Memory Reduction Research Articles

University Student Sleep Quality: Some Health Insights regarding Light Exposure

Young adult sleep quality is reported to be compromised in the literature with shortened sleep intervals, daytime napping, excessive daytime sleepiness and excessive wakeups. The description of young adult sleep quality commonly includes the rationale that the independent schedule of the young adults coupled with their preferred social schedule, work and academic schedules leaves the planning for sleep to last place. Despite mentation and memory reductions with sleep deprivation secondary to poor sleep quality documented in young adults, the poor sleep quality prevails. This study is focused on the elements of arousal during a young adult’s day that may predispose them to elevated activation and difficulty relaxing sufficiently to fall asleep. It was hypothesized that light exposure was elevated in young adults with poor sleep quality. Light exposure in terms of overhead bright lights in classrooms and laboratories of the academic environment, bright light in terms of video monitors/TVs/tablets/phone exposure is sufficiently stimulating to cause cognitive activation was light exposure.

Prior-Guided Deep Interference Mitigation for FMCW Radars

In this paper, the interference mitigation problem is tackled as a regression problem. A prior-guided deep learning (DL) based interference mitigation approach is proposed for frequency modulated continuous wave (FMCW) radars. Considering the complex-valued nature of radar signals, complex-valued convolutional neural network, which is different from the conventional real-valued counterparts, is utilized as an architecture for implementation. Meanwhile, as the desired beat signals of FMCW radars and interferences exhibit different distributions in the time-frequency domain, this prior feature is exploited as a regularization term to avoid overfitting of the learned representation. The effectiveness and accuracy of our proposed complex-valued fully convolutional network (CV-FCN) based interference mitigation approach are verified and analyzed through both simulated and measured radar signals. Compared with the real-valued counterparts, the CV-FCN shows a better interference mitigation performance with a potential of half memory reduction in low Signal to Interference plus Noise Ratio (SINR) scenarios. The average SINR of interfered signals has been improved from -9.13 dB to 10.46 dB. Moreover, the CV-FCN trained using only simulated data can be directly utilized for interference mitigation in various measured radar signals and shows a superior generalization capability. Furthermore, by incorporating the prior feature, the CV-FCN trained on only 1/8 of the full data achieves comparable performance as that on the full dataset in low SINR scenarios, and the training procedure converges faster.

Demeter: A Fast and Energy-Efficient Food Profiler Using Hyperdimensional Computing in Memory

Food profiling is an essential step in any food monitoring system needed to prevent health risks and potential frauds in the food industry. Significant improvements in sequencing technologies are pushing food profiling to become the main computational bottleneck. State-of-the-art profilers are unfortunately too costly for food profiling. Our goal is to design a food profiler that solves the main limitations of existing profilers, namely (1) working on massive data structures and (2) incurring considerable data movement, for a real-time monitoring system. To this end, we propose Demeter, the first platform-independent framework for food profiling. Demeter overcomes the first limitation through the use of hyperdimensional computing (HDC) and efficiently performs the accurate few-species classification required in food profiling. We overcome the second limitation by the use of an in-memory hardware accelerator for Demeter (named Acc-Demeter) based on memristor devices. Acc-Demeter actualizes several domain-specific optimizations and exploits the inherent characteristics of memristors to improve the overall performance and energy consumption of Acc-Demeter. We compare Demeter’s accuracy with other industrial food profilers using detailed software modeling.We synthesize Acc-Demeter’s required hardware using UMC’s 65nm library by considering an accurate PCM model based on silicon-based prototypes. Our evaluations demonstrate that Acc-Demeter achieves a (1) throughput improvement of 192× and 724× and (2) memory reduction of 36× and 33× compared to Kraken2 and MetaCache (2 state-of-the-art profilers), respectively, on typical food-related databases. Demeter maintains an acceptable profiling accuracy (within 2% of existing tools) and incurs a very low area overhead.

Immune cell profile and immune-related gene expression of obese peripheral blood and liver tissue.

