs. 20%), or one of two equally likely colors. This strongly suggests that probabilistic target color expectations had no impact on search preparation. In marked contrast, subsequent target selection processes were strongly affected by these expectations. We discuss possible explanations for this clear dissociation in the effects of expectations on preparatory search template activation and search target selection, respectively.