Recruitment of a long-term memory supporting neural network during repeated maintenance of a multi-item abstract visual image in working memory

Humans can flexibly transfer information between different memory systems. Information in visual working memory (VWM) can for instance be stored in long-term memory (LTM). Conversely, information can be retrieved from LTM and temporarily held in WM when needed. It has previously been suggested that a neural transition from parietal- to midfrontal activity during repeated visual search reflects transfer of information from WM to LTM. Whether this neural transition indeed reflects consolidation and is also observed when memorizing a rich visual scene (rather than responding to a single target), is not known. To investigate this, we employed an EEG paradigm, in which abstract six-item colour-arrays were repeatedly memorized and explicitly visualized, or merely attended to. Importantly, we tested the functional significance of a potential neural shift for longer-term consolidation in a subsequent recognition task. Our results show a gradually enhanced- and sustained modulation of the midfrontal P170 component and a decline in parietal CDA, during repeated WM maintenance. Improved recollection/visualization of memoranda upon WM-cueing, was associated with contralateral parietal- and right temporal activity. Importantly, only colour-arrays previously held in WM, induced a greater midfrontal P170-response, together with left temporal- and late centro-parietal activity, upon re-exposure. These findings provide evidence for recruitment of an LTM-supporting neural network which facilitates visual WM maintenance.

Ground Target Tracking Using an Airborne Angle-Only Sensor with Terrain Uncertainty and Sensor Biases

Airborne angle-only sensors can be used to track stationary or mobile ground targets. In order to make the problem observable in 3-dimensions (3-D), the height of the target (i.e., the height of the terrain) from the sea-level is needed to be known. In most of the existing works, the terrain height is assumed to be known accurately. However, the terrain height is usually obtained from Digital Terrain Elevation Data (DTED), which has different resolution levels. Ignoring the terrain height uncertainty in a tracking algorithm will lead to a bias in the estimated states. In addition to the terrain uncertainty, another common source of uncertainty in angle-only sensors is the sensor biases. Both these uncertainties must be handled properly to obtain better tracking accuracy. In this paper, we propose algorithms to estimate the sensor biases with the target(s) of opportunity and algorithms to track targets with terrain and sensor bias uncertainties. Sensor bias uncertainties can be reduced by estimating the biases using the measurements from the target(s) of opportunity with known horizontal positions. This step can be an optional step in an angle-only tracking problem. In this work, we have proposed algorithms to pick optimal targets of opportunity to obtain better bias estimation and algorithms to estimate the biases with the selected target(s) of opportunity. Finally, we provide a filtering framework to track the targets with terrain and bias uncertainties. The Posterior Cramer–Rao Lower Bound (PCRLB), which provides the lower bound on achievable estimation error, is derived for the single target filtering with an angle-only sensor with terrain uncertainty and measurement biases. The effectiveness of the proposed algorithms is verified by Monte Carlo simulations. The simulation results show that sensor biases can be estimated accurately using the target(s) of opportunity and the tracking accuracies of the targets can be improved significantly using the proposed algorithms when the terrain and bias uncertainties are present.

Sample Selection in Randomized Trials With Multiple Target Populations

Practitioners and policymakers often want estimates of the effect of an intervention for their local community, e.g., region, state, county. In the ideal, these multiple population average treatment effect (ATE) estimates will be considered in the design of a single randomized trial. Methods for sample selection for generalizing the sample ATE to date, however, focus only on the case of a single target population. In this paper, I provide a framework for sample selection in the multiple population case, including three compromise allocations. I situate the methods in an example and conclude with a discussion of the implications for the design of randomized evaluations more generally.

The Beneficial Effects of Combining Anti-Aβ Antibody NP106 and Curcumin Analog TML-6 on the Treatment of Alzheimer’s Disease in APP/PS1 Mice

Alzheimer’s disease (AD) is a progressive neurodegenerative disease with a multifactorial etiology. A multitarget treatment that modulates multifaceted biological functions might be more effective than a single-target approach. Here, the therapeutic efficacy of combination treatment using anti-Aβ antibody NP106 and curcumin analog TML-6 versus monotherapy was investigated in an APP/PS1 mouse model of AD. Our data demonstrate that both combination treatment and monotherapy attenuated brain Aβ and improved the nesting behavioral deficit to varying degrees. Importantly, the combination treatment group had the lowest Aβ levels, and insoluble forms of Aβ were reduced most effectively. The nesting performance of APP/PS1 mice receiving combination treatment was better than that of other APP/PS1 groups. Further findings indicate that enhanced microglial Aβ phagocytosis and lower levels of proinflammatory cytokines were concurrent with the aforementioned effects of NP106 in combination with TML-6. Intriguingly, combination treatment also normalized the gut microbiota of APP/PS1 mice to levels resembling the wild-type control. Taken together, combination treatment outperformed NP106 or TML-6 monotherapy in ameliorating Aβ pathology and the nesting behavioral deficit in APP/PS1 mice. The superior effect might result from a more potent modulation of microglial function, cerebral inflammation, and the gut microbiota. This innovative treatment paradigm confers a new avenue to develop more efficacious AD treatments.