Obesity is associated with changes in immune cell subpopulations. However, tissue and blood obesity-responsive immune phenotypic pathways have not been contrasted. Here, the local niche immune cell population and gene expression in fatty liver is compared to peripheral blood of obese individuals. The Cibersort algorithm enumerated increased fractions of memory CD4+ T lymphocytes and reductions in natural killer and memory B cells in obese liver tissue and obese blood, with similar reductions found in nonalcoholic fatty liver disease tissue. Gene expression analysis identified inflammatory immune signatures of regulatory CD4+ T cells with inferred Th1, Th17, Th2, or Treg phenotypes that differed between liver and blood. Our study suggests that the local tissue-specific immune phenotype in the liver differs from the obese peripheral circulation, with the latter reflective of multisystemic persistent inflammation that is characteristic of obesity.

Long-term effect of neonatal antagonism of ionotropic glutamate receptors on dendritic spines and cognitive function in rats

Glutamate is the most abundant excitatory neurotransmitter in the hippocampus where mediates its actions by activating glutamate receptors. The activation of these receptors is essential for the maintenance and dynamics of dendritic spines and plasticity that correlate with learning and memory processes during neurodevelopment and adulthood. We studied in adults the effect of blocking ionotropic glutamate receptors (NMDAR, AMPAR, and KAR) functions at neonatal age (PD1-PD15) with their respective antagonists D-AP5, GYKI-53655 and UBP-302. We first evaluated memory using a new object recognition test in adults. Second, we evaluated the levels of glial fibrillary acidic protein, synaptophysin and actin with immunohistochemistry in the CA1, CA3, and dentate gyrus regions of the hippocampus and, finally, the number of dendritic spines and their dynamics using Golgi-Cox staining. We found that ionotropic glutamate receptor function blockade at neonatal age causes a reduction in short and long-term memory in adulthood and a reduction in the expression of synaptophysin and actin protein levels in the hippocampus regions studied. This blockade also reduced the number of dendritic spines and modified dendritic dynamics in the CA1 region. The antagonism of the three types of ionotropic glutamate receptors reduced the mushrooms and bifurcated types of spines and increased the thin spines. The number of stubby spines was reduced by D-AP5, increased by UPB-302, and not affected by GYKI-53655. Our results indicate that the blockade of neonatal ionotropic glutamate receptors produces alterations that persist until adulthood.

Transient Global Amnesia: A Case Report

Transient global amnesia (TGA) is a clinical syndrome characterized by the sudden onset reduction of anterograde memory. Many etiologies were debated, such as migraine, focal ischemia, venous flow abnormalities, and epileptic phenomena. A large proportion of TGA cases are preceded by stressful events, which may be emotional or physical. Here we are reporting a case of TGA in a 60-year-old woman triggered by emotional stress.

Milk fat globule membrane attenuates high fat diet-induced neuropathological changes in obese Ldlr-/-.Leiden mice.

Milk-fat globule membrane (MFGM) is a complex structure secreted by the mammary gland and present in mammalian milk. MFGM contains lipids and glycoproteins as well as gangliosides, which may be involved in myelination processes. Notably, myelination and thereby white matter integrity are often altered in obesity. Furthermore, MFGM interventions showed beneficial effects in obesity by affecting inflammatory processes and the microbiome. In this study, we investigated the impact of a dietary MFGM intervention on fat storage, neuroinflammatory processes and myelination in a rodent model of high fat diet (HFD)-induced obesity. 12-week-old male low density lipoprotein receptor-deficient Leiden mice were exposed to a HFD, a HFD enriched with 3% whey protein lipid concentrate (WPC) high in MFGM components, or a low fat diet. The impact of MFGM supplementation during 24-weeks of HFD-feeding was examined over time by analyzing body weight and fat storage, assessing cognitive tasks and MRI scanning, analyzing myelinization with polarized light imaging and examining neuroinflammation using immunohistochemistry. We found in this study that 24 weeks of HFD-feeding induced excessive fat storage, increased systolic blood pressure, altered white matter integrity, decreased functional connectivity, induced neuroinflammation and impaired spatial memory. Notably, supplementation with 3% WPC high in MFGM components restored HFD-induced neuroinflammation and attenuated the reduction in hippocampal-dependent spatial memory and hippocampal functional connectivity. We showed that supplementation with WPC high in MFGM components beneficially contributed to hippocampal-dependent spatial memory, functional connectivity in the hippocampus and anti-inflammatory processes in HFD-induced obesity in rodents. Current knowledge regarding exact biological mechanisms underlying these effects should be addressed in future studies.

Quick and robust feature selection: the strength of energy-efficient sparse training for autoencoders

Major complications arise from the recent increase in the amount of high-dimensional data, including high computational costs and memory requirements. Feature selection, which identifies the most relevant and informative attributes of a dataset, has been introduced as a solution to this problem. Most of the existing feature selection methods are computationally inefficient; inefficient algorithms lead to high energy consumption, which is not desirable for devices with limited computational and energy resources. In this paper, a novel and flexible method for unsupervised feature selection is proposed. This method, named QuickSelection (The code is available at: https://github.com/zahraatashgahi/QuickSelection), introduces the strength of the neuron in sparse neural networks as a criterion to measure the feature importance. This criterion, blended with sparsely connected denoising autoencoders trained with the sparse evolutionary training procedure, derives the importance of all input features simultaneously. We implement QuickSelection in a purely sparse manner as opposed to the typical approach of using a binary mask over connections to simulate sparsity. It results in a considerable speed increase and memory reduction. When tested on several benchmark datasets, including five low-dimensional and three high-dimensional datasets, the proposed method is able to achieve the best trade-off of classification and clustering accuracy, running time, and maximum memory usage, among widely used approaches for feature selection. Besides, our proposed method requires the least amount of energy among the state-of-the-art autoencoder-based feature selection methods.

Effect of metformin treatment on memory and hippocampal neurogenesis decline correlated with oxidative stress induced by methotrexate in rats

Metformin is currently used as a first-line drug to treat patients with type 2 diabetes. Previous studies have demonstrated that metformin has antioxidant properties and reduces neuroinflammation and hippocampal neuronal cell loss, which eventually improves memory. Methotrexate (MTX) is an antimetabolite chemotherapeutic agent reported to activate cognitive impairment found in many patients. Moreover, MTX negatively affects the spatial working memory, related to neurogenesis reduction in animal models. Therefore, the present study aimed to investigate the antioxidant effect of metformin on the reduction of memory and neurogenesis caused by MTX. Male Sprague-Dawley rats were divided into four groups: control, MTX, metformin, and MTX+metformin. MTX (75 mg/kg, i.v.) was administered on days 7 and 14. Rats were administered metformin (200 mg/kg, i.p.) for 14 days. Memory was determined using novel object location (NOL) and novel object recognition (NOR) tests. Furthermore, cell cycle arrest was quantified by p21 immunostaining. Levels of neuronal protein expression, scavenging enzymes activity, and malondialdehyde (MDA) level changes in the hippocampus and prefrontal cortex were investigated. Rats receiving only MTX showed memory impairment. Decreases in scavenging enzyme activity and BDNF, DCX, and Nrf2 protein expressions levels were detected in the MTX-treated rats. In addition, MTX significantly increased p21-positive cell numbers and MDA levels. However, these adverse MTX effects were counteracted by co-administration with metformin. These results demonstrate that metformin can improve memory impairments, increase BDNF, DCX and Nrf2 protein expressions and antioxidant capacities, and decrease MDA levels in MTX-treated rats.

Neurological, neuropsychiatric and psychiatric symptoms during COVID-19 infection and after recovery: a systematic review of observational studies

Involvement of Fibrinogen(Fg) and fibrin during various pathologies associated with neuroinflammatory diseases associated with memory reduction are well known. Elevated level of Fg, called hyperfibrinogenemia (HFg) (e.g. ≥ 4±0.1 mg/ml of plasma during inflammation vs.~2±0.1 mg/ml of normal plasma [1]), accompanies inflammatory diseases such as stroke [2,3], hypertension [1,4], diabetes [5] and Traumatic Brain Injury (TBI) [6-9]. Blood level of Fg increases during inflammation in general [10].