Optimization of multiplex PCR composition to screen for SARS-CoV-2 variants of concern

Background: The ongoing COVID-19 pandemic has led to the emergence of several variants of concern. To rapidly identify those variants, screening samples for whole-genome sequencing (WGS) prioritization could be performed. Objective: We optimized the polymerase chain reaction (PCR) screening method to identify the mutation in spike and ORF1a regions. Methods: We adopted primers targeting mutation in spike and ORF1a region from another study. We optimized the PCR screening method using kits readily available in Indonesia. Firstly, we compared N1 and N2 primers as internal positive control. We also compared GoTaq® 1-Step RT-qPCR System and Indonesia TFRIC-19 BioCOV-19 for the multiplex reaction. We used the optimized composition to screen SARS-CoV-2 positive samples from April – June 2021. Samples with spike and/or ORF1a target failure were subjected to whole genome sequencing (WGS). Results: The results demonstrated the N2 BioCOV-19 reaction as the optimized multiplex PCR composition for spike and ORF1a mutations screening. Whole-genome sequencing has shown that a sample with spike and ORF1a targets failure to be Alpha variant, while other samples with single target failure as non-variants of concern. Therefore, a multiplex RT-PCR composition has been optimized to detect mutation in spike and ORF1a regions. Conclusion: We have optimized a multiplex RT-PCR composition to detect mutation in spike and ORF1a regions.

SA-GAN: Stain Acclimation Generative Adversarial Network for Histopathology Image Analysis

Histopathological image analysis is an examination of tissue under a light microscope for cancerous disease diagnosis. Computer-assisted diagnosis (CAD) systems work well by diagnosing cancer from histopathology images. However, stain variability in histopathology images is inevitable due to the use of different staining processes, operator ability, and scanner specifications. These stain variations present in histopathology images affect the accuracy of the CAD systems. Various stain normalization techniques have been developed to cope with inter-variability issues, allowing standardizing the appearance of images. However, in stain normalization, these methods rely on the single reference image rather than incorporate color distributions of the entire dataset. In this paper, we design a novel machine learning-based model that takes advantage of whole dataset distributions as well as color statistics of a single target image instead of relying only on a single target image. The proposed deep model, called stain acclimation generative adversarial network (SA-GAN), consists of one generator and two discriminators. The generator maps the input images from the source domain to the target domain. Among discriminators, the first discriminator forces the generated images to maintain the color patterns as of target domain. While second discriminator forces the generated images to preserve the structure contents as of source domain. The proposed model is trained using a color attribute metric, extracted from a selected template image. Therefore, the designed model not only learns dataset-specific staining properties but also image-specific textural contents. Evaluated results on four different histopathology datasets show the efficacy of SA-GAN to acclimate stain contents and enhance the quality of normalization by obtaining the highest values of performance metrics. Additionally, the proposed method is also evaluated for multiclass cancer type classification task, showing a 6.9% improvement in accuracy on ICIAR 2018 hidden test data.

Experimental Investigation on the Ranging Resolution of a FMCW Lidar

In some previous reports about frequency-modulated continuous-wave (FMCW) Lidar, observing the longer waveform of a de-chirped signal is considered an effective scheme for further improving the ranging resolution. In this work, the ranging resolution of a FMCW Lidar is experimentally investigated, and the feasibility of such a scheme is tested. During the experiment, a FMCW signal is generated via a Mach–Zehnder modulator in the transmitted port. In the received port, the de-chirped signal is extracted based on a homodyne detection scheme and is analyzed by an electrical spectrum analyzer. Two different methods are adopted to determine the ranging resolution. One is based on a single target, for which the ranging resolution is obtained through inspecting the shift of spectral peak position as the target moves. The other is based on two targets, for which the ranging resolution is acquired through inspecting the variation of spectrum distribution as the spacing of two targets changes. The experimental results demonstrate that extending the observed duration of the de-chirped signal cannot improve the ranging resolution, and the corresponding physical mechanism is revealed.

Involvement of the Orexinergic System in Feeding

To know the processes involved in feeding, the dysregulation of hypothalamic neuropeptides promoting anorexigenic/orexigenic mechanisms must be investigated. Many neuropeptides are involved in this behavior and in overweight/obesity. Current pharmacological strategies for the treatment of obesity are unfortunately not very effective and, hence, new therapeutic strategies must be investigated and developed. Due to the crucial role played by orexins in feeding behavior, the aim of this review is to update the involvement of the orexinergic system in this behavior. The studies performed in experimental animal models and humans and the relationships between the orexinergic system and other substances are mentioned and discussed. Promising research lines on the orexinergic system are highlighted (signaling pathways, heterogeneity of the hypothalamic orexinergic neurons, receptor-receptor interaction, and sex differences). Each of the orexin 1 and 2 receptors plays a unique role in energy metabolism, exerting a differential function in obesity. Additional preclinical/clinical studies must be carried out to demonstrate the beneficial effects mediated by orexin receptor antagonists. Because therapies applied are in general ineffective when they are directed against a single target, the best option for successful anti-obesity treatments is the development of combination therapies as well as the development of new and more specific orexin receptor antagonists.