Efficient Visualization of Large-Scale Oblique Photogrammetry Models in Unreal Engine

Oblique photogrammetry models are indispensable for implementing digital twins of cities. Geographic information system researchers have proposed plenty of methods to load and visualize these city-scaled scenes. However, when the area viewed changes quickly in real-time rendering, current methods still require excessive GPU calculation and memory occupation. In this study, we propose a data organization method in which we merged all quadtrees and used a binary encoding method to encode nodes in a merged tree so that the parent–child relationship between the tree nodes could be calculated using rapid binary operations. After that, we developed a strategy to cancel the loading of redundant nodes based on the parent–child relationship, which helped to reduce the hard disk loading time and the amount of memory occupied in visualization. Moreover, we introduced a parameter to measure the area of the triangle mesh per pixel to achieve unified data scheduling under different production standards. We implemented our method based on Unreal Engine (UE), and three experiments were designed to illustrate the advantages of our methods in index acceleration, frame time, and memory reduction. The results show that our methods can significantly improve visualization fluency and reduce memory usage.

Pineal Parenchymal Tumours

Involvement of Fibrinogen(Fg) and fibrin during various pathologies associated with neuroinflammatory diseases associated with memory reduction are well known. Elevated level of Fg, called hyperfibrinogenemia (HFg) (e.g. ≥ 4±0.1 mg/ml of plasma during inflammation vs.~2±0.1 mg/ml of normal plasma [1]), accompanies inflammatory diseases such as stroke [2,3], hypertension [1,4], diabetes [5] and Traumatic Brain Injury (TBI) [6-9]. Blood level of Fg increases during inflammation in general [10].

The Effects of Fibrinogen's Interactions with Its Neuronal Receptors, Intercellular Adhesion Molecule-1 and Cellular Prion Protein.

Neuroinflammatory diseases, such as Alzheimer’s disease (AD) and traumatic brain injury (TBI), are associated with the extravascular deposition of the fibrinogen (Fg) derivative fibrin and are accompanied with memory impairment. We found that during the hyperfibrinogenemia that typically occurs during AD and TBI, extravasated Fg was associated with amyloid beta and astrocytic cellular prion protein (PrPC). These effects coincided with short-term memory (STM) reduction and neurodegeneration. However, the mechanisms of a direct Fg–neuron interaction and its functional role in neurodegeneration are still unclear. Cultured mouse brain neurons were treated with Fg in the presence or absence of function-blockers of its receptors, PrPC or intercellular adhesion molecule-1 (ICAM-1). Associations of Fg with neuronal PrPC and ICAM-1 were characterized. The expression of proinflammatory marker interleukin 6 (IL-6) and the generation of reactive oxygen species (ROS), mitochondrial superoxide, and nitrite in neurons were assessed. Fg-induced neuronal death was also evaluated. A strong association of Fg with neuronal PrPC and ICAM-1, accompanied with overexpression of IL-6 and enhanced generation of ROS, mitochondrial superoxide, and nitrite as well as the resulting neuronal death, was found. These effects were reduced by blocking the function of neuronal PrPC and ICAM-1, suggesting that the direct interaction of Fg with its neuronal receptors can induce overexpression of IL-6 and increase the generation of ROS, nitrite, and mitochondrial superoxide, ultimately leading to neuronal death. These effects can be a mechanism of neurodegeneration and the resultant memory reduction seen during TBI and AD.

Conductive mixed-order generalized dispersion model for noble metals in the optical regime.

Various dispersion models can be expressed as special cases of the Generalized Dispersion Model (GDM), which is composed of a series of Padé polynomials. While important for its broad applicability, we found that some materials with Drude dispersive terms can be accurately modeled by mixing a 1st order Padé polynomial with an extra conductivity term. This conductivity term can be separated from the auxiliary differential equation (ADE). Therefore, the proposed mixed-order model can achieve the same accuracy with fewer unknowns, thus realizing higher computational efficiency and lower memory consumption. For examples, we derive the model parameters and corresponding numerical errors for noble metals including Au, Ag, and Al in the optical regime. Finally, the proposed model's efficiency improvements are validated through implementation within a Discontinuous Galerkin Time Domain (DGTD) framework. The proposed model can achieve up to 12.5% efficiency improvement in theory compared to the conventional GDM with the same accuracy. A numerical example validates that, in practice, 9% memory reduction and 11% acceleration can be realized.

Trapezoid-grid finite difference frequency domain method for seismic wave simulation

Abstract The large computational cost and memory requirement for the finite difference frequency domain (FDFD) method limit its applications in seismic wave simulation, especially for large models. For conventional FDFD methods, the discretisation based on minimum model velocity leads to oversampling in high-velocity regions. To reduce the oversampling of the conventional FDFD method, we propose a trapezoid-grid FDFD scheme to improve the efficiency of wave modeling. To alleviate the difficulty of processing irregular grids, we transform trapezoid grids in the Cartesian coordinate system to square grids in the trapezoid coordinate system. The regular grid sizes in the trapezoid coordinate system correspond to physical grid sizes increasing with depth, which is consistent with the increasing trend of seismic velocity. We derive the trapezoid coordinate system Helmholtz equation and the corresponding absorbing boundary condition, then get the FDFD stencil by combining the central difference method and the average-derivative method (ADM). Dispersion analysis indicates that our method can satisfy the requirement of maximum phase velocity error less than $1\%$ with appropriate parameters. Numerical tests on the Marmousi model show that, compared with the regular-grid ADM 9-point FDFD scheme, our method can achieve about $80\%$ computation efficiency improvement and $80\%$ memory reduction for comparable accuracy.

Clinical Application of Platelet Mapping Using Thromboelastography in Neurology Patients –Is It Needed?

Involvement of Fibrinogen(Fg) and fibrin during various pathologies associated with neuroinflammatory diseases associated with memory reduction are well known. Elevated level of Fg, called hyperfibrinogenemia (HFg) (e.g. ≥ 4±0.1 mg/ml of plasma during inflammation vs.~2±0.1 mg/ml of normal plasma [1]), accompanies inflammatory diseases such as stroke [2,3], hypertension [1,4], diabetes [5] and Traumatic Brain Injury (TBI) [6-9]. Blood level of Fg increases during inflammation in general [10].

Practical selection of representative sets of RNA-seq samples using a hierarchical approach.

MotivationDespite numerous RNA-seq samples available at large databases, most RNA-seq analysis tools are evaluated on a limited number of RNA-seq samples. This drives a need for methods to select a representative subset from all available RNA-seq samples to facilitate comprehensive, unbiased evaluation of bioinformatics tools. In sequence-based approaches for representative set selection (e.g. a k-mer counting approach that selects a subset based on k-mer similarities between RNA-seq samples), because of the large numbers of available RNA-seq samples and of k-mers/sequences in each sample, computing the full similarity matrix using k-mers/sequences for the entire set of RNA-seq samples in a large database (e.g. the SRA) has memory and runtime challenges; this makes direct representative set selection infeasible with limited computing resources.ResultsWe developed a novel computational method called ‘hierarchical representative set selection’ to handle this challenge. Hierarchical representative set selection is a divide-and-conquer-like algorithm that breaks representative set selection into sub-selections and hierarchically selects representative samples through multiple levels. We demonstrate that hierarchical representative set selection can achieve summarization quality close to that of direct representative set selection, while largely reducing runtime and memory requirements of computing the full similarity matrix (up to 8.4× runtime reduction and 5.35× memory reduction for 10 000 and 12 000 samples respectively that could be practically run with direct subset selection). We show that hierarchical representative set selection substantially outperforms random sampling on the entire SRA set of RNA-seq samples, making it a practical solution to representative set selection on large databases like the SRA.Availability and implementationThe code is available at https://github.com/Kingsford-Group/hierrepsetselection and https://github.com/Kingsford-Group/jellyfishsim.Supplementary information Supplementary data are available at Bioinformatics online.

Chronic cerebral lipocalin 2 exposure elicits hippocampal neuronal dysfunction and cognitive impairment

Lipocalin 2 (LCN2) is a pleiotropic molecule that is induced in the central nervous system (CNS) in several acute and chronic pathologies. The acute induction of LCN2 evolved as a beneficial process, aimed at combating bacterial infection through the sequestration of iron from pathogens, while the role of LCN2 during chronic, non-infectious disease remains unclear, and recent studies suggest that LCN2 is neurotoxic. However, whether LCN2 is sufficient to induce behavioral and cognitive alterations remains unclear. In this paper, we sought to address the role of cerebral LCN2 on cognition in both acute and chronic settings. We demonstrate that LCN2 is robustly induced in the CNS during both acute and chronic inflammatory conditions, including LPS-based sepsis and cancer cachexia. In vivo, LPS challenge results in a global induction of LCN2 in the central nervous system, while cancer cachexia results in a distribution specific to the vasculature. Similar to these in vivo observations, in vitro modeling demonstrated that both glia and cerebral endothelium produce and secrete LCN2 when challenged with LPS, while only cerebral endothelium secrete LCN2 when challenged with cancer-conditioned medium. Chronic, but not short-term, cerebral LCN2 exposure resulted in reduced hippocampal neuron staining intensity, an increase in newborn neurons, microglial activation, and increased CNS immune cell infiltration, while gene set analyses suggested these effects were mediated through melanocortin-4 receptor independent mechanisms. RNA sequencing analyses of primary hippocampal neurons revealed a distinct transcriptome associated with prolonged LCN2 exposure, and ontology analysis was suggestive of altered neurite growth and abnormal spatial learning. Indeed, LCN2-treated hippocampal neurons display blunted neurite processes, and mice exposed to prolonged cerebral LCN2 levels experienced a reduction in spatial reference memory as indicated by Y-maze assessment. These findings implicate LCN2 as a pathologic mediator of cognitive decline in the setting of chronic disease.

Exposure To Urban Air Pollution Nanoparticles andCNS Disease

Involvement of Fibrinogen(Fg) and fibrin during various pathologies associated with neuroinflammatory diseases associated with memory reduction are well known. Elevated level of Fg, called hyperfibrinogenemia (HFg) (e.g. ≥ 4±0.1 mg/ml of plasma during inflammation vs.~2±0.1 mg/ml of normal plasma [1]), accompanies inflammatory diseases such as stroke [2,3], hypertension [1,4], diabetes [5] and Traumatic Brain Injury (TBI) [6-9]. Blood level of Fg increases during inflammation in general [10].

Verbal working memory and processing speed: Correlations with the severity of attention deficit and emotional dysregulation in adult ADHD.

ObjectivesThe Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM‐5), emphasizes symptoms severity with regard to the diagnosis of attention deficit hyperactivity disorder (ADHD). Many clinicians use neuropsychological test results as objective measures of cognitive functions as part of the diagnostic work‐up. The aim of this study was to investigate whether the psychometric test results regarding verbal working memory and processing speed are useful as indicators of the severity of attention deficits and emotional dysregulation in adults with ADHD.MethodsThis observational cross‐sectional clinical study included 418 adults diagnosed with ADHD according to the DSM‐5. Attention deficit severity was defined based on the inattentive subscale of the Adult ADHD Self‐Report Scale. Emotional dysregulation was assessed with the Deficient Emotional Self‐Regulation scale. Verbal working memory was measured with the Working Memory Index (WMI), and processing speed was measured with the Processing Speed Index (PSI) from the Wechsler Adult Intelligence Scale, third edition.ResultsThe full‐scale intelligence quotients of the participants were in the normal range, with expected reductions in verbal working memory and processing speed. Only processing speed was associated with attention deficits (β = −.056, p = .003). The association between the psychometric test result for verbal working memory and processing speed and that between the severity of attention deficits and emotional dysregulation were weak (R2 < .1) and mostly non‐significant.ConclusionThe psychometric index scores for verbal working memory (WMI) and processing speed (PSI) seem to have limited utility as indicators of the severity of attention deficits and emotional dysregulation in adult ADHD patients